migrated code to public repository

README.md

@@ -1 +1,22 @@
-# etra2016_3dgaze
+### 3D Gaze Estimation from 2D Pupil Positions on Monocular Head-Mounted Eye Trackers
+**Mohsen Mansouryar, Julian Steil, Yusuke Sugano and Andreas Bulling**
+
+published at ETRA 2016
+
+code -> Contains main scripts. below you can see a list of commands and the results they produce:
+- cmd: python parallax_analysis.py pts |-> result: plot of calibration and test points.
+- cmd: python parallax_analysis.py 2d3d |-> result: plot of 2D-to-2D againt 3D-to-3D mapping over all number of calibration depths.
+- cmd: python parallax_analysis.py 2d2d_2d3d |-> result: plot comparing parallax error over five different test depths for three calibration depths of 1.0m, 1.5m, and 2.0m between 2D-to-2D and 3D-to-3D mapping.
+- cmd: python parallax_2D3D_3Cdepths.py |-> result: plot comparing average angular error of the two mapping techniques when 3 calibration depths are used together. (depths 1 to 5 correspond to test depths 1.0m to 2.0m)
+
+code/pupil -> Modules directly used from PUPIL source code for baseline 2D-to-2D mapping and data stream correlation.
+
+code/recording -> Scripts related to dataset recording and marker visualization and tracking. script dependencies are python 2's openCV and ArUco library. more information regarding each module is documented where required.
+
+code/results -> Contains gaze estimation results for both 2D-to-2D and 2D-to-3D mapping approaches with multiple calibration depths on data from participants. data files in the root directory of each method correspond to single depth calibration results. data format is described inside README.txt inside each method directory. the results are also available via /BS/3D_Gaze_Tracking/work/results
+
+code/Visualization -> Creation of figures for the paper
+1CalibrationDepth.py -> 2D-to-2D vs. 2D-to-3D with one calibration depth
+3CalibrationDepths.py -> 2D-to-2D vs. 2D-to-3D with three calibration depth
+EffectDistanceDifference1CalibrationDepth.py -> Effect of different distances to the original calibration depth
+EffectNumberofClusters.py -> Effect of the number of clusters 

code/.gitignore

@@ -493,3 +493,7 @@ FakesAssemblies/
 **/*.Server/GeneratedArtifacts
 **/*.Server/ModelManifest.xml
 _Pvt_Extensions
+
+
+# Custom ignore rules
+*.npy

code/README.md

@@ -1 +1,38 @@
 
+- Participants data is accessible under /BS/3D_Gaze_Tracking/archive00/participants/
+	- only the data for the following 14 participants is used in the study:
+		'p10', 'p16', 'p13', 'p24', 'p5', 'p14', 'p26', 'p12', 'p20', 'p7', 'p15', 'p11', 'p21', 'p25'
+
+- Eye camera's intrinsic parameters are accessible under /BS/3D_Gaze_Tracking/archive00/eye_camera_images/
+
+- Scene camera's intrinsic parameters are accessible under /BS/3D_Gaze_Tracking/archive00/scene_camera/
+
+- geom.py contains the Pinhole camera model along with a couple of geometry related methods.
+
+- minimize.py contains code on least square minimization using numpy.
+
+- vector.py contains a fully python version of VPython's vector object used for vector processing.
+
+- sim.py contains the main GazeSimulation object used for simulation and visualization. this object also 
+contains methods for handling real world data.
+
+- parallax_analysis.py mainly uses GazeSimulation object to perform different experiments on simulation/real-
+world data. (parallax_2D3D_3Cdepths.py is just a single experiment separated from this module)
+
+- Scripts regarding real-world data processing including marker tracking, marker movement detection, frame 
+extraction, ... are inside recording package.
+
+- recording/tracker.py uses a slightly modified version of aruco_test script to silently track markers in 
+the scene, log the output of ArUco, and compute 2D and 3D position of the center of marker given the ArUco output. (the modified aruco_test is included in recording package)
+
+- recording/process_recordings.py contains the main code for detecting intervals in which marker is not 
+moving using sklearn's AgglomerativeClustering. the method performs well almost always but in a few cases manual annotation was required to get the correct output. output of this detection is depicted in marker_motion.png inside each recording's data files. a sample output for a test recording (16 points) is located under /BS/3D_Gaze_Tracking/work/marker_motion.png
+
+- recording/util/check_pupil_positions.py is used during recording to ensure accurate pupil detection. 
+example usage: 
+	python check_pupil_positions.py /BS/3D_Gaze_Tracking/archive00/participants/p16/2015_09_29/000/
+	
+- recording/util/SingleMarkerVisualizer/ contains the main Processing script for visualizing a moving marker 
+during the recording experiments. the code uses Processing version 3.
+
+

code/Visualization/1CalibrationDepth.py

@@ -0,0 +1,944 @@
+import os, sys
+import seaborn
+from pylab import rcParams
+import cv2
+
+import numpy as np
+from numpy import linalg as LA
+from time import time
+from itertools import combinations
+
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import *
+import matplotlib.patches as mpatches
+
+# activate latex text rendering
+#rc('text', usetex=True)
+
+def main(argv):
+#    path = str(argv[0])
+    path ="/home/mmbrian/3D_Gaze_Tracking/work/results/2D2D/"
+
+    Data1 = np.load(path + "p5_2d2d_all.npy")
+    Data2 = np.load(path + "p7_2d2d_all.npy")
+    Data3 = np.load(path + "p10_2d2d_all.npy")
+    Data4 = np.load(path + "p11_2d2d_all.npy")
+    Data5 = np.load(path + "p12_2d2d_all.npy")
+    Data6 = np.load(path + "p13_2d2d_all.npy")
+    Data7 = np.load(path + "p14_2d2d_all.npy")
+    Data8 = np.load(path + "p15_2d2d_all.npy")
+    Data9 = np.load(path + "p16_2d2d_all.npy")
+    Data10 = np.load(path + "p20_2d2d_all.npy")
+    Data11 = np.load(path + "p21_2d2d_all.npy")
+    Data12 = np.load(path + "p24_2d2d_all.npy")
+    Data13 = np.load(path + "p25_2d2d_all.npy")
+    Data14 = np.load(path + "p26_2d2d_all.npy")
+    
+    Data = [Data1,Data2,Data3,Data4,Data5,Data6,Data7,Data8,Data9,Data10,Data11,Data12,Data13,Data14]
+    
+    Participantmean = []
+    for i in xrange(5):
+        Participantmean.append(float(0))
+    yerrup = []
+    for i in xrange(5):
+        yerrup.append(float(0))
+    yerrdown = []
+    for i in xrange(5):
+        yerrdown.append(float(0))
+        
+    maxvalue = []
+    for i in xrange(5):
+        maxvalue.append(float(0))
+    minvalue = []
+    for i in xrange(5):
+        minvalue.append(float(0))
+        
+    Activitymax = []
+    for i in xrange(5):
+        Activitymax.append([])
+        for j in xrange(15):
+            Activitymax[i].append(float(0))
+
+    Activitymin = []
+    for i in xrange(5):
+        Activitymin.append([])
+        for j in xrange(15):
+            Activitymin[i].append(float(0))
+            
+    AngularerrorC1 = []
+    for i in xrange(5):
+        AngularerrorC1.append([])
+        for j in xrange(14):
+            AngularerrorC1[i].append(float(0))
+            
+    AngularerrorC2 = []
+    for i in xrange(5):
+        AngularerrorC2.append([])
+        for j in xrange(14):
+            AngularerrorC2[i].append(float(0))
+            
+    AngularerrorC3 = []
+    for i in xrange(5):
+        AngularerrorC3.append([])
+        for j in xrange(14):
+            AngularerrorC3[i].append(float(0))
+            
+    AngularerrorC4 = []
+    for i in xrange(5):
+        AngularerrorC4.append([])
+        for j in xrange(14):
+            AngularerrorC4[i].append(float(0))
+            
+    AngularerrorC5 = []
+    for i in xrange(5):
+        AngularerrorC5.append([])
+        for j in xrange(14):
+            AngularerrorC5[i].append(float(0))
+    
+# C1
+    distance = 1.0
+    i = 0
+    while i < 14:
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.0 and Data[i][j][0] == distance:
+                AngularerrorC1[0][i] = Data[i][j][7]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            print "i: ", i," j: ", j
+            if Data[i][j][1] == 1.25 and Data[i][j][0] == distance:
+                print Data[i][j][7]
+                AngularerrorC1[1][i] = Data[i][j][7]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.5 and Data[i][j][0] == distance:
+                AngularerrorC1[2][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1 
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.75 and Data[i][j][0] == distance:
+                AngularerrorC1[3][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 2.0 and Data[i][j][0] == distance:
+                AngularerrorC1[4][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1
+        i += 1
+
+    print "AngularerrorC1: ", AngularerrorC1[0]
+    print "AngularerrorC1: ", AngularerrorC1[1]
+    print "AngularerrorC1: ", AngularerrorC1[2]
+    print "AngularerrorC1: ", AngularerrorC1[3]
+    print "AngularerrorC1: ", AngularerrorC1[4]
+
+    meanC1D1 = np.mean(AngularerrorC1[0])
+    meanC1D2 = np.mean(AngularerrorC1[1])
+    meanC1D3 = np.mean(AngularerrorC1[2])
+    meanC1D4 = np.mean(AngularerrorC1[3])
+    meanC1D5 = np.mean(AngularerrorC1[4])
+
+    stdC1D1 = np.std(AngularerrorC1[0])
+    stdC1D2 = np.std(AngularerrorC1[1])
+    stdC1D3 = np.std(AngularerrorC1[2])
+    stdC1D4 = np.std(AngularerrorC1[3])
+    stdC1D5 = np.std(AngularerrorC1[4])
+    
+    meanC1 = [meanC1D1,meanC1D2,meanC1D3,meanC1D4,meanC1D5]
+    stdC1 = [stdC1D1,stdC1D2,stdC1D3,stdC1D4,stdC1D5]
+
+# C2
+    distance = 1.25
+    i = 0
+    while i < 14:
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.0 and Data[i][j][0] == distance:
+                AngularerrorC2[0][i] = Data[i][j][7]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            print "i: ", i," j: ", j
+            if Data[i][j][1] == 1.25 and Data[i][j][0] == distance:
+                print Data[i][j][7]
+                AngularerrorC2[1][i] = Data[i][j][7]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.5 and Data[i][j][0] == distance:
+                AngularerrorC2[2][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1 
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.75 and Data[i][j][0] == distance:
+                AngularerrorC2[3][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 2.0 and Data[i][j][0] == distance:
+                AngularerrorC2[4][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1
+        i += 1
+
+    print "AngularerrorC2: ", AngularerrorC2[0]
+    print "AngularerrorC2: ", AngularerrorC2[1]
+    print "AngularerrorC2: ", AngularerrorC2[2]
+    print "AngularerrorC2: ", AngularerrorC2[3]
+    print "AngularerrorC2: ", AngularerrorC2[4]
+
+    meanC2D1 = np.mean(AngularerrorC2[0])
+    meanC2D2 = np.mean(AngularerrorC2[1])
+    meanC2D3 = np.mean(AngularerrorC2[2])
+    meanC2D4 = np.mean(AngularerrorC2[3])
+    meanC2D5 = np.mean(AngularerrorC2[4])
+
+    stdC2D1 = np.std(AngularerrorC2[0])
+    stdC2D2 = np.std(AngularerrorC2[1])
+    stdC2D3 = np.std(AngularerrorC2[2])
+    stdC2D4 = np.std(AngularerrorC2[3])
+    stdC2D5 = np.std(AngularerrorC2[4])
+    
+    meanC2 = [meanC2D1,meanC2D2,meanC2D3,meanC2D4,meanC2D5]
+    stdC2 = [stdC2D1,stdC2D2,stdC2D3,stdC2D4,stdC2D5]
+
+# C3
+    distance = 1.5
+    i = 0
+    while i < 14:
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.0 and Data[i][j][0] == distance:
+                AngularerrorC3[0][i] = Data[i][j][7]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            print "i: ", i," j: ", j
+            if Data[i][j][1] == 1.25 and Data[i][j][0] == distance:
+                print Data[i][j][7]
+                AngularerrorC3[1][i] = Data[i][j][7]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.5 and Data[i][j][0] == distance:
+                AngularerrorC3[2][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1 
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.75 and Data[i][j][0] == distance:
+                AngularerrorC3[3][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 2.0 and Data[i][j][0] == distance:
+                AngularerrorC3[4][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1
+        i += 1
+
+    print "AngularerrorC3: ", AngularerrorC3[0]
+    print "AngularerrorC3: ", AngularerrorC3[1]
+    print "AngularerrorC3: ", AngularerrorC3[2]
+    print "AngularerrorC3: ", AngularerrorC3[3]
+    print "AngularerrorC3: ", AngularerrorC3[4]
+
+    meanC3D1 = np.mean(AngularerrorC3[0])
+    meanC3D2 = np.mean(AngularerrorC3[1])
+    meanC3D3 = np.mean(AngularerrorC3[2])
+    meanC3D4 = np.mean(AngularerrorC3[3])
+    meanC3D5 = np.mean(AngularerrorC3[4])
+
+    stdC3D1 = np.std(AngularerrorC3[0])
+    stdC3D2 = np.std(AngularerrorC3[1])
+    stdC3D3 = np.std(AngularerrorC3[2])
+    stdC3D4 = np.std(AngularerrorC3[3])
+    stdC3D5 = np.std(AngularerrorC3[4])
+    
+    meanC3 = [meanC3D1,meanC3D2,meanC3D3,meanC3D4,meanC3D5]
+    stdC3 = [stdC3D1,stdC3D2,stdC3D3,stdC3D4,stdC3D5]
+
+# C4
+    distance = 1.75
+    i = 0
+    while i < 14:
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.0 and Data[i][j][0] == distance:
+                AngularerrorC4[0][i] = Data[i][j][7]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            print "i: ", i," j: ", j
+            if Data[i][j][1] == 1.25 and Data[i][j][0] == distance:
+                print Data[i][j][7]
+                AngularerrorC4[1][i] = Data[i][j][7]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.5 and Data[i][j][0] == distance:
+                AngularerrorC4[2][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1 
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.75 and Data[i][j][0] == distance:
+                AngularerrorC4[3][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 2.0 and Data[i][j][0] == distance:
+                AngularerrorC4[4][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1
+        i += 1
+
+    print "AngularerrorC4: ", AngularerrorC4[0]
+    print "AngularerrorC4: ", AngularerrorC4[1]
+    print "AngularerrorC4: ", AngularerrorC4[2]
+    print "AngularerrorC4: ", AngularerrorC4[3]
+    print "AngularerrorC4: ", AngularerrorC4[4]
+
+    meanC4D1 = np.mean(AngularerrorC4[0])
+    meanC4D2 = np.mean(AngularerrorC4[1])
+    meanC4D3 = np.mean(AngularerrorC4[2])
+    meanC4D4 = np.mean(AngularerrorC4[3])
+    meanC4D5 = np.mean(AngularerrorC4[4])
+
+    stdC4D1 = np.std(AngularerrorC4[0])
+    stdC4D2 = np.std(AngularerrorC4[1])
+    stdC4D3 = np.std(AngularerrorC4[2])
+    stdC4D4 = np.std(AngularerrorC4[3])
+    stdC4D5 = np.std(AngularerrorC4[4])
+    
+    meanC4 = [meanC4D1,meanC4D2,meanC4D3,meanC4D4,meanC4D5]
+    stdC4 = [stdC4D1,stdC4D2,stdC4D3,stdC4D4,stdC4D5]
+
+# C5
+    distance = 2.0
+    i = 0
+    while i < 14:
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.0 and Data[i][j][0] == distance:
+                AngularerrorC5[0][i] = Data[i][j][7]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            print "i: ", i," j: ", j
+            if Data[i][j][1] == 1.25 and Data[i][j][0] == distance:
+                print Data[i][j][7]
+                AngularerrorC5[1][i] = Data[i][j][7]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.5 and Data[i][j][0] == distance:
+                AngularerrorC5[2][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1 
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 1.75 and Data[i][j][0] == distance:
+                AngularerrorC5[3][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Data[i][j][1] == 2.0 and Data[i][j][0] == distance:
+                AngularerrorC5[4][i] = Data[i][j][7]
+                j = 25
+            else:
+                j += 1
+        i += 1
+
+    print "AngularerrorC5: ", AngularerrorC5[0]
+    print "AngularerrorC5: ", AngularerrorC5[1]
+    print "AngularerrorC5: ", AngularerrorC5[2]
+    print "AngularerrorC5: ", AngularerrorC5[3]
+    print "AngularerrorC5: ", AngularerrorC5[4]
+
+    meanC5D1 = np.mean(AngularerrorC5[0])
+    meanC5D2 = np.mean(AngularerrorC5[1])
+    meanC5D3 = np.mean(AngularerrorC5[2])
+    meanC5D4 = np.mean(AngularerrorC5[3])
+    meanC5D5 = np.mean(AngularerrorC5[4])
+
+    stdC5D1 = np.std(AngularerrorC5[0])
+    stdC5D2 = np.std(AngularerrorC5[1])
+    stdC5D3 = np.std(AngularerrorC5[2])
+    stdC5D4 = np.std(AngularerrorC5[3])
+    stdC5D5 = np.std(AngularerrorC5[4])
+    
+    meanC5 = [meanC5D1,meanC5D2,meanC5D3,meanC5D4,meanC5D5]
+    stdC5 = [stdC5D1,stdC5D2,stdC5D3,stdC5D4,stdC5D5]
+    
+    #######################################################################################
+    
+    
+    path ="/home/mmbrian/3D_Gaze_Tracking/work/results/2D3D/"
+
+    Datatwo1 = np.load(path + "p5_2d3d_all.npy")
+    Datatwo2 = np.load(path + "p7_2d3d_all.npy")
+    Datatwo3 = np.load(path + "p10_2d3d_all.npy")
+    Datatwo4 = np.load(path + "p11_2d3d_all.npy")
+    Datatwo5 = np.load(path + "p12_2d3d_all.npy")
+    Datatwo6 = np.load(path + "p13_2d3d_all.npy")
+    Datatwo7 = np.load(path + "p14_2d3d_all.npy")
+    Datatwo8 = np.load(path + "p15_2d3d_all.npy")
+    Datatwo9 = np.load(path + "p16_2d3d_all.npy")
+    Datatwo10 = np.load(path + "p20_2d3d_all.npy")
+    Datatwo11 = np.load(path + "p21_2d3d_all.npy")
+    Datatwo12 = np.load(path + "p24_2d3d_all.npy")
+    Datatwo13 = np.load(path + "p25_2d3d_all.npy")
+    Datatwo14 = np.load(path + "p26_2d3d_all.npy")
+    
+    Datatwo = [Datatwo1,Datatwo2,Datatwo3,Datatwo4,Datatwo5,Datatwo6,Datatwo7,Datatwo8,Datatwo9,Datatwo10,Datatwo11,Datatwo12,Datatwo13,Datatwo14]
+    
+    Participantmean = []
+    for i in xrange(5):
+        Participantmean.append(float(0))
+    yerrup = []
+    for i in xrange(5):
+        yerrup.append(float(0))
+    yerrdown = []
+    for i in xrange(5):
+        yerrdown.append(float(0))
+        
+    maxvalue = []
+    for i in xrange(5):
+        maxvalue.append(float(0))
+    minvalue = []
+    for i in xrange(5):
+        minvalue.append(float(0))
+        
+    Activitymax = []
+    for i in xrange(5):
+        Activitymax.append([])
+        for j in xrange(15):
+            Activitymax[i].append(float(0))
+
+    Activitymin = []
+    for i in xrange(5):
+        Activitymin.append([])
+        for j in xrange(15):
+            Activitymin[i].append(float(0))
+            
+    AngularerrortwoC1 = []
+    for i in xrange(5):
+        AngularerrortwoC1.append([])
+        for j in xrange(14):
+            AngularerrortwoC1[i].append(float(0))
+            
+    AngularerrortwoC2 = []
+    for i in xrange(5):
+        AngularerrortwoC2.append([])
+        for j in xrange(14):
+            AngularerrortwoC2[i].append(float(0))
+            
+    AngularerrortwoC3 = []
+    for i in xrange(5):
+        AngularerrortwoC3.append([])
+        for j in xrange(14):
+            AngularerrortwoC3[i].append(float(0))
+            
+    AngularerrortwoC4 = []
+    for i in xrange(5):
+        AngularerrortwoC4.append([])
+        for j in xrange(14):
+            AngularerrortwoC4[i].append(float(0))
+            
+    AngularerrortwoC5 = []
+    for i in xrange(5):
+        AngularerrortwoC5.append([])
+        for j in xrange(14):
+            AngularerrortwoC5[i].append(float(0))
+    
+# C1
+    distance = 1.0
+    i = 0
+    while i < 14:
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.0 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC1[0][i] = Datatwo[i][j][2]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            print "i: ", i," j: ", j
+            if Datatwo[i][j][1] == 1.25 and Datatwo[i][j][0] == distance:
+                print Datatwo[i][j][7]
+                AngularerrortwoC1[1][i] = Datatwo[i][j][2]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.5 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC1[2][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1 
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.75 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC1[3][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 2.0 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC1[4][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1
+        i += 1
+
+    print "AngularerrortwoC1: ", AngularerrortwoC1[0]
+    print "AngularerrortwoC1: ", AngularerrortwoC1[1]
+    print "AngularerrortwoC1: ", AngularerrortwoC1[2]
+    print "AngularerrortwoC1: ", AngularerrortwoC1[3]
+    print "AngularerrortwoC1: ", AngularerrortwoC1[4]
+
+    meantwoC1D1 = np.mean(AngularerrortwoC1[0])
+    meantwoC1D2 = np.mean(AngularerrortwoC1[1])
+    meantwoC1D3 = np.mean(AngularerrortwoC1[2])
+    meantwoC1D4 = np.mean(AngularerrortwoC1[3])
+    meantwoC1D5 = np.mean(AngularerrortwoC1[4])
+
+    stdtwoC1D1 = np.std(AngularerrortwoC1[0])
+    stdtwoC1D2 = np.std(AngularerrortwoC1[1])
+    stdtwoC1D3 = np.std(AngularerrortwoC1[2])
+    stdtwoC1D4 = np.std(AngularerrortwoC1[3])
+    stdtwoC1D5 = np.std(AngularerrortwoC1[4])
+    
+    meantwoC1 = [meantwoC1D1,meantwoC1D2,meantwoC1D3,meantwoC1D4,meantwoC1D5]
+    stdtwoC1 = [stdtwoC1D1,stdtwoC1D2,stdtwoC1D3,stdtwoC1D4,stdtwoC1D5]
+
+# C2
+    distance = 1.25
+    i = 0
+    while i < 14:
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.0 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC2[0][i] = Datatwo[i][j][2]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            print "i: ", i," j: ", j
+            if Datatwo[i][j][1] == 1.25 and Datatwo[i][j][0] == distance:
+                print Datatwo[i][j][7]
+                AngularerrortwoC2[1][i] = Datatwo[i][j][2]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.5 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC2[2][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1 
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.75 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC2[3][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 2.0 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC2[4][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1
+        i += 1
+
+    print "AngularerrortwoC2: ", AngularerrortwoC2[0]
+    print "AngularerrortwoC2: ", AngularerrortwoC2[1]
+    print "AngularerrortwoC2: ", AngularerrortwoC2[2]
+    print "AngularerrortwoC2: ", AngularerrortwoC2[3]
+    print "AngularerrortwoC2: ", AngularerrortwoC2[4]
+
+    meantwoC2D1 = np.mean(AngularerrortwoC2[0])
+    meantwoC2D2 = np.mean(AngularerrortwoC2[1])
+    meantwoC2D3 = np.mean(AngularerrortwoC2[2])
+    meantwoC2D4 = np.mean(AngularerrortwoC2[3])
+    meantwoC2D5 = np.mean(AngularerrortwoC2[4])
+
+    stdtwoC2D1 = np.std(AngularerrortwoC2[0])
+    stdtwoC2D2 = np.std(AngularerrortwoC2[1])
+    stdtwoC2D3 = np.std(AngularerrortwoC2[2])
+    stdtwoC2D4 = np.std(AngularerrortwoC2[3])
+    stdtwoC2D5 = np.std(AngularerrortwoC2[4])
+    
+    meantwoC2 = [meantwoC2D1,meantwoC2D2,meantwoC2D3,meantwoC2D4,meantwoC2D5]
+    stdtwoC2 = [stdtwoC2D1,stdtwoC2D2,stdtwoC2D3,stdtwoC2D4,stdtwoC2D5]
+
+# C3
+    distance = 1.5
+    i = 0
+    while i < 14:
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.0 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC3[0][i] = Datatwo[i][j][2]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            print "i: ", i," j: ", j
+            if Datatwo[i][j][1] == 1.25 and Datatwo[i][j][0] == distance:
+                print Datatwo[i][j][7]
+                AngularerrortwoC3[1][i] = Datatwo[i][j][2]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.5 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC3[2][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1 
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.75 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC3[3][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 2.0 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC3[4][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1
+        i += 1
+
+    print "AngularerrortwoC3: ", AngularerrortwoC3[0]
+    print "AngularerrortwoC3: ", AngularerrortwoC3[1]
+    print "AngularerrortwoC3: ", AngularerrortwoC3[2]
+    print "AngularerrortwoC3: ", AngularerrortwoC3[3]
+    print "AngularerrortwoC3: ", AngularerrortwoC3[4]
+
+    meantwoC3D1 = np.mean(AngularerrortwoC3[0])
+    meantwoC3D2 = np.mean(AngularerrortwoC3[1])
+    meantwoC3D3 = np.mean(AngularerrortwoC3[2])
+    meantwoC3D4 = np.mean(AngularerrortwoC3[3])
+    meantwoC3D5 = np.mean(AngularerrortwoC3[4])
+
+    stdtwoC3D1 = np.std(AngularerrortwoC3[0])
+    stdtwoC3D2 = np.std(AngularerrortwoC3[1])
+    stdtwoC3D3 = np.std(AngularerrortwoC3[2])
+    stdtwoC3D4 = np.std(AngularerrortwoC3[3])
+    stdtwoC3D5 = np.std(AngularerrortwoC3[4])
+    
+    meantwoC3 = [meantwoC3D1,meantwoC3D2,meantwoC3D3,meantwoC3D4,meantwoC3D5]
+    stdtwoC3 = [stdtwoC3D1,stdtwoC3D2,stdtwoC3D3,stdtwoC3D4,stdtwoC3D5]
+
+# C4
+    distance = 1.75
+    i = 0
+    while i < 14:
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.0 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC4[0][i] = Datatwo[i][j][2]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            print "i: ", i," j: ", j
+            if Datatwo[i][j][1] == 1.25 and Datatwo[i][j][0] == distance:
+                print Datatwo[i][j][7]
+                AngularerrortwoC4[1][i] = Datatwo[i][j][2]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.5 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC4[2][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1 
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.75 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC4[3][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 2.0 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC4[4][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1
+        i += 1
+
+    print "AngularerrortwoC4: ", AngularerrortwoC4[0]
+    print "AngularerrortwoC4: ", AngularerrortwoC4[1]
+    print "AngularerrortwoC4: ", AngularerrortwoC4[2]
+    print "AngularerrortwoC4: ", AngularerrortwoC4[3]
+    print "AngularerrortwoC4: ", AngularerrortwoC4[4]
+
+    meantwoC4D1 = np.mean(AngularerrortwoC4[0])
+    meantwoC4D2 = np.mean(AngularerrortwoC4[1])
+    meantwoC4D3 = np.mean(AngularerrortwoC4[2])
+    meantwoC4D4 = np.mean(AngularerrortwoC4[3])
+    meantwoC4D5 = np.mean(AngularerrortwoC4[4])
+
+    stdtwoC4D1 = np.std(AngularerrortwoC4[0])
+    stdtwoC4D2 = np.std(AngularerrortwoC4[1])
+    stdtwoC4D3 = np.std(AngularerrortwoC4[2])
+    stdtwoC4D4 = np.std(AngularerrortwoC4[3])
+    stdtwoC4D5 = np.std(AngularerrortwoC4[4])
+    
+    meantwoC4 = [meantwoC4D1,meantwoC4D2,meantwoC4D3,meantwoC4D4,meantwoC4D5]
+    stdtwoC4 = [stdtwoC4D1,stdtwoC4D2,stdtwoC4D3,stdtwoC4D4,stdtwoC4D5]
+
+# C5
+    distance = 2.0
+    i = 0
+    while i < 14:
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.0 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC5[0][i] = Datatwo[i][j][2]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            print "i: ", i," j: ", j
+            if Datatwo[i][j][1] == 1.25 and Datatwo[i][j][0] == distance:
+                print Datatwo[i][j][7]
+                AngularerrortwoC5[1][i] = Datatwo[i][j][2]
+                break
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.5 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC5[2][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1 
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 1.75 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC5[3][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1
+        j = 0
+        while j < 25:
+            if Datatwo[i][j][1] == 2.0 and Datatwo[i][j][0] == distance:
+                AngularerrortwoC5[4][i] = Datatwo[i][j][2]
+                j = 25
+            else:
+                j += 1
+        i += 1
+
+    print "AngularerrortwoC5: ", AngularerrortwoC5[0]
+    print "AngularerrortwoC5: ", AngularerrortwoC5[1]
+    print "AngularerrortwoC5: ", AngularerrortwoC5[2]
+    print "AngularerrortwoC5: ", AngularerrortwoC5[3]
+    print "AngularerrortwoC5: ", AngularerrortwoC5[4]
+
+    meantwoC5D1 = np.mean(AngularerrortwoC5[0])
+    meantwoC5D2 = np.mean(AngularerrortwoC5[1])
+    meantwoC5D3 = np.mean(AngularerrortwoC5[2])
+    meantwoC5D4 = np.mean(AngularerrortwoC5[3])
+    meantwoC5D5 = np.mean(AngularerrortwoC5[4])
+
+    stdtwoC5D1 = np.std(AngularerrortwoC5[0])
+    stdtwoC5D2 = np.std(AngularerrortwoC5[1])
+    stdtwoC5D3 = np.std(AngularerrortwoC5[2])
+    stdtwoC5D4 = np.std(AngularerrortwoC5[3])
+    stdtwoC5D5 = np.std(AngularerrortwoC5[4])
+    
+    meantwoC5 = [meantwoC5D1,meantwoC5D2,meantwoC5D3,meantwoC5D4,meantwoC5D5]
+    stdtwoC5 = [stdtwoC5D1,stdtwoC5D2,stdtwoC5D3,stdtwoC5D4,stdtwoC5D5]
+
+    
+    
+    
+    
+    ######################################################################################
+
+    N = 5
+    ind = np.asarray([0.25,1.25,2.25,3.25,4.25])
+
+    width = 0.5       # the width of the bars
+
+    x1 = [0.375,1.375,2.375,3.375,4.375]
+    x2 = [0.425,1.425,2.425,3.425,4.425]
+    x3 = [0.475,1.475,2.475,3.475,4.475]
+    x4 = [0.525,1.525,2.525,3.525,4.525]
+    x5 = [0.575,1.575,2.575,3.575,4.575]
+    x6 = [0.625,1.625,2.625,3.625,4.625]
+
+    fig = plt.figure(figsize=(14.0, 10.0))
+
+    
+    
+    ax = fig.add_subplot(111)
+    
+
+
+#    rects1 = ax.bar(ind, Participantmean,width, color='r',edgecolor='black',)#, hatch='//')
+    rects1 = ax.errorbar(x1, meanC1,yerr=[stdC1,stdC1],fmt='o',color='blue',ecolor='blue',lw=3, capsize=5, capthick=2)
+    plt.plot(x1, meanC1, marker="o", linestyle='-',lw=3,color='blue',label = r'2D-to-2D Calibration Depth 1')
+    
+#    rects2 =ax.errorbar(x2, meanC2,yerr=[stdC2,stdC2],fmt='o',color='red',ecolor='red',lw=3, capsize=5, capthick=2)
+#    plt.plot(x2, meanC2, marker="o", linestyle='-',lw=3,color='red')
+    
+    rects3 = ax.errorbar(x3, meanC3,yerr=[stdC3,stdC3],fmt='o',color='orange',ecolor='orange',lw=3, capsize=5, capthick=2)
+    plt.plot(x3, meanC3, marker="o", linestyle='-',lw=3,color='orange',label = r'2D-to-2D Calibration Depth 3')
+#    
+#    rects4 =ax.errorbar(x4, meanC4,yerr=[stdC4,stdC4],fmt='o',color='green',ecolor='green',lw=3, capsize=5, capthick=2)
+#    plt.plot(x4, meanC4, marker="o", linestyle='-',lw=3,color='green')
+    
+    rects5 =ax.errorbar(x5, meanC5,yerr=[stdC5,stdC5],fmt='o',color='red',ecolor='red',lw=3, capsize=5, capthick=2)
+    plt.plot(x5, meanC5, marker="o", linestyle='-',lw=3,color='red',label = r'2D-to-2D Calibration Depth 5')
+    
+    
+
+#    rects1 = ax.bar(ind, Participantmean,width, color='r',edgecolor='black',)#, hatch='//')
+    rects2 = ax.errorbar(x2, meantwoC1,yerr=[stdtwoC1,stdtwoC1],fmt='o',color='blue',ecolor='blue',lw=3, capsize=5, capthick=2)
+    plt.plot(x2, meantwoC1, marker="o", linestyle='--',lw=3,color='blue',label = r'2D-to-3D Calibration Depth 1')
+    
+#    rects2 =ax.errorbar(x2, meanC2,yerr=[stdC2,stdC2],fmt='o',color='red',ecolor='red',lw=3, capsize=5, capthick=2)
+#    plt.plot(x2, meanC2, marker="o", linestyle='-',lw=3,color='red')
+    
+    rects4 = ax.errorbar(x4, meantwoC3,yerr=[stdtwoC3,stdtwoC3],fmt='o',color='orange',ecolor='orange',lw=3, capsize=5, capthick=2)
+    plt.plot(x4, meantwoC3, marker="o", linestyle='--',lw=3,color='orange',label = r'2D-to-3D Calibration Depth 3')
+#    
+#    rects4 =ax.errorbar(x4, meanC4,yerr=[stdC4,stdC4],fmt='o',color='green',ecolor='green',lw=3, capsize=5, capthick=2)
+#    plt.plot(x4, meanC4, marker="o", linestyle='-',lw=3,color='green')
+    
+    rects6 =ax.errorbar(x6, meantwoC5,yerr=[stdtwoC5,stdtwoC5],fmt='o',color='red',ecolor='red',lw=3, capsize=5, capthick=2)
+    plt.plot(x6, meantwoC5, marker="o", linestyle='--',lw=3,color='red',label = r'2D-to-3D Calibration Depth 5')
+
+
+    ax.set_ylabel('Angular Error',fontsize=22)
+    ax.set_xlabel('Depth',fontsize=22)
+    ax.set_xticks(ind+0.25)
+    ax.set_xticklabels( ('D1', 'D2', 'D3','D4', 'D5') ,fontsize=16)
+    
+    TOPICs = [0.0,0.5,1.5,2.5,3.5,4.5,5.0]#,110]#,120]
+    print TOPICs
+    LABELs = ["",r'D1 - 1m',r'D2 - 1.25m', r'D3 - 1.5m', r'D4 - 1.75m', r'D5 - 2.0m', ""]#, ""]#, ""]
+    
+#    fig.canvas.set_window_title('Distance Error Correlation')
+    plt.xticks(TOPICs, LABELs,fontsize=18)
+
+#    legend([rects1,rects2,rects3,rects4,rects5], [r'\LARGE\textbf{Calibration Distance 1}', r'\LARGE\textbf{Calibration Distance 2}',r'\LARGE\textbf{Calibration Distance 3}', r'\LARGE\textbf{Calibration Distance 4}',r'\LARGE\textbf{Calibration Distance 5}'], loc='lower right') 
+    legend(fontsize=20,loc='best')
+
+    TOPICS = [-4.0,-2.0, 0.0,2.0,4.0,6.0,8.0,10.0,12,14,16,18,20,22,24]#,110]#,120]
+    print TOPICS
+    LABELS = [r'', r'',r'0',r'2',  r'4', r'6', r'8', r'10',  r'12',  r'14', r'16',  r'18', r'20',  r'22', r'24']#, ""]#, ""]
+    
+#    fig.canvas.set_window_title('Accuracy - Activity Statistics')
+    plt.yticks(TOPICS, LABELS,fontsize=18)
+
+    def autolabel(rects):
+        # attach some text labels
+        for rect in rects:
+            height = rect.get_height()
+            ax.text(0.26+rect.get_x()+rect.get_width()/2., height +0.35, "%.2f"%float(height),
+                    ha='center', va='bottom',fontweight='bold',fontsize=13.5)
+
+#    autolabel(rects1)
+    
+    
+    left  = 0.1  # the left side of the subplots of the figure
+    right = 0.975    # the right side of the subplots of the figure
+    bottom = 0.075   # the bottom of the subplots of the figure
+    top = 0.925      # the top of the subplots of the figure
+    wspace = 0.2   # the amount of width reserved for blank space between subplots
+    hspace = 0.4   # the amount of height reserved for white space between subplots
+
+    plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
+    plt.show()
+    
+    
+    means = [meanC1, meanC2, meanC3, meanC4, meanC5]
+    print meanC1
+    print meanC2
+    print meanC3
+    print meanC4
+    print meanC5
+    
+    fixationinfos_list_path = "MeansC1D2D2.npy"
+    fixationinfos_list_csv_path = "MeansC1D2D2.csv"
+    np.save(fixationinfos_list_path,np.asarray(means))
+    np.savetxt(fixationinfos_list_csv_path,np.asarray(means), delimiter=",", fmt="%f")
+    
+##    fixationinfos_list_path = "Activitymin_"+str(activity)+".npy"
+##    fixationinfos_list_csv_path = "Activitymin_"+str(activity)+".csv"
+##    np.save(fixationinfos_list_path,np.asarray(Activitymin))
+##    np.savetxt(fixationinfos_list_csv_path,np.asarray(Activitymin), delimiter=",", fmt="%s")
+
+
+        
+if __name__ == "__main__":
+    main(sys.argv[1:])
+

code/Visualization/EffectDistanceDifference1CalibrationDepth.py

@@ -0,0 +1,200 @@
+import os, sys
+import seaborn
+from pylab import rcParams
+import cv2
+
+import numpy as np
+from numpy import linalg as LA
+from time import time
+from itertools import combinations
+
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import *
+import matplotlib.patches as mpatches
+
+# activate latex text rendering
+#rc('text', usetex=True)
+
+def main(argv):
+#    path = str(argv[0])
+#    path ="/home/Julian/3D_Pupil_Project/work/results/2D2D/"
+
+#    Data1 = np.load(path + "p5_2d2d_all.npy")
+#    Data2 = np.load(path + "p7_2d2d_all.npy")
+#    Data3 = np.load(path + "p10_2d2d_all.npy")
+#    Data4 = np.load(path + "p11_2d2d_all.npy")
+#    Data5 = np.load(path + "p12_2d2d_all.npy")
+#    Data6 = np.load(path + "p13_2d2d_all.npy")
+#    Data7 = np.load(path + "p14_2d2d_all.npy")
+#    Data8 = np.load(path + "p15_2d2d_all.npy")
+#    Data9 = np.load(path + "p16_2d2d_all.npy")
+#    Data10 = np.load(path + "p20_2d2d_all.npy")
+#    Data11 = np.load(path + "p21_2d2d_all.npy")
+#    Data12 = np.load(path + "p24_2d2d_all.npy")
+#    Data13 = np.load(path + "p25_2d2d_all.npy")
+    D2D2 = np.load("MeansC1D2D2.npy")
+    D2D3 = np.load("MeansC1D2D3.npy")
+    
+    
+    mean02D2D = np.mean([D2D2[0][0],D2D2[1][1], D2D2[2][2], D2D2[3][3], D2D2[4][4]])
+    mean12D2D = np.mean([D2D2[0][1],D2D2[1][2], D2D2[2][3], D2D2[3][4]])
+    mean22D2D = np.mean([D2D2[0][2],D2D2[1][3], D2D2[2][4]])
+    mean32D2D = np.mean([D2D2[0][3],D2D2[1][4]])
+    mean42D2D = np.mean([D2D2[0][4]])
+    
+#    mean2D2D = [mean02D2D,mean12D2D,mean22D2D,mean32D2D,mean42D2D]
+    
+    minmean02D2D = np.mean([D2D2[1][0],D2D2[2][1], D2D2[3][2], D2D2[4][3]])
+    minmean12D2D = np.mean([D2D2[2][0],D2D2[3][1], D2D2[4][2]])
+    minmean22D2D = np.mean([D2D2[3][0],D2D2[4][1]])
+    minmean32D2D = np.mean([D2D2[4][0]])
+    
+#    minmean2D2D = [minmean02D2D,minmean12D2D,minmean22D2D,minmean32D2D]
+    
+    std02D2D = np.std([D2D2[0][0],D2D2[1][1], D2D2[2][2], D2D2[3][3], D2D2[4][4]])
+    std12D2D = np.std([D2D2[0][1],D2D2[1][2], D2D2[2][3], D2D2[3][4]])
+    std22D2D = np.std([D2D2[0][2],D2D2[1][3], D2D2[2][4]])
+    std32D2D = np.std([D2D2[0][3],D2D2[1][4]])
+    std42D2D = np.std([D2D2[0][4]])
+    
+#    std2D2D = [std02D2D,std12D2D,std22D2D,std32D2D,std42D2D]
+    
+    minstd02D2D = np.std([D2D2[1][0],D2D2[2][1], D2D2[3][2], D2D2[4][3]])
+    minstd12D2D = np.std([D2D2[2][0],D2D2[3][1], D2D2[4][2]])
+    minstd22D2D = np.std([D2D2[3][0],D2D2[4][1]])
+    minstd32D2D = np.std([D2D2[4][0]])
+    
+#    minstd2D2D = [minstd02D2D,minstd12D2D,minstd22D2D,minstd32D2D]
+    
+    mean2D2D = [minmean32D2D,minmean22D2D,minmean12D2D,minmean02D2D,mean02D2D,mean12D2D,mean22D2D,mean32D2D,mean42D2D]
+    std2D2D = [minstd32D2D,minstd22D2D,minstd12D2D,minstd02D2D,std02D2D,std12D2D,std22D2D,std32D2D,std42D2D]
+    
+    mean02D3D = np.mean([D2D3[0][0],D2D3[1][1], D2D3[2][2], D2D3[3][3], D2D3[4][4]])
+    mean12D3D = np.mean([D2D3[0][1],D2D3[1][2], D2D3[2][3], D2D3[3][4]])
+    mean22D3D = np.mean([D2D3[0][2],D2D3[1][3], D2D3[2][4]])
+    mean32D3D = np.mean([D2D3[0][3],D2D3[1][4]])
+    mean42D3D = np.mean([D2D3[0][4]])
+    
+#    mean2D3D = [mean02D3D,mean12D3D,mean22D3D,mean32D3D,mean42D3D]
+    
+    std02D3D = np.std([D2D3[0][0],D2D3[1][1], D2D3[2][2], D2D3[3][3], D2D3[4][4]])
+    std12D3D = np.std([D2D3[0][1],D2D3[1][2], D2D3[2][3], D2D3[3][4]])
+    std22D3D = np.std([D2D3[0][2],D2D3[1][3], D2D3[2][4]])
+    std32D3D = np.std([D2D3[0][3],D2D3[1][4]])
+    std42D3D = np.std([D2D3[0][4]])
+    
+#    std2D3D = [std02D3D,std12D3D,std22D3D,std32D3D,std42D3D]
+    
+    minmean02D3D = np.mean([D2D3[1][0],D2D3[2][1], D2D3[3][2], D2D3[4][3]])
+    minmean12D3D = np.mean([D2D3[2][0],D2D3[3][1], D2D3[4][2]])
+    minmean22D3D = np.mean([D2D3[3][0],D2D3[4][1]])
+    minmean32D3D = np.mean([D2D3[4][0]])
+    
+#    minmean2D3D = [minmean02D3D,minmean12D3D,minmean22D3D,minmean32D3D]
+    
+    minstd02D3D = np.std([D2D3[1][0],D2D3[2][1], D2D3[3][2], D2D3[4][3]])
+    minstd12D3D = np.std([D2D3[2][0],D2D3[3][1], D2D3[4][2]])
+    minstd22D3D = np.std([D2D3[3][0],D2D3[4][1]])
+    minstd32D3D = np.std([D2D3[4][0]])
+    
+#    minstd2D3D = [minstd02D3D,minstd12D3D,minstd22D3D,minstd32D3D]
+
+    mean2D3D = [minmean32D3D,minmean22D3D,minmean12D3D,minmean02D3D,mean02D3D,mean12D3D,mean22D3D,mean32D3D,mean42D3D]
+    std2D3D = [minstd32D3D,minstd22D3D,minstd12D3D,minstd02D3D,std02D3D,std12D3D,std22D3D,std32D3D,std42D3D]
+
+    N = 5
+    ind = np.asarray([0.25,1.25,2.25,3.25,4.25])
+
+    width = 0.5       # the width of the bars
+
+#    x1 = [0.4,1.4,2.4,3.4,4.4]
+    x2 = [-3.55,-2.55,-1.55,-0.55,0.45,1.45,2.45,3.45,4.45]
+#    x3 = [0.5,1.5,2.5,3.5,4.5]
+    x4 = [-3.45,-2.45,-1.45,-0.45,0.55,1.55,2.55,3.55,4.55]
+#    x5 = [0.6,1.6,2.6,3.6,4.6]
+
+    fig = plt.figure(figsize=(14.0, 10.0))
+
+    
+    
+    ax = fig.add_subplot(111)
+    
+#    print mean2D2D
+#    print mean2D3D
+    
+#    ax.axhline(linewidth=2, y = np.mean(mean2D2D),color='r')
+#    ax.axhline(linewidth=2, y = np.mean(mean2D3D),color='blue')
+    
+    
+#    ax.axhline(linewidth=2, y = minvaluevalue,color='black')
+    
+#    ax.text(0.98, Participantmeanvalue+0.5, "Mean %.2f" % Participantmeanvalue,fontsize=12, fontweight='bold',color='r')
+#    ax.text(0.98, maxvaluevalue+0.5, "Maximum %.2f" % maxvaluevalue,fontsize=12, fontweight='bold',color='black')
+#    ax.text(0.98, minvaluevalue+0.5, "Minimum %.2f" % minvaluevalue,fontsize=12, fontweight='bold', color='black')
+
+#    rects1 = ax.bar(ind, Participantmean,width, color='r',edgecolor='black',)#, hatch='//')
+    rects1 = ax.errorbar(x2, mean2D2D,yerr=[std2D2D,std2D2D],fmt='o',color='red',ecolor='red',lw=3, capsize=5, capthick=2)
+    plt.plot(x2, mean2D2D, marker="o", linestyle='-',lw=3,color='red',label = r'2D-to-2D')
+    
+    rects2 =ax.errorbar(x4, mean2D3D,yerr=[std2D3D,std2D3D],fmt='o',color='blue',ecolor='blue',lw=3, capsize=5, capthick=2)
+    plt.plot(x4, mean2D3D, marker="o", linestyle='-',lw=3,color='blue', label = r'2D-to-3D')
+    
+    legend(fontsize=20,loc='upper right')
+    
+#    rects3 = ax.errorbar(x3, meanC3,yerr=[stdC3,stdC3],fmt='o',color='black',ecolor='black',lw=3, capsize=5, capthick=2)
+#    plt.plot(x3, meanC3, marker="o", linestyle='-',lw=3,color='black')
+#    
+#    rects4 =ax.errorbar(x4, meanC4,yerr=[stdC4,stdC4],fmt='o',color='green',ecolor='green',lw=3, capsize=5, capthick=2)
+#    plt.plot(x4, meanC4, marker="o", linestyle='-',lw=3,color='green')
+#    
+#    rects5 =ax.errorbar(x5, meanC5,yerr=[stdC5,stdC5],fmt='o',color='orange',ecolor='orange',lw=3, capsize=5, capthick=2)
+#    plt.plot(x5, meanC5, marker="o", linestyle='-',lw=3,color='orange')
+
+
+    ax.set_ylabel(r'Angular Error',fontsize=22)
+    ax.set_xlabel(r'Distance Difference',fontsize=22)
+    ax.set_xticks(ind+0.25)
+    ax.set_xticklabels( ('D1', 'D2', 'D3','D4', 'D5') ,fontsize=18)
+    
+    TOPICs = [-3.5,-2.5,-1.5,-0.5,0.5,1.5,2.5,3.5,4.5]#,110]#,120]
+    print TOPICs
+    LABELs = [r'-1m',r'-0.75m', r'-0.5m', r'-0.25m', r'0m',r'0.25m', r'0.5m', r'0.75m', r'1.0m', ""]#, ""]#, ""]
+    
+#    fig.canvas.set_window_title('Distance Error Correlation')
+    plt.xticks(TOPICs, LABELs,fontsize=18)
+
+#    legend([rects1,rects2], [r'\LARGE\textbf{2D2D}', r'\LARGE\textbf{2D3D}'], loc='lower right') 
+
+    TOPICS = [0.5,1.0,1.5,2.0,2.5,3.0,3.5,4.0,4.5,5.0,5.5,6.0,6.5,7.0,7.5,8.0,8.5]#,110]#,120]
+    print TOPICS
+    LABELS = [r'0.5',r'1', r'1.5', r'2', r'2.5', r'3', r'3.5', r'4', r'4.5', r'5', r'5.5', r'6', r'6.5', r'7', r'7.5', r'8', r'8.5']#, ""]#, ""]
+    
+#    fig.canvas.set_window_title('Accuracy - Activity Statistics')
+    plt.yticks(TOPICS, LABELS,fontsize=18)
+
+    def autolabel(rects):
+        # attach some text labels
+        for rect in rects:
+            height = rect.get_height()
+            ax.text(0.26+rect.get_x()+rect.get_width()/2., height +0.35, "%.2f"%float(height),
+                    ha='center', va='bottom',fontweight='bold',fontsize=13.5)
+
+#    autolabel(rects1)
+    
+    
+    left  = 0.1  # the left side of the subplots of the figure
+    right = 0.975    # the right side of the subplots of the figure
+    bottom = 0.075   # the bottom of the subplots of the figure
+    top = 0.925      # the top of the subplots of the figure
+    wspace = 0.2   # the amount of width reserved for blank space between subplots
+    hspace = 0.4   # the amount of height reserved for white space between subplots
+
+    plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
+    plt.show()
+    
+
+
+        
+if __name__ == "__main__":
+    main(sys.argv[1:])
+

code/Visualization/EffectNumberofClusters.py

@@ -0,0 +1,350 @@
+import os, sys
+import seaborn
+from pylab import rcParams
+import cv2
+
+import numpy as np
+from numpy import linalg as LA
+from time import time
+from itertools import combinations
+
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import *
+import matplotlib.patches as mpatches
+
+# activate latex text rendering
+#rc('text', usetex=True)
+
+def main(argv):
+
+    C12D2D = np.load("MeansC1D2D2.npy")
+    C12D3D = np.load("MeansC1D2D3.npy")
+    
+    C22D2D = np.load("MeansC2D2D2.npy")
+    C22D3D = np.load("MeansC2D2D3.npy")
+    
+    C32D2D = np.load("MeansC3D2D2.npy")
+    C32D3D = np.load("MeansC3D2D3.npy")
+    
+    C42D2D = np.load("MeansC4D2D2.npy")
+    C42D3D = np.load("MeansC4D2D3.npy")
+    
+    C52D2D = np.load("MeansC5D2D2.npy")
+    C52D3D = np.load("MeansC5D2D3.npy")
+    
+    
+    summeC12D2D = [] 
+    summeC22D2D = [] 
+    summeC32D2D = [] 
+    summeC42D2D = [] 
+    summeC52D2D = [] 
+    
+    summeC12D3D = [] 
+    summeC22D3D = [] 
+    summeC32D3D = [] 
+    summeC42D3D = [] 
+    summeC52D3D = [] 
+    
+
+    i = 0
+    while i < len(C12D2D):
+        j = 0
+        while j < len(C12D2D[0]):
+            summeC12D2D.append(C12D2D[i][j])
+            j += 1
+        i += 1
+        
+    i = 0
+    while i < len(C22D2D):
+        j = 0
+        while j < len(C22D2D[0]):
+            summeC22D2D.append(C22D2D[i][j])
+            j += 1
+        i += 1
+        
+    i = 0
+    while i < len(C32D2D):
+        j = 0
+        while j < len(C32D2D[0]):
+            summeC32D2D.append(C32D2D[i][j])
+            j += 1
+        i += 1
+        
+    i = 0
+    while i < len(C42D2D):
+        j = 0
+        while j < len(C42D2D[0]):
+            summeC42D2D.append(C42D2D[i][j])
+            j += 1
+        i += 1
+        
+    i = 0
+    while i < len(C52D2D):
+        j = 0
+        while j < len(C52D2D[0]):
+            summeC52D2D.append(C52D2D[i][j])
+            j += 1
+        i += 1
+        
+    i = 0
+    while i < len(C12D3D):
+        j = 0
+        while j < len(C12D3D[0]):
+            summeC12D3D.append(C12D3D[i][j])
+            j += 1
+        i += 1
+        
+    i = 0
+    while i < len(C22D3D):
+        j = 0
+        while j < len(C22D3D[0]):
+            summeC22D3D.append(C22D3D[i][j])
+            j += 1
+        i += 1
+        
+    i = 0
+    while i < len(C32D3D):
+        j = 0
+        while j < len(C32D3D[0]):
+            summeC32D3D.append(C32D3D[i][j])
+            j += 1
+        i += 1
+        
+    i = 0
+    while i < len(C42D3D):
+        j = 0
+        while j < len(C42D3D[0]):
+            summeC42D3D.append(C42D3D[i][j])
+            j += 1
+        i += 1
+        
+    i = 0
+    while i < len(C52D3D):
+        j = 0
+        while j < len(C52D3D[0]):
+            summeC52D3D.append(C52D3D[i][j])
+            j += 1
+        i += 1
+        
+        
+    mean1 = np.mean(summeC12D2D)
+    mean2 = np.mean(summeC22D2D) 
+    mean3 = np.mean(summeC32D2D) 
+    mean4 = np.mean(summeC42D2D) 
+    mean5 = np.mean(summeC52D2D) 
+    
+    mean6 = np.mean(summeC12D3D) 
+    mean7 = np.mean(summeC22D3D)
+    mean8 = np.mean(summeC32D3D) 
+    mean9 = np.mean(summeC42D3D) 
+    mean10 = np.mean(summeC52D3D) 
+    
+    std1 = np.std(summeC12D2D)
+    std2 = np.std(summeC22D2D) 
+    std3 = np.std(summeC32D2D) 
+    std4 = np.std(summeC42D2D) 
+    std5 = np.std(summeC52D2D) 
+    
+    std6 = np.std(summeC12D3D) 
+    std7 = np.std(summeC22D3D)
+    std8 = np.std(summeC32D3D) 
+    std9 = np.std(summeC42D3D) 
+    std10 = np.std(summeC52D3D) 
+        
+#    i = 0
+#    while i < len(C12D2D):
+#        j = 0
+#        while j < len(C12D2D[0]):
+#            summeC12D2D.append(C12D2D[i][j])
+#            j += 1
+#        i += 1
+        
+        
+#    print summeC12D2D
+        
+#    i = 0
+#    minimum2 = 100
+#    while i < len(C22D2D):
+#        print np.mean(C22D2D[i])
+#        if np.mean(C22D2D[i]) < minimum2:
+#            minimum2 = np.mean(C22D2D[i])
+#        i += 1
+#        
+#    i = 0
+#    minimum3 = 100
+#    while i < len(C32D2D):
+#        print np.mean(C32D2D[i])
+#        if np.mean(C32D2D[i]) < minimum3:
+#            minimum3 = np.mean(C32D2D[i])
+#        i += 1
+#        
+#    i = 0
+#    minimum4 = 100
+#    while i < len(C42D2D):
+#        print np.mean(C42D2D[i])
+#        if np.mean(C42D2D[i]) < minimum4:
+#            minimum4 = np.mean(C42D2D[i])
+#        i += 1
+#        
+#    i = 0
+#    minimum5 = 100
+#    while i < len(C52D2D):
+#        print np.mean(C52D2D[i])
+#        if np.mean(C52D2D[i]) < minimum5:
+#            minimum5 = np.mean(C52D2D[i])
+#        i += 1
+#        
+#    i = 0
+#    minimum6 = 100
+#    while i < len(C12D3D):
+#        print np.mean(C12D3D[i])
+#        if np.mean(C12D3D[i]) < minimum6:
+#            minimum6 = np.mean(C12D3D[i])
+#        i += 1
+#        
+#    i = 0
+#    minimum7 = 100
+#    while i < len(C22D3D):
+#        print np.mean(C22D3D[i])
+#        if np.mean(C22D3D[i]) < minimum7:
+#            minimum7 = np.mean(C22D3D[i])
+#        i += 1
+#        
+#    i = 0
+#    minimum8 = 100
+#    while i < len(C32D3D):
+#        print np.mean(C32D3D[i])
+#        if np.mean(C32D3D[i]) < minimum8:
+#            minimum8 = np.mean(C32D3D[i])
+#        i += 1
+#        
+#    i = 0
+#    minimum9 = 100
+#    while i < len(C42D3D):
+#        print np.mean(C42D3D[i])
+#        if np.mean(C42D3D[i]) < minimum9:
+#            minimum9 = np.mean(C42D3D[i])
+#        i += 1
+
+#    i = 0
+#    minimum10 = 100
+#    while i < len(C52D3D):
+#        print np.mean(C52D3D[i])
+#        if np.mean(C52D3D[i]) < minimum10:
+#            minimum10 = np.mean(C52D3D[i])
+#        i += 1
+#        
+    mean2D2D = [mean1,mean2,mean3,mean4,mean5]
+    mean2D3D = [mean6,mean7,mean8,mean9,mean10]
+    
+    std2D2D = [std1,std2,std3,std4,std5]
+    std2D3D = [std6,std7,std8,std9,std10]
+        
+#    print minimum1
+        
+#    i = 0
+#    minimum2 = 100
+#    while i < 5:
+#        if np.mean(C12D2D[i]) < minimum:
+#            minimum1 = np.mean(C12D2D[i])
+#        i += 1
+    
+
+    
+    
+
+    N = 5
+    ind = np.asarray([0.25,1.25,2.25,3.25,4.25])
+
+    width = 0.5       # the width of the bars
+
+#    x1 = [0.4,1.4,2.4,3.4,4.4]
+    x2 = [0.45,1.45,2.45,3.45,4.45]
+#    x3 = [0.5,1.5,2.5,3.5,4.5]
+    x4 = [0.55,1.55,2.55,3.55,4.55]
+#    x5 = [0.6,1.6,2.6,3.6,4.6]
+
+    fig = plt.figure(figsize=(14.0, 10.0))
+
+    
+    
+    ax = fig.add_subplot(111)
+    
+#    print mean2D2D
+#    print mean2D3D
+    
+#    ax.axhline(linewidth=2, y = np.mean(mean2D2D),color='r')
+#    ax.axhline(linewidth=2, y = np.mean(mean2D3D),color='blue')
+    
+#    ax.axhline(linewidth=2, y = minvaluevalue,color='black')
+    
+#    ax.text(0.98, Participantmeanvalue+0.5, "Mean %.2f" % Participantmeanvalue,fontsize=12, fontweight='bold',color='r')
+#    ax.text(0.98, maxvaluevalue+0.5, "Maximum %.2f" % maxvaluevalue,fontsize=12, fontweight='bold',color='black')
+#    ax.text(0.98, minvaluevalue+0.5, "Minimum %.2f" % minvaluevalue,fontsize=12, fontweight='bold', color='black')
+
+#    rects1 = ax.bar(ind, Participantmean,width, color='r',edgecolor='black',)#, hatch='//')
+    rects1 = ax.errorbar(x2, mean2D2D,yerr=[std2D2D,std2D2D],fmt='o',color='red',ecolor='red',lw=3, capsize=5, capthick=2)
+    plt.plot(x2, mean2D2D, marker="o", linestyle='-',lw=3,color='red',label = r'2D-to-2D')
+    
+    rects2 =ax.errorbar(x4, mean2D3D,yerr=[std2D3D,std2D3D],fmt='o',color='blue',ecolor='blue',lw=3, capsize=5, capthick=2)
+    plt.plot(x4, mean2D3D, marker="o", linestyle='-',lw=3,color='blue', label = r'2D-to-3D')
+    
+    legend(fontsize=20,loc='upper right')
+    
+#    rects3 = ax.errorbar(x3, meanC3,yerr=[stdC3,stdC3],fmt='o',color='black',ecolor='black',lw=3, capsize=5, capthick=2)
+#    plt.plot(x3, meanC3, marker="o", linestyle='-',lw=3,color='black')
+#    
+#    rects4 =ax.errorbar(x4, meanC4,yerr=[stdC4,stdC4],fmt='o',color='green',ecolor='green',lw=3, capsize=5, capthick=2)
+#    plt.plot(x4, meanC4, marker="o", linestyle='-',lw=3,color='green')
+#    
+#    rects5 =ax.errorbar(x5, meanC5,yerr=[stdC5,stdC5],fmt='o',color='orange',ecolor='orange',lw=3, capsize=5, capthick=2)
+#    plt.plot(x5, meanC5, marker="o", linestyle='-',lw=3,color='orange')
+
+
+    ax.set_ylabel(r'Angular Error',fontsize=22)
+    ax.set_xlabel(r'Number of Calibration Depths',fontsize=22)
+    ax.set_xticks(ind+0.25)
+    ax.set_xticklabels( ('D1', 'D2', 'D3','D4', 'D5') ,fontsize=18)
+    
+    TOPICs = [0.0,0.5,1.5,2.5,3.5,4.5,5.0]#,110]#,120]
+    print TOPICs
+    LABELs = ["",r'1',r'2', r'3', r'4', r'5', ""]#, ""]#, ""]
+    
+#    fig.canvas.set_window_title('Distance Error Correlation')
+    plt.xticks(TOPICs, LABELs,fontsize=18)
+
+#    legend([rects1,rects2], [r'\LARGE\textbf{2D2D}', r'\LARGE\textbf{2D3D}'], loc='lower right') 
+
+    TOPICS = [0.5,1,1.5,2,2.5,3,3.5,4,4.5,5]#,110]#,120]
+    print TOPICS
+    LABELS = [r'0.5',  r'1',r'1.5', r'2',r'2.5', r'3',r'3.5', r'4',r'4.5',r'5']#, ""]#, ""]
+    
+#    fig.canvas.set_window_title('Accuracy - Activity Statistics')
+    plt.yticks(TOPICS, LABELS,fontsize=18)
+
+    def autolabel(rects):
+        # attach some text labels
+        for rect in rects:
+            height = rect.get_height()
+            ax.text(0.26+rect.get_x()+rect.get_width()/2., height +0.35, "%.2f"%float(height),
+                    ha='center', va='bottom',fontweight='bold',fontsize=13.5)
+
+#    autolabel(rects1)
+    
+    
+    left  = 0.1  # the left side of the subplots of the figure
+    right = 0.975    # the right side of the subplots of the figure
+    bottom = 0.075   # the bottom of the subplots of the figure
+    top = 0.925      # the top of the subplots of the figure
+    wspace = 0.2   # the amount of width reserved for blank space between subplots
+    hspace = 0.4   # the amount of height reserved for white space between subplots
+
+    plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
+    plt.show()
+    
+
+
+        
+if __name__ == "__main__":
+    main(sys.argv[1:])
+

code/Visualization/EffectNumberofClusters_plus_Simulation.py

@@ -0,0 +1,418 @@
+from __future__ import division
+
+import os, sys
+import seaborn
+from pylab import rcParams
+import cv2
+
+import numpy as np
+from numpy import linalg as LA
+from time import time
+from itertools import combinations
+
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import *
+import matplotlib.patches as mpatches
+
+sys.path.append('..') # so we can import modules from `code` directory
+from minimize import findInitialW, _q, g, minimizeEnergy, g3D3D, findW3D3D
+from minimize import g as gaze_ray
+from geom import getSphericalCoords, getAngularDiff
+from vector import Vector as v
+
+from sim import GazeSimulation
+from recording.util.tools import is_outlier
+from recording.tracker import readCameraParams
+from geom import getRotationMatrix
+from parallax_analysis import Experiment
+
+'''
+Change lines 48 to 61 accordingly
+'''
+
+
+class Parallax2Dto3DMapping(Experiment):
+    '''
+    IMPORTANT!
+    In all experiments, scene camera's rvec = (0, 0, 0) i.e. the corresponding rotation matrix is the identity matrix therefore
+    I have not included the dot production with this rotation matrix to convert points in world coordinates
+    into scene camera coordinates. however, one should know that if the scene camera is rotated differentl7y
+    this transformation is essential. I would add the corresponding computations later on.
+    '''
+
+    def __run__(self):
+        # Processing real world data
+        ######################################################################################################
+        print '> Processing real world data...'
+
+        C12D2D = np.load("../results/MeansC1D2D2.npy")
+        C12D3D = np.load("../results/MeansC1D2D3.npy")
+
+        C22D2D = np.load("../results/MeansC2D2D2.npy")
+        C22D3D = np.load("../results/MeansC2D2D3.npy")
+
+        C32D2D = np.load("../results/MeansC3D2D2.npy")
+        C32D3D = np.load("../results/MeansC3D2D3.npy")
+
+        C42D2D = np.load("../results/MeansC4D2D2.npy")
+        C42D3D = np.load("../results/MeansC4D2D3.npy")
+
+        C52D2D = np.load("../results/MeansC5D2D2.npy")
+        C52D3D = np.load("../results/MeansC5D2D3.npy")
+
+
+        summeC12D2D = [] 
+        summeC22D2D = [] 
+        summeC32D2D = [] 
+        summeC42D2D = [] 
+        summeC52D2D = [] 
+
+        summeC12D3D = [] 
+        summeC22D3D = [] 
+        summeC32D3D = [] 
+        summeC42D3D = [] 
+        summeC52D3D = [] 
+
+
+        i = 0
+        while i < len(C12D2D):
+            j = 0
+            while j < len(C12D2D[0]):
+                summeC12D2D.append(C12D2D[i][j])
+                j += 1
+            i += 1
+            
+        i = 0
+        while i < len(C22D2D):
+            j = 0
+            while j < len(C22D2D[0]):
+                summeC22D2D.append(C22D2D[i][j])
+                j += 1
+            i += 1
+            
+        i = 0
+        while i < len(C32D2D):
+            j = 0
+            while j < len(C32D2D[0]):
+                summeC32D2D.append(C32D2D[i][j])
+                j += 1
+            i += 1
+            
+        i = 0
+        while i < len(C42D2D):
+            j = 0
+            while j < len(C42D2D[0]):
+                summeC42D2D.append(C42D2D[i][j])
+                j += 1
+            i += 1
+            
+        i = 0
+        while i < len(C52D2D):
+            j = 0
+            while j < len(C52D2D[0]):
+                summeC52D2D.append(C52D2D[i][j])
+                j += 1
+            i += 1
+            
+        i = 0
+        while i < len(C12D3D):
+            j = 0
+            while j < len(C12D3D[0]):
+                summeC12D3D.append(C12D3D[i][j])
+                j += 1
+            i += 1
+            
+        i = 0
+        while i < len(C22D3D):
+            j = 0
+            while j < len(C22D3D[0]):
+                summeC22D3D.append(C22D3D[i][j])
+                j += 1
+            i += 1
+            
+        i = 0
+        while i < len(C32D3D):
+            j = 0
+            while j < len(C32D3D[0]):
+                summeC32D3D.append(C32D3D[i][j])
+                j += 1
+            i += 1
+            
+        i = 0
+        while i < len(C42D3D):
+            j = 0
+            while j < len(C42D3D[0]):
+                summeC42D3D.append(C42D3D[i][j])
+                j += 1
+            i += 1
+            
+        i = 0
+        while i < len(C52D3D):
+            j = 0
+            while j < len(C52D3D[0]):
+                summeC52D3D.append(C52D3D[i][j])
+                j += 1
+            i += 1
+            
+            
+        mean1 = np.mean(summeC12D2D)
+        mean2 = np.mean(summeC22D2D) 
+        mean3 = np.mean(summeC32D2D) 
+        mean4 = np.mean(summeC42D2D) 
+        mean5 = np.mean(summeC52D2D) 
+
+        mean6 = np.mean(summeC12D3D) 
+        mean7 = np.mean(summeC22D3D)
+        mean8 = np.mean(summeC32D3D) 
+        mean9 = np.mean(summeC42D3D) 
+        mean10 = np.mean(summeC52D3D) 
+
+        std1 = np.std(summeC12D2D)
+        std2 = np.std(summeC22D2D) 
+        std3 = np.std(summeC32D2D) 
+        std4 = np.std(summeC42D2D) 
+        std5 = np.std(summeC52D2D) 
+
+        std6 = np.std(summeC12D3D) 
+        std7 = np.std(summeC22D3D)
+        std8 = np.std(summeC32D3D) 
+        std9 = np.std(summeC42D3D) 
+        std10 = np.std(summeC52D3D) 
+            
+        mean2D2D_real = [mean1,mean2,mean3,mean4,mean5]
+        mean2D3D_real = [mean6,mean7,mean8,mean9,mean10]
+
+        std2D2D_real = [std1,std2,std3,std4,std5]
+        std2D3D_real = [std6,std7,std8,std9,std10]
+        ######################################################################################################
+        # Simulation
+        print '> Processing simulation data...'
+        ######################################################################################################
+        sim = GazeSimulation(log = False)
+
+        sim.place_eyeball_on_scene_camera = False
+        sim.setEyeRelativeToSceneCamera(v(-65, -33, -73))
+        # sim.setEyeRelativeToSceneCamera(v(-65, -33, 0)) # assuming eyeball and scene camera are coplanar i.e. e = (e.x, e.y, 0)
+
+        sim.setCalibrationDepth(1 * 1000) # mm, wrt scene camera
+        sim.setTestDepth(1.5 * 1000)
+        sim.calibration_grid = True
+        sim.calibration_random_depth = False
+        sim.test_grid = True
+        sim.test_random_depth = False
+        sim.test_random_fixed_depth = False
+
+        depths = map(lambda d:d*1000, [1, 1.25, 1.5, 1.75, 2.0])
+        print '> Computing results for multiple calibration depths...'
+        results, results_std = [], []
+        for num_of_calibration_depths in xrange(1, 6): # from 1 calibration depths to 5
+            print '> Considering only %s calibration depth(s)...' %num_of_calibration_depths
+            sim.reset() 
+            aae_2ds_aae = []
+            aae_2ds_phe = []
+            aae_3ds_aae = []
+            aae_3ds_phe = []
+            aae_3D3Ds = [] # angular error
+
+            for calibs in combinations(depths, num_of_calibration_depths):
+                # Now calibs is a set of depths from each of which we need calibration data
+                print '> Current calibration depths', calibs
+                calibs = list(calibs)
+                cp, ct = [], []
+                sim.reset() 
+                sim.setCalibrationDepth(calibs)
+                # Perform calibration
+                sim.runCalibration()
+                cp, ct, p3d = sim.tr_pupil_locations, sim.calibration_points, sim.tr_3d_pupil_locations
+                # target positions are computed relative to the scene CCS
+                ti = map(lambda target: v(target) - v(sim.scene_camera.t), ct)
+                # Computing pupil pose for each gaze
+                ni = map(lambda p: (v(p)-v(sim.sclera_pos)).norm(), p3d) # ground truth gaze vectors
+                
+                w, e, w0 = minimizeEnergy(cp, ti)
+                e = v(e)
+
+                # transforming pupil pose to eye camera CS
+                eyeR = np.array(sim.eye_camera.R[:3])
+                ni = map(lambda pose: eyeR.dot(np.array(pose)), ni) 
+                
+                R, e3d3d = minimizeEnergy(ni, ti, pose_given=True)
+                e3d3d = v(e3d3d)
+                # R = LA.inv(R)
+
+                # Now we have calibration data from multiple depths, we can test on all depths
+                for test_depth in depths:
+                    sim.setTestDepth(test_depth)
+                    aae_2d_aae, aae_2d_phe, aae_2d_std, _ = sim.runTest() # last one is PHE std
+                    aae_2ds_aae.append((aae_2d_aae, aae_2d_std))
+                    aae_2ds_phe.append(aae_2d_phe)
+                    # Fetching test points
+                    t, p, p3d = sim.test_points, sim.te_pupil_locations, sim.te_3d_pupil_locations
+                    t = map(lambda target: v(target) - v(sim.scene_camera.t), t) # target coords in scene CCS
+
+                    # 3D3D
+                    t_3d3d = t[:]
+
+                    ni = map(lambda p: v(v(p)-v(sim.sclera_pos)).norm(), p3d) # ground truth gaze vectors
+                    # transforming pupil pose to eye camera CS
+                    ni = map(lambda r: v(eyeR.dot(np.array(r))), ni)
+
+                    # applying estimated rotation to pose vector in eye camera coordinates (Rn)
+                    # R is estimated rotation between scene camera and eye coordinate system (not eye camera!)
+                    # in other words, R is the rotation part of e
+                    Rni = map(lambda n: v(R.dot(np.array(n))), ni) # now ready to compare Rn with t-e
+                    # Intersecting gaze rays originating from the eye with the planes defined by each
+                    # target. then we can simply compute angular error between each intersection and 
+                    # the corresponding 3D target
+                    gis = map(lambda vec: v(vec), Rni) # gaze rays originating from eyeball
+                    # we multiply g such that it hits t's z-plane i.e. multiply all coordinates by factor (t.z-e.z)/g.z
+                    # then we add e to the final g so that it originates from scene camera. now both g and t are in the
+                    # same coordinate system and originate from the same point, so we can compare them
+                    gprimes = map(lambda tg: v(((tg[0].z - e3d3d.z)/tg[1].z)*tg[1] + e3d3d), zip(t_3d3d, gis))
+                    AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, t_3d3d))
+
+                    N = len(t)
+                    AAE = np.mean(AE)
+                    STD = np.std(AE)
+                    m, M = min(AE), max(AE)
+                    aae_3D3Ds.append((AAE, STD))
+
+
+                    qi = map(_q, p) # computing feature vectors from raw pupil coordinates in 2D
+                    # computing unit gaze vectors corresponding to pupil positions
+                    # here we use the computed mapping matrix w
+                    gis = map(lambda q: g(q, w), qi) 
+                    
+                    # Intersecting gaze rays originating from the eye with the planes defined by each
+                    # target. then we can simply compute angular error between each intersection and 
+                    # the corresponding 3D target
+                    t = map(lambda vec: v(vec), t)
+                    gis = map(lambda vec: v(vec), gis)
+                    gprimes = map(lambda tg: v(((tg[0].z - e.z)/tg[1].z)*tg[1] + e), zip(t, gis))
+
+                    AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, t))
+                    N = len(t)
+                    AAE = np.mean(AE)
+                    STD = np.std(AE)
+                    m, M = min(AE), max(AE)
+
+                    # Computing physical distance error (in meters)
+                    PHE = list((u-v).mag/1000 for u,v in zip(t, gprimes))
+                    N = len(t)
+                    APHE = np.mean(PHE)
+                    PHE_STD = np.std(PHE)
+                    PHE_m, PHE_M = min(PHE), max(PHE)
+
+                    aae_3ds_aae.append((AAE, STD))
+                    aae_3ds_phe.append((PHE, PHE_STD))
+            
+            # results only contains AAE
+            results.append([np.mean(np.array(aae_2ds_aae)[:,0]), np.mean(np.array(aae_3ds_aae)[:,0]), np.mean(np.array(aae_3D3Ds)[:,0])])
+            results_std.append([np.std(np.array(aae_2ds_aae)[:,0]), np.std(np.array(aae_3ds_aae)[:,0]), np.std(np.array(aae_3D3Ds)[:,0])])
+        ######################################################################################################
+        # Plotting
+        print '> Plotting...'
+        ######################################################################################################
+        # New plot code based on EffectNumberofClusters.py
+        mean2D2D = [res[0] for res in results]
+        mean2D3D = [res[1] for res in results]
+        mean3D3D = [res[2] for res in results]
+        std2D2D = [res[0] for res in results_std]
+        std2D3D = [res[1] for res in results_std]
+        std3D3D = [res[2] for res in results_std]
+
+
+        N = 5
+        ind = np.asarray([0.25,1.25,2.25,3.25,4.25])
+
+        width = 0.5       # the width of the bars
+
+        #    x1 = [0.4,1.4,2.4,3.4,4.4]
+        x2 = [0.45,1.45,2.45,3.45,4.45]
+        #    x3 = [0.5,1.5,2.5,3.5,4.5]
+        x4 = [0.55,1.55,2.55,3.55,4.55]
+        #    x5 = [0.6,1.6,2.6,3.6,4.6]
+        x6 = [0.50,1.50,2.50,3.50,4.50]
+
+        fig = plt.figure(figsize=(14.0, 10.0))
+
+        ax = fig.add_subplot(111)
+
+        rrects1 = ax.errorbar(x2, mean2D2D_real,yerr=[std2D2D_real,std2D2D_real],fmt='o',color='red',ecolor='red',lw=3, capsize=8, capthick=3)
+        plt.plot(x2, mean2D2D_real, marker="o", linestyle='-',lw=3,color='red',label = r'2D-to-2D')
+
+        rrects2 =ax.errorbar(x4, mean2D3D_real,yerr=[std2D3D_real,std2D3D_real],fmt='o',color='blue',ecolor='blue',lw=3, capsize=8, capthick=3)
+        plt.plot(x4, mean2D3D_real, marker="o", linestyle='-',lw=3,color='blue', label = r'2D-to-3D')
+
+
+        rects1 = ax.errorbar(x2, mean2D2D,yerr=[std2D2D,std2D2D],fmt='o',color='red',ecolor='red',lw=3, capsize=5, capthick=2)
+        plt.plot(x2, mean2D2D, marker="o", linestyle='--',lw=3,color='red',label = r'2D-to-2D Simulation')
+
+        rects2 =ax.errorbar(x4, mean2D3D,yerr=[std2D3D,std2D3D],fmt='o',color='blue',ecolor='blue',lw=3, capsize=5, capthick=2)
+        plt.plot(x4, mean2D3D, marker="o", linestyle='--',lw=3,color='blue', label = r'2D-to-3D Simulation')
+
+        rects3 =ax.errorbar(x6, mean3D3D,yerr=[std3D3D,std3D3D],fmt='o',color='orange',ecolor='orange',lw=3, capsize=5, capthick=2)
+        plt.plot(x6, mean3D3D, marker="o", linestyle='--',lw=3,color='orange', label = r'3D-to-3D Simulation')
+
+        legend(fontsize=20,loc='upper right')
+
+        #    rects3 = ax.errorbar(x3, meanC3,yerr=[stdC3,stdC3],fmt='o',color='black',ecolor='black',lw=3, capsize=5, capthick=2)
+        #    plt.plot(x3, meanC3, marker="o", linestyle='-',lw=3,color='black')
+        #    
+        #    rects4 =ax.errorbar(x4, meanC4,yerr=[stdC4,stdC4],fmt='o',color='green',ecolor='green',lw=3, capsize=5, capthick=2)
+        #    plt.plot(x4, meanC4, marker="o", linestyle='-',lw=3,color='green')
+        #    
+        #    rects5 =ax.errorbar(x5, meanC5,yerr=[stdC5,stdC5],fmt='o',color='orange',ecolor='orange',lw=3, capsize=5, capthick=2)
+        #    plt.plot(x5, meanC5, marker="o", linestyle='-',lw=3,color='orange')
+
+
+        ax.set_ylabel(r'Angular Error',fontsize=22)
+        ax.set_xlabel(r'Number of Calibration Depths',fontsize=22)
+        ax.set_xticks(ind+0.25)
+        ax.set_xticklabels( ('D1', 'D2', 'D3','D4', 'D5') ,fontsize=18)
+
+        TOPICs = [0.0,0.5,1.5,2.5,3.5,4.5,5.0]#,110]#,120]
+        print TOPICs
+        LABELs = ["",r'1',r'2', r'3', r'4', r'5', ""]#, ""]#, ""]
+
+        #    fig.canvas.set_window_title('Distance Error Correlation')
+        plt.xticks(TOPICs, LABELs,fontsize=18)
+
+        #    legend([rects1,rects2], [r'\LARGE\textbf{2D2D}', r'\LARGE\textbf{2D3D}'], loc='lower right') 
+
+        TOPICS = [0.0, 0.5,1,1.5,2,2.5,3,3.5,4,4.5,5]#,110]#,120]
+        print TOPICS
+        LABELS = [r'0.0', r'0.5',  r'1',r'1.5', r'2',r'2.5', r'3',r'3.5', r'4',r'4.5',r'5']#, ""]#, ""]
+
+        #    fig.canvas.set_window_title('Accuracy - Activity Statistics')
+        plt.yticks(TOPICS, LABELS,fontsize=18)
+
+        def autolabel(rects):
+            # attach some text labels
+            for rect in rects:
+                height = rect.get_height()
+                ax.text(0.26+rect.get_x()+rect.get_width()/2., height +0.35, "%.2f"%float(height),
+                ha='center', va='bottom',fontweight='bold',fontsize=13.5)
+
+            #    autolabel(rects1)
+
+
+        left  = 0.1  # the left side of the subplots of the figure
+        right = 0.975    # the right side of the subplots of the figure
+        bottom = 0.075   # the bottom of the subplots of the figure
+        top = 0.925      # the top of the subplots of the figure
+        wspace = 0.2   # the amount of width reserved for blank space between subplots
+        hspace = 0.4   # the amount of height reserved for white space between subplots
+
+        plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
+        plt.show()
+        ######################################################################################################
+
+
+def main():
+    ex = Parallax2Dto3DMapping()
+    ex.performExperiment()
+        
+if __name__ == "__main__":
+    main()
+

code/copy_missing_data.py

@@ -0,0 +1,36 @@
+'''
+Copies all missing files from ROOT to DEST for selected PARTICIPANTS
+'''
+import os, sys
+import shutil
+
+PARTICIPANTS = ['p10', 'p16', 'p13', 'p24', 'p5', 'p14', 'p26', 'p12', 'p20', 'p7', 'p15', 'p11', 'p21', 'p25']
+
+DEST = '/home/mmbrian/3D_Gaze_Tracking/archive00/participants/'
+ROOT = '/home/mmbrian/HiWi/etra2016_mohsen/code/recording/data/participants/'
+
+def main():
+	processed = 0
+	for p in os.listdir(ROOT):
+		if p in PARTICIPANTS:
+			print '> Copying missing files for participant', p
+			d1 = os.path.join(ROOT, p)
+			d1 = os.path.join(d1, os.listdir(d1)[0]) # ../p_i/../
+			for d2 in os.listdir(d1): # per recording
+				root_path = os.path.join(d1, d2)
+				dest_path = os.path.join(DEST, p)
+				dest_path = os.path.join(dest_path, os.listdir(dest_path)[0], d2)
+				print '> From:', root_path, 'To:', dest_path			
+				processPath(root_path, dest_path)
+			processed+=1
+			print '> Processed %s participants.' % processed
+
+def processPath(root_path, dest_path):
+	dest_files = os.listdir(dest_path)
+	for f in os.listdir(root_path): # every file from root path
+		if not f in dest_files: # copy if file does not exist in destination
+			print '> Copying', f
+			shutil.copyfile(os.path.join(root_path, f), os.path.join(dest_path, f))
+
+if __name__ == '__main__':
+	main()

code/experiment.py

@@ -0,0 +1,441 @@
+from __future__ import division
+import numpy as np
+from numpy import linalg as LA
+
+from time import time
+
+from sklearn import linear_model
+
+from sim import GazeSimulation
+from svis import Ray
+from minimize import findInitialW, _q, g, minimizeEnergy, minimizeEnergyGivenE, minimizeUsingScaleVector
+
+from geom import getSphericalCoords, getAngularDiff
+
+import visual as vs
+# from visual import vector as v # for vector operations
+from vector import Vector as v # for vector operations
+
+class Experiment:
+
+	def performExperiment(self):
+		print 'Running Experiment...'
+		start = time()
+		self.__run__()
+		self.runningTime = time() - start
+		print 'Running Time:', self.runningTime
+		if self.minimizationTime:
+			print 'Minimization Time:', self.minimizationTime
+
+	def __run__(self):
+		pass
+
+
+class Experiment1(Experiment):
+
+	def __run__(self):
+		'''
+		Simply evaluates 2D gaze estimation (using pupil calibration method)
+		'''
+		sim = GazeSimulation(log = False)
+		## Uncomment for shifting eyeball to scene camera coordinates
+		sim.scene_eye_distance_factor = 0.6 # controls how much to move eyeball towards scene camera [0-1]
+		sim.place_eyeball_on_scene_camera = False
+
+		# calibration_grid, calibration_random_depth,	test_grid, test_random_depth, test_random_fixed_depth
+		experiments = [
+			[True, 				False,				False,  		True,				False],
+			[True, 				False,				False,  		False,				False],
+			[True, 				False,				False,  		False,				True],
+			[False, 			True,				False,  		True,				False],
+			[False, 			True,				False,  		False,				False],
+			[False, 			True,				False,  		False,				True],
+			[False, 			False,				False,  		True,				False],
+			[False, 			False,				False,  		False,				False],
+			[False, 			False,				False,  		False,				True],
+			# [True, 				False,				True,  			False,				False],
+		]
+
+		nsim = 100
+		for experiment in experiments:
+			res = [sim.runCustomSimulation(experiment) for n in xrange(nsim)]
+			res = filter(lambda aae: aae>=0, res)
+			M = max(res)
+			m = min(res)
+			aae = sum(res) / len(res)
+			print 'AAE: %2.5f - Min AAE: %2.5f | Max AAE: %2.5f' % (aae, m, M)
+
+		eye_scene_diff = v(sim.sclera_pos) - v(sim.scene_camera.t)
+		print "Eyeball-SceneCamera distance was", eye_scene_diff.mag
+
+		self.sim = sim
+		self.minimizationTime = -1
+
+
+class Experiment1p5(Experiment):
+
+	def __run__(self):
+		'''
+		Read 3D gaze estimation experiment
+		'''
+		sim = GazeSimulation(log = False)
+		## Uncomment for shifting eyeball to scene camera coordinates
+		sim.place_eyeball_on_scene_camera = True
+		sim.scene_eye_distance_factor = 0.6
+		# sim.scene_eye_distance_factor = 1.0
+
+		# 	calibration_grid, calibration_random_depth,	test_grid, test_random_depth, test_random_fixed_depth
+		# -> grid setting
+		# con = [True, 				False,				True,  		False,				True]	
+		# -> general setting with grid learning
+		# con = [True, 				False,				False,  		True,				False]
+		con = [True, 				False,				False,  		False,				False]
+		# -> general setting with random learning
+		# con = [False, 				True,				False,  		True,				False]	
+		sim.num_calibration = 36
+		sim.num_test = 25
+		aae_2d = sim.runCustomSimulation(con)
+
+		## Fetching the calibration points
+		t, p = sim.calibration_points, sim.tr_pupil_locations
+		# target positions are computed relative to the scene CCS
+		t = map(lambda target: v(target) - v(sim.scene_camera.t), t) # verified
+		# p = sim.eye_camera.getNormalizedPts(p)
+
+		start = time()
+
+		eye_scene_diff = v(sim.sclera_pos) - v(sim.scene_camera.t)
+		e = np.array(eye_scene_diff) # eyeball coords in scene CCS
+		print 'Real e', e
+		e_org = e[:]
+		print 'Eye-Scene distance', eye_scene_diff.mag
+		# w, w0 = minimizeEnergyGivenE(p, t, e)
+
+		## Finding the optimal transformation matrix by minimizing the nonlinear energy
+		# w0 is the initial w by solving the leastsq with e=(0,0,0)
+		# w is by solving the leastsq again optimizing for both e and w
+		w, e, w0 = minimizeEnergy(p, t)
+		
+		# here were are initializing minimization with an e close to the ground
+		# truth e by adding random perturbations to it
+		# w, e, w0 = minimizeEnergy(p, t, e + np.random.rand(1, 3)[0])
+
+		print 'Estimated e', e, '( Distance:', LA.norm(e - e_org) , ')'
+		self.minimizationTime = time() - start
+		print
+
+		sim.w = w
+
+		## Fetching test points
+		t, p = sim.test_points, sim.te_pupil_locations
+		t = map(lambda target: v(target) - v(sim.scene_camera.t), t) # target coords in scene CCS
+
+		# closest point distance to scene camera
+		cDist = min(v(pt).mag for pt in t)
+		# farthest point distance to scene camera
+		fDist = max(v(pt).mag for pt in t)
+		# average point distance to scene camera
+		avgDist = sum(v(pt).mag for pt in t)/len(t) 
+
+
+		# p = sim.eye_camera.getNormalizedPts(p)
+		# print
+		# print p
+		# print
+
+
+		qi = map(_q, p) # computing feature vectors from raw pupil coordinates in 2D
+		# computing unit gaze vectors corresponding to pupil positions
+		# here we use the computed mapping matrix w
+		gis = map(lambda q: g(q, w), qi) 
+		gis0 = map(lambda q: g(q, w0), qi) 
+
+		# now we can compare unit gaze vectors with their corresponding gaze rays t
+		# normalizing gaze rays first
+		t = map(lambda vec: v(vec).norm(), t)
+		# TODO: compare spherical coordinates instead
+
+
+		AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gis, t))
+		N = len(t)
+		AAE = sum(AE)/N
+		VAR = sum((ae - AAE)**2 for ae in AE)/N
+		print 'AAE:', AAE, '\nVariance:', VAR, 'STD:', np.sqrt(VAR), '\nMin:', min(AE), 'Max:', max(AE), '(N=' + str(N) + ')'
+		print 'Target Distances: m=%s M=%s Avg=%s' % (cDist, fDist, avgDist)
+
+		AE0 = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gis0, t))
+		AAE0 = sum(AE0)/N
+		print 'AAE (only optimizing W for e=(0,0,0)):', AAE0
+
+		print 'AAE (2D-to-2D mapping):', aae_2d
+		print ('Improvement (2D-2D vs 2D-3D): %s' % round((aae_2d - AAE)*100/aae_2d, 2)) + '%'
+		print ('Improvement (2D-2D vs 2D-2D): %s' % round((aae_2d - AAE0)*100/aae_2d, 2)) + '%'
+
+
+		sim.display_test_points = True
+		sim.display_calibration_points = False
+
+		## Visualizing gaze vector
+		ray_scale_factor = 120
+		ground_truth_gaze_rays = []
+		for pt in t:
+			ground_truth_gaze_rays.append(Ray(sim.scene_camera.t, ray_scale_factor, v(pt), vs.color.green))
+		sim.rays = ground_truth_gaze_rays
+
+		estimated_gaze_rays = []
+		for pt in gis:
+			estimated_gaze_rays.append(Ray(sim.scene_camera.t, ray_scale_factor, v(pt), vs.color.red))
+		sim.rays.extend(estimated_gaze_rays)
+
+		AE = []
+		for pair in zip(t, gis):
+			gt_r, e_r = pair
+			base = v(sim.scene_camera.t)
+			gt_ray_endpoint = base + (v(gt_r) * ray_scale_factor)
+			e_ray_endpoint = base + (v(e_r) * ray_scale_factor)
+			AE.append(getAngularDiff(gt_ray_endpoint, e_ray_endpoint, base))
+
+			diff = gt_ray_endpoint - e_ray_endpoint
+			sim.rays.append(Ray(e_ray_endpoint, diff.mag, diff.norm(), vs.color.orange))
+		
+		# Recomputing AAE (using law of cosines) sum(AE)/N
+		assert(round(sum(AE)/N - AAE) == 0)
+
+		self.sim = sim
+		print
+
+def p3dEx():
+	ex = Experiment1p5()
+	ex.performExperiment()
+	return ex
+
+def experiment2(s = 1):
+	sim = GazeSimulation(log = False)
+	sim.place_eyeball_on_scene_camera = True
+
+	# 2/3 for calibration, 1/3 for test
+	# sim.num_calibration = 66
+	# sim.num_test = 33
+	# sim.reset()
+
+	# 	calibration_grid, calibration_random_depth,	test_grid, test_random_depth, test_random_fixed_depth
+	# con = [True, 				False,				False,  		False,				True]
+	# con = [False, 				True,				False,  		True,				False]
+	con = [True, 				False,				True,  		False,				True]	
+	# sim.runSimulation()
+	# sim.num_calibration = 36
+	sim.num_calibration = 25
+	sim.num_test = 25
+	sim.runCustomSimulation(con)
+
+	# retrieving calibration points (t for 3D target positions and p for 2D pupil locations)
+	t, p = sim.calibration_points, sim.tr_pupil_locations
+	# t = sim.tr_target_projections # projections of target points
+	# target positions are computed relative to the scene CCS
+	t = map(lambda target: v(target) - v(sim.scene_camera.t), t) # verified
+	
+	# p = sim.eye_camera.getNormalizedPts(p)
+	
+	# computing the initial mapping matrix w by solving the energy minimization 
+	# for e=(0, 0, 0)
+	# w = findInitialW(p, t, True)
+	# print w
+	# w, e = minimizeEnergy(p, t)
+	# print w
+	w = np.array([[s, 0],
+				  [0, s]])
+	# w = minimizeUsingScaleVector(p, t)
+
+	# e = np.array(v(sim.sclera_pos) - v(sim.scene_camera.t)) # eyeball coords in scene CCS
+	# w = minimizeEnergyGivenE(p, t, e)
+
+	t, p = sim.test_points, sim.te_pupil_locations
+	t = map(lambda target: v(target) - v(sim.scene_camera.t), t) # target coords in scene CCS
+	
+	# TODO, is this necessary? (due to eye camera rotation)
+	p = map(lambda pt: np.array([-pt[0], pt[1]]), p)
+
+	# p = sim.eye_camera.getNormalizedPts(p)
+
+	# this w can be used for 2D-2D mapping (apparently!) testing it
+	qi = map(_q, p) # computing feature vectors from raw pupil coordinates in 2D
+	# computing unit gaze vectors corresponding to pupil positions
+	# here we use the computed mapping matrix w
+	gis = map(lambda q: g(q, w), qi) 
+
+	# now we can compare unit gaze vectors with their corresponding gaze rays t
+	# normalizing gaze rays
+	t = map(lambda vec: v(vec).norm(), t)
+	SAE = sum(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gis, t))
+	# return SAE / len(t)
+
+	sim.display_test_points = True
+	sim.display_calibration_points = False
+	## Visualizing gaze vector
+	ray_scale_factor = 120
+	ground_truth_gaze_rays = []
+	for pt in t:
+		ground_truth_gaze_rays.append(Ray(sim.scene_camera.t, ray_scale_factor, v(pt), vs.color.green))
+	sim.rays = ground_truth_gaze_rays
+
+	estimated_gaze_rays = []
+	for pt in gis:
+		estimated_gaze_rays.append(Ray(sim.scene_camera.t, ray_scale_factor, v(pt), vs.color.red))
+	sim.rays.extend(estimated_gaze_rays)
+
+	AE = []
+	for pair in zip(t, gis):
+		gt_r, e_r = pair
+		base = v(sim.scene_camera.t)
+		gt_ray_endpoint = base + (v(gt_r) * ray_scale_factor)
+		e_ray_endpoint = base + (v(e_r) * ray_scale_factor)
+		AE.append(getAngularDiff(gt_ray_endpoint, e_ray_endpoint, base))
+
+		diff = gt_ray_endpoint - e_ray_endpoint
+		sim.rays.append(Ray(gt_ray_endpoint, diff.mag, -diff.norm(), vs.color.orange))
+
+	return sim
+	# print e
+
+def experiment3():
+	sim = GazeSimulation(log = False)
+	# 2/3 for calibration, 1/3 for test
+	# sim.num_calibration = 66
+	# sim.num_test = 33
+	# sim.reset()
+
+	# 	calibration_grid, calibration_random_depth,	test_grid, test_random_depth, test_random_fixed_depth
+	con = [True, 				False,				False,  		True,				False]
+	# con = [False, 				True,				False,  		True,				False]
+	# sim.runSimulation()
+	sim.runCustomSimulation(con)
+	
+	# retrieving calibration points (t for 3D target positions and p for 2D pupil locations)
+	t, p = sim.calibration_points, sim.tr_pupil_locations
+	# t = sim.tr_target_projections # projections of target points
+	# target positions are computed relative to the scene CCS
+	t = map(lambda target: v(target) - v(sim.scene_camera.t), t)
+	
+	# p = sim.eye_camera.getNormalizedPts(p)
+	
+	# Applying regression using LARS (Least Angle Regression)
+	qi = map(_q, p)
+	clf = linear_model.Lars(n_nonzero_coefs=np.inf) # n_nonzero_coefs=1
+	t = map(lambda vec: v(vec).norm(), t)
+	clf.fit(qi, t)
+
+	t, p = sim.test_points, sim.te_pupil_locations
+	t = map(lambda target: v(target) - v(sim.scene_camera.t), t)
+	# p = sim.eye_camera.getNormalizedPts(p)
+
+	# this w can be used for 2D-2D mapping (apparently!) testing it
+	qi = map(_q, p) # computing feature vectors from raw pupil coordinates in 2D
+	# computing unit gaze vectors corresponding to pupil positions
+	# here we use the computed mapping matrix w
+	gis = map(lambda q: clf.predict(q)[0], qi) 
+
+	# now we can compare unit gaze vectors with their corresponding gaze rays t
+	# normalizing gaze rays
+	t = map(lambda vec: v(vec).norm(), t)
+	SAE = sum(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gis, t))
+	print SAE / len(t)
+	# print e
+
+def experimentGT():
+	## Here we are using the ground truth gaze vectors (from the eye) and we compare
+	## them with ground truth vectors initiating from the scene camera
+	## In case eyeball and scene camera are located at the same point, the AAE should
+	## be reported as zero, this is only to make sure we're doing the right thing
+
+	sim = GazeSimulation(log = False)
+	# Uncomment to shift eyeball to scene camera
+	# sim.place_eyeball_on_scene_camera = True
+
+	# 2/3 for calibration, 1/3 for test
+	# sim.num_calibration = 66
+	# sim.num_test = 33
+	# sim.reset()
+
+	# 	calibration_grid, calibration_random_depth,	test_grid, test_random_depth, test_random_fixed_depth
+	con = [True, 				False,				False,  		True,				False]
+	# con = [False, 				True,				False,  		True,				False]
+	# sim.runSimulation()
+	
+	sim.runCustomSimulation(con)
+	
+	# retrieving calibration points (t for 3D target positions and p for 2D pupil locations)
+	t, p, tp = sim.calibration_points, sim.tr_pupil_locations, sim.tr_target_projections
+	# t = sim.tr_target_projections # projections of target points
+	# target positions are computed relative to the scene CCS
+	
+	t_org = t[:]
+	t = map(lambda target: v(target) - v(sim.scene_camera.t), t)
+	
+	rays_from_scene = []
+	for pt in t:
+		rays_from_scene.append(Ray(sim.scene_camera.t, v(pt).mag, v(pt).norm(), vs.color.red))
+	sim.rays = rays_from_scene
+
+	
+	rays_from_scene_to_target_projections = []
+	base = v(sim.scene_camera.t) + v(sim.scene_camera.direction) * sim.scene_camera.f
+	for pp in tp:
+		# print pp
+		# since we wanna draw rays starting from scene camera,
+		# ray is relative to scene CCS
+		ray = base + v(pp[0], pp[1], 0) - v(sim.scene_camera.t) 
+		rays_from_scene_to_target_projections.append(Ray(v(sim.scene_camera.t), ray.mag * 4, ray.norm(), vs.color.orange))
+	sim.rays.extend(rays_from_scene_to_target_projections)
+
+	t = map(lambda vec: v(vec).norm(), t)
+	
+	alpha_GT = []
+	_deg = lambda triple: (np.degrees(triple[0]), np.degrees(triple[1]), triple[2])
+	print 
+	for pt in t:
+		alpha_GT.append(_deg(getSphericalCoords(pt[0], pt[1], pt[2])))
+		# print _deg(getSphericalCoords(pt[0], pt[1], pt[2])), pt[2]
+	print 
+
+	# starting from eyeball
+	base = v(sim.sclera_pos)
+	# direction vector from eyeball towards eye camera
+	d = -v(sim.eye_camera.direction).norm()
+	# calculating distance between eyeball and image plane of the eye camera
+	dist = (v(sim.eye_camera.t) - v(sim.sclera_pos)).mag - sim.eye_camera.f
+
+	p = map(lambda p: np.array((-p[0], p[1])), p) # -x due to orientation
+	sf = 4
+	p = map(lambda p: p.dot(np.array([[sf, 0], 
+									  [0 , sf]])), p)
+	gis = map(lambda p: (d*dist + v(p[0], p[1], 0)).norm(), p)
+
+	alpha_test = []
+
+	rays_from_eye = []
+	for pt in gis:
+		alpha_test.append(_deg(getSphericalCoords(pt[0], pt[1], pt[2])))
+		rays_from_eye.append(Ray(sim.sclera_pos, 200, pt, vs.color.yellow))
+	sim.rays.extend(rays_from_eye)
+
+	# gis = map(lambda t: (v(t) - v(sim.sclera_pos)).norm(), t_org)
+	
+	# now we can compare unit gaze vectors with their corresponding gaze rays t
+	# normalizing gaze rays
+	
+	# for _t, _gis in zip(t, gis): print _t, _gis
+	SAE = sum(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gis, t))
+	print SAE / len(t)
+	return sim
+
+
+
+# experiment1()
+
+# for s in np.arange(1, 20, 0.25):
+# 	print experiment2(s), s
+
+# experiment3()
+
+# experimentGT()
+# experiment1p5()

code/geom.py

@@ -0,0 +1,288 @@
+from __future__ import division
+import numpy as np
+from random import sample
+
+def_fov = np.pi * 2./3
+
+def generatePoints(n, min_xyz, max_xyz, grid=False, randomZ=True, randFixedZ=False, depth=None, offset=0.5, xoffset=0, yoffset=0, zoffset=0):
+  if randFixedZ: # means all points have the same z but z is chosen at random between max and min z
+    z = min_xyz[2] + np.random.random() * (max_xyz[2] - min_xyz[2])
+  else: # same depth
+    if not isinstance(depth, list) and not depth: # depth is exactly the middle of min and max z
+        z = min_xyz[2] + (max_xyz[2] - min_xyz[2]) / 2
+    else:
+        z = depth
+  if not grid: # compute randomly
+    xr, yr, zr = max_xyz[0] - min_xyz[0], max_xyz[1] - min_xyz[1], max_xyz[2] - min_xyz[2]
+
+    xr = np.random.rand(1, n)[0] * xr + min_xyz[0]
+    yr = np.random.rand(1, n)[0] * yr + min_xyz[1]
+    if randomZ:
+        zr = np.random.rand(1, n)[0] * zr + min_xyz[2]
+    else:
+        zr = np.ones((1, n))[0] * z
+    return zip(xr, yr, zr)
+  else: # compute points on a mXm grid when m = sqrt(n)
+    m = int(np.sqrt(n))
+    gwx = (max_xyz[0] - min_xyz[0]) / m
+    gwy = (max_xyz[1] - min_xyz[1]) / m
+    zr = max_xyz[2] - min_xyz[2]
+    if randomZ:
+        return [(min_xyz[0] + (i+offset) * gwx + xoffset, 
+                 min_xyz[1] + (j+offset) * gwy + yoffset,     
+                 np.random.random() * zr + min_xyz[2] + zoffset) for i in xrange(m) for j in xrange(m)]
+    else:
+        if not isinstance(depth, list):
+            ret = [(min_xyz[0] + (i+offset) * gwx + xoffset, # offset .5
+                     min_xyz[1] + (j+offset) * gwy + yoffset, # offset .5
+                     z + zoffset) for i in xrange(m) for j in xrange(m)]
+            # return ret
+            return sample(ret, len(ret)) # this shuffles the points
+        else:
+            ret = []
+            for dz in depth:
+                ret.extend([(min_xyz[0] + (i+offset) * gwx + xoffset, 
+                             min_xyz[1] + (j+offset) * gwy + yoffset, 
+                             dz + zoffset) for i in xrange(m) for j in xrange(m)])
+            # return ret
+            return sample(ret, len(ret)) # this shuffles the points
+
+
+def getSphericalCoords(x, y, z):
+    '''
+    According to our coordinate system, this returns the 
+    spherical coordinates of a 3D vector.
+    A vector originating from zero and pointing to the positive Z direction (no X or Y deviation)
+    will correspond to (teta, phi) = (0, 90)  (in degrees)
+    The coordinate system we are using is similar to https://en.wikipedia.org/wiki/File:3D_Spherical_2.svg
+
+         Y
+         |
+         |
+         |______X
+        / 
+       /
+      /
+     Z   
+
+    with a CounterClockwise rotation of the axis vectors
+    '''
+    r = np.sqrt(x*x + y*y + z*z)
+    teta = np.arctan(x/z)
+    phi = np.arccos(y/r)
+    return teta, phi, r
+
+def getAngularDiff(T, E, C):
+    '''
+    T is the target point
+    E is the estimated target
+    C is camera center
+    Returns angular error
+
+    (using law of cosines: http://mathcentral.uregina.ca/QQ/database/QQ.09.07/h/lucy1.html)
+    '''
+    t = (E - C).mag
+    e = (C - T).mag
+    c = (T - E).mag
+    return np.degrees(np.arccos((e*e + t*t - c*c)/(2*e*t)))
+
+def getRotationMatrixFromAngles(r):
+    '''
+    Returns a rotation matrix by combining elemental rotations 
+    around x, y', and z''
+
+    It also appends a zero row, so the end result looks like:
+    [R_11 R_12 R_13]
+    [R_11 R_12 R_13]
+    [R_11 R_12 R_13]
+    [0    0    0   ] 
+    '''
+    cos = map(np.cos, r)
+    sin = map(np.sin, r)
+    Rx = np.array([
+        [1,      0,       0],
+        [0, cos[0], -sin[0]],
+        [0, sin[0], cos[0]]])
+    Ry = np.array([
+        [ cos[1], 0, sin[1]],
+        [ 0     , 1,      0],
+        [-sin[1], 0, cos[1]]])
+    Rz = np.array([
+        [cos[2], -sin[2], 0],
+        [sin[2],  cos[2], 0],
+        [0     ,       0, 1]])
+    R = Rz.dot(Ry.dot(Rx))
+    return np.concatenate((R, [[0, 0, 0]]))
+
+# import cv2
+# def getRotationMatrix(a, b):
+#     y = a[1] - b[1]
+#     z = a[2] - b[2]
+#     x = a[0] - b[0]
+#     rotx = np.arctan(y/z)
+#     roty = np.arctan(x*np.cos(rotx)/z)
+#     rotz = np.arctan(np.cos(rotx)/(np.sin(rotx)*np.sin(roty)))
+#     return cv2.Rodrigues(np.array([rotx, roty, rotz]))[0]
+
+def getRotationMatrix(a, b):
+    '''
+    Computes the rotation matrix that maps unit vector a to unit vector b
+    
+    It also augments a zero row, so the end result looks like:
+    [R_11 R_12 R_13]
+    [R_11 R_12 R_13]
+    [R_11 R_12 R_13]
+    [0    0    0   ] 
+
+    (simply slice the output like R = output[:3] to get only the rotation matrix)
+
+    based on the solution here:
+    https://math.stackexchange.com/questions/180418/calculate-rotation-matrix-to-align-vector-a-to-vector-b-in-3d
+    '''
+    a, b = np.array(a), np.array(b)
+    v = np.cross(a, b, axis=0)
+    s = np.linalg.norm(v) # sine of angle
+    c = a.dot(b) # cosine of angle
+
+    vx = np.array([
+        [0    , -v[2],  v[1]],
+        [v[2] ,     0, -v[0]],
+        [-v[1],  v[0],    0]]) 
+
+    if s == 0: # a == b
+        return np.concatenate((np.eye(3), [[0, 0, 0]]))
+    if c == 1:
+        return np.concatenate((np.eye(3) + vx, [[0, 0, 0]]))
+    return np.concatenate((np.eye(3) + vx + vx.dot(vx)*((1-c)/s/s), [[0, 0, 0]]))
+
+
+
+
+class PinholeCamera:
+    '''
+    Models a basic Pinhole Camera with 9 degrees of freedom
+    '''
+    # Intrinsic parameters
+    f = 1 # focal length
+    p = (0, 0) # position of principal point in the image plane
+    # Extrinsic parameters
+    # this rotation corresponds to a camera setting pointing towards (0, 0, -1)
+    r = (0, 0, 0) # rotations in x, y', and z'' planes respectively 
+    t = (0, 0, 0) # camera center translation w.r.t world coordinate system (with no rotation)
+    #
+    # Using the above parameters we can construct the camera matrix
+    #
+    #                      [f 0 p.x 0]
+    # P = K[R|t] where K = [0 f p.y 0] is the camera calibration matrix (full projection matrix)
+    #                      [0 0  1  0]
+    #
+    # and thus we have x = PX for every point X in the word coordinate system 
+    # and its corresponding projection in the camera image plane x
+    # NOTE: points are assumed to be represented by homogeneous vectors
+    #
+    # Other parameters
+    label = ''
+
+    direction = (0, 0, 1) # camera direction
+    fov = def_fov # field of view (both horizontal and vertical)
+    image_width = 2*f*np.tan(fov/2.)
+    ################################################
+    def __init__(self, label, f = 1, r = (0, 0, 0), t = (0, 0, 0), direction = (0, 0, 1), fov=def_fov):
+        self.label = label
+        self.f = f
+        self.r = r
+        self.direction = direction
+        self.t = t
+        self.setFOV(fov)
+        self.recomputeCameraMatrix(True, True)
+
+    def recomputeCameraMatrix(self, changedRotation, changedIntrinsic):
+        if changedRotation:
+            # # Computing rotation matrix using elemental rotations
+            self.R = getRotationMatrixFromAngles(self.r)
+            # by default if rotation is 0 then camera optical axis points to positive Z
+            self.direction = np.array([0, 0, 1, 0]).dot(self.R) 
+
+        # Computing the extrinsic matrix
+        _t = -self.R.dot(np.array(self.t)) # t = -RC
+        self.Rt = np.concatenate((self.R, np.array([[_t[0], _t[1], _t[2], 1]]).T), axis=1)
+        # instead of the above, we could also represent translation matrix as [I|-C] and
+        #                              [R -RC]
+        # then compute Rt as R[I|-C] = [0   1]                   [R]      [-RC]
+        # but we're basically do the same thing by concatenating [0] with [  1]
+
+        if changedIntrinsic:
+            # Computing intrinsic matrix
+            f, px, py = self.f, self.p[0], self.p[1]
+            self.K = np.array([
+                [f, 0, px, 0],
+                [0, f, py, 0],
+                [0, 0, 1,  0]])
+        # Full Camera Projection Matrix
+        self.P = self.K.dot(self.Rt)
+        
+    ################################################
+    ## Intrinsic parameter setters
+    def setF(self, f, auto_adjust = True):
+        self.f = f
+        if auto_adjust:
+            self.setFOV(self.fov)
+            self.recomputeCameraMatrix(False, True)
+    def setP(self, p, auto_adjust = True):
+        self.p = p
+        if auto_adjust:
+            self.recomputeCameraMatrix(False, True)
+    def setFOV(self, fov):
+        self.fov = fov
+        self.image_width = 2*self.f*np.tan(fov/2.)
+    ################################################
+    ## Extrinsic parameter setters
+    def setT(self, t, auto_adjust = True):
+        self.t = t
+        if auto_adjust:
+            self.recomputeCameraMatrix(False, False)
+    def setR(self, r, auto_adjust = True):
+      self.r = r
+      if auto_adjust:
+          self.recomputeCameraMatrix(True, False)
+    ################################################
+    def project(self, p):
+        '''
+        Computes projection of a point p in world coordinate system
+        using its homogeneous vector coordinates [x, y, z, 1]
+        '''
+        if len(p) < 4: p = (p[0], p[1], p[2], 1)
+        projection = self.P.dot(np.array(p))
+        # dividing by the Z value to get a 2D point from homogeneous coordinates
+        return np.array((projection[0], projection[1]))/projection[2]
+
+    def getNormalizedPts(self, pts):
+        '''
+        Returns normalized x and y coordinates in range [0, 1]
+        '''
+        px, py = self.p[0], self.p[1]
+        return map(lambda p:np.array([p[0] - px + self.image_width/2, 
+                                      p[1] - py + self.image_width/2]) / self.image_width, pts)
+    def getDenormalizedPts(self, pts):
+        '''
+        Returns original points in the camera image coordinate plane from normalized points
+        '''
+        px, py = self.p[0], self.p[1]
+        offset = np.array([self.image_width/2-px, self.image_width/2-py])
+        return map(lambda p:(p*self.image_width)-offset, pts)
+
+class Camera(PinholeCamera):
+    default_radius = 2.0
+
+    def updateShape(self):
+        c = v(self.t)
+        # Update camera center
+        self.center.pos = c
+        # Update the arrow
+        self.dir.pos = c
+        self.dir.axis = v(self.direction) * self.f
+        # Update image plane
+        self.img_plane.pos = c + self.dir.axis
+        self.img_plane.length = self.image_width
+        self.img_plane.height = self.image_width
+        # TODO: handle rotation of image plane

code/minimize.py

@@ -0,0 +1,131 @@
+from __future__ import division
+import numpy as np 
+from numpy import linalg as LA
+from scipy import optimize
+from sklearn.preprocessing import PolynomialFeatures as P
+from vector import Vector as v
+import cv2
+
+M = 7 # this is the size of the feature vector
+_q = lambda p: np.array([p[0], p[1], p[0]*p[0], p[1]*p[1], p[0]*p[1], p[0]*p[1]*p[0]*p[1], 1])
+
+def alpha(q, w):
+	'''
+	q is the polynomial representation of a 2D pupil point p
+	w is the Mx2 parameter matrix (M is the length of our feature vector) 
+	(could be initialized as a zero matrix np.zeros((M, 2)))
+	returns alpha_i = (theta_i, phi_i)
+	'''
+	theta = np.dot(w[:, 0], q)
+	phi = np.dot(w[:, 1], q)
+	return theta, phi
+
+def g(q, w):
+	'''
+	computes the unit gaze ray originating from the eye corresponding to q
+	'''
+	theta, phi = alpha(q, w)
+	sin = map(np.sin, (theta, phi))
+	cos = map(np.cos, (theta, phi))
+	## This is THE definition, maps (0,0) to (0,0,1) and it produces a unit vector
+	return np.array([sin[0], 
+					 cos[0]*sin[1],
+					 cos[0]*cos[1]])
+def g3D3D(pose):
+	theta, phi = pose
+	sin = map(np.sin, (theta, phi))
+	cos = map(np.cos, (theta, phi))
+	return np.array([sin[0], 
+					 cos[0]*sin[1],
+					 cos[0]*cos[1]])
+
+####################################################################################
+## Initial attempt to solve E(w, 0) Using a similiar idea to: 
+## http://scipy-lectures.github.io/advanced/mathematical_optimization/#id28
+
+def getWfromRowRepr(w):
+	w1 = w[:M] # 1st col
+	w2 = w[M:] # 2nd col
+	return np.array([w1, w2]).transpose()
+
+def getRfromRowRepr(w):
+	return np.array([w[:3], w[3:6], w[6:]])	
+
+def f(w, qi, ti):
+	'''
+	E(\bm{w}) = \sum_{i=1}^N | \bm{(\theta_i, \phi_i)} - \bm{q}_i\bm{w}|^2
+	'''
+	w = getWfromRowRepr(w)
+	alpha_i = map(lambda q: alpha(q, w), qi) # theta, phi
+	# we compare the estimated polar coordinates with polar coordinates corresponding
+	# to ti as in the 2D to 2D case
+	p_ti = map(lambda g: [np.arctan(g.x/g.z), np.arctan(g.y/g.z)], map(v,ti))
+	return map(lambda pair: np.sqrt((pair[0][0]-pair[1][0])**2 + (pair[0][1]-pair[1][1])**2), zip(p_ti, alpha_i)) 
+
+def findInitialW(p, t, retMatrix = True):
+	ti = map(np.array, t)
+	qi = map(_q, p)
+
+	w0 = np.zeros(2*M)
+	wr = optimize.leastsq(f, w0[:], args=(qi, ti))
+	if retMatrix:
+		return getWfromRowRepr(wr[0])
+	else:
+		return wr[0]
+
+# tub function > 0 in -pi:pi, positive outside
+tub = lambda x: max(-x-np.pi, 0, x-np.pi)
+tub_weight = 10000
+def f3D3D(w, ni, ti):
+	'''
+	E_{\textmd{3Dto3D}}(\bm{R}, \bm{e}) = \sum_{i=1}^N |\bm{R} \bm{n}_i  \times (\bm{t}_i - \bm{e}) |^2
+	'''
+	rvec = w[:3]
+	tvec = w[3:] # e
+	R = cv2.Rodrigues(np.array(rvec))[0]
+	return map(lambda (t, n): LA.norm(np.cross(R.dot(n), t-tvec)), zip(ti, ni)) + \
+	[tub_weight * tub(w[0]), tub_weight * tub(w[1]), tub_weight * tub(w[2])]
+	# this distance measure works if we initialize the minimization by np.array([0.,0.,0.,0.,0.,0.]) 
+	# return map(lambda (t, n): -np.dot(R.dot(n), t-tvec)/LA.norm(t-tvec) + 1, zip(ti, ni))
+	
+def findW3D3D(po, t):
+	'''
+	po stands for pose i.e. a vector in the direction of gaze for each pupil
+	'''
+	ti = map(np.array, t) # ground truth data in scene camera coordinate system
+	ni = map(np.array, po) # pupil pose in eye coordinate system (normal to pupil disc)
+	# w0 = np.array([0.,0.,0.,0.,0.,0.]) # rvec (roll, pitch, yaw), tvec (x, y, z)
+	w0 = np.array([0.,np.pi,0.,0.,0.,0.]) # rvec (roll, pitch, yaw), tvec (x, y, z)
+	wr = optimize.leastsq(f3D3D, w0[:], args=(ni, ti))
+	return wr[0]
+####################################################################################
+## Solving target energy using e0 = (0, 0, 0) and w0 from above
+
+def minimizeEnergy(p, t, e0 = None, pose_given = False):
+	if e0 is None:
+		e0 = np.array([0, 0, 0])
+
+	if pose_given:
+		w = findW3D3D(p, t)
+		return cv2.Rodrigues(np.array(w[:3]))[0], w[3:] # R, e
+	else:
+		w0 = findInitialW(p, t, False)
+		qi = map(_q, p)
+
+	ti = map(np.array, t)
+	we0 = np.concatenate((w0, e0))
+
+	wer = optimize.leastsq(f2D3D, we0[:], args=(qi, ti))[0]
+	w = getWfromRowRepr(wer[:2*M])
+	e = wer[2*M:]
+	return w, e, getWfromRowRepr(w0)
+
+def f2D3D(we, qi, ti):
+	'''
+	E_{\textmd{2Dto3D}}(\bm{w}, \bm{e}) = \sum_{i=1}^N |\bm{g}(\bm{q}_i\bm{w}) \times (\bm{t}_i - \bm{e})|^2
+	'''
+	# extracting parameters from the combined vector
+	w = getWfromRowRepr(we[:2*M])
+	e = we[2*M:]
+	gis = map(lambda q: g(q, w), qi)
+	return map(lambda pair: LA.norm(np.cross(pair[1], pair[0]-e)), zip(ti, gis)) 

code/parallax_2D3D_3Cdepths.py

@@ -0,0 +1,249 @@
+from __future__ import division
+
+import os
+import seaborn
+from pylab import rcParams
+import cv2
+
+import numpy as np
+from numpy import linalg as LA
+from time import time
+from itertools import combinations
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+
+from minimize import findInitialW, _q, g, minimizeEnergy, g3D3D, findW3D3D
+from minimize import g as gaze_ray
+from geom import getSphericalCoords, getAngularDiff
+from vector import Vector as v
+# from experiment import Experiment
+from sim import GazeSimulation
+from recording.util.tools import is_outlier
+from recording.tracker import readCameraParams
+from geom import getRotationMatrix
+
+
+class Experiment:
+	def performExperiment(self):
+		print 'Running Experiment...'
+		start = time()
+		self.__run__()
+		self.runningTime = time() - start
+		print 'Running Time:', self.runningTime
+	def __run__(self):
+		pass
+
+class Parallax2Dto3DMapping(Experiment):
+	def __run__(self):
+		sim = GazeSimulation(log = False)
+
+		sim.place_eyeball_on_scene_camera = False
+		sim.setEyeRelativeToSceneCamera(v(-65, -33, -73))
+		# sim.setEyeRelativeToSceneCamera(v(-65, -33, 0)) # assuming eyeball and scene camera are coplanar i.e. e = (e.x, e.y, 0)
+		sim.setCalibrationDepth(1 * 1000) # mm, wrt scene camera
+		sim.setTestDepth(1.5 * 1000)
+		sim.calibration_grid = True
+		sim.calibration_random_depth = False
+		sim.test_grid = True
+		sim.test_random_depth = False
+		sim.test_random_fixed_depth = False
+
+		depths = map(lambda d:d*1000, [1, 1.25, 1.5, 1.75, 2.0])
+		print '> Computing results for multiple calibration depths...'
+		results = []
+		for num_of_calibration_depths in xrange(1, 6): # from 1 calibration depths to 5
+			print '> Considering only %s calibration depth(s)...' %num_of_calibration_depths
+			sim.reset()	
+			if num_of_calibration_depths != 3: continue
+
+			for calibs in combinations(depths, num_of_calibration_depths):
+				aae_2ds_aae = []
+				aae_2ds_phe = []
+				aae_3ds_aae = []
+				aae_3ds_phe = []
+				aae_3D3Ds = [] # angular error
+
+				# Now calibs is a set of depths, from each of those we need calibration data
+				# print calibs
+				if not calibs in [(1000., 1250., 1500.), (1250., 1500., 1750.), (1500., 1750., 2000.)]:
+					continue
+				print 'curr calibs', calibs
+				calibs = list(calibs)
+				cp, ct = [], []
+				sim.reset()	
+				sim.setCalibrationDepth(calibs)
+
+				# Perform calibration
+				sim.runCalibration()
+				cp, ct, p3d = sim.tr_pupil_locations, sim.calibration_points, sim.tr_3d_pupil_locations
+
+				# target positions are computed relative to the scene CCS
+				ti = map(lambda target: v(target) - v(sim.scene_camera.t), ct)
+				# Computing pupil pose for each gaze
+				ni = map(lambda p: (v(p)-v(sim.sclera_pos)).norm(), p3d) # ground truth gaze vectors
+				
+				w, e, w0 = minimizeEnergy(cp, ti)
+				e = v(e)
+
+				# transforming pupil pose to eye camera CS
+				eyeR = np.array(sim.eye_camera.R[:3])
+				ni = map(lambda pose: eyeR.dot(np.array(pose)), ni) 
+				
+				R, e3d3d = minimizeEnergy(ni, ti, pose_given=True)
+				e3d3d = v(e3d3d)
+				# R = LA.inv(R)
+
+				# Now we have calibration data from multiple depths, we can test on all depths
+				for test_depth in depths:
+					sim.setTestDepth(test_depth)
+					aae_2d_aae, aae_2d_phe, aae_2d_std, _ = sim.runTest() # 2nd one is PHE
+					aae_2ds_aae.append((aae_2d_aae, aae_2d_std))
+					aae_2ds_phe.append(aae_2d_phe)
+
+					# Fetching test points
+					t, p, p3d = sim.test_points, sim.te_pupil_locations, sim.te_3d_pupil_locations
+					t = map(lambda target: v(target) - v(sim.scene_camera.t), t) # target coords in scene CCS
+
+					# 3D3D
+					t_3d3d = t[:]
+
+					ni = map(lambda p: v(v(p)-v(sim.sclera_pos)).norm(), p3d) # ground truth gaze vectors
+					# transforming pupil pose to eye camera CS
+					ni = map(lambda r: v(eyeR.dot(np.array(r))), ni)
+
+					# applying estimated rotation to pose vector in eye camera coordinates (Rn)
+					# R is estimated rotation between scene camera and eye coordinate system (not eye camera!)
+					# in other words, R is the rotation part of e
+					Rni = map(lambda n: v(R.dot(np.array(n))), ni) # now ready to compare Rn with t-e
+					# Intersecting gaze rays originating from the eye with the planes defined by each
+					# target. then we can simply compute angular error between each intersection and 
+					# the corresponding 3D target
+					gis = map(lambda vec: v(vec), Rni) # gaze rays originating from eyeball
+					# we multiply g such that it hits t's z-plane i.e. multiply all coordinates by factor (t.z-e.z)/g.z
+					# then we add e to the final g so that it originates from scene camera. now both g and t are in the
+					# same coordinate system and originate from the same point, so we can compare them
+					gprimes = map(lambda tg: v(((tg[0].z - e3d3d.z)/tg[1].z)*tg[1] + e3d3d), zip(t_3d3d, gis))
+					AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, t_3d3d))
+
+					N = len(t)
+					AAE = np.mean(AE)
+					STD = np.std(AE)
+					m, M = min(AE), max(AE)
+					aae_3D3Ds.append((AAE, STD))
+
+
+					qi = map(_q, p) # computing feature vectors from raw pupil coordinates in 2D
+					# computing unit gaze vectors corresponding to pupil positions
+					# here we use the computed mapping matrix w
+					gis = map(lambda q: g(q, w), qi) 
+					
+					# Intersecting gaze rays originating from the eye with the planes defined by each
+					# target. then we can simply compute angular error between each intersection and 
+					# the corresponding 3D target
+					t = map(lambda vec: v(vec), t)
+					gis = map(lambda vec: v(vec), gis)
+					gprimes = map(lambda tg: v(((tg[0].z - e.z)/tg[1].z)*tg[1] + e), zip(t, gis))
+
+					AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, t))
+					N = len(t)
+					AAE = np.mean(AE)
+					STD = np.std(AE)
+					m, M = min(AE), max(AE)
+
+					# Computing physical distance error (in meters)
+					PHE = list((u-v).mag/1000 for u,v in zip(t, gprimes))
+					N = len(t)
+					APHE = np.mean(PHE)
+					PHE_STD = np.std(PHE)
+					PHE_m, PHE_M = min(PHE), max(PHE)
+
+					aae_3ds_aae.append((AAE, STD))
+					aae_3ds_phe.append((PHE, PHE_STD))
+				
+
+				results.append([calibs, aae_2ds_aae, aae_3ds_aae, aae_3D3Ds])
+
+		plt.ylabel('Angular Error')
+		plt.xlabel('Depth')
+
+		fig = plt.figure(figsize=(14.0, 10.0))
+		ax = fig.add_subplot(111)
+
+		clrs = ['blue', 'red', 'orange']
+		depth_map = {1000.:1, 1250.:2, 1500.:3, 1750.:4, 2000.:5}
+		cdlabel = lambda c: ' + '.join(map(str, map(lambda d: depth_map[d], c)))
+
+		x1 = [0.375,1.375,2.375,3.375,4.375]
+		x2 = [0.425,1.425,2.425,3.425,4.425]
+		x3 = [0.475,1.475,2.475,3.475,4.475]
+		x4 = [0.525,1.525,2.525,3.525,4.525]
+		x5 = [0.575,1.575,2.575,3.575,4.575]
+		x6 = [0.625,1.625,2.625,3.625,4.625]
+		xrange_2d2d = [x1, x3, x5]
+		xrange_2d3d = [x2, x4, x6]
+
+		for i in [0, 1, 2]:
+			res = results[i]
+			calibs = res[0]
+
+			_xrange = xrange_2d2d[i]
+			aae_2d2d = np.array(res[1])[:,0]
+			std_2d2d = np.array(res[1])[:,1]
+			rects1 = ax.errorbar(_xrange, aae_2d2d,yerr=[std_2d2d, std_2d2d],fmt='o',color=clrs[i],ecolor=clrs[i],lw=3, capsize=5, capthick=2)
+			plt.plot(_xrange, aae_2d2d, marker="o", linestyle='-',lw=3,color=clrs[i],label = '2D-to-2D Calibration Depth '+cdlabel(calibs))
+
+		for i in [0, 1, 2]:
+			res = results[i]
+			calibs = res[0]
+
+			_xrange = xrange_2d3d[i]
+			aae_2d3d = np.array(res[2])[:,0]
+			std_2d3d = np.array(res[2])[:,1]
+			rects2 = ax.errorbar(_xrange, aae_2d3d,yerr=[std_2d3d, std_2d3d],fmt='o',color=clrs[i],ecolor=clrs[i],lw=3, capsize=5, capthick=2)
+			plt.plot(_xrange, aae_2d3d, marker="o", linestyle='--',lw=3,color=clrs[i],label ='2D-to-3D Calibration Depth '+cdlabel(calibs))
+
+		for i in [0, 1, 2]:
+			res = results[i]
+			calibs = res[0]
+
+			_xrange = xrange_2d2d[i]
+			aae_3d3d = np.array(res[3])[:,0]
+			std_3d3d = np.array(res[3])[:,1]
+			rects3 = ax.errorbar(_xrange, aae_3d3d,yerr=[std_3d3d, std_3d3d],fmt='o',color=clrs[i],ecolor=clrs[i],lw=3, capsize=5, capthick=2)
+			plt.plot(_xrange, aae_3d3d, marker="o", linestyle='-.',lw=3,color=clrs[i],label ='3D-to-3D Calibration Depth '+cdlabel(calibs))
+
+		ax.set_ylim(0, 1.5)
+
+		ax.set_ylabel(r'Angular Error',fontsize=22, fontweight='bold')
+		ax.set_xlabel(r'Depth',fontsize=22, fontweight='bold')
+		
+		TOPICS = [-0.2, 0, 0.2, 0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8,2.0,2.2,2.4]#,110]#,120]
+		LABELS = [r'', r'0', r'0.2',r'0.4',r'0.6',  r'0.8', r'1.0', r'1.2', r'1.4',  r'1.6',  r'1.8', r'2.0',  r'2.2', r'2.4']#, ""]#, ""]
+
+		plt.yticks(TOPICS, LABELS,fontsize=18)
+
+		# plt.legend(fontsize=20)
+		plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
+           ncol=3, mode="expand", borderaxespad=0., fontsize=15)
+		plt.yticks(fontsize='18')
+
+		TOPICs = [0.0,0.5,1.5,2.5,3.5,4.5,5.0]
+		LABELs = ['', 'D1 - 1m', 'D2 - 1.25m', 'D3 - 1.5m', 'D4 - 1.75m', 'D5 - 2.0m', '']
+		# ax.set_xticklabels(LABELs,fontsize=18)
+		plt.xticks(TOPICs, LABELs,fontsize=18)
+		
+		left  = 0.1  # the left side of the subplots of the figure
+		right = 0.975    # the right side of the subplots of the figure
+		bottom = 0.075   # the bottom of the subplots of the figure
+		top = 0.925      # the top of the subplots of the figure
+		wspace = 0.2   # the amount of width reserved for blank space between subplots
+		hspace = 0.4   # the amount of height reserved for white space between subplots
+
+		plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
+		plt.show()
+
+if __name__ == '__main__':
+	# This also does 3D gaze estimation and plots estimation results for both 3D and 2D estimation
+	ex = Parallax2Dto3DMapping()
+	ex.performExperiment()

code/parallax_analysis.py

@@ -0,0 +1,996 @@
+from __future__ import division
+
+import os, sys
+import seaborn
+from pylab import rcParams
+import cv2
+
+import numpy as np
+from numpy import linalg as LA
+from time import time
+from itertools import combinations
+
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import legend
+import matplotlib.patches as mpatches
+
+from minimize import findInitialW, _q, g, minimizeEnergy, g3D3D, findW3D3D
+from minimize import g as gaze_ray
+from geom import getSphericalCoords, getAngularDiff
+from vector import Vector as v
+
+from sim import GazeSimulation
+from recording.util.tools import is_outlier
+from recording.tracker import readCameraParams
+from geom import getRotationMatrix
+
+import pdb
+
+PARTICIPANTS = ['p10', 'p16', 'p13', 'p24', 'p5', 'p14', 'p26', 'p12', 'p20', 'p7', 'p15', 'p11', 'p21', 'p25']
+
+class Experiment:
+	def performExperiment(self):
+		print 'Running Experiment...'
+		start = time()
+		self.__run__()
+		self.runningTime = time() - start
+		print 'Running Time:', self.runningTime
+	def __run__(self):
+		pass
+
+class Parallax2Dto2DMapping(Experiment):
+	def __run__(self):
+		sim = GazeSimulation(log = False)
+
+		sim.place_eyeball_on_scene_camera = False
+		
+		sim.setEyeRelativeToSceneCamera(v(-65, -33, -73))
+
+		sim.setCalibrationDepth(1 * 1000) # mm, wrt scene camera
+		sim.setTestDepth(1.5 * 1000)
+
+		sim.calibration_grid = True
+		sim.calibration_random_depth = False
+		sim.test_grid = True
+		sim.test_random_depth = False
+		sim.test_random_fixed_depth = False
+
+		sim.reset()
+
+		print 'scene_camera', sim.scene_camera.t
+		print 'calibration'
+		print len(sim.calibration_points)
+		# print sim.calibration_points
+		print min(np.array(sim.calibration_points)[:,0]) - sim.scene_camera.t[0], max(np.array(sim.calibration_points)[:,0]) - sim.scene_camera.t[0]
+		print min(np.array(sim.calibration_points)[:,1]) - sim.scene_camera.t[1], max(np.array(sim.calibration_points)[:,1]) - sim.scene_camera.t[1]
+		print 'depths', set(np.array(sim.calibration_points)[:,2])
+		print 'test'
+		print len(sim.test_points)
+		print min(np.array(sim.test_points)[:,0]) - sim.scene_camera.t[0], max(np.array(sim.test_points)[:,0]) - sim.scene_camera.t[0]
+		print min(np.array(sim.test_points)[:,1]) - sim.scene_camera.t[1], max(np.array(sim.test_points)[:,1]) - sim.scene_camera.t[1]
+		print 'depths', set(np.array(sim.test_points)[:,2])
+
+		
+		
+		plt.ylabel('Y (mm)')
+		plt.xlabel('X (mm)')
+		plt.plot(np.array(sim.calibration_points)[:,0], np.array(sim.calibration_points)[:,1], 'bo')
+		plt.plot(np.array(sim.test_points)[:,0], np.array(sim.test_points)[:,1], 'ro')
+		plt.show()
+
+		# self.sim = sim
+		# visualize the setting in windows
+
+class Parallax2Dto3DMapping(Experiment):
+	'''
+	IMPORTANT!
+	In all experiments, scene camera's rvec = (0, 0, 0) i.e. the corresponding rotation matrix is the identity matrix therefore
+	I have not included the dot production with this rotation matrix to convert points in world coordinates
+	into scene camera coordinates. however, one should know that if the scene camera is rotated differentl7y
+	this transformation is essential. I would add the corresponding computations later on.
+	'''
+
+	def __run__(self):
+		sim = GazeSimulation(log = False)
+
+		sim.place_eyeball_on_scene_camera = False
+		sim.setEyeRelativeToSceneCamera(v(-65, -33, -73))
+		# sim.setEyeRelativeToSceneCamera(v(-65, -33, 0)) # assuming eyeball and scene camera are coplanar i.e. e = (e.x, e.y, 0)
+
+		sim.setCalibrationDepth(1 * 1000) # mm, wrt scene camera
+		sim.setTestDepth(1.5 * 1000)
+		sim.calibration_grid = True
+		sim.calibration_random_depth = False
+		sim.test_grid = True
+		sim.test_random_depth = False
+		sim.test_random_fixed_depth = False
+
+		depths = map(lambda d:d*1000, [1, 1.25, 1.5, 1.75, 2.0])
+		print '> Computing results for multiple calibration depths...'
+		results, results_std = [], []
+		for num_of_calibration_depths in xrange(1, 6): # from 1 calibration depths to 5
+			print '> Considering only %s calibration depth(s)...' %num_of_calibration_depths
+			sim.reset()	
+			aae_2ds_aae = []
+			aae_2ds_phe = []
+			aae_3ds_aae = []
+			aae_3ds_phe = []
+			aae_3D3Ds = [] # angular error
+
+			for calibs in combinations(depths, num_of_calibration_depths):
+				# Now calibs is a set of depths from each of which we need calibration data
+				print 'Current calibration depths', calibs
+				calibs = list(calibs)
+				cp, ct = [], []
+				sim.reset()	
+				sim.setCalibrationDepth(calibs)
+				# Perform calibration
+				sim.runCalibration()
+				cp, ct, p3d = sim.tr_pupil_locations, sim.calibration_points, sim.tr_3d_pupil_locations
+				# target positions are computed relative to the scene CCS
+				ti = map(lambda target: v(target) - v(sim.scene_camera.t), ct)
+				# Computing pupil pose for each gaze
+				ni = map(lambda p: (v(p)-v(sim.sclera_pos)).norm(), p3d) # ground truth gaze vectors
+				
+				w, e, w0 = minimizeEnergy(cp, ti)
+				e = v(e)
+
+				# transforming pupil pose to eye camera CS
+				eyeR = np.array(sim.eye_camera.R[:3])
+				ni = map(lambda pose: eyeR.dot(np.array(pose)), ni) 
+				
+				R, e3d3d = minimizeEnergy(ni, ti, pose_given=True)
+				# R = LA.inv(R)
+				e3d3d = v(e3d3d)
+
+				# Now we have calibration data from multiple depths, we can test on all depths
+				for test_depth in depths:
+					sim.setTestDepth(test_depth)
+					aae_2d_aae, aae_2d_phe, aae_2d_std, _ = sim.runTest() # last one is PHE std
+					aae_2ds_aae.append((aae_2d_aae, aae_2d_std))
+					aae_2ds_phe.append(aae_2d_phe)
+					# Fetching test points
+					t, p, p3d = sim.test_points, sim.te_pupil_locations, sim.te_3d_pupil_locations
+					t = map(lambda target: v(target) - v(sim.scene_camera.t), t) # target coords in scene CCS
+
+					# 3D3D
+					t_3d3d = t[:]
+
+					ni = map(lambda p: v(v(p)-v(sim.sclera_pos)).norm(), p3d) # ground truth gaze vectors
+					# transforming pupil pose to eye camera CS
+					ni = map(lambda r: v(eyeR.dot(np.array(r))), ni)
+
+					# applying estimated rotation to pose vector in eye camera coordinates (Rn)
+					# R is estimated rotation between scene camera and eye coordinate system (not eye camera!)
+					# in other words, R is the rotation part of e
+					Rni = map(lambda n: v(R.dot(np.array(n))), ni) # now ready to compare Rn with t-e
+					# Intersecting gaze rays originating from the eye with the planes defined by each
+					# target. then we can simply compute angular error between each intersection and 
+					# the corresponding 3D target
+					gis = map(lambda vec: v(vec), Rni) # gaze rays originating from eyeball
+					# we multiply g such that it hits t's z-plane i.e. multiply all coordinates by factor (t.z-e.z)/g.z
+					# then we add e to the final g so that it originates from scene camera. now both g and t are in the
+					# same coordinate system and originate from the same point, so we can compare them
+					gprimes = map(lambda tg: v(((tg[0].z - e3d3d.z)/tg[1].z)*tg[1] + e3d3d), zip(t_3d3d, gis))
+					AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, t_3d3d))
+
+					N = len(t)
+					AAE = np.mean(AE)
+					STD = np.std(AE)
+					m, M = min(AE), max(AE)
+					aae_3D3Ds.append((AAE, STD))
+
+
+					qi = map(_q, p) # computing feature vectors from raw pupil coordinates in 2D
+					# computing unit gaze vectors corresponding to pupil positions
+					# here we use the computed mapping matrix w
+					gis = map(lambda q: g(q, w), qi) 
+					
+					# Intersecting gaze rays originating from the eye with the planes defined by each
+					# target. then we can simply compute angular error between each intersection and 
+					# the corresponding 3D target
+					t = map(lambda vec: v(vec), t)
+					gis = map(lambda vec: v(vec), gis)
+					gprimes = map(lambda tg: v(((tg[0].z - e.z)/tg[1].z)*tg[1] + e), zip(t, gis))
+
+					AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, t))
+					N = len(t)
+					AAE = np.mean(AE)
+					STD = np.std(AE)
+					m, M = min(AE), max(AE)
+
+					# Computing physical distance error (in meters)
+					PHE = list((u-v).mag/1000 for u,v in zip(t, gprimes))
+					N = len(t)
+					APHE = np.mean(PHE)
+					PHE_STD = np.std(PHE)
+					PHE_m, PHE_M = min(PHE), max(PHE)
+
+					aae_3ds_aae.append((AAE, STD))
+					aae_3ds_phe.append((PHE, PHE_STD))
+			
+			# results only contains AAE
+			results.append([np.mean(np.array(aae_2ds_aae)[:,0]), np.mean(np.array(aae_3ds_aae)[:,0]), np.mean(np.array(aae_3D3Ds)[:,0])])
+			results_std.append([np.std(np.array(aae_2ds_aae)[:,0]), np.std(np.array(aae_3ds_aae)[:,0]), np.std(np.array(aae_3D3Ds)[:,0])])
+		
+		# Old plot code
+		######################################################################################################
+		# plt.ylabel('Angular Error')
+		# plt.xlabel('Depth')
+
+		# fig = plt.figure(figsize=(14.0, 10.0))
+		# ax = fig.add_subplot(111)
+
+		# clrs = ['b', 'r', 'orange']
+
+
+		# _xrange = [0.5,1.5,2.5,3.5,4.5]
+		# ax.plot(_xrange, [res[0] for res in results], 'r', label='2D-to-2D', marker="o", linestyle='-',lw=3)
+		# ax.plot(_xrange, [res[1] for res in results], 'b', label='2D-to-3D', marker="o", linestyle='-',lw=3)
+		# ax.plot(_xrange, [res[2] for res in results], 'g', label='3D-to-3D', marker="o", linestyle='-',lw=3)
+
+		# ax.set_ylabel(r'Angular Error',fontsize=22, fontweight='bold')
+		# ax.set_xlabel(r'Number of Calibration Depths',fontsize=22, fontweight='bold')
+		
+		# plt.legend(fontsize=20)
+		# # plt.legend(loc="upper left", ncol=3, title=r"$d_c$")
+		# # plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
+		# # 				ncol=5, mode="expand", borderaxespad=0., fontsize=20)
+		# # plt.xticks(fontsize='18')
+		# plt.yticks(fontsize='18')
+
+
+		# TOPICs = [0.0,0.5,1.5,2.5,3.5,4.5,5.0]
+		# LABELs = ['', '1', '2', '3', '4', '5', '']
+		# # ax.set_xticklabels(LABELs,fontsize=18)
+		# plt.xticks(TOPICs, LABELs,fontsize=18)
+		
+		# left  = 0.1  # the left side of the subplots of the figure
+		# right = 0.975    # the right side of the subplots of the figure
+		# bottom = 0.075   # the bottom of the subplots of the figure
+		# top = 0.925      # the top of the subplots of the figure
+		# wspace = 0.2   # the amount of width reserved for blank space between subplots
+		# hspace = 0.4   # the amount of height reserved for white space between subplots
+
+		# plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
+		# plt.show()
+		######################################################################################################
+		# New plot code based on EffectNumberofClusters.py
+		mean2D2D = [res[0] for res in results]
+		mean2D3D = [res[1] for res in results]
+		mean3D3D = [res[2] for res in results]
+		std2D2D = [res[0] for res in results_std]
+		std2D3D = [res[1] for res in results_std]
+		std3D3D = [res[2] for res in results_std]
+
+
+		N = 5
+		ind = np.asarray([0.25,1.25,2.25,3.25,4.25])
+
+		width = 0.5       # the width of the bars
+
+		#    x1 = [0.4,1.4,2.4,3.4,4.4]
+		x2 = [0.45,1.45,2.45,3.45,4.45]
+		#    x3 = [0.5,1.5,2.5,3.5,4.5]
+		x4 = [0.55,1.55,2.55,3.55,4.55]
+		#    x5 = [0.6,1.6,2.6,3.6,4.6]
+		x6 = [0.50,1.50,2.50,3.50,4.50]
+
+		fig = plt.figure(figsize=(14.0, 10.0))
+
+		ax = fig.add_subplot(111)
+
+		#    print mean2D2D
+		#    print mean2D3D
+
+		#    ax.axhline(linewidth=2, y = np.mean(mean2D2D),color='r')
+		#    ax.axhline(linewidth=2, y = np.mean(mean2D3D),color='blue')
+
+		#    ax.axhline(linewidth=2, y = minvaluevalue,color='black')
+
+		#    ax.text(0.98, Participantmeanvalue+0.5, "Mean %.2f" % Participantmeanvalue,fontsize=12, fontweight='bold',color='r')
+		#    ax.text(0.98, maxvaluevalue+0.5, "Maximum %.2f" % maxvaluevalue,fontsize=12, fontweight='bold',color='black')
+		#    ax.text(0.98, minvaluevalue+0.5, "Minimum %.2f" % minvaluevalue,fontsize=12, fontweight='bold', color='black')
+
+		#    rects1 = ax.bar(ind, Participantmean,width, color='r',edgecolor='black',)#, hatch='//')
+		rects1 = ax.errorbar(x2, mean2D2D,yerr=[std2D2D,std2D2D],fmt='o',color='red',ecolor='red',lw=3, capsize=5, capthick=2)
+		plt.plot(x2, mean2D2D, marker="o", linestyle='-',lw=3,color='red',label = r'2D-to-2D')
+
+		rects2 =ax.errorbar(x4, mean2D3D,yerr=[std2D3D,std2D3D],fmt='o',color='blue',ecolor='blue',lw=3, capsize=5, capthick=2)
+		plt.plot(x4, mean2D3D, marker="o", linestyle='-',lw=3,color='blue', label = r'2D-to-3D')
+
+		rects3 =ax.errorbar(x6, mean3D3D,yerr=[std3D3D,std3D3D],fmt='o',color='blue',ecolor='blue',lw=3, capsize=5, capthick=2)
+		plt.plot(x6, mean3D3D, marker="o", linestyle='-',lw=3,color='blue', label = r'3D-to-3D')
+
+		legend(fontsize=20,loc='upper right')
+
+		#    rects3 = ax.errorbar(x3, meanC3,yerr=[stdC3,stdC3],fmt='o',color='black',ecolor='black',lw=3, capsize=5, capthick=2)
+		#    plt.plot(x3, meanC3, marker="o", linestyle='-',lw=3,color='black')
+		#    
+		#    rects4 =ax.errorbar(x4, meanC4,yerr=[stdC4,stdC4],fmt='o',color='green',ecolor='green',lw=3, capsize=5, capthick=2)
+		#    plt.plot(x4, meanC4, marker="o", linestyle='-',lw=3,color='green')
+		#    
+		#    rects5 =ax.errorbar(x5, meanC5,yerr=[stdC5,stdC5],fmt='o',color='orange',ecolor='orange',lw=3, capsize=5, capthick=2)
+		#    plt.plot(x5, meanC5, marker="o", linestyle='-',lw=3,color='orange')
+
+
+		ax.set_ylabel(r'Angular Error',fontsize=22)
+		ax.set_xlabel(r'Number of Calibration Depths',fontsize=22)
+		ax.set_xticks(ind+0.25)
+		ax.set_xticklabels( ('D1', 'D2', 'D3','D4', 'D5') ,fontsize=18)
+
+		TOPICs = [0.0,0.5,1.5,2.5,3.5,4.5,5.0]#,110]#,120]
+		print TOPICs
+		LABELs = ["",r'1',r'2', r'3', r'4', r'5', ""]#, ""]#, ""]
+
+		#    fig.canvas.set_window_title('Distance Error Correlation')
+		plt.xticks(TOPICs, LABELs,fontsize=18)
+
+		#    legend([rects1,rects2], [r'\LARGE\textbf{2D2D}', r'\LARGE\textbf{2D3D}'], loc='lower right') 
+
+		TOPICS = [0.5,1,1.5,2,2.5,3,3.5,4,4.5,5]#,110]#,120]
+		print TOPICS
+		LABELS = [r'0.5',  r'1',r'1.5', r'2',r'2.5', r'3',r'3.5', r'4',r'4.5',r'5']#, ""]#, ""]
+
+		#    fig.canvas.set_window_title('Accuracy - Activity Statistics')
+		plt.yticks(TOPICS, LABELS,fontsize=18)
+
+		def autolabel(rects):
+			# attach some text labels
+			for rect in rects:
+				height = rect.get_height()
+				ax.text(0.26+rect.get_x()+rect.get_width()/2., height +0.35, "%.2f"%float(height),
+				ha='center', va='bottom',fontweight='bold',fontsize=13.5)
+
+			#    autolabel(rects1)
+
+
+		left  = 0.1  # the left side of the subplots of the figure
+		right = 0.975    # the right side of the subplots of the figure
+		bottom = 0.075   # the bottom of the subplots of the figure
+		top = 0.925      # the top of the subplots of the figure
+		wspace = 0.2   # the amount of width reserved for blank space between subplots
+		hspace = 0.4   # the amount of height reserved for white space between subplots
+
+		plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
+		plt.show()
+		######################################################################################################
+
+class Parallax3Dto3DMapping(Experiment): # GT pupil pose instead of estimating the pose
+	'''
+	'''
+	def __run__(self):
+		sim = GazeSimulation(log = False)
+		sim.place_eyeball_on_scene_camera = False
+		sim.setEyeRelativeToSceneCamera(v(-65, -33, -73))
+		# sim.setEyeRelativeToSceneCamera(v(-65, -33, 0)) # assuming eyeball and scene camera are coplanar i.e. e = (e.x, e.y, 0)
+		sim.setCalibrationDepth(1 * 1000) # mm, wrt scene camera
+		sim.setTestDepth(1.5 * 1000)
+
+		sim.calibration_grid = True
+		sim.calibration_random_depth = False
+		sim.test_grid = True
+		sim.test_random_depth = False
+		sim.test_random_fixed_depth = False
+
+		cdepths = [1, 1.5, 2.0]
+		tdepths = np.linspace(1, 2, 5)
+
+		results = []
+		for i, cdepth in enumerate(cdepths):
+			sim.setCalibrationDepth(cdepth * 1000)
+			# Performing calibration
+			sim.runCalibration()
+
+			aae_2Ds = []
+			aae_3Ds = []
+			aae_3D3Ds = []
+			std_2Ds = []
+			std_3Ds = []
+			std_3D3Ds = []
+			
+			# Fetching calibration points
+			t, p3d, p2d = sim.calibration_points, sim.tr_3d_pupil_locations, sim.tr_pupil_locations
+			# target positions are computed relative to the scene CCS
+			ti = map(lambda target: v(target) - v(sim.scene_camera.t), t)
+			# Computing pupil pose for each gaze
+			ni = map(lambda p: (v(p)-v(sim.sclera_pos)).norm(), p3d) # ground truth gaze vectors
+
+			eye_scene_diff = v(sim.sclera_pos) - v(sim.scene_camera.t)
+			e = np.array(eye_scene_diff) # eyeball coords in scene CCS
+			e_org = v(e[:])
+
+			# transforming pose vector to eye camera coordinates
+			eyeR = np.array(sim.eye_camera.R[:3]) # result is identical to cv2.Rodrigues(np.array([0., np.pi, 0.]))[0]
+			ni = map(lambda pose: eyeR.dot(np.array(pose)), ni)
+
+			R, e3d3d = minimizeEnergy(ni, ti, pose_given=True)
+			e3d3d = v(e3d3d)
+			# R = LA.inv(R)
+
+			w2d3d, e2d3d, w0 = minimizeEnergy(p2d, ti)
+			e2d3d = v(e2d3d)
+			
+			print
+			print R
+			print 'e3d3d', e3d3d, 'distance =', (e3d3d-e_org).mag
+			print 'e2d3d', e2d3d, 'distance =', (e2d3d-e_org).mag
+			print 'real e', e_org
+			print
+
+			for j, tdepth in enumerate(tdepths):
+
+				sim.setTestDepth(tdepth * 1000)
+				aae_2d, _, std_2d, _ = sim.runTest()
+				aae_2Ds.append(aae_2d)
+				std_2Ds.append(std_2d)
+				
+				# Fetching test points
+				t, p3d, p2d = sim.test_points, sim.te_3d_pupil_locations, sim.te_pupil_locations
+				ti = map(lambda target: v(target) - v(sim.scene_camera.t), t) # target coords in scene CCS
+				ni = map(lambda p: v(v(p)-v(sim.sclera_pos)).norm(), p3d) # ground truth gaze vectors
+				
+				# transforming pose vector to eye camera coordinates
+				ni = map(lambda r: v(eyeR.dot(np.array(r))), ni)
+				
+				# applying estimated rotation to pose vector in eye camera coordinates (Rn)
+				ni = map(lambda n: v(R.dot(np.array(n))), ni) # now ready to compare Rn with t-e
+
+				# 3D to 3D 
+				# Intersecting gaze rays originating from the eye with the planes defined by each
+				# target. then we can simply compute angular error between each intersection and 
+				# the corresponding 3D target
+				gis = map(lambda vec: v(vec), ni)
+				gprimes = map(lambda tg: v(((tg[0].z - e3d3d.z)/tg[1].z)*tg[1] + e3d3d), zip(ti, gis))
+				AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, ti))
+				# AE = list(np.degrees(np.arccos(v(p[0]).dot(p[1])/v(p[0]).mag/v(p[1]).mag)) for p in zip(gprimes, ti))
+
+				N = len(ti)
+				AAE = np.mean(AE)
+				STD_3D3D = np.std(AE)
+				m, M = min(AE), max(AE)
+				aae_3D3Ds.append(AAE)
+				std_3D3Ds.append(STD_3D3D)
+
+				# 2D to 3D 
+				qi = map(_q, p2d) # computing feature vectors from raw pupil coordinates in 2D
+				# computing unit gaze vectors corresponding to pupil positions
+				# here we use the computed mapping matrix w
+				gis = map(lambda q: gaze_ray(q, w2d3d), qi) 
+				
+				# Intersecting gaze rays originating from the eye with the planes defined by each
+				# target. then we can simply compute angular error between each intersection and 
+				# the corresponding 3D target
+				gis = map(lambda vec: v(vec), gis)
+				gprimes = map(lambda tg: v(((tg[0].z - e2d3d.z)/tg[1].z)*tg[1] + e2d3d), zip(ti, gis))
+
+				AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, ti))
+				# AE = list(np.degrees(np.arccos(v(p[0]).dot(p[1])/v(p[0]).mag/v(p[1]).mag)) for p in zip(gprimes, ti))
+
+				N = len(t)
+				AAE = np.mean(AE)
+				STD_2D3D = np.std(AE)
+				m, M = min(AE), max(AE)
+				# Computing physical distance error (in meters)
+				PHE = list((u-v).mag for u,v in zip(ti, gprimes))
+				N = len(ti)
+				APHE = np.mean(PHE)
+				PHE_STD = np.std(PHE)
+				PHE_m, PHE_M = min(PHE), max(PHE)
+
+				# aae_3Ds.append((AAE, STD, PHE, PHE_STD))
+				aae_3Ds.append(AAE)
+				std_3Ds.append(STD_2D3D)
+				# break
+
+			print 'depth', cdepth, 'finished.'
+			results.append([aae_2Ds, aae_3Ds, aae_3D3Ds, std_2Ds, std_3Ds, std_3D3Ds])
+
+
+		clrs = ['r', 'g', 'b', 'k', 'o']
+		colors = ['blue', 'orange', 'red', 'black', 'orange']
+		patches = []
+
+		fig = plt.figure(figsize=(14.0, 10.0))
+		ax = fig.add_subplot(111)
+
+		dmap = {0: '1', 1:'3', 2:'5'}
+
+		x1 = [0.375,1.375,2.375,3.375,4.375]
+		x2 = [0.425,1.425,2.425,3.425,4.425]
+		x3 = [0.475,1.475,2.475,3.475,4.475]
+		x4 = [0.525,1.525,2.525,3.525,4.525]
+		x5 = [0.575,1.575,2.575,3.575,4.575]
+		x6 = [0.625,1.625,2.625,3.625,4.625]
+		xrange_2d2d = [x1, x3, x5]
+		xrange_2d3d = [x2, x4, x6]
+
+		for i in [0, 1, 2]:
+			cdepth_results = results[i]
+			_xrange = xrange_2d2d[i]
+			aae_2d2d = cdepth_results[0]
+			std_2d2d = cdepth_results[3]
+			rects1 = ax.errorbar(_xrange, aae_2d2d,yerr=[std_2d2d, std_2d2d],fmt='o',color=colors[i],ecolor=colors[i],lw=3, capsize=5, capthick=2)
+			plt.plot(_xrange, aae_2d2d, marker="o", linestyle='-',lw=3,color=colors[i],label = '2D-to-2D Calibration Depth ' + dmap[i])
+
+		for i in [0, 1, 2]:
+			cdepth_results = results[i]
+			_xrange = xrange_2d3d[i]
+			aae_2d3d = cdepth_results[1]
+			std_2d3d = cdepth_results[4]
+			rects2 = ax.errorbar(_xrange, aae_2d3d,yerr=[std_2d3d, std_2d3d],fmt='o',color=colors[i],ecolor=colors[i],lw=3, capsize=5, capthick=2)
+			plt.plot(_xrange, aae_2d3d, marker="o", linestyle='--',lw=3,color=colors[i],label = '2D-to-3D Calibration Depth ' + dmap[i])
+
+		for i in [0, 1, 2]:
+			cdepth_results = results[i]
+			_xrange = xrange_2d2d[i]
+			aae_3d3d = cdepth_results[2]
+			std_3d3d = cdepth_results[5]
+			rects3 = ax.errorbar(_xrange, aae_3d3d,yerr=[std_3d3d, std_3d3d],fmt='o',color=colors[i],ecolor=colors[i],lw=3, capsize=5, capthick=2)
+			plt.plot(_xrange, aae_3d3d, marker="o", linestyle='-.',lw=3,color=colors[i],label = '3D-to-3D Calibration Depth ' + dmap[i])
+
+
+		ax.set_ylabel(r'\textbf{Angular Error}',fontsize=22)
+		ax.set_xlabel(r'\textbf{Depth}',fontsize=22)
+		# ax.set_ylim((0, 2.4))
+
+		TOPICS = [-0.2, 0, 0.2, 0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8,2.0,2.2,2.4]#,110]#,120]
+		LABELS = [r'', r'0', r'0.2',r'0.4',r'0.6',  r'0.8', r'1.0', r'1.2', r'1.4',  r'1.6',  r'1.8', r'2.0',  r'2.2', r'2.4']#, ""]#, ""]
+
+		plt.yticks(TOPICS, LABELS,fontsize=18)
+
+		plt.xlabel('Depth')
+		plt.ylabel('Angular Error')
+
+		plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
+           ncol=3, mode="expand", borderaxespad=0., fontsize=18)
+
+		TOPICs = [0.0,0.5,1.5,2.5,3.5,4.5,5.0]
+		LABELs = ['', 'D1 - 1m', 'D2 - 1.25m', 'D3 - 1.5m', 'D4 - 1.75m', 'D5 - 2.0m', '']
+		plt.xticks(TOPICs, LABELs,fontsize=18)
+		
+		left  = 0.1  # the left side of the subplots of the figure
+		right = 0.975    # the right side of the subplots of the figure
+		bottom = 0.075   # the bottom of the subplots of the figure
+		top = 0.925      # the top of the subplots of the figure
+		wspace = 0.2   # the amount of width reserved for blank space between subplots
+		hspace = 0.4   # the amount of height reserved for white space between subplots
+
+		plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
+		plt.show()
+
+		# self.sim = sim
+
+ROOT_DATA_DIR = '/home/mmbrian/HiWi/etra2016_mohsen/code/recording/data/participants'
+class Parallax2Dto2DRealData(Experiment):
+	'''
+	First it runs single calibration depth vs test depth over all combinations of depths per participant (25 cases)
+	Then we perform estimation with data from multiple calibration depths for each test depth
+	'''
+	def __run__(self):
+		sim = GazeSimulation(log = False)
+		camera_matrix, dist_coeffs = readCameraParams()
+		
+		root_result_path = 'results/2d2d/'
+		if not os.path.exists(root_result_path):
+			os.makedirs(root_result_path)
+
+		errors = []
+		for d1 in os.listdir(ROOT_DATA_DIR):
+			if d1.startswith('p'): # every participant
+				if not d1 in PARTICIPANTS:
+					continue
+				participant_label = d1
+				participant_results = []
+				d2 = os.path.join(ROOT_DATA_DIR, d1) # .../pi/
+				d2 = os.path.join(d2, os.listdir(d2)[0]) # .../pi/../
+				print '> Processing participant', d1
+				participant_experiment = {}
+				for d3 in os.listdir(d2): # every recording
+					d4 = os.path.join(d2, d3) # .../pi/../00X/
+
+					intervals_dir = os.path.join(d4, 'gaze_intervals.npy')
+					if not os.path.isfile(intervals_dir):
+						# Participant not completely processed
+						print '> One or more recordings need processing...'
+						break
+					else:
+
+						p = np.load(os.path.join(d4, 'p.npy'))
+						break_participant = False
+						for point in p:
+							if np.isnan(point[0]):
+								# For this gaze position, not input with nonzero confidence exist
+								break_participant = True
+								break
+						if break_participant:
+							print '> One or more recordings miss gaze coords...'
+							break
+						# print p
+						t2d = np.load(os.path.join(d4, 't2d.npy'))
+						t3d = np.load(os.path.join(d4, 't3d.npy'))
+						participant_experiment[d3] = [p, t2d, t3d]
+
+				if len(participant_experiment) == 10:
+					print '> All recordings processed'
+					keys = sorted(participant_experiment.keys())
+					depths = zip(keys[::2], keys[1::2])
+					for calib_depth in depths:
+						cp, ct = participant_experiment[calib_depth[0]][0], participant_experiment[calib_depth[0]][1]
+						cdepth_value = getDepth(calib_depth)
+						# Perform calibration
+						sim.perform2D2DCalibrationOnReadData(cp, ct, (1280, 720))
+						for test_depth in depths:
+							tdepth_value = getDepth(test_depth)
+							tp, tt, tt3d = participant_experiment[test_depth[1]][0], participant_experiment[test_depth[1]][1], participant_experiment[test_depth[1]][2]
+							error = sim.run2D2DTestOnRealData(tp, tt, (1280, 720), tt3d, camera_matrix, dist_coeffs)
+							participant_results.append([cdepth_value, tdepth_value] + error)
+					print len(participant_results), 'combinations processed...'
+					np.save('results/2d2d/%s_2d2d_all.npy' % participant_label, np.array(participant_results))
+					np.savetxt('results/2d2d/%s_2d2d_all.csv' % participant_label, np.array(participant_results), delimiter=",")
+
+					print '> Computing results for multiple calibration depths...'
+					for num_of_calibration_depths in xrange(2, 6): # from 2 calibration depths to 5
+						participant_results = []
+						print '> Computing results for combining %s calibration depths...' % num_of_calibration_depths
+						for calibs in combinations(depths, num_of_calibration_depths):
+							# Now calibs is a set of depths, from each of those we need calibration data
+							cp, ct = [], []
+							calib_depths_label = []
+							for calib in calibs:
+								if len(cp):
+									cp = np.concatenate((cp, participant_experiment[calib[0]][0]), axis=0)
+									ct = np.concatenate((ct, participant_experiment[calib[0]][1]), axis=0)
+								else:
+									cp = participant_experiment[calib[0]][0]
+									ct = participant_experiment[calib[0]][1]
+								calib_depths_label.append(getDepth(calib))
+							# Perform calibration
+							sim.perform2D2DCalibrationOnReadData(cp, ct, (1280, 720))
+							# Now we have calibration data from multiple depths, we can test on all depths
+							for test_depth in depths:
+								tdepth_value = getDepth(test_depth)
+								tp, tt, tt3d = participant_experiment[test_depth[1]][0], participant_experiment[test_depth[1]][1], participant_experiment[test_depth[1]][2]
+								error = sim.run2D2DTestOnRealData(tp, tt, (1280, 720), tt3d, camera_matrix, dist_coeffs)
+								participant_results.append(calib_depths_label + [tdepth_value] + error)
+						print len(participant_results), 'combinations processed...'
+						result_path = 'results/2d2d/%s_calibration_depths/' % num_of_calibration_depths
+						if not os.path.exists(result_path):
+							os.makedirs(result_path)
+						np.save(result_path + '%s.npy' % participant_label, np.array(participant_results))
+						np.savetxt(result_path + '%s.csv' % participant_label, np.array(participant_results), delimiter=",")
+
+
+		print 'done.'
+		# plt.plot(tdrange, aaes)
+		# plt.show()
+
+def getDepth(depth_experiments):
+	_map = {'000': 1, '002': 1.25, '004': 1.5, '006': 1.75, '008': 2.0}
+	return _map[depth_experiments[0]]
+
+class Parallax2Dto3DRealData(Experiment):
+	def __run__(self):
+		sim = GazeSimulation(log = False)
+		aae_3ds = []
+		root_result_path = '/home/mmbrian/3D_Gaze_Tracking/work/results/2D3D/'
+		if not os.path.exists(root_result_path):
+			os.makedirs(root_result_path)
+
+		for d1 in os.listdir(ROOT_DATA_DIR):
+			if d1.startswith('p'): # every participant
+				if not d1 in PARTICIPANTS:
+					continue
+				participant_label = d1
+				participant_results = []
+				d2 = os.path.join(ROOT_DATA_DIR, d1) # .../pi/
+				d2 = os.path.join(d2, os.listdir(d2)[0]) # .../pi/../
+				print '> Processing participant', d1
+				participant_experiment = {}
+				for d3 in os.listdir(d2): # every recording
+					d4 = os.path.join(d2, d3) # .../pi/../00X/
+
+					intervals_dir = os.path.join(d4, 'gaze_intervals.npy')
+					if not os.path.isfile(intervals_dir):
+						# Participant not completely processed
+						print '> One or more recordings need processing...'
+						break
+					else:
+
+						p = np.load(os.path.join(d4, 'p.npy'))
+						# p = np.load(os.path.join(d4, 'p_mean.npy'))
+						break_participant = False
+						for point in p:
+							if np.isnan(point[0]):
+								# For this gaze position, not input with nonzero confidence exist
+								break_participant = True
+								break
+						if break_participant:
+							print '> One or more recordings miss gaze coords...'
+							break
+						# print p
+						t2d = np.load(os.path.join(d4, 't2d.npy'))
+						t3d = np.load(os.path.join(d4, 't3d.npy'))
+						# t2d = np.load(os.path.join(d4, 't2d_mean.npy'))
+						# t3d = np.load(os.path.join(d4, 't3d_mean.npy'))
+						participant_experiment[d3] = [p, t2d, t3d]
+
+				if len(participant_experiment) == 10:
+					print '> All recordings processed'
+					keys = sorted(participant_experiment.keys())
+					depths = zip(keys[::2], keys[1::2])
+					for calib_depth in depths:
+						cp, ct3d = participant_experiment[calib_depth[0]][0], participant_experiment[calib_depth[0]][2]
+						cdepth_value = getDepth(calib_depth)
+
+						# Performing calibration
+						w, e, w0 = minimizeEnergy(cp, ct3d)
+						e = v(e)
+						for test_depth in depths:
+							tdepth_value = getDepth(test_depth)
+							tp, tt3d = participant_experiment[test_depth[1]][0], participant_experiment[test_depth[1]][2]
+
+							t, p = tt3d, tp
+							qi = map(_q, p) # computing feature vectors from raw pupil coordinates in 2D
+							# computing unit gaze vectors corresponding to pupil positions
+							# here we use the computed mapping matrix w
+							gis = map(lambda q: g(q, w), qi)
+							# Intersecting gaze rays originating from the eye with the planes defined by each
+							# target. then we can simply compute angular error between each intersection and 
+							# the corresponding 3D target
+							t = map(lambda vec: v(vec), t)
+							gis = map(lambda vec: v(vec), gis)
+							gprimes = map(lambda tg: v(((tg[0].z - e.z)/tg[1].z)*tg[1] + e), zip(t, gis))
+
+							AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, t))
+							N = len(t)
+							AAE = sum(AE)/N
+							VAR = sum((ae - AAE)**2 for ae in AE)/N
+							STD = np.sqrt(VAR)
+							m, M = min(AE), max(AE)
+
+							# Computing physical distance error (in meters)
+							PHE = list((u-v).mag for u,v in zip(t, gprimes))
+							N = len(t)
+							APHE = sum(PHE)/N
+							PHE_VAR = sum((phe - APHE)**2 for phe in PHE)/N
+							PHE_STD = np.sqrt(VAR)
+							PHE_m, PHE_M = min(PHE), max(PHE)
+
+							participant_results.append([cdepth_value, tdepth_value] + [AAE, VAR, STD, m, M, APHE, PHE_VAR, PHE_STD, PHE_m, PHE_M])
+
+					print len(participant_results), 'combinations processed...'
+					np.save(os.path.join(root_result_path, '%s_2d3d_all.npy' % participant_label), np.array(participant_results))
+					np.savetxt(os.path.join(root_result_path, '%s_2d3d_all.csv' % participant_label), np.array(participant_results), delimiter=",")
+
+					print '> Computing results for multiple calibration depths...'
+					for num_of_calibration_depths in xrange(2, 6): # from 2 calibration depths to 5
+						participant_results = []
+						print '> Computing results for combining %s calibration depths...' % num_of_calibration_depths
+						for calibs in combinations(depths, num_of_calibration_depths):
+							# Now calibs is a set of depths, from each of those we need calibration data
+							cp, ct3d = [], []
+							calib_depths_label = []
+							for calib in calibs:
+								if len(cp):
+									cp = np.concatenate((cp, participant_experiment[calib[0]][0]), axis=0)
+									ct3d = np.concatenate((ct3d, participant_experiment[calib[0]][2]), axis=0)
+								else:
+									cp = participant_experiment[calib[0]][0]
+									ct3d = participant_experiment[calib[0]][2]
+								calib_depths_label.append(getDepth(calib))
+							# Performing calibration
+							w, e, w0 = minimizeEnergy(cp, ct3d)
+							e = v(e)
+							# Now we have calibration data from multiple depths, we can test on all depths
+							for test_depth in depths:
+								tdepth_value = getDepth(test_depth)
+								tp, tt3d = participant_experiment[test_depth[1]][0], participant_experiment[test_depth[1]][2]
+
+								t, p = tt3d, tp
+								qi = map(_q, p) 
+								gis = map(lambda q: g(q, w), qi)
+
+								t = map(lambda vec: v(vec), t)
+								gis = map(lambda vec: v(vec), gis)
+								gprimes = map(lambda tg: v(((tg[0].z - e.z)/tg[1].z)*tg[1] + e), zip(t, gis))
+
+								AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, t))
+								N = len(t)
+								AAE = sum(AE)/N
+								VAR = sum((ae - AAE)**2 for ae in AE)/N
+								STD = np.sqrt(VAR)
+								m, M = min(AE), max(AE)
+
+								# Computing physical distance error (in meters)
+								PHE = list((u-v).mag for u,v in zip(t, gprimes))
+								N = len(t)
+								APHE = sum(PHE)/N
+								PHE_VAR = sum((phe - APHE)**2 for phe in PHE)/N
+								PHE_STD = np.sqrt(VAR)
+								PHE_m, PHE_M = min(PHE), max(PHE)
+
+								participant_results.append(calib_depths_label + [tdepth_value] + [AAE, VAR, STD, m, M, APHE, PHE_VAR, PHE_STD, PHE_m, PHE_M])
+						
+						print len(participant_results), 'combinations processed...'
+						result_path = os.path.join(root_result_path, '%s_calibration_depths/' % num_of_calibration_depths)
+						if not os.path.exists(result_path):
+							os.makedirs(result_path)
+						np.save(result_path + '%s.npy' % participant_label, np.array(participant_results))
+						np.savetxt(result_path + '%s.csv' % participant_label, np.array(participant_results), delimiter=",")
+		
+		print 'done.'
+
+
+
+class Parallax3Dto3DRealData(Experiment):
+	def __run__(self):
+		sim = GazeSimulation(log = False)
+		aae_3ds = []
+
+		root_result_path = '/home/mmbrian/3D_Gaze_Tracking/work/results/3D3D/'
+		root_pose_path = '/home/mmbrian/3D_Gaze_Tracking/work/Marker_Eye_Images/ImagesUndist/'
+		root_data_path = '/home/mmbrian/HiWi/etra2016_mohsen/code/recording/data/participants/'
+		take_only_nearest_neighbor_for_calibration = True
+
+		participants = ['p14']
+
+		if not os.path.exists(root_result_path):
+			os.makedirs(root_result_path)
+
+		for d1 in os.listdir(root_data_path):
+			if d1.startswith('p'): # every participant
+				if not d1 in participants:
+				# if not d1 in PARTICIPANTS:
+					continue
+				participant_label = d1
+				participant_results = []
+				d2 = os.path.join(root_data_path, d1) # .../pi/
+				d2 = os.path.join(d2, os.listdir(d2)[0]) # .../pi/../
+				print '> Processing participant', d1
+				participant_experiment = {}
+				for d3 in os.listdir(d2): # every recording
+					d4 = os.path.join(d2, d3) # .../pi/../00X/
+					
+					# pose_info = np.loadtxt(open(os.path.join(root_pose_path+d1+'/'+d3, "null_pupils.csv"),"rb"),delimiter=";")
+					pose_info = np.loadtxt(open(os.path.join(root_pose_path+d1+'/'+d3, "simple_pupils.csv"),"rb"),delimiter=";")
+					frames_numbers = pose_info[:, 0]
+					pose_estimates = pose_info[:,4:7]
+					pose_info = dict(zip(frames_numbers, pose_estimates))
+					p_frames = np.load(os.path.join(d4, 'p_frames.npy'))
+					# print d4
+					# Fetching pose information for every target
+					poses = []
+					for target in p_frames:
+						pose = []
+						for fn in target: # all frames corresponding to this pupil 
+							# first fn corresponds to the nearest neighbor
+							# for test use all correspondents from these 3 or 2 estimates
+							# i.e. each pose-marker creates a correspondence so 3*16=48 correspondents for test
+							# for calibration compare two cases, one similar to above take all the 75 correspondents
+							# and the other taking only the pose corresponding to nearest neighbor which results in
+							# the same number of correspondents as target markers
+							try:
+								pose.append(pose_info[fn])
+							except KeyError, err:
+								print err
+						poses.append(pose)
+
+					t2d = np.load(os.path.join(d4, 't2d.npy'))
+					t3d = np.load(os.path.join(d4, 't3d.npy'))
+					participant_experiment[d3] = [poses, t2d, t3d]
+
+				keys = sorted(participant_experiment.keys())
+				depths = zip(keys[::2], keys[1::2])
+
+				for calib_depth in depths:
+					pose_data, ct3d = participant_experiment[calib_depth[0]][0], participant_experiment[calib_depth[0]][2]
+					cdepth_value = getDepth(calib_depth)
+					if take_only_nearest_neighbor_for_calibration:
+						pose = np.array(list(p[0] for p in pose_data))
+						calib_3ds = ct3d[:]
+					else:
+						calib_3ds = []
+						pose = []
+						for i, p3d in enumerate(ct3d):
+							for p in pose_data[i]:
+								pose.append(p)
+								calib_3ds.append(p3d)
+					# Performing calibration
+					# First we convert gaze rays to actual pupil pose in our right hand coordinate system
+					# _pose = [(np.arctan(g.x/g.z), np.arctan(g.y/g.z)) for g in map(v, pose)]
+					_pose = map(v, pose)
+					print '> Running tests for calibration depth', cdepth_value
+					if any(g.z == 0 for g in _pose):
+						print 'Calibration is flawed'
+						# print pose
+					else:
+						print 'Calibration data is okay'
+						# print [g.mag for g in map(v, pose)]
+					# w, e, w0 = minimizeEnergy(_pose, calib_3ds, pose_given=True)
+					R, e = minimizeEnergy(pose, calib_3ds, pose_given=True)
+					# R = LA.inv(R)
+					print 'R', R
+					print 'e', e
+
+					e = v(e)
+					for test_depth in depths:
+						tdepth_value = getDepth(test_depth)
+						tpose_data, tt3d = participant_experiment[test_depth[1]][0], participant_experiment[test_depth[1]][2]
+
+						test_3ds = []
+						tpose = []
+						for i, p3d in enumerate(tt3d):
+							for p in tpose_data[i]:
+								tpose.append(p)
+								test_3ds.append(p3d)
+						
+						# applying estimated rotation to bring pose vectors to scene camera coordinates
+						tpose = map(lambda p: v(R.dot(np.array(p))), tpose)
+
+						if any(g.z == 0 for g in map(v, tpose)):
+							print 'Test depth', tdepth_value, 'is flawed'
+							
+						gis = map(lambda vec: v(vec), tpose)
+						t = map(lambda vec: v(vec), test_3ds)
+						gprimes = map(lambda tg: v(((tg[0].z - e.z)/tg[1].z)*tg[1] + e), zip(t, gis))
+						# AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gprimes, t))
+						AE = list(np.degrees(np.arccos(v(p[0]).dot(p[1])/v(p[0]).mag/v(p[1]).mag)) for p in zip(gprimes, t))
+
+						AAE = np.mean(AE)
+						STD = np.std(AE)
+						m, M = min(AE), max(AE)
+
+						# Computing physical distance error (in meters)
+						PHE = list((u-v).mag for u,v in zip(t, gprimes))
+						APHE = np.mean(PHE)
+						PHE_STD = np.std(PHE)
+						PHE_m, PHE_M = min(PHE), max(PHE)
+
+						print 'Calibration', cdepth_value, 'Test', tdepth_value, AAE, 'degrees', APHE, 'meters'
+						participant_results.append([cdepth_value, tdepth_value] + [AAE, STD, m, M, APHE, PHE_STD, PHE_m, PHE_M])
+
+				print len(participant_results), 'combinations processed...'
+				np.save(os.path.join(root_result_path, '%s_3d3d_all.npy' % participant_label), np.array(participant_results))
+				np.savetxt(os.path.join(root_result_path, '%s_3d3d_all.csv' % participant_label), np.array(participant_results), delimiter=",")
+
+
+def main():
+	if len(sys.argv) <= 1:
+		print 'Please select a mode.'
+		return
+	mode = sys.argv[1]
+
+	if mode == 'pts':
+		# Performs an experiment by fixing calibration depth and testing for different test depths
+		# to investigate parallax error in 2D to 2D mapping
+		ex = Parallax2Dto2DMapping()
+		ex.performExperiment()
+	if mode == '2d3d':
+		# This also does 3D gaze estimation and plots estimation results for both 3D and 2D estimation
+		ex = Parallax2Dto3DMapping()
+		ex.performExperiment()
+
+	# ex = Parallax2Dto3DMappingEye()
+	# ex.performExperiment()i
+	if mode == '2d2d_2d3d':
+		ex = Parallax3Dto3DMapping()
+		ex.performExperiment()
+
+	# ex = Parallax2Dto2DRealData()
+	# ex.performExperiment()
+
+	# ex = Parallax2Dto3DRealData()
+	# ex.performExperiment()	
+
+	if mode == '3d3d_real':
+		ex = Parallax3Dto3DRealData()
+		ex.performExperiment()	
+
+
+if __name__ == '__main__':
+	main()
+
+
+
+

code/pupil/calibrate.py

@@ -0,0 +1,255 @@
+'''
+(*)~----------------------------------------------------------------------------------
+ Pupil - eye tracking platform
+ Copyright (C) 2012-2015  Pupil Labs
+
+ Distributed under the terms of the CC BY-NC-SA License.
+ License details are in the file license.txt, distributed as part of this software.
+----------------------------------------------------------------------------------~(*)
+'''
+
+import numpy as np
+#logging
+import logging
+logger = logging.getLogger(__name__)
+
+
+def get_map_from_cloud(cal_pt_cloud,screen_size=(2,2),threshold = 35,return_inlier_map=False,return_params=False):
+    """
+    we do a simple two pass fitting to a pair of bi-variate polynomials
+    return the function to map vector
+    """
+    # fit once using all avaiable data
+    model_n = 7
+    cx,cy,err_x,err_y = fit_poly_surface(cal_pt_cloud,model_n)
+    err_dist,err_mean,err_rms = fit_error_screen(err_x,err_y,screen_size)
+    if cal_pt_cloud[err_dist<=threshold].shape[0]: #did not disregard all points..
+        # fit again disregarding extreme outliers
+        cx,cy,new_err_x,new_err_y = fit_poly_surface(cal_pt_cloud[err_dist<=threshold],model_n)
+        map_fn = make_map_function(cx,cy,model_n)
+        new_err_dist,new_err_mean,new_err_rms = fit_error_screen(new_err_x,new_err_y,screen_size)
+
+        logger.info('first iteration. root-mean-square residuals: %s, in pixel' %err_rms)
+        logger.info('second iteration: ignoring outliers. root-mean-square residuals: %s in pixel',new_err_rms)
+
+        logger.info('used %i data points out of the full dataset %i: subset is %i percent' \
+            %(cal_pt_cloud[err_dist<=threshold].shape[0], cal_pt_cloud.shape[0], \
+            100*float(cal_pt_cloud[err_dist<=threshold].shape[0])/cal_pt_cloud.shape[0]))
+
+        if return_inlier_map and return_params:
+            return map_fn,err_dist<=threshold,(cx,cy,model_n)
+        if return_inlier_map and not return_params:
+            return map_fn,err_dist<=threshold
+        if return_params and not return_inlier_map:
+            return map_fn,(cx,cy,model_n)
+        return map_fn
+    else: # did disregard all points. The data cannot be represented by the model in a meaningful way:
+        map_fn = make_map_function(cx,cy,model_n)
+        logger.info('First iteration. root-mean-square residuals: %s in pixel, this is bad!'%err_rms)
+        logger.warning('The data cannot be represented by the model in a meaningfull way.')
+
+        if return_inlier_map and return_params:
+            return map_fn,err_dist<=threshold,(cx,cy,model_n)
+        if return_inlier_map and not return_params:
+            return map_fn,err_dist<=threshold
+        if return_params and not return_inlier_map:
+            return map_fn,(cx,cy,model_n)
+        return map_fn
+
+
+
+def fit_poly_surface(cal_pt_cloud,n=7):
+    M = make_model(cal_pt_cloud,n)
+    U,w,Vt = np.linalg.svd(M[:,:n],full_matrices=0)
+    V = Vt.transpose()
+    Ut = U.transpose()
+    pseudINV = np.dot(V, np.dot(np.diag(1/w), Ut))
+    cx = np.dot(pseudINV, M[:,n])
+    cy = np.dot(pseudINV, M[:,n+1])
+    # compute model error in world screen units if screen_res specified
+    err_x=(np.dot(M[:,:n],cx)-M[:,n])
+    err_y=(np.dot(M[:,:n],cy)-M[:,n+1])
+    return cx,cy,err_x,err_y
+
+def fit_error_screen(err_x,err_y,(screen_x,screen_y)):
+    err_x *= screen_x/2.
+    err_y *= screen_y/2.
+    err_dist=np.sqrt(err_x*err_x + err_y*err_y)
+    err_mean=np.sum(err_dist)/len(err_dist)
+    err_rms=np.sqrt(np.sum(err_dist*err_dist)/len(err_dist))
+    return err_dist,err_mean,err_rms
+
+def make_model(cal_pt_cloud,n=7):
+    n_points = cal_pt_cloud.shape[0]
+
+    if n==3:
+        X=cal_pt_cloud[:,0]
+        Y=cal_pt_cloud[:,1]
+        Ones=np.ones(n_points)
+        ZX=cal_pt_cloud[:,2]
+        ZY=cal_pt_cloud[:,3]
+        M=np.array([X,Y,Ones,ZX,ZY]).transpose()
+
+    elif n==7:
+        X=cal_pt_cloud[:,0]
+        Y=cal_pt_cloud[:,1]
+        XX=X*X
+        YY=Y*Y
+        XY=X*Y
+        XXYY=XX*YY
+        Ones=np.ones(n_points)
+        ZX=cal_pt_cloud[:,2]
+        ZY=cal_pt_cloud[:,3]
+        M=np.array([X,Y,XX,YY,XY,XXYY,Ones,ZX,ZY]).transpose()
+
+    elif n==9:
+        X=cal_pt_cloud[:,0]
+        Y=cal_pt_cloud[:,1]
+        XX=X*X
+        YY=Y*Y
+        XY=X*Y
+        XXYY=XX*YY
+        XXY=XX*Y
+        YYX=YY*X
+        Ones=np.ones(n_points)
+        ZX=cal_pt_cloud[:,2]
+        ZY=cal_pt_cloud[:,3]
+        M=np.array([X,Y,XX,YY,XY,XXYY,XXY,YYX,Ones,ZX,ZY]).transpose()
+    else:
+        raise Exception("ERROR: Model n needs to be 3, 7 or 9")
+    return M
+
+
+def make_map_function(cx,cy,n):
+    if n==3:
+        def fn((X,Y)):
+            x2 = cx[0]*X + cx[1]*Y +cx[2]
+            y2 = cy[0]*X + cy[1]*Y +cy[2]
+            return x2,y2
+
+    elif n==7:
+        def fn((X,Y)):
+            x2 = cx[0]*X + cx[1]*Y + cx[2]*X*X + cx[3]*Y*Y + cx[4]*X*Y + cx[5]*Y*Y*X*X +cx[6]
+            y2 = cy[0]*X + cy[1]*Y + cy[2]*X*X + cy[3]*Y*Y + cy[4]*X*Y + cy[5]*Y*Y*X*X +cy[6]
+            return x2,y2
+
+    elif n==9:
+        def fn((X,Y)):
+            #          X         Y         XX         YY         XY         XXYY         XXY         YYX         Ones
+            x2 = cx[0]*X + cx[1]*Y + cx[2]*X*X + cx[3]*Y*Y + cx[4]*X*Y + cx[5]*Y*Y*X*X + cx[6]*Y*X*X + cx[7]*Y*Y*X + cx[8]
+            y2 = cy[0]*X + cy[1]*Y + cy[2]*X*X + cy[3]*Y*Y + cy[4]*X*Y + cy[5]*Y*Y*X*X + cy[6]*Y*X*X + cy[7]*Y*Y*X + cy[8]
+            return x2,y2
+    else:
+        raise Exception("ERROR: Model n needs to be 3, 7 or 9")
+
+    return fn
+
+
+def preprocess_data(pupil_pts,ref_pts):
+    '''small utility function to deal with timestamped but uncorrelated data
+    input must be lists that contain dicts with at least "timestamp" and "norm_pos"
+    '''
+    cal_data = []
+
+    if len(ref_pts)<=2:
+        return cal_data
+
+    cur_ref_pt = ref_pts.pop(0)
+    next_ref_pt = ref_pts.pop(0)
+    while True:
+        matched = []
+        while pupil_pts:
+            #select all points past the half-way point between current and next ref data sample
+            if pupil_pts[0]['timestamp'] <=(cur_ref_pt['timestamp']+next_ref_pt['timestamp'])/2.:
+                matched.append(pupil_pts.pop(0))
+            else:
+                for p_pt in matched:
+                    #only use close points
+                    if abs(p_pt['timestamp']-cur_ref_pt['timestamp']) <= 1/15.: #assuming 30fps + slack
+                        data_pt = p_pt["norm_pos"][0], p_pt["norm_pos"][1],cur_ref_pt['norm_pos'][0],cur_ref_pt['norm_pos'][1]
+                        cal_data.append(data_pt)
+                break
+        if ref_pts:
+            cur_ref_pt = next_ref_pt
+            next_ref_pt = ref_pts.pop(0)
+        else:
+            break
+    return cal_data
+
+
+
+# if __name__ == '__main__':
+#     import matplotlib.pyplot as plt
+#     from matplotlib import cm
+#     from mpl_toolkits.mplot3d import Axes3D
+
+#     cal_pt_cloud = np.load('cal_pt_cloud.npy')
+#     # plot input data
+#     # Z = cal_pt_cloud
+#     # ax.scatter(Z[:,0],Z[:,1],Z[:,2], c= "r")
+#     # ax.scatter(Z[:,0],Z[:,1],Z[:,3], c= "b")
+
+#     # fit once
+#     model_n = 7
+#     cx,cy,err_x,err_y = fit_poly_surface(cal_pt_cloud,model_n)
+#     map_fn = make_map_function(cx,cy,model_n)
+#     err_dist,err_mean,err_rms = fit_error_screen(err_x,err_y,(1280,720))
+#     print err_rms,"in pixel"
+#     threshold =15 # err_rms*2
+
+#     # fit again disregarding crass outlines
+#     cx,cy,new_err_x,new_err_y = fit_poly_surface(cal_pt_cloud[err_dist<=threshold],model_n)
+#     map_fn = make_map_function(cx,cy,model_n)
+#     new_err_dist,new_err_mean,new_err_rms = fit_error_screen(new_err_x,new_err_y,(1280,720))
+#     print new_err_rms,"in pixel"
+
+#     print "using %i datapoints out of the full dataset %i: subset is %i percent" \
+#         %(cal_pt_cloud[err_dist<=threshold].shape[0], cal_pt_cloud.shape[0], \
+#         100*float(cal_pt_cloud[err_dist<=threshold].shape[0])/cal_pt_cloud.shape[0])
+
+#     # plot residuals
+#     fig_error = plt.figure()
+#     plt.scatter(err_x,err_y,c="y")
+#     plt.scatter(new_err_x,new_err_y)
+#     plt.title("fitting residuals full data set (y) and better subset (b)")
+
+
+#     # plot projection of eye and world vs observed data
+#     X,Y,ZX,ZY = cal_pt_cloud.transpose().copy()
+#     X,Y = map_fn((X,Y))
+#     X *= 1280/2.
+#     Y *= 720/2.
+#     ZX *= 1280/2.
+#     ZY *= 720/2.
+#     fig_projection = plt.figure()
+#     plt.scatter(X,Y)
+#     plt.scatter(ZX,ZY,c='y')
+#     plt.title("world space projection in pixes, mapped and observed (y)")
+
+#     # plot the fitting functions 3D plot
+#     fig = plt.figure()
+#     ax = fig.gca(projection='3d')
+#     outliers =cal_pt_cloud[err_dist>threshold]
+#     inliers = cal_pt_cloud[err_dist<=threshold]
+#     ax.scatter(outliers[:,0],outliers[:,1],outliers[:,2], c= "y")
+#     ax.scatter(outliers[:,0],outliers[:,1],outliers[:,3], c= "y")
+#     ax.scatter(inliers[:,0],inliers[:,1],inliers[:,2], c= "r")
+#     ax.scatter(inliers[:,0],inliers[:,1],inliers[:,3], c= "b")
+#     Z = cal_pt_cloud
+#     X = np.linspace(min(Z[:,0])-.2,max(Z[:,0])+.2,num=30,endpoint=True)
+#     Y = np.linspace(min(Z[:,1])-.2,max(Z[:,1]+.2),num=30,endpoint=True)
+#     X, Y = np.meshgrid(X,Y)
+#     ZX,ZY = map_fn((X,Y))
+#     ax.plot_surface(X, Y, ZX, rstride=1, cstride=1, linewidth=.1, antialiased=True,alpha=0.4,color='r')
+#     ax.plot_surface(X, Y, ZY, rstride=1, cstride=1, linewidth=.1, antialiased=True,alpha=0.4,color='b')
+#     plt.xlabel("Pupil x in Eye-Space")
+#     plt.ylabel("Pupil y Eye-Space")
+#     plt.title("Z: Gaze x (blue) Gaze y (red) World-Space, yellow=outliers")
+
+#     # X,Y,_,_ = cal_pt_cloud.transpose()
+
+#     # pts= map_fn((X,Y))
+#     # import cv2
+#     # pts = np.array(pts,dtype=np.float32).transpose()
+#     # print cv2.convexHull(pts)[:,0]
+#     plt.show()

code/pupil/file_methods.py

@@ -0,0 +1,65 @@
+'''
+(*)~----------------------------------------------------------------------------------
+ Pupil - eye tracking platform
+ Copyright (C) 2012-2015  Pupil Labs
+
+ Distributed under the terms of the CC BY-NC-SA License.
+ License details are in the file license.txt, distributed as part of this software.
+----------------------------------------------------------------------------------~(*)
+'''
+
+import cPickle as pickle
+import os
+import logging
+logger = logging.getLogger(__name__)
+
+class Persistent_Dict(dict):
+	"""a dict class that uses pickle to save inself to file"""
+	def __init__(self, file_path):
+		super(Persistent_Dict, self).__init__()
+		self.file_path = os.path.expanduser(file_path)
+		try:
+			with open(self.file_path,'rb') as fh:
+				try:
+					self.update(pickle.load(fh))
+				except: #KeyError,EOFError
+					logger.warning("Session settings file '%s'could not be read. Will overwrite on exit."%self.file_path)
+		except IOError:
+			logger.debug("Session settings file '%s' not found. Will make new one on exit."%self.file_path)
+
+
+	def save(self):
+		d = {}
+		d.update(self)
+		try:
+			with open(self.file_path,'wb') as fh:
+				pickle.dump(d,fh,-1)
+		except IOError:
+			logger.warning("Could not save session settings to '%s'"%self.file_path)
+
+
+	def close(self):
+		self.save()
+
+
+def load_object(file_path):
+	file_path = os.path.expanduser(file_path)
+	with open(file_path,'rb') as fh:
+		return pickle.load(fh)
+
+def save_object(object,file_path):
+	file_path = os.path.expanduser(file_path)
+	with open(file_path,'wb') as fh:
+		pickle.dump(object,fh,-1)
+
+if __name__ == '__main__':
+	logging.basicConfig(level=logging.DEBUG)
+	# settings = Persistent_Dict("~/Desktop/test")
+	# settings['f'] = "this is a test"
+	# settings['list'] = ["list 1","list2"]
+	# settings.close()
+
+	# save_object("string",'test')
+	# print load_object('test')
+	settings = Persistent_Dict('~/Desktop/pupil_settings/user_settings_eye')
+	print settings['roi']

code/pupil/methods.py

@@ -0,0 +1,661 @@
+'''
+(*)~----------------------------------------------------------------------------------
+ Pupil - eye tracking platform
+ Copyright (C) 2012-2015  Pupil Labs
+
+ Distributed under the terms of the CC BY-NC-SA License.
+ License details are in the file license.txt, distributed as part of this software.
+----------------------------------------------------------------------------------~(*)
+'''
+
+import numpy as np
+try:
+    import numexpr as ne
+except:
+    ne = None
+import cv2
+import logging
+logger = logging.getLogger(__name__)
+
+
+
+class Roi(object):
+    """this is a simple 2D Region of Interest class
+    it is applied on numpy arrays for convenient slicing
+    like this:
+
+    roi_array_slice = full_array[r.view]
+    # do something with roi_array_slice
+
+    this creates a view, no data copying done
+    """
+    def __init__(self, array_shape):
+        self.array_shape = array_shape
+        self.lX = 0
+        self.lY = 0
+        self.uX = array_shape[1]
+        self.uY = array_shape[0]
+        self.nX = 0
+        self.nY = 0
+
+    @property
+    def view(self):
+        return slice(self.lY,self.uY,),slice(self.lX,self.uX)
+
+    @view.setter
+    def view(self, value):
+        raise Exception('The view field is read-only. Use the set methods instead')
+
+    def add_vector(self,(x,y)):
+        """
+        adds the roi offset to a len2 vector
+        """
+        return (self.lX+x,self.lY+y)
+
+    def sub_vector(self,(x,y)):
+        """
+        subs the roi offset to a len2 vector
+        """
+        return (x-self.lX,y-self.lY)
+
+    def set(self,vals):
+        if vals is not None and len(vals) is 5:
+            if vals[-1] == self.array_shape:
+                self.lX,self.lY,self.uX,self.uY,_ = vals
+            else:
+                logger.info('Image size has changed: Region of Interest has been reset')
+        elif vals is not None and len(vals) is 4:
+            self.lX,self.lY,self.uX,self.uY= vals
+
+    def get(self):
+        return self.lX,self.lY,self.uX,self.uY,self.array_shape
+
+
+
+def bin_thresholding(image, image_lower=0, image_upper=256):
+    binary_img = cv2.inRange(image, np.asarray(image_lower),
+                np.asarray(image_upper))
+
+    return binary_img
+
+def make_eye_kernel(inner_size,outer_size):
+    offset = (outer_size - inner_size)/2
+    inner_count = inner_size**2
+    outer_count = outer_size**2-inner_count
+    val_inner = -1.0 / inner_count
+    val_outer = -val_inner*inner_count/outer_count
+    inner = np.ones((inner_size,inner_size),np.float32)*val_inner
+    kernel = np.ones((outer_size,outer_size),np.float32)*val_outer
+    kernel[offset:offset+inner_size,offset:offset+inner_size]= inner
+    return kernel
+
+def dif_gaus(image, lower, upper):
+        lower, upper = int(lower-1), int(upper-1)
+        lower = cv2.GaussianBlur(image,ksize=(lower,lower),sigmaX=0)
+        upper = cv2.GaussianBlur(image,ksize=(upper,upper),sigmaX=0)
+        # upper +=50
+        # lower +=50
+        dif = lower-upper
+        # dif *= .1
+        # dif = cv2.medianBlur(dif,3)
+        # dif = 255-dif
+        dif = cv2.inRange(dif, np.asarray(200),np.asarray(256))
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
+        dif = cv2.dilate(dif, kernel, iterations=2)
+        dif = cv2.erode(dif, kernel, iterations=1)
+        # dif = cv2.max(image,dif)
+        # dif = cv2.dilate(dif, kernel, iterations=1)
+        return dif
+
+def equalize(image, image_lower=0.0, image_upper=255.0):
+    image_lower = int(image_lower*2)/2
+    image_lower +=1
+    image_lower = max(3,image_lower)
+    mean = cv2.medianBlur(image,255)
+    image = image - (mean-100)
+    # kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))
+    # cv2.dilate(image, kernel, image, iterations=1)
+    return image
+
+
+def erase_specular(image,lower_threshold=0.0, upper_threshold=150.0):
+    """erase_specular: removes specular reflections
+            within given threshold using a binary mask (hi_mask)
+    """
+    thresh = cv2.inRange(image,
+                np.asarray(float(lower_threshold)),
+                np.asarray(256.0))
+
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7,7))
+    hi_mask = cv2.dilate(thresh, kernel, iterations=2)
+
+    specular = cv2.inpaint(image, hi_mask, 2, flags=cv2.INPAINT_TELEA)
+    # return cv2.max(hi_mask,image)
+    return specular
+
+
+def find_hough_circles(img):
+    circles = cv2.HoughCircles(pupil_img,cv2.cv.CV_HOUGH_GRADIENT,1,20,
+                            param1=50,param2=30,minRadius=0,maxRadius=80)
+    if circles is not None:
+        circles = np.uint16(np.around(circles))
+        for i in circles[0,:]:
+            # draw the outer circle
+            cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2)
+            # draw the center of the circle
+            cv2.circle(img,(i[0],i[1]),2,(0,0,255),3)
+
+
+
+def chessboard(image, pattern_size=(9,5)):
+    status, corners = cv2.findChessboardCorners(image, pattern_size, flags=4)
+    if status:
+        mean = corners.sum(0)/corners.shape[0]
+        # mean is [[x,y]]
+        return mean[0], corners
+    else:
+        return None
+
+
+def curvature(c):
+    try:
+        from vector import Vector
+    except:
+        return
+    c = c[:,0]
+    curvature = []
+    for i in xrange(len(c)-2):
+        #find the angle at i+1
+        frm = Vector(c[i])
+        at = Vector(c[i+1])
+        to = Vector(c[i+2])
+        a = frm -at
+        b = to -at
+        angle = a.angle(b)
+        curvature.append(angle)
+    return curvature
+
+
+
+def GetAnglesPolyline(polyline,closed=False):
+    """
+    see: http://stackoverflow.com/questions/3486172/angle-between-3-points
+    ported to numpy
+    returns n-2 signed angles
+    """
+
+    points = polyline[:,0]
+
+    if closed:
+        a = np.roll(points,1,axis=0)
+        b = points
+        c = np.roll(points,-1,axis=0)
+    else:
+        a = points[0:-2] # all "a" points
+        b = points[1:-1] # b
+        c = points[2:]  # c points
+    # ab =  b.x - a.x, b.y - a.y
+    ab = b-a
+    # cb =  b.x - c.x, b.y - c.y
+    cb = b-c
+    # float dot = (ab.x * cb.x + ab.y * cb.y); # dot product
+    # print 'ab:',ab
+    # print 'cb:',cb
+
+    # float dot = (ab.x * cb.x + ab.y * cb.y) dot product
+    # dot  = np.dot(ab,cb.T) # this is a full matrix mulitplication we only need the diagonal \
+    # dot = dot.diagonal() #  because all we look for are the dotproducts of corresponding vectors (ab[n] and cb[n])
+    dot = np.sum(ab * cb, axis=1) # or just do the dot product of the correspoing vectors in the first place!
+
+    # float cross = (ab.x * cb.y - ab.y * cb.x) cross product
+    cros = np.cross(ab,cb)
+
+    # float alpha = atan2(cross, dot);
+    alpha = np.arctan2(cros,dot)
+    return alpha*(180./np.pi) #degrees
+    # return alpha #radians
+
+# if ne:
+#     def GetAnglesPolyline(polyline):
+#         """
+#         see: http://stackoverflow.com/questions/3486172/angle-between-3-points
+#         ported to numpy
+#         returns n-2 signed angles
+#         same as above but implemented using numexpr
+#         SLOWER than just numpy!
+#         """
+
+#         points = polyline[:,0]
+#         a = points[0:-2] # all "a" points
+#         b = points[1:-1] # b
+#         c = points[2:]  # c points
+#         ax,ay = a[:,0],a[:,1]
+#         bx,by = b[:,0],b[:,1]
+#         cx,cy = c[:,0],c[:,1]
+#         # abx =  '(bx - ax)'
+#         # aby =  '(by - ay)'
+#         # cbx =  '(bx - cx)'
+#         # cby =  '(by - cy)'
+#         # # float dot = (ab.x * cb.x + ab.y * cb.y) dot product
+#         # dot = '%s * %s + %s * %s' %(abx,cbx,aby,cby)
+#         # # float cross = (ab.x * cb.y - ab.y * cb.x) cross product
+#         # cross = '(%s * %s - %s * %s)' %(abx,cby,aby,cbx)
+#         # # float alpha = atan2(cross, dot);
+#         # alpha = "arctan2(%s,%s)" %(cross,dot)
+#         # term = '%s*%s'%(alpha,180./np.pi)
+#         term = 'arctan2(((bx - ax) * (by - cy) - (by - ay) * (bx - cx)),(bx - ax) * (bx - cx) + (by - ay) * (by - cy))*57.2957795131'
+#         return ne.evaluate(term)
+
+
+
+def split_at_angle(contour, curvature, angle):
+    """
+    contour is array([[[108, 290]],[[111, 290]]], dtype=int32) shape=(number of points,1,dimension(2) )
+    curvature is a n-2 list
+    """
+    segments = []
+    kink_index = [i for i in range(len(curvature)) if curvature[i] < angle]
+    for s,e in zip([0]+kink_index,kink_index+[None]): # list of slice indecies 0,i0,i1,i2,None
+        if e is not None:
+            segments.append(contour[s:e+1]) #need to include the last index
+        else:
+            segments.append(contour[s:e])
+    return segments
+
+
+def find_kink(curvature, angle):
+    """
+    contour is array([[[108, 290]],[[111, 290]]], dtype=int32) shape=(number of points,1,dimension(2) )
+    curvature is a n-2 list
+    """
+    kinks = []
+    kink_index = [i for i in range(len(curvature)) if abs(curvature[i]) < angle]
+    return kink_index
+
+def find_change_in_general_direction(curvature):
+    """
+    return indecies of where the singn of curvature has flipped
+    """
+    curv_pos = curvature > 0
+    split = []
+    currently_pos = curv_pos[0]
+    for c, is_pos in zip(range(curvature.shape[0]),curv_pos):
+        if is_pos !=currently_pos:
+            currently_pos = is_pos
+            split.append(c)
+    return split
+
+
+def find_kink_and_dir_change(curvature,angle):
+    split = []
+    if curvature.shape[0] == 0:
+        return split
+    curv_pos = curvature > 0
+    currently_pos = curv_pos[0]
+    for idx,c, is_pos in zip(range(curvature.shape[0]),curvature,curv_pos):
+        if (is_pos !=currently_pos) or abs(c) < angle:
+            currently_pos = is_pos
+            split.append(idx)
+    return split
+
+
+def find_slope_disc(curvature,angle = 15):
+    # this only makes sense when your polyline is longish
+    if len(curvature)<4:
+        return []
+
+    i = 2
+    split_idx = []
+    for anchor1,anchor2,candidate in zip(curvature,curvature[1:],curvature[2:]):
+        base_slope = anchor2-anchor1
+        new_slope = anchor2 - candidate
+        dif = abs(base_slope-new_slope)
+        if dif>=angle:
+            split_idx.add(i)
+        print i,dif
+        i +=1
+
+    return split_list
+
+def find_slope_disc_test(curvature,angle = 15):
+    # this only makes sense when your polyline is longish
+    if len(curvature)<4:
+        return []
+    # mean = np.mean(curvature)
+    # print '------------------- start'
+    i = 2
+    split_idx = set()
+    for anchor1,anchor2,candidate in zip(curvature,curvature[1:],curvature[2:]):
+        base_slope = anchor2-anchor1
+        new_slope = anchor2 - candidate
+        dif = abs(base_slope-new_slope)
+        if dif>=angle:
+            split_idx.add(i)
+        # print i,dif
+        i +=1
+    i-= 3
+    for anchor1,anchor2,candidate in zip(curvature[::-1],curvature[:-1:][::-1],curvature[:-2:][::-1]):
+        avg = (anchor1+anchor2)/2.
+        dif = abs(avg-candidate)
+        if dif>=angle:
+            split_idx.add(i)
+        # print i,dif
+        i -=1
+    split_list = list(split_idx)
+    split_list.sort()
+    # print split_list
+    # print '-------end'
+    return split_list
+
+
+def points_at_corner_index(contour,index):
+    """
+    contour is array([[[108, 290]],[[111, 290]]], dtype=int32) shape=(number of points,1,dimension(2) )
+    #index n-2 because the curvature is n-2 (1st and last are not exsistent), this shifts the index (0 splits at first knot!)
+    """
+    return [contour[i+1] for i in index]
+
+
+def split_at_corner_index(contour,index):
+    """
+    contour is array([[[108, 290]],[[111, 290]]], dtype=int32) shape=(number of points,1,dimension(2) )
+    #index n-2 because the curvature is n-2 (1st and last are not exsistent), this shifts the index (0 splits at first knot!)
+    """
+    segments = []
+    index = [i+1 for i in index]
+    for s,e in zip([0]+index,index+[10000000]): # list of slice indecies 0,i0,i1,i2,
+        segments.append(contour[s:e+1])# +1 is for not loosing line segments
+    return segments
+
+
+def convexity_defect(contour, curvature):
+    """
+    contour is array([[[108, 290]],[[111, 290]]], dtype=int32) shape=(number of points,1,dimension(2) )
+    curvature is a n-2 list
+    """
+    kinks = []
+    mean = np.mean(curvature)
+    if mean>0:
+        kink_index = [i for i in range(len(curvature)) if curvature[i] < 0]
+    else:
+        kink_index = [i for i in range(len(curvature)) if curvature[i] > 0]
+    for s in kink_index: # list of slice indecies 0,i0,i1,i2,None
+        kinks.append(contour[s+1]) # because the curvature is n-2 (1st and last are not exsistent)
+    return kinks,kink_index
+
+
+def is_round(ellipse,ratio,tolerance=.8):
+    center, (axis1,axis2), angle = ellipse
+
+    if axis1 and axis2 and abs( ratio - min(axis2,axis1)/max(axis2,axis1)) <  tolerance:
+        return True
+    else:
+        return False
+
+def size_deviation(ellipse,target_size):
+    center, axis, angle = ellipse
+    return abs(target_size-max(axis))
+
+
+
+
+
+def circle_grid(image, pattern_size=(4,11)):
+    """Circle grid: finds an assymetric circle pattern
+    - circle_id: sorted from bottom left to top right (column first)
+    - If no circle_id is given, then the mean of circle positions is returned approx. center
+    - If no pattern is detected, function returns None
+    """
+    status, centers = cv2.findCirclesGridDefault(image, pattern_size, flags=cv2.CALIB_CB_ASYMMETRIC_GRID)
+    if status:
+        return centers
+    else:
+        return None
+
+def calibrate_camera(img_pts, obj_pts, img_size):
+    # generate pattern size
+    camera_matrix = np.zeros((3,3))
+    dist_coef = np.zeros(4)
+    rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_pts, img_pts,
+                                                    img_size, camera_matrix, dist_coef)
+    return camera_matrix, dist_coefs
+
+def gen_pattern_grid(size=(4,11)):
+    pattern_grid = []
+    for i in xrange(size[1]):
+        for j in xrange(size[0]):
+            pattern_grid.append([(2*j)+i%2,i,0])
+    return np.asarray(pattern_grid, dtype='f4')
+
+
+
+def normalize(pos, (width, height),flip_y=False):
+    """
+    normalize return as float
+    """
+    x = pos[0]
+    y = pos[1]
+    x /=float(width)
+    y /=float(height)
+    if flip_y:
+        return x,1-y
+    return x,y
+
+def denormalize(pos, (width, height), flip_y=False):
+    """
+    denormalize
+    """
+    x = pos[0]
+    y = pos[1]
+    x *= width
+    if flip_y:
+        y = 1-y
+    y *= height
+    return x,y
+
+
+
+def dist_pts_ellipse(((ex,ey),(dx,dy),angle),points):
+    """
+    return unsigned euclidian distances of points to ellipse
+    """
+    pts = np.float64(points)
+    rx,ry = dx/2., dy/2.
+    angle = (angle/180.)*np.pi
+    # ex,ey =ex+0.000000001,ey-0.000000001 #hack to make 0 divisions possible this is UGLY!!!
+    pts = pts - np.array((ex,ey)) # move pts to ellipse appears at origin , with this we copy data -deliberatly!
+
+    M_rot = np.mat([[np.cos(angle),-np.sin(angle)],[np.sin(angle),np.cos(angle)]])
+    pts = np.array(pts*M_rot) #rotate so that ellipse axis align with coordinate system
+    # print "rotated",pts
+
+    pts /= np.array((rx,ry)) #normalize such that ellipse radii=1
+    # print "normalize",norm_pts
+    norm_mag = np.sqrt((pts*pts).sum(axis=1))
+    norm_dist = abs(norm_mag-1) #distance of pt to ellipse in scaled space
+    # print 'norm_mag',norm_mag
+    # print 'norm_dist',norm_dist
+    ratio = (norm_dist)/norm_mag #scale factor to make the pts represent their dist to ellipse
+    # print 'ratio',ratio
+    scaled_error = np.transpose(pts.T*ratio) # per vector scalar multiplication: makeing sure that boradcasting is done right
+    # print "scaled error points", scaled_error
+    real_error = scaled_error*np.array((rx,ry))
+    # print "real point",real_error
+    error_mag = np.sqrt((real_error*real_error).sum(axis=1))
+    # print 'real_error',error_mag
+    # print 'result:',error_mag
+    return error_mag
+
+
+if ne:
+    def dist_pts_ellipse(((ex,ey),(dx,dy),angle),points):
+        """
+        return unsigned euclidian distances of points to ellipse
+        same as above but uses numexpr for 2x speedup
+        """
+        pts = np.float64(points)
+        pts.shape=(-1,2)
+        rx,ry = dx/2., dy/2.
+        angle = (angle/180.)*np.pi
+        # ex,ey = ex+0.000000001 , ey-0.000000001 #hack to make 0 divisions possible this is UGLY!!!
+        x = pts[:,0]
+        y = pts[:,1]
+        # px = '((x-ex) * cos(angle) + (y-ey) * sin(angle))/rx'
+        # py = '(-(x-ex) * sin(angle) + (y-ey) * cos(angle))/ry'
+        # norm_mag = 'sqrt(('+px+')**2+('+py+')**2)'
+        # norm_dist = 'abs('+norm_mag+'-1)'
+        # ratio = norm_dist + "/" + norm_mag
+        # x_err  = ''+px+'*'+ratio+'*rx'
+        # y_err =  ''+py+'*'+ratio+'*ry'
+        # term = 'sqrt(('+x_err+')**2 + ('+y_err+')**2 )'
+        term = 'sqrt((((x-ex) * cos(angle) + (y-ey) * sin(angle))/rx*abs(sqrt((((x-ex) * cos(angle) + (y-ey) * sin(angle))/rx)**2+((-(x-ex) * sin(angle) + (y-ey) * cos(angle))/ry)**2)-1)/sqrt((((x-ex) * cos(angle) + (y-ey) * sin(angle))/rx)**2+((-(x-ex) * sin(angle) + (y-ey) * cos(angle))/ry)**2)*rx)**2 + ((-(x-ex) * sin(angle) + (y-ey) * cos(angle))/ry*abs(sqrt((((x-ex) * cos(angle) + (y-ey) * sin(angle))/rx)**2+((-(x-ex) * sin(angle) + (y-ey) * cos(angle))/ry)**2)-1)/sqrt((((x-ex) * cos(angle) + (y-ey) * sin(angle))/rx)**2+((-(x-ex) * sin(angle) + (y-ey) * cos(angle))/ry)**2)*ry)**2 )'
+        error_mag = ne.evaluate(term)
+        return error_mag
+
+
+
+def metric(l):
+    """
+    example metric for search
+    """
+    # print 'evaluating', idecies
+    global evals
+    evals +=1
+    return sum(l) < 3
+
+
+
+
+def pruning_quick_combine(l,fn,seed_idx=None,max_evals=1e20,max_depth=5):
+    """
+    l is a list of object to quick_combine.
+    the evaluation fn should accept idecies to your list and the list
+    it should return a binary result on wether this set is good
+
+    this search finds all combinations but assumes:
+        that a bad subset can not be bettered by adding more nodes
+        that a good set may not always be improved by a 'passing' superset (purging subsets will revoke this)
+
+    if all items and their combinations pass the evaluation fn you get n**2 -1 solutions
+    which leads to (2**n - 1) calls of your evaluation fn
+
+    it needs more evaluations than finding strongly connected components in a graph because:
+    (1,5) and (1,6) and (5,6) may work but (1,5,6) may not pass evaluation, (n,m) being list idx's
+
+    """
+    if seed_idx:
+        non_seed_idx = [i for i in range(len(l)) if i not in seed_idx]
+    else:
+        #start from every item
+        seed_idx = range(len(l))
+        non_seed_idx = []
+    mapping =  seed_idx+non_seed_idx
+    unknown = [[node] for node in range(len(seed_idx))]
+    # print mapping
+    results = []
+    prune = []
+    while unknown and max_evals:
+        path = unknown.pop(0)
+        max_evals -= 1
+        # print '@idx',[mapping[i] for i in path]
+        # print '@content',path
+        if not len(path) > max_depth:
+            # is this combination even viable, or did a subset fail already?
+            if not any(m.issubset(set(path)) for m in prune):
+                #we have not tested this and a subset of this was sucessfull before
+                if fn([l[mapping[i]] for i in path]):
+                    # yes this was good, keep as solution
+                    results.append([mapping[i] for i in path])
+                    # lets explore more by creating paths to each remaining node
+                    decedents = [path+[i] for i in range(path[-1]+1,len(mapping)) ]
+                    unknown.extend(decedents)
+                else:
+                    # print "pruning",path
+                    prune.append(set(path))
+    return results
+
+
+
+
+# def is_subset(needle,haystack):
+#     """ Check if needle is ordered subset of haystack in O(n)
+#     taken from:
+#     http://stackoverflow.com/questions/1318935/python-list-filtering-remove-subsets-from-list-of-lists
+#     """
+
+#     if len(haystack) < len(needle): return False
+
+#     index = 0
+#     for element in needle:
+#         try:
+#            index = haystack.index(element, index) + 1
+#         except ValueError:
+#             return False
+#     else:
+#        return True
+
+# def filter_subsets(lists):
+#     """ Given list of lists, return new list of lists without subsets
+#     taken from:
+#     http://stackoverflow.com/questions/1318935/python-list-filtering-remove-subsets-from-list-of-lists
+#     """
+
+#     for needle in lists:
+#         if not any(is_subset(needle, haystack) for haystack in lists
+#             if needle is not haystack):
+#             yield needle
+
+def filter_subsets(l):
+    return [m for i, m in enumerate(l) if not any(set(m).issubset(set(n)) for n in (l[:i] + l[i+1:]))]
+
+
+
+if __name__ == '__main__':
+    # tst = []
+    # for x in range(10):
+    #   tst.append(gen_pattern_grid())
+    # tst = np.asarray(tst)
+    # print tst.shape
+
+
+    #test polyline
+    #    *-*   *
+    #    |  \  |
+    #    *   *-*
+    #    |
+    #  *-*
+    pl = np.array([[[0, 0]],[[0, 1]],[[1, 1]],[[2, 1]],[[2, 2]],[[1, 3]],[[1, 4]],[[2,4]]], dtype=np.int32)
+    curvature = GetAnglesPolyline(pl,closed=0)
+    print curvature
+    curvature = GetAnglesPolyline(pl,closed=1)
+    # print curvature
+    # print find_curv_disc(curvature)
+    # idx =  find_kink_and_dir_change(curvature,60)
+    # print idx
+    # print split_at_corner_index(pl,idx)
+    # ellipse = ((0,0),(np.sqrt(2),np.sqrt(2)),0)
+    # pts = np.array([(0,1),(.5,.5),(0,-1)])
+    # # print pts.dtype
+    # print dist_pts_ellipse(ellipse,pts)
+    # print pts
+    # # print test()
+
+    # l = [1,2,1,0,1,0]
+    # print len(l)
+    # # evals = 0
+    # # r = quick_combine(l,metric)
+    # # # print r
+    # # print filter_subsets(r)
+    # # print evals
+
+    # evals = 0
+    # r = pruning_quick_combine(l,metric,[2])
+    # print r
+    # print filter_subsets(r)
+    # print evals
+
+
+
+
+
+

code/pupil/player_methods.py

@@ -0,0 +1,157 @@
+'''
+(*)~----------------------------------------------------------------------------------
+ Pupil - eye tracking platform
+ Copyright (C) 2012-2015  Pupil Labs
+
+ Distributed under the terms of the CC BY-NC-SA License.
+ License details are in the file license.txt, distributed as part of this software.
+----------------------------------------------------------------------------------~(*)
+'''
+
+import os
+import cv2
+import numpy as np
+#logging
+import logging
+logger = logging.getLogger(__name__)
+from file_methods import save_object
+
+
+def correlate_data(data,timestamps):
+    '''
+    data:  dict of data :
+        will have at least:
+            timestamp: float
+
+    timestamps: timestamps list to correlate  data to
+
+    this takes a data list and a timestamps list and makes a new list
+    with the length of the number of timestamps.
+    Each slot conains a list that will have 0, 1 or more assosiated data points.
+
+    Finnaly we add an index field to the data_point with the assosiated index
+    '''
+    timestamps = list(timestamps)
+    data_by_frame = [[] for i in timestamps]
+
+    frame_idx = 0
+    data_index = 0
+
+
+    while True:
+        try:
+            datum = data[data_index]
+            # we can take the midpoint between two frames in time: More appropriate for SW timestamps
+            ts = ( timestamps[frame_idx]+timestamps[frame_idx+1] ) / 2.
+            # or the time of the next frame: More appropriate for Sart Of Exposure Timestamps (HW timestamps).
+            # ts = timestamps[frame_idx+1]
+        except IndexError:
+            # we might loose a data point at the end but we dont care
+            break
+
+        if datum['timestamp'] <= ts:
+            datum['index'] = frame_idx
+            data_by_frame[frame_idx].append(datum)
+            data_index +=1
+        else:
+            frame_idx+=1
+
+    return data_by_frame
+
+
+
+def update_recording_0v4_to_current(rec_dir):
+    logger.info("Updatig recording from v0.4x format to current version")
+    gaze_array = np.load(os.path.join(rec_dir,'gaze_positions.npy'))
+    pupil_array = np.load(os.path.join(rec_dir,'pupil_positions.npy'))
+    gaze_list = []
+    pupil_list = []
+
+    for datum in pupil_array:
+        ts, confidence, id, x, y, diameter = datum[:6]
+        pupil_list.append({'timestamp':ts,'confidence':confidence,'id':id,'norm_pos':[x,y],'diameter':diameter})
+
+    pupil_by_ts = dict([(p['timestamp'],p) for p in pupil_list])
+
+    for datum in gaze_array:
+        ts,confidence,x,y, = datum
+        gaze_list.append({'timestamp':ts,'confidence':confidence,'norm_pos':[x,y],'base':[pupil_by_ts.get(ts,None)]})
+
+    pupil_data = {'pupil_positions':pupil_list,'gaze_positions':gaze_list}
+    try:
+        save_object(pupil_data,os.path.join(rec_dir, "pupil_data"))
+    except IOError:
+        pass
+
+def update_recording_0v3_to_current(rec_dir):
+    logger.info("Updatig recording from v0.3x format to current version")
+    pupilgaze_array = np.load(os.path.join(rec_dir,'gaze_positions.npy'))
+    gaze_list = []
+    pupil_list = []
+
+    for datum in pupilgaze_array:
+        gaze_x,gaze_y,pupil_x,pupil_y,ts,confidence = datum
+        #some bogus size and confidence as we did not save it back then
+        pupil_list.append({'timestamp':ts,'confidence':confidence,'id':0,'norm_pos':[pupil_x,pupil_y],'diameter':50})
+        gaze_list.append({'timestamp':ts,'confidence':confidence,'norm_pos':[gaze_x,gaze_y],'base':[pupil_list[-1]]})
+
+    pupil_data = {'pupil_positions':pupil_list,'gaze_positions':gaze_list}
+    try:
+        save_object(pupil_data,os.path.join(rec_dir, "pupil_data"))
+    except IOError:
+        pass
+
+def is_pupil_rec_dir(rec_dir):
+    if not os.path.isdir(rec_dir):
+        logger.error("No valid dir supplied")
+        return False
+    meta_info_path = os.path.join(rec_dir,"info.csv")
+    try:
+        with open(meta_info_path) as info:
+            meta_info = dict( ((line.strip().split('\t')) for line in info.readlines() ) )
+            info = meta_info["Capture Software Version"]
+    except:
+        logger.error("Could not read info.csv file: Not a valid Pupil recording.")
+        return False
+    return True
+
+
+
+
+def transparent_circle(img,center,radius,color,thickness):
+    center = tuple(map(int,center))
+    rgb = [255*c for c in color[:3]] # convert to 0-255 scale for OpenCV
+    alpha = color[-1]
+    radius = int(radius)
+    if thickness > 0:
+        pad = radius + 2 + thickness
+    else:
+        pad = radius + 3
+    roi = slice(center[1]-pad,center[1]+pad),slice(center[0]-pad,center[0]+pad)
+
+    try:
+        overlay = img[roi].copy()
+        cv2.circle(overlay,(pad,pad), radius=radius, color=rgb, thickness=thickness, lineType=cv2.cv.CV_AA)
+        opacity = alpha
+        cv2.addWeighted(overlay, opacity, img[roi], 1. - opacity, 0, img[roi])
+    except:
+        logger.debug("transparent_circle would have been partially outsize of img. Did not draw it.")
+
+
+def transparent_image_overlay(pos,overlay_img,img,alpha):
+    """
+    Overlay one image with another with alpha blending
+    In player this will be used to overlay the eye (as overlay_img) over the world image (img)
+    Arguments:
+        pos: (x,y) position of the top left corner in numpy row,column format from top left corner (numpy coord system)
+        overlay_img: image to overlay
+        img: destination image
+        alpha: 0.0-1.0
+    """
+    roi = slice(pos[1],pos[1]+overlay_img.shape[0]),slice(pos[0],pos[0]+overlay_img.shape[1])
+    try:
+        cv2.addWeighted(overlay_img,alpha,img[roi],1.-alpha,0,img[roi])
+    except:
+        logger.debug("transparent_image_overlay was outside of the world image and was not drawn")
+    pass
+

code/recording/aruco_test.cpp

@@ -0,0 +1,242 @@
+/*****************************************************************************************
+Copyright 2011 Rafael Muñoz Salinas. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are
+permitted provided that the following conditions are met:
+
+   1. Redistributions of source code must retain the above copyright notice, this list of
+      conditions and the following disclaimer.
+
+   2. Redistributions in binary form must reproduce the above copyright notice, this list
+      of conditions and the following disclaimer in the documentation and/or other materials
+      provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY Rafael Muñoz Salinas ''AS IS'' AND ANY EXPRESS OR IMPLIED
+WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL Rafael Muñoz Salinas OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
+ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+The views and conclusions contained in the software and documentation are those of the
+authors and should not be interpreted as representing official policies, either expressed
+or implied, of Rafael Muñoz Salinas.
+********************************************************************************************/
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include "aruco.h"
+#include "cvdrawingutils.h"
+#include <opencv2/highgui/highgui.hpp>
+using namespace cv;
+using namespace aruco;
+
+string TheInputVideo;
+string TheIntrinsicFile;
+float TheMarkerSize = -1;
+int ThePyrDownLevel;
+MarkerDetector MDetector;
+VideoCapture TheVideoCapturer;
+vector< Marker > TheMarkers;
+Mat TheInputImage, TheInputImageCopy;
+CameraParameters TheCameraParameters;
+void cvTackBarEvents(int pos, void *);
+bool readCameraParameters(string TheIntrinsicFile, CameraParameters &CP, Size size);
+
+pair< double, double > AvrgTime(0, 0); // determines the average time required for detection
+double ThresParam1, ThresParam2;
+int iThresParam1, iThresParam2;
+int waitTime = 0;
+
+/************************************
+ *
+ *
+ *
+ *
+ ************************************/
+bool readArguments(int argc, char **argv) {
+    if (argc < 2) {
+        cerr << "Invalid number of arguments" << endl;
+        cerr << "Usage: (in.avi|live[:idx_cam=0]) [intrinsics.yml] [size]" << endl;
+        return false;
+    }
+    TheInputVideo = argv[1];
+    if (argc >= 3)
+        TheIntrinsicFile = argv[2];
+    if (argc >= 4)
+        TheMarkerSize = atof(argv[3]);
+
+    if (argc == 3)
+        cerr << "NOTE: You need makersize to see 3d info!!!!" << endl;
+    return true;
+}
+
+int findParam(std::string param, int argc, char *argv[]) {
+    for (int i = 0; i < argc; i++)
+        if (string(argv[i]) == param)
+            return i;
+
+    return -1;
+}
+/************************************
+ *
+ *
+ *
+ *
+ ************************************/
+int main(int argc, char **argv) {
+    try {
+        if (readArguments(argc, argv) == false) {
+            return 0;
+        }
+        // parse arguments
+
+        // read from camera or from  file
+        if (TheInputVideo.find("live") != string::npos) {
+            int vIdx = 0;
+            // check if the :idx is here
+            char cad[100];
+            if (TheInputVideo.find(":") != string::npos) {
+                std::replace(TheInputVideo.begin(), TheInputVideo.end(), ':', ' ');
+                sscanf(TheInputVideo.c_str(), "%s %d", cad, &vIdx);
+            }
+            cout << "Opening camera index " << vIdx << endl;
+            TheVideoCapturer.open(vIdx);
+            waitTime = 10;
+        } else
+            TheVideoCapturer.open(TheInputVideo);
+        // check video is open
+        if (!TheVideoCapturer.isOpened()) {
+            cerr << "Could not open video" << endl;
+            return -1;
+        }
+        bool isVideoFile = false;
+        if (TheInputVideo.find(".avi") != std::string::npos || TheInputVideo.find("live") != string::npos)
+            isVideoFile = true;
+        // read first image to get the dimensions
+        TheVideoCapturer >> TheInputImage;
+
+        // read camera parameters if passed
+        if (TheIntrinsicFile != "") {
+            TheCameraParameters.readFromXMLFile(TheIntrinsicFile);
+            TheCameraParameters.resize(TheInputImage.size());
+        }
+        // Configure other parameters
+        if (ThePyrDownLevel > 0)
+            MDetector.pyrDown(ThePyrDownLevel);
+
+
+        // Create gui
+
+        // cv::namedWindow("thres", 1);
+        // cv::namedWindow("in", 1);
+
+        MDetector.setThresholdParams(7, 7);
+        MDetector.setThresholdParamRange(2, 0);
+        // 	MDetector.enableLockedCornersMethod(true);
+        //         MDetector.setCornerRefinementMethod ( MarkerDetector::SUBPIX );
+        MDetector.getThresholdParams(ThresParam1, ThresParam2);
+        iThresParam1 = ThresParam1;
+        iThresParam2 = ThresParam2;
+        // cv::createTrackbar("ThresParam1", "in", &iThresParam1, 25, cvTackBarEvents);
+        // cv::createTrackbar("ThresParam2", "in", &iThresParam2, 13, cvTackBarEvents);
+
+        char key = 0;
+        int index = 0;
+        // capture until press ESC or until the end of the video
+        TheVideoCapturer.retrieve(TheInputImage);
+
+        do {
+
+            // copy image
+
+            index++; // number of images captured
+            double tick = (double)getTickCount(); // for checking the speed
+            // Detection of markers in the image passed
+            MDetector.detect(TheInputImage, TheMarkers, TheCameraParameters, TheMarkerSize);
+            // chekc the speed by calculating the mean speed of all iterations
+            AvrgTime.first += ((double)getTickCount() - tick) / getTickFrequency();
+            AvrgTime.second++;
+            cout << "\rTime detection=" << 1000 * AvrgTime.first / AvrgTime.second << " milliseconds nmarkers=" << TheMarkers.size() << std::flush;
+
+            // print marker info and draw the markers in image
+            TheInputImage.copyTo(TheInputImageCopy);
+
+            for (unsigned int i = 0; i < TheMarkers.size(); i++) {
+                cout << endl << TheMarkers[i];
+                TheMarkers[i].draw(TheInputImageCopy, Scalar(0, 0, 255), 1);
+            }
+            if (TheMarkers.size() != 0)
+                cout << endl;
+            // print other rectangles that contains no valid markers
+            /**     for (unsigned int i=0;i<MDetector.getCandidates().size();i++) {
+                     aruco::Marker m( MDetector.getCandidates()[i],999);
+                     m.draw(TheInputImageCopy,cv::Scalar(255,0,0));
+                 }*/
+
+
+
+            // draw a 3d cube in each marker if there is 3d info
+            if (TheCameraParameters.isValid())
+                for (unsigned int i = 0; i < TheMarkers.size(); i++) {
+                    CvDrawingUtils::draw3dCube(TheInputImageCopy, TheMarkers[i], TheCameraParameters);
+                    CvDrawingUtils::draw3dAxis(TheInputImageCopy, TheMarkers[i], TheCameraParameters);
+                }
+            // DONE! Easy, right?
+            // show input with augmented information and  the thresholded image
+            // cv::imshow("in", TheInputImageCopy);
+            // cv::imshow("thres", MDetector.getThresholdedImage());
+
+            // key = cv::waitKey(waitTime); // wait for key to be pressed
+            // key = cv::waitKey(10); // wait for key to be pressed
+            key = 10;
+            if (isVideoFile)
+                TheVideoCapturer.retrieve(TheInputImage);
+
+        } while (key != 27 && (TheVideoCapturer.grab() || !isVideoFile));
+
+    } catch (std::exception &ex)
+
+    {
+        cout << "Exception :" << ex.what() << endl;
+    }
+}
+/************************************
+ *
+ *
+ *
+ *
+ ************************************/
+
+void cvTackBarEvents(int pos, void *) {
+    if (iThresParam1 < 3)
+        iThresParam1 = 3;
+    if (iThresParam1 % 2 != 1)
+        iThresParam1++;
+    if (ThresParam2 < 1)
+        ThresParam2 = 1;
+    ThresParam1 = iThresParam1;
+    ThresParam2 = iThresParam2;
+    MDetector.setThresholdParams(ThresParam1, ThresParam2);
+    // recompute
+    MDetector.detect(TheInputImage, TheMarkers, TheCameraParameters);
+    TheInputImage.copyTo(TheInputImageCopy);
+    for (unsigned int i = 0; i < TheMarkers.size(); i++)
+        TheMarkers[i].draw(TheInputImageCopy, Scalar(0, 0, 255), 1);
+    // print other rectangles that contains no valid markers
+    /*for (unsigned int i=0;i<MDetector.getCandidates().size();i++) {
+        aruco::Marker m( MDetector.getCandidates()[i],999);
+        m.draw(TheInputImageCopy,cv::Scalar(255,0,0));
+    }*/
+
+    // draw a 3d cube in each marker if there is 3d info
+    if (TheCameraParameters.isValid())
+        for (unsigned int i = 0; i < TheMarkers.size(); i++)
+            CvDrawingUtils::draw3dCube(TheInputImageCopy, TheMarkers[i], TheCameraParameters);
+
+    // cv::imshow("in", TheInputImageCopy);
+    // cv::imshow("thres", MDetector.getThresholdedImage());
+}

code/recording/collect_images.py

@@ -0,0 +1,89 @@
+from __future__ import division
+import os, sys, cv2
+import numpy as np
+import shutil
+
+# PARTICIPANTS = ['p10', 'p16', 'p13', 'p24', 'p5', 'p14', 'p26', 'p12', 'p20', 'p7', 'p15', 'p11', 'p21', 'p25']
+# PARTICIPANTS = ['p21']
+# ROOT = '/BS/3D_Gaze_Tracking/archive00/participants/'
+# DEST = '/BS/3D_Gaze_Tracking/work/Eye_Images/'
+DEST = '/home/mmbrian/3D_Gaze_Tracking/work/Marker_Eye_Images_7/'
+ROOT = '/home/mmbrian/HiWi/etra2016_mohsen/code/recording/data/participants'
+
+def main():
+	# distCoeffs = np.load("/BS/3D_Gaze_Tracking/work/dist.npy")
+	# cameraMatrix = np.load("/BS/3D_Gaze_Tracking/work/cameraMatrix.npy")
+	distCoeffs = np.load("dist.npy")
+	cameraMatrix = np.load("cameraMatrix.npy")
+
+	PARTICIPANTS = [sys.argv[1]]
+
+	processed = 0
+	for p in os.listdir(ROOT):
+		if p in PARTICIPANTS:
+			print '> Collecting images for participant', p
+			d1 = os.path.join(ROOT, p)
+			d1 = os.path.join(d1, os.listdir(d1)[0]) # ../p_i/../
+			for d2 in os.listdir(d1):
+				path = os.path.join(d1, d2)
+				print '> Processing', path			
+				processPath(path, p, d2, cameraMatrix, distCoeffs)
+			processed+=1
+			print '> Processed %s participants.' % processed
+
+def processPath(path = None, participant = None, experiment = None, cameraMatrix = None, distCoeffs = None):
+	if not path:
+		path = sys.argv[1]
+	raw_images_dir = os.path.join(DEST, 'ImagesRaw')
+	raw_images_dir = os.path.join(raw_images_dir, participant)
+	raw_images_dir = os.path.join(raw_images_dir, experiment)
+	undist_images_dir = os.path.join(DEST, 'ImagesUndist')
+	undist_images_dir = os.path.join(undist_images_dir, participant)
+	undist_images_dir = os.path.join(undist_images_dir, experiment)
+	if not os.path.exists(raw_images_dir):
+		os.makedirs(raw_images_dir)
+	else:
+		print '> Already processed.'
+		return
+	if not os.path.exists(undist_images_dir):
+		os.makedirs(undist_images_dir)
+	else:
+		print '> Already processed.'
+		return
+	# else:
+	# 	print '> Removing old images...'
+	# 	shutil.rmtree(raw_images_dir)
+	# 	return
+	# 	os.makedirs(raw_images_dir)
+
+	p_frames = np.load(os.path.join(path, 'p_frames.npy'))
+	frames = sorted(reduce(lambda l1,l2: list(l1)+list(l2), p_frames))
+
+	cap = cv2.VideoCapture(os.path.join(path, 'eye0.mp4'))
+	fc_eye = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
+	fps_eye = int(cap.get(cv2.cv.CV_CAP_PROP_FPS))
+
+	eye_frame = 0
+	world_frame = 0
+	status, img = cap.read() # extract the first frame
+	while status:
+		try:
+			if eye_frame == frames[world_frame]:
+				save_dir = os.path.join(raw_images_dir, 'img_%s.png' %(frames[world_frame]))
+				cv2.imwrite(save_dir, img)
+
+				save_dir = os.path.join(undist_images_dir, 'img_%s.png' %(frames[world_frame]))
+				undistImg = cv2.undistort(img, cameraMatrix, distCoeffs)
+				cv2.imwrite(save_dir, undistImg)
+				world_frame+=1
+		except:
+			break
+		eye_frame+=1
+		status, img = cap.read()
+	cap.release()
+	# print '> Removing world video...'
+	# os.remove(world_video)
+	print "> Processed %d frames." % (world_frame)
+
+if __name__ == '__main__':
+	main()

code/recording/data/.gitignore

@@ -0,0 +1,3 @@
+*.jpg
+*.JPG
+*.mkv

code/recording/data/display_setting.txt

@@ -0,0 +1,238 @@
+name of display:    :0
+version number:    11.0
+vendor string:    The X.Org Foundation
+vendor release number:    11600000
+X.Org version: 1.16.0
+maximum request size:  16777212 bytes
+motion buffer size:  256
+bitmap unit, bit order, padding:    32, LSBFirst, 32
+image byte order:    LSBFirst
+number of supported pixmap formats:    7
+supported pixmap formats:
+    depth 1, bits_per_pixel 1, scanline_pad 32
+    depth 4, bits_per_pixel 8, scanline_pad 32
+    depth 8, bits_per_pixel 8, scanline_pad 32
+    depth 15, bits_per_pixel 16, scanline_pad 32
+    depth 16, bits_per_pixel 16, scanline_pad 32
+    depth 24, bits_per_pixel 32, scanline_pad 32
+    depth 32, bits_per_pixel 32, scanline_pad 32
+keycode range:    minimum 8, maximum 255
+focus:  window 0x240000b, revert to Parent
+number of extensions:    29
+    BIG-REQUESTS
+    Composite
+    DAMAGE
+    DOUBLE-BUFFER
+    DPMS
+    DRI2
+    DRI3
+    GLX
+    Generic Event Extension
+    MIT-SCREEN-SAVER
+    MIT-SHM
+    Present
+    RANDR
+    RECORD
+    RENDER
+    SECURITY
+    SGI-GLX
+    SHAPE
+    SYNC
+    X-Resource
+    XC-MISC
+    XFIXES
+    XFree86-DGA
+    XFree86-VidModeExtension
+    XINERAMA
+    XInputExtension
+    XKEYBOARD
+    XTEST
+    XVideo
+default screen number:    0
+number of screens:    1
+
+screen #0:
+  dimensions:    3286x1080 pixels (869x286 millimeters)
+  resolution:    96x96 dots per inch
+  depths (7):    24, 1, 4, 8, 15, 16, 32
+  root window id:    0xbd
+  depth of root window:    24 planes
+  number of colormaps:    minimum 1, maximum 1
+  default colormap:    0x42
+  default number of colormap cells:    256
+  preallocated pixels:    black 0, white 16777215
+  options:    backing-store WHEN MAPPED, save-unders NO
+  largest cursor:    256x256
+  current input event mask:    0xda4033
+    KeyPressMask             KeyReleaseMask           EnterWindowMask          
+    LeaveWindowMask          KeymapStateMask          StructureNotifyMask      
+    SubstructureNotifyMask   SubstructureRedirectMask PropertyChangeMask       
+    ColormapChangeMask       
+  number of visuals:    20
+  default visual id:  0x40
+  visual:
+    visual id:    0x40
+    class:    TrueColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0x41
+    class:    DirectColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xab
+    class:    TrueColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xac
+    class:    TrueColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xad
+    class:    TrueColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xae
+    class:    TrueColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xaf
+    class:    TrueColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xb0
+    class:    TrueColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xb1
+    class:    TrueColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xb2
+    class:    TrueColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xb3
+    class:    DirectColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xb4
+    class:    DirectColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xb5
+    class:    DirectColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xb6
+    class:    DirectColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xb7
+    class:    DirectColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xb8
+    class:    DirectColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xb9
+    class:    DirectColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xba
+    class:    DirectColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0xbb
+    class:    DirectColor
+    depth:    24 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+  visual:
+    visual id:    0x7e
+    class:    TrueColor
+    depth:    32 planes
+    available colormap entries:    256 per subfield
+    red, green, blue masks:    0xff0000, 0xff00, 0xff
+    significant bits in color specification:    8 bits
+
+
+Screen 0: minimum 8 x 8, current 3286 x 1080, maximum 32767 x 32767
+LVDS1 connected primary 1366x768+0+0 (normal left inverted right x axis y axis) 344mm x 194mm
+   1366x768       60.0*+
+   1360x768       59.8     60.0  
+   1024x768       60.0  
+   800x600        60.3     56.2  
+   640x480        59.9  
+VGA1 connected 1920x1080+1366+0 (normal left inverted right x axis y axis) 533mm x 300mm
+   1920x1080      60.0*+
+   1600x1200      60.0  
+   1280x1024      60.0  
+   1152x864       75.0  
+   1024x768       60.0  
+   800x600        60.3  
+   640x480        60.0     59.9  
+HDMI1 disconnected (normal left inverted right x axis y axis)
+DP1 disconnected (normal left inverted right x axis y axis)
+VIRTUAL1 disconnected (normal left inverted right x axis y axis)
+
+
+
+256 pixels for the marker, 1920px is the width of screen
+and as I measurred it is 121cm in width
+==> 256./1920 * 121 ~ 16cm is the width of our marker (also measured it, seems legit)

code/recording/data/eye_camera/2015_10_03/000/info.csv

@@ -0,0 +1,14 @@
+Recording Name	2015_10_03
+Start Date	03.10.2015
+Start Time	17:48:58
+Duration Time	00:03:00
+Eye Mode	monocular
+Duration Time	00:03:00
+World Camera Frames	4309
+World Camera Resolution	720x1280
+Capture Software Version	0.5.7
+User	mmbrian
+Platform	Linux
+Machine	Brian
+Release	3.16.0-49-generic
+Version	#65~14.04.1-Ubuntu SMP Wed Sep 9 10:03:23 UTC 2015

code/recording/data/eye_camera/2015_10_03/000/user_info.csv

@@ -0,0 +1,2 @@
+name	
+additional_field	change_me

code/recording/data/eye_camera/2015_10_03/001/info.csv

@@ -0,0 +1,14 @@
+Recording Name	2015_10_03
+Start Date	03.10.2015
+Start Time	18:06:58
+Duration Time	00:03:37
+Eye Mode	monocular
+Duration Time	00:03:37
+World Camera Frames	5215
+World Camera Resolution	720x1280
+Capture Software Version	0.5.7
+User	mmbrian
+Platform	Linux
+Machine	Brian
+Release	3.16.0-49-generic
+Version	#65~14.04.1-Ubuntu SMP Wed Sep 9 10:03:23 UTC 2015

code/recording/data/eye_camera/2015_10_03/001/user_info.csv

@@ -0,0 +1,2 @@
+name	
+additional_field	change_me

code/recording/data/eye_camera/eye_camera_RMS0p39.yml

@@ -0,0 +1,20 @@
+%YAML:1.0
+calibration_time: "Sat 03 Oct 2015 18:42:34 CEST"
+nframes: 30
+image_width: 640
+image_height: 360
+board_width: 8
+board_height: 6
+square_size: 0.00122
+flags: 0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 678.75284504,    0.        ,  301.29715044,    0.        ,
+        667.94939515,  209.10259404,    0.        ,    0.        ,    1.        ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ 0.12812696, -0.13076179,  0.00631552, -0.01349366, -2.10210424]

code/recording/data/frames_005/frame_300.txt

@@ -0,0 +1,9 @@
+18=(376.883,493.993) (367.418,432.916) (429.433,424.199) (438.674,484.834) Txyz=-0.138299 0.0442204 0.497911 Rxyz=-3.10292 0.221729 -0.0411571 
+130=(557.896,467.496) (549.296,407.464) (609.848,398.895) (617.985,458.804) Txyz=-0.0245979 0.0282039 0.504007 Rxyz=-3.08427 0.211876 -0.115737 
+301=(748.172,549.641) (740.68,491.47) (799.57,482.841) (806.625,541.024) Txyz=0.0988437 0.0832783 0.516703 Rxyz=-3.05697 0.208073 -0.108889 
+351=(573.305,576.144) (564.956,517.414) (624.868,508.643) (632.845,566.939) Txyz=-0.0150596 0.0992645 0.509626 Rxyz=-3.04795 0.206556 -0.108844 
+399=(720.388,332.456) (712.982,271.43) (773.556,263.268) (780.206,324.203) Txyz=0.0793988 -0.0583554 0.504764 Rxyz=-3.05028 0.207139 -0.10664 
+456=(542.036,356.791) (533.264,295.4) (594.619,287.303) (602.898,348.62) Txyz=-0.0341846 -0.0425535 0.498523 Rxyz=-3.05347 0.204274 -0.0319069 
+608=(358.957,381.543) (348.602,319.251) (411.724,311.228) (421.632,373.302) Txyz=-0.147483 -0.0265521 0.491548 Rxyz=-3.05284 0.204884 -0.0436 
+659=(734.247,442.123) (726.628,382.526) (786.131,374.246) (793.406,433.659) Txyz=0.0889698 0.0122942 0.510193 Rxyz=-3.07904 0.209142 -0.116808 
+707=(394.177,604.361) (384.954,544.505) (446.419,535.311) (455.177,594.624) Txyz=-0.129612 0.116083 0.506384 Rxyz=-3.09125 0.216361 -0.0913303 

code/recording/data/frames_006/frame_300.txt

@@ -0,0 +1,10 @@
+18=(337.292,468.882) (324.79,404.901) (390.651,394.692) (402.499,457.982) Txyz=-0.154857 0.0256425 0.473357 Rxyz=-3.04908 0.244502 -0.076421 
+130=(527.352,437.35) (516.93,374.763) (580.082,364.783) (589.941,426.958) Txyz=-0.0420971 0.00724924 0.483541 Rxyz=-3.06316 0.24193 -0.165829 
+301=(725.485,517.338) (716.988,457.961) (777.014,447.674) (785.252,507.095) Txyz=0.0819062 0.0595391 0.502175 Rxyz=-3.02787 0.238118 -0.13582 
+351=(546.056,549.115) (536.021,488.78) (598.013,478.114) (607.443,538.074) Txyz=-0.0314374 0.0779249 0.492233 Rxyz=-3.02323 0.23531 -0.158536 
+399=(693.675,293.595) (685.114,229.493) (747.716,220.171) (755.538,283.927) Txyz=0.0601648 -0.081201 0.484901 Rxyz=-3.01722 0.233661 -0.14594 
+456=(507.989,321.903) (497.092,257.244) (561.524,247.809) (571.784,312.326) Txyz=-0.052872 -0.0630324 0.476038 Rxyz=-3.02846 0.233055 -0.0858767 
+608=(313.964,351.087) (300.582,284.895) (368.329,275.447) (380.611,341.093) Txyz=-0.165616 -0.0445972 0.465881 Rxyz=-3.02714 0.234292 -0.108089 
+659=(709.625,407.24) (701.037,345.51) (762.366,335.935) (770.285,397.398) Txyz=0.0707768 -0.0109919 0.492042 Rxyz=-3.03724 0.23253 -0.158296 
+707=(359.279,582.974) (347.403,521.27) (412.05,510.011) (423.197,571.361) Txyz=-0.145168 0.0967286 0.484355 Rxyz=-3.05352 0.247565 -0.117771 
+

code/recording/data/frames_007/frame_300.txt

@@ -0,0 +1,24 @@
+173=(699.455,499.98) (691.307,438.567) (752.606,429.954) (760.682,490.932) Txyz=0.0649598 0.047518 0.494322 Rxyz=-3.09118 0.21103 -0.110259 
+243=(558.056,453.388) (549.028,391.358) (611.729,382.452) (620.144,444.44) Txyz=-0.0231608 0.0178109 0.487122 Rxyz=-3.06591 0.212071 -0.107365 
+288=(509.858,595.993) (501.039,534.333) (563.524,524.831) (572.148,586.371) Txyz=-0.0533126 0.106812 0.490712 Rxyz=-3.10034 0.213607 -0.101361 
+325=(690.555,434.231) (682.66,372.562) (744.469,363.995) (751.952,425.615) Txyz=0.0593093 0.0062092 0.491782 Rxyz=-3.06472 0.206759 -0.107539 
+347=(363.361,550.855) (353.573,487.91) (418.325,478.379) (427.426,540.869) Txyz=-0.141701 0.0769299 0.483147 Rxyz=-3.10662 0.220845 -0.0927639 
+356=(480.526,396.484) (470.666,333.017) (534.73,324.316) (543.978,387.486) Txyz=-0.0699126 -0.0175944 0.480141 Rxyz=-3.06477 0.215313 -0.0848652 
+358=(432.44,540.204) (423.087,477.693) (486.636,468.411) (495.612,530.387) Txyz=-0.10047 0.0711499 0.486519 Rxyz=-3.1106 0.220745 -0.0922127 
+392=(442.289,606.858) (433.197,544.676) (496.376,535.089) (505.121,596.737) Txyz=-0.0950006 0.113034 0.489782 Rxyz=-3.11116 0.21787 -0.102892 
+399=(708.4,565.16) (700.069,504.329) (761.353,495.298) (769.126,555.978) Txyz=0.0705462 0.0888017 0.496123 Rxyz=-3.07587 0.210512 -0.114342 
+449=(548.411,386.917) (539.147,323.647) (602.773,315.279) (611.129,378.093) Txyz=-0.0286112 -0.0233864 0.481884 Rxyz=-3.01791 0.206092 -0.143664 
+528=(624.563,443.811) (616.147,381.957) (678.347,373.238) (686.266,434.894) Txyz=0.0180922 0.0120028 0.488909 Rxyz=-3.05213 0.209138 -0.118358 
+554=(490.591,463.222) (481.316,400.801) (544.603,391.939) (553.41,454.083) Txyz=-0.0645677 0.0236915 0.485569 Rxyz=-3.08837 0.214389 -0.108485 
+559=(373.466,617.896) (364.062,555.286) (428.305,545.501) (437.448,607.679) Txyz=-0.136017 0.118491 0.485214 Rxyz=-3.10462 0.217903 -0.0966003 
+571=(615.48,377.577) (607.189,314.515) (670.369,305.988) (677.674,368.791) Txyz=0.0125728 -0.0290879 0.482861 Rxyz=-2.98808 0.202386 -0.146367 
+655=(342.345,415.599) (330.378,351.444) (396.27,342.763) (407.057,406.633) Txyz=-0.151935 -0.00585876 0.474021 Rxyz=-3.0196 0.207689 -0.0499907 
+660=(500.258,529.687) (491.297,467.655) (554.109,458.558) (562.902,520.323) Txyz=-0.0588932 0.0651044 0.487798 Rxyz=-3.09854 0.214559 -0.0948563 
+664=(576.63,585.616) (568.056,524.065) (629.927,514.768) (638.399,575.89) Txyz=-0.0118773 0.100667 0.49203 Rxyz=-3.0937 0.211299 -0.126647 
+735=(352.832,483.454) (343.004,420.029) (407.747,411.093) (417.501,473.927) Txyz=-0.147205 0.0354299 0.480331 Rxyz=3.16704 -0.223919 0.076249 
+737=(633.714,509.651) (625.26,448.175) (686.896,439.242) (695.163,500.561) Txyz=0.0237445 0.0533222 0.49199 Rxyz=-3.09203 0.212088 -0.117316 
+782=(642.802,575.179) (634.357,514.125) (695.692,504.957) (704.056,565.739) Txyz=0.0294044 0.0947937 0.494568 Rxyz=-3.08374 0.21306 -0.111104 
+786=(412.026,405.993) (401.221,342.19) (465.902,333.555) (475.958,397.018) Txyz=-0.11102 -0.011715 0.477259 Rxyz=-3.03908 0.211348 -0.0405065 
+787=(567.301,519.555) (558.638,457.877) (620.81,448.837) (629.291,510.389) Txyz=-0.0175444 0.0592119 0.489873 Rxyz=-3.09406 0.212163 -0.109216 
+842=(422.538,473.23) (412.683,410.393) (476.666,401.431) (486.064,463.924) Txyz=-0.10576 0.0295403 0.482561 Rxyz=-3.08552 0.217952 -0.0668197 
+914=(682.094,368.247) (674.609,305.389) (737.422,296.877) (743.864,359.666) Txyz=0.0536882 -0.0348577 0.485769 Rxyz=-2.99698 0.201008 -0.128513 

code/recording/data/frames_010/frame_300.txt

@@ -0,0 +1,24 @@
+173=(723.927,447.07) (715.643,384.723) (778.576,375.426) (786.258,437.702) Txyz=0.0790166 0.0136108 0.484619 Rxyz=-3.04246 0.219174 -0.091972 
+243=(579.827,400.658) (570.222,337.215) (634.345,327.765) (643.039,391.221) Txyz=-0.00925742 -0.0150964 0.476483 Rxyz=-3.00322 0.218584 -0.10575 
+288=(532.679,544.107) (522.959,482.482) (585.978,472.648) (595.254,534.363) Txyz=-0.0388098 0.0736001 0.484942 Rxyz=-3.05784 0.224629 -0.0772962 
+325=(715.033,380.428) (706.95,317.043) (770.634,307.64) (777.861,370.973) Txyz=0.0731347 -0.0274384 0.480342 Rxyz=-3.00927 0.218851 -0.0908707 
+347=(384.833,499.337) (373.829,436.216) (438.669,426.422) (449.139,489.416) Txyz=-0.127388 0.0447468 0.477125 Rxyz=-3.06431 0.229277 -0.0535486 
+356=(500.555,342.84) (489.461,277.411) (554.996,268.146) (565.07,333.476) Txyz=-0.0562687 -0.0497284 0.468424 Rxyz=-2.98365 0.21573 -0.033857 
+358=(454.078,488.67) (443.545,425.754) (507.685,416.119) (517.61,478.697) Txyz=-0.0861319 0.0385554 0.479532 Rxyz=-3.06777 0.229777 -0.0678913 
+392=(465.08,554.994) (454.885,493.098) (518.413,483.204) (527.941,544.889) Txyz=-0.0804108 0.0801372 0.484963 Rxyz=-3.0663 0.226716 -0.0831945 
+399=(732.737,512.68) (724.51,451.377) (786.84,442.078) (794.647,503.35) Txyz=0.0850416 0.0548219 0.489455 Rxyz=-3.04315 0.219029 -0.0818131 
+449=(569.515,332.769) (559.638,267.491) (624.982,258.069) (633.689,323.424) Txyz=-0.0150215 -0.0558136 0.469471 Rxyz=-2.95079 0.213833 -0.102914 
+528=(647.503,390.54) (638.787,327.07) (702.535,317.713) (710.537,381.069) Txyz=0.0319074 -0.0212714 0.47804 Rxyz=-3.00741 0.218382 -0.102638 
+554=(511.635,410.921) (501.203,347.341) (565.709,337.896) (575.31,401.427) Txyz=-0.0505453 -0.00890131 0.475441 Rxyz=-3.02306 0.221724 -0.0744128 
+559=(396.416,566.132) (385.832,503.765) (449.873,493.88) (460.227,555.802) Txyz=-0.121709 0.0862522 0.482184 Rxyz=-3.06672 0.23014 -0.062468 
+571=(638.16,322.645) (629.505,257.342) (694.553,247.905) (701.927,313.41) Txyz=0.0261123 -0.0619344 0.470728 Rxyz=-2.95023 0.212263 -0.114807 
+655=(360.505,363.079) (347.015,297.287) (413.74,288.078) (426.117,353.694) Txyz=-0.138416 -0.0372608 0.46466 Rxyz=-2.97141 0.2152 -0.031309 
+660=(522.304,477.958) (512.311,415.374) (575.931,405.843) (585.329,468.295) Txyz=-0.0447246 0.0322643 0.480759 Rxyz=-3.0578 0.225199 -0.0738372 
+664=(599.766,533.608) (590.643,472.009) (653.283,462.292) (661.946,523.804) Txyz=0.00257891 0.0672197 0.485662 Rxyz=-3.04987 0.219942 -0.114197 
+735=(373.089,431.793) (361.365,367.453) (426.782,358.194) (437.982,422.065) Txyz=-0.132966 0.00355214 0.471777 Rxyz=-3.03753 0.22429 -0.0346352 
+737=(657.115,457.277) (648.23,394.901) (711.199,385.518) (719.5,447.765) Txyz=0.0378741 0.0197911 0.483178 Rxyz=-3.04552 0.22143 -0.0968667 
+782=(666.4,523.065) (657.721,461.573) (720.035,452.19) (728.332,513.475) Txyz=0.0438073 0.0610251 0.487713 Rxyz=-3.04853 0.219505 -0.0967276 
+786=(430.999,352.987) (418.778,287.389) (484.649,278.121) (495.913,343.557) Txyz=-0.0973854 -0.0435011 0.466652 Rxyz=-2.98462 0.217406 -0.0168059 
+787=(589.799,467.54) (580.54,405.102) (643.78,395.603) (652.536,457.897) Txyz=-0.00336534 0.0259621 0.481289 Rxyz=-3.03955 0.220231 -0.11357 
+842=(442.786,421.342) (431.874,357.423) (496.653,347.995) (507.008,411.669) Txyz=-0.0918841 -0.0026965 0.474376 Rxyz=-3.05417 0.227679 -0.0500495 
+914=(706.368,312.641) (699.076,247.258) (764.04,237.726) (770.078,303.24) Txyz=0.0671587 -0.0679941 0.472159 Rxyz=-2.93732 0.21098 -0.108571 

code/recording/data/participants/.gitignore

@@ -0,0 +1,2 @@
+/p*
+/Hosna

code/recording/data/participants/notes.txt

@@ -0,0 +1,15 @@
+- for participants P1 to P11, eyetracker had to be readjusted during the experiment so that pupil could still be tracked with high confidence. eyetracker was rarely readjusted between two recordings at the same depth, mostly it was in between two depths. as a consequence, usage of different depth calibrations for a test recording would have another source of error caused by slighly repositioning the cameras.
+
+- p12 to p26 > no readjusting the tracker
+- p23 > a minor adjustment for better pupil tracking confidence from 2nd depth
+- p25 is myself. my head was slightly looking down or up in between recordings so even for a fixed depth the data could result in large errors! (not so useful). it's quite hard to do better all by myself.
+
+- for p6, 001 is splited into 001 and 002 but the movement from test point 15 to 16 is lost, so from 001 only extact data for the first 15 points, and  extract point 16 from the entire 002
+- for p9, depth 0, calibration video, the screen is a bit outside of the screen from left, therefore at least two marker points are lost (cannot be tracked by ArUco). it might be better not to use the data from this depth or at least not use the left edge of the grids.
+
+
+- p1 to p11 + p23 had minor adjustments to eye camera (and in terms scene camera) in between recordings
+- p12 to p22 + p24 and p26 didn't have any camera adjustments in between recordings
+- p25 is data recorded from myself
+- p6 and p9 are special cases to be handled separately
+- data for Hosna is not useful as the eye camera wasn't robustly tracking the eye (less than 20% frames with nonzero confidence)

code/recording/data/scene_camera/2015_10_03/000/info.csv

@@ -0,0 +1,14 @@
+Recording Name	2015_10_03
+Start Date	03.10.2015
+Start Time	20:15:10
+Duration Time	00:00:52
+Eye Mode	monocular
+Duration Time	00:00:52
+World Camera Frames	1265
+World Camera Resolution	720x1280
+Capture Software Version	0.5.7
+User	mmbrian
+Platform	Linux
+Machine	Brian
+Release	3.16.0-49-generic
+Version	#65~14.04.1-Ubuntu SMP Wed Sep 9 10:03:23 UTC 2015

code/recording/data/scene_camera/2015_10_03/000/user_info.csv

@@ -0,0 +1,2 @@
+name	
+additional_field	change_me

code/recording/data_grabber.py

@@ -0,0 +1,61 @@
+import os
+from tracker import performMarkerTracking
+
+
+ROOT_DATA_DIR = '/home/mmbrian/HiWi/etra2016_mohsen/code/recording/data/participants'
+SQUARE_SIZE = '0.16'
+
+def main(force = False):
+	'''
+	Processes all the participants recordings and performs marker tracking on each video and stores marker data in a npy file near the video
+	'''
+	c = 0
+	for d1 in os.listdir(ROOT_DATA_DIR):
+		if d1.startswith('p'): # every participant
+			d2 = os.path.join(ROOT_DATA_DIR, d1) # .../pi/
+			d2 = os.path.join(d2, os.listdir(d2)[0]) # .../pi/../
+			for d3 in os.listdir(d2): # every recording
+				d4 = os.path.join(d2, d3) # .../pi/../00X/
+				print '> Processing', d4
+				frames_dir = os.path.join(d4, '_aruco_frames.npy')
+				if os.path.isfile(frames_dir):
+					if not force: # already processed this recording
+						print '> Recording already processed...'
+						continue
+					else: # video processed, but forced to process again
+						# remove old file
+						print '> Reprocessing file:', frames_dir
+						print '> Removing old marker file...'
+						os.remove(frames_dir)
+
+				world_path = os.path.join(d4, 'world.avi')
+
+				log_dir = os.path.join(d4, '_ArUco.log')
+				read_log = False
+				if os.path.isfile(log_dir):
+					read_log = True
+				else: # no log available > process video
+					if not os.path.isfile(world_path): # .avi file exists
+						# Creating avi
+						print '> AVI video does not exists, generating it from mp4 source file...'
+						os.popen('avconv -i ' + os.path.join(d4, 'world.mp4') + ' -c:a copy -c:v copy ' + world_path + ' -v quiet')
+				
+				# Perform tracking...
+				performMarkerTracking(read_from_log = read_log, log = True, log_dir = log_dir, 
+										recording_video = world_path + ' ', frames_dir = frames_dir, 
+										square_size = SQUARE_SIZE) # in case of relocating camera.yml input the new path as cam_math
+				if not read_log:
+					# Removing avi file
+					print '> Removing avi file:', world_path
+					os.remove(world_path)
+				c += 1
+				print '> Processed %s recordings so far...' % c
+				# print '> Halting...'
+				# return
+
+if __name__ == '__main__':
+	main(force=False)
+	print 'Finished.'
+
+
+

code/recording/eye_world_correlation.py

@@ -0,0 +1,85 @@
+'''
+(*)~----------------------------------------------------------------------------------
+ author:
+ Julian Steil
+ Master Thesis (2014): 
+ Discovery of eye movement patterns in long-term human visual behaviour using topic models
+----------------------------------------------------------------------------------~(*)
+'''
+import sys,os
+import numpy as np
+
+PARTICIPANTS = ['p10', 'p16', 'p13', 'p24', 'p5', 'p14', 'p26', 'p12', 'p20', 'p7', 'p15', 'p11', 'p21', 'p25']
+
+ROOT = '/home/mmbrian/HiWi/etra2016_mohsen/code/recording/data/participants'
+timestamps_world_path = 'world_timestamps.npy'
+timestamps_eye_path = 'eye0_timestamps.npy'
+
+def main():
+    for p in os.listdir(ROOT):
+        if p in PARTICIPANTS:
+            print '> Correlating eye-world images for', p
+            d1 = os.path.join(ROOT, p)
+            d1 = os.path.join(d1, os.listdir(d1)[0]) # ../p_i/../
+            for d2 in os.listdir(d1):
+                path = os.path.join(d1, d2)
+                print '> Processing', path
+                process(path)
+    print 'Done.'
+
+def process(root):
+    timestamps_world = list(np.load(os.path.join(root, timestamps_world_path)))
+    timestamps_eye = list(np.load(os.path.join(root, timestamps_eye_path)))
+    
+    no_frames_eye = len(timestamps_eye)
+    no_frames_world = len(timestamps_world)
+
+    # Detection of Synchronization-Matchings to initialize the correlation-matrix
+    frame_idx_world = 0
+    frame_idx_eye = 0
+    while (frame_idx_world < no_frames_world):
+        # if the current world_frame is before the mean of the current eye frame timestamp and the next eyeframe timestamp
+        if timestamps_world[frame_idx_world] <= (timestamps_eye[frame_idx_eye]+timestamps_eye[frame_idx_eye+1])/2.:
+            frame_idx_world+=1
+        else:
+            if frame_idx_eye >= no_frames_eye-2:
+                break
+            frame_idx_eye+=1
+
+
+    no_of_matched_frames = frame_idx_eye
+    print "no_of_matched_frames: ", no_of_matched_frames
+
+    # Synchonizing eye and world cam
+    print no_frames_eye, no_frames_world
+    correlation = []
+    for i in xrange(no_frames_world):
+        correlation.append([])
+        for j in xrange(1):
+            correlation[i].append(float(0))
+
+    frame_idx_world = 0
+    frame_idx_eye = 0
+    while (frame_idx_world < no_frames_world):
+        # print frame_idx_world,frame_idx_eye
+        # if the current world_frame is before the mean of the current eye frame timestamp and the next eye timestamp
+        if timestamps_world[frame_idx_world] <= (timestamps_eye[frame_idx_eye]+timestamps_eye[frame_idx_eye+1])/2.:
+            correlation[frame_idx_world][0] = frame_idx_eye
+            frame_idx_world+=1
+        else:
+            if frame_idx_eye >= no_frames_eye-2:
+                frame_idx_eye += 1
+                while (frame_idx_world < no_frames_world):
+                    correlation[frame_idx_world][1] = frame_idx_eye
+                    frame_idx_world+=1
+                break
+            frame_idx_eye+=1
+
+    correlation_list_path = "eye_world_correlation.npy"
+    correlation_list_csv_path = "eye_world_correlation.csv"
+    np.save(os.path.join(root, correlation_list_path),np.asarray(correlation))
+    np.savetxt(os.path.join(root, correlation_list_csv_path),np.asarray(correlation), delimiter=",", fmt="%f")
+    
+
+if __name__ == '__main__':
+    main()

code/recording/opencv/calibrate_and_save.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python
+'''
+This is a modified version of opencv's calibrate.py that stores the camera 
+parameters in the YAML format required by ArUco. an example of this format
+is given by:
+
+%YAML:1.0
+calibration_time: "Sa 08 Aug 2015 16:32:35 CEST"
+nr_of_frames: 30
+image_width: 752
+image_height: 480
+board_width: 8
+board_height: 6
+square_size: 24.
+fix_aspect_ratio: 1.
+# flags:  +fix_aspect_ratio +fix_principal_point +zero_tangent_dist
+flags: 14
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 418.164617459, 0., 372.480325679, 0., 417.850564673, 229.985538918, 0., 0., 1.]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ -0.3107371474, 0.1187673445, -0.0002552599, -0.0001158436, -0.0233324616]
+
+(Reference: https://wiki.mpi-inf.mpg.de/d2/ArUco)
+
+How to use:
+- Copy and paste this in /<OPENCV_SOURCE_DIR>/samples/python2
+- Run it like 
+	python calibrate_and_save.py --save "/<PATH_TO_SAVE_CAMERA_MATRIX>/camera.yml" "/<PATH_TO_IMAGE_DIR>/*.png" 
+- Then you can use ArUco like
+    ./<PATH_TO_ARUCO_SRC>/build/utils/aruco_test_gl live /<PATH_TO_SAVE_CAMERA_MATRIX>/camera.yml 0.039
+
+More on calibration: http://www.janeriksolem.net/2014/05/how-to-calibrate-camera-with-opencv-and.html
+
+UPDATE:
+Alternatively, you can follow the steps below
+- After disabling integrated webcam:
+    echo "0" > /sys/bus/usb/devices/2-1.6/bConfigurationValue
+    (need to first find which usb device corresponds to your webcam)
+- Plug in pupil tracker and use the official precompiled cpp script like:
+    /<PATH_TO_OPENCV>/build/bin/cpp-example-calibration -w 8 -h 6 -s 0.039 -o camera.yml -op
+'''
+
+import time
+
+import numpy as np
+import cv2, cv
+import os
+from common import splitfn
+
+
+def saveCameraParams(save_dir, nframes, w, h, bw, bh, square_size, 
+					camera_matrix, distortion_coefficients, fix_aspect_ratio = None, flags = 0):
+	time_str = time.strftime('%a %d %b %Y %H:%M:%S %Z')
+	lines = []
+	lines.append('%YAML:1.0')
+	lines.append('calibration_time: "%s"' %time_str)
+	lines.append('nframes: %s' %nframes)
+	lines.append('image_width: %s' %w)
+	lines.append('image_height: %s' %h)
+	lines.append('board_width: %s' %bw)
+	lines.append('board_height: %s' %bh)
+	lines.append('square_size: %s' %square_size)
+	if fix_aspect_ratio:
+		lines.append('fix_aspect_ratio: %s' %fix_aspect_ratio)
+	lines.append('flags: %s' %flags)
+	lines.append('camera_matrix: !!opencv-matrix')
+	lines.append('   rows: 3')
+	lines.append('   cols: 3')
+	lines.append('   dt: d')
+	lines.append('   data: %s' %repr(camera_matrix.reshape(1,9)[0])[6:-1]) # [6:-1] removes "array(" and ")"
+	lines.append('distortion_coefficients: !!opencv-matrix')
+	lines.append('   rows: 5')
+	lines.append('   cols: 1')
+	lines.append('   dt: d')
+	lines.append('   data: %s' %repr(distortion_coefficients)[6:-1])
+	with open(save_dir, 'w') as f:
+		f.writelines(map(lambda l: l+'\n', lines))
+
+
+def readCameraParams(cam_mat = None):
+    '''
+    Reads an openCV camera.yml file and returns camera_matrix and distortion_coefficients
+    '''
+    if not cam_mat:
+        cam_mat = CAMERA_MATRIX
+
+    data = ''.join(open(cam_mat.strip(), 'r').readlines()).replace('\n', '').lower()
+    try:
+        ind1 = data.index('[', data.index('camera_matrix'))
+        ind2 = data.index(']', ind1)
+        camera_matrix = eval(data[ind1:ind2+1])
+        camera_matrix = np.array([camera_matrix[:3],
+                                  camera_matrix[3:6],
+                                  camera_matrix[6:]])
+        ind1 = data.index('[', data.index('distortion_coefficients'))
+        ind2 = data.index(']', ind1)
+        dist_coeffs = np.array(eval(data[ind1:ind2+1]))
+        return camera_matrix, dist_coeffs
+    except Exception:
+        print 'Could not load camera parameters'
+        print 'Invalid camera.yml file.'
+
+if __name__ == '__main__':
+    import sys, getopt
+    from glob import glob
+
+    args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug=', 'square_size='])
+    args = dict(args)
+    try: img_mask = img_mask[0]
+    except: img_mask = '../cpp/left*.jpg'
+    # print 'mask is', img_mask
+    # img_mask = img_mask.replace('10.png', '*.png')
+    img_names = glob(img_mask)
+    debug_dir = args.get('--debug')
+    square_size = float(args.get('--square_size', 1.0))
+    square_size = 0.00122
+
+    save_dir = args.get('--save')
+
+    pattern_size = (8, 6)
+
+    pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 )
+    pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
+    pattern_points *= square_size
+
+    obj_points = []
+    img_points = []
+    h, w = 0, 0
+    for fn in img_names:
+        print 'processing %s...' % fn,
+        img = cv2.imread(fn, 0)
+        h, w = img.shape[:2]
+        found, corners = cv2.findChessboardCorners(img, pattern_size)
+        if found:
+            term = ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1 )
+            cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
+        if debug_dir:
+            vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+            cv2.drawChessboardCorners(vis, pattern_size, corners, found)
+            path, name, ext = splitfn(fn)
+            cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
+        if not found:
+            print 'chessboard not found'
+            continue
+        img_points.append(corners.reshape(-1, 2))
+        obj_points.append(pattern_points)
+
+        print 'ok'
+
+    # rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h))
+
+    root_dir = '/home/mmbrian/Pictures/eye_camera_images/'
+    cameraMatrixguess, distCoeffsguess = readCameraParams(os.path.join(root_dir,'_camera.yml'))
+    print "cameraM: ", cameraMatrixguess
+    print "dist: ", distCoeffsguess
+
+    cameraMatrixguess[1][1] = cameraMatrixguess[0][0]
+    cameraMatrixguess[0][2] = 320
+    cameraMatrixguess[1][2] = 180
+    
+    # Calibrate camera intrinsics
+    rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w,h),
+    cameraMatrixguess, distCoeffsguess, None, None, flags = \
+    cv.CV_CALIB_USE_INTRINSIC_GUESS + cv.CV_CALIB_FIX_PRINCIPAL_POINT + cv.CV_CALIB_FIX_ASPECT_RATIO)
+    np.save(os.path.join(root_dir,'dist.npy'), dist_coefs)
+    np.save(os.path.join(root_dir,'cameraMatrix.npy'), camera_matrix)
+    np.savetxt(os.path.join(root_dir,'dist.csv'),np.asarray(dist_coefs), delimiter=";", fmt="%s")
+    np.savetxt(os.path.join(root_dir,"cameraMatrix.csv"),np.asarray(camera_matrix), delimiter=";", fmt="%s")
+
+    print "RMS:", rms
+    print "camera matrix:\n", camera_matrix
+    print "distortion coefficients: ", dist_coefs.ravel()
+
+    print 'Width:', w, 'Height:', h
+    print 'nframes:', len(img_names)
+    print 'square_size:', square_size
+    print 'board_width:', pattern_size[0]
+    print 'board_height:', pattern_size[1]
+
+    saveCameraParams(save_dir, len(img_names), w, h, pattern_size[0], pattern_size[1], square_size, 
+    				camera_matrix, dist_coefs.ravel())
+    print "Saved camera matrix to", save_dir
+    
+    cv2.destroyAllWindows()
+
+
+

code/recording/process_recordings.py

@@ -0,0 +1,454 @@
+from __future__ import division
+'''
+For each experiment, this script tracks movement of the marker in the video from the information in aruco_frames.npy
+It then correlates this information with gaze data from pupil_positions.npy
+finally, for every target in the video (25 targets in calibration, 16 in test), it maps 3D marker position (mean position over the duration of pause)
+to the gaze position (mean position over the pause duration) and stores this info together with the projected 2D marker position in a separate npy file.
+the resulting file contains the ground truth data for this experiment.
+'''
+import os, sys
+import numpy as np
+import matplotlib.pyplot as plt
+from pylab import rcParams
+from scipy.ndimage.filters import gaussian_filter1d as g1d
+from scipy import signal
+
+from sklearn.neighbors import NearestNeighbors as knn
+
+# from sklearn import svm
+from sklearn.cluster import AgglomerativeClustering
+
+from tracker import readCameraParams, Marker
+from util.tools import is_outlier, moving_average
+
+sys.path.append('..') # so we can import from pupil
+from pupil import player_methods
+from vector import Vector as v
+
+import pdb
+
+ROOT_DATA_DIR = '/home/mmbrian/HiWi/etra2016_mohsen/code/recording/data/participants'
+
+def unifiy_markers_per_frame(marker_data):
+	'''
+	Since ArUco sometimes detects a marker twice in a frame, we need to either ignore one or somehow compute their mean.
+	Also this method maps each final marker to its center's 3D and 2D position wrt scene camera
+	'''
+	camera_matrix, dist_coeffs = readCameraParams() # in case of relocating camera.yml input the new path as cam_math
+	mdata, mrdata = [], []
+	for fn in xrange(len(marker_data)):
+		if len(marker_data[fn]) > 0:
+			markers = map(lambda m: Marker.fromList(m), marker_data[fn])
+			markers = map(lambda m: np.array([np.array(m.getCenter()), 
+											  np.array(m.getCenterInImage(camera_matrix, dist_coeffs))]), markers)
+			marker = sum(markers)/len(markers)
+			marker = [marker[0][0], marker[0][1], marker[0][2], marker[1][0], marker[1][1]]
+			# marker_data[fn] = marker
+			mdata.append(marker)
+			mrdata.append(marker)
+		else: # if marker is not detected, assign last detected position to this frame
+			# marker_data[fn] = marker_data[fn-1]
+			mdata.append(mdata[fn-1])
+			mrdata.append([]) # this contains real marker information (all tracked positions)
+	# return marker_data
+	return np.array(mdata), mrdata
+
+def fix_labels(labels, window = 2, elements = [0, 1], outliers = []):
+	labels = list(labels)
+	for i in xrange(window, len(labels)-window):
+		neighborhood = labels[i-window:i+window+1]
+		if outliers[i]: # removing this label from decision making
+			neighborhood = neighborhood[:i] + neighborhood[i+1:]
+		element_counts = [list(neighborhood).count(e) for e in elements]
+		dominant_element = elements[element_counts.index(max(element_counts))]
+		labels[i] = dominant_element
+	return labels
+
+def find_intervals(labels, mean, marker_speed):
+	'''
+	Given the label information of frame to frame motion speed, this method returns the frame
+	intervals for which the marker is either "moving" or "not moving"
+	Notice that len(labels) equals the number of frames minus one
+	'''
+	nm_label = labels[0]
+	intervals = []
+	curr_label, start, end = -1, -1, -1
+	not_moving = 0
+	for i in xrange(len(labels)):
+		if curr_label < 0: # first label
+			curr_label = labels[i]
+			start = i
+		else:
+			if labels[i] != curr_label: # label changed
+				end = i
+				intervals.append([start, end, curr_label])
+				if curr_label == nm_label: not_moving+=1
+				curr_label = labels[i]
+				start = i+1
+	end = len(labels)
+	intervals.append([start, end, curr_label])
+	if curr_label == nm_label: not_moving+=1
+
+	# Now we do a post check to see if two non moving intervals are very close to each other,
+	# the middle interval is most likely a misclassification
+	# computing average interval length for moving intervals
+	if (len(intervals) > 49 and not_moving > 25) or (len(intervals)>31 and not_moving>16):
+		ret = merge_intervals(intervals, nm_label, mean, marker_speed, remove_outliers=True)
+		return ret, sum(1 for e in ret if e[2] == nm_label)
+	else:
+		return intervals, not_moving
+	
+	
+
+def merge_intervals(intervals, nm_label, mean, marker_speed, remove_outliers=True):
+	mlength = np.array([seg[1] - seg[0] for seg in intervals if seg[2] != nm_label])
+	nmlength = np.array([seg[1] - seg[0] for seg in intervals if seg[2] == nm_label])
+	if remove_outliers:
+		mlength_outliers = mlength[is_outlier(mlength, thresh=3.5)]
+		avg_m_length = (sum(mlength)-sum(mlength_outliers))/(mlength.size - mlength_outliers.size)
+		
+		nmlength_outliers = nmlength[is_outlier(nmlength, thresh=3.5)]
+		avg_nm_length = (sum(nmlength)-sum(nmlength_outliers))/(nmlength.size - nmlength_outliers.size)
+	else:
+		avg_m_length = sum(mlength)/mlength.size
+		avg_nm_length = sum(nmlength)/nmlength.size
+
+	thresh = 3.5 # removes a moving interval if average length is at least this time larger than its length
+	i = 1
+	ret = []
+	ret.append(intervals[0])
+	while i < len(intervals):
+		length = intervals[i][1] - intervals[i][0]
+		ratio, label = 1, intervals[i][2]
+		if label == nm_label:
+			ratio = avg_nm_length/length
+		else:
+			ratio = avg_m_length/length
+		if ratio>=thresh: # average length is at least 2 times larger than the length of this interval
+			# replace this interval by merge the two not moving intervals around it
+			# check if average of elements in this interval is greater than mean
+			if np.mean(marker_speed[intervals[i][0]:intervals[i][1]]) < mean:
+				last_intv = ret.pop()
+				ret.append([last_intv[0], intervals[i+1][1], 1-label])
+				print 'Merged two intervals'
+				i+=2
+				continue
+		else: 
+			pass
+		ret.append(intervals[i])
+		i+=1
+	return ret
+
+
+# def main(force=False):
+# 	rcParams['figure.figsize'] = 15, 7
+# 	recordings_processed = 0
+# 	recordings_successful = 0
+# 	for d1 in os.listdir(ROOT_DATA_DIR):
+# 		if d1.startswith('p'): # every participant
+# 			d2 = os.path.join(ROOT_DATA_DIR, d1) # .../pi/
+# 			d2 = os.path.join(d2, os.listdir(d2)[0]) # .../pi/../
+# 			for d3 in os.listdir(d2): # every recording
+# 				d4 = os.path.join(d2, d3) # .../pi/../00X/
+# 				print '> Processing', d4
+# 				frames_dir = os.path.join(d4, '_aruco_frames.npy')
+# 				if not os.path.isfile(frames_dir): # the recording is not yet processed for marker tracking 
+# 					print '> Recording does not contain marker data...'
+# 					continue
+# 				intervals_dir = os.path.join(d4, 'gaze_intervals.npy')
+# 				if os.path.isfile(intervals_dir):
+# 					print '> Recording already processed...'
+# 					if force:
+# 						print '> Processing again...'
+# 					else:
+# 						continue
+
+# 				marker_data = np.load(frames_dir)
+# 				# marker_data includes data on tracked markers per frame
+# 				# it's a list with as many entries as the number of video frames, each entry 
+# 				# has a list of tracked markers, each marker item has marker id, marker corners, Rvec, Tvec
+# 				wt = np.load(os.path.join(d4, 'world_timestamps.npy'))
+# 				# Processing pupil positions
+# 				pp = np.load(os.path.join(d4, 'pupil_positions.npy')) # timestamp	confidence	id	pos_x	pos_y	diameter
+# 				# pos_x and pos_y are normalized (Origin 0,0 at the bottom left and 1,1 at the top right)
+# 				# converting each element to dictionary for correlation
+# 				pp = map(lambda e: dict(zip(['timestamp', 'conf', 'id', 'x', 'y', 'diam'], e)), pp)
+# 				pp_by_frame = player_methods.correlate_data(pp, wt)
+
+# 				# Keeping only pupil positions with nonzero confidence
+# 				pp_by_frame = map(lambda l: filter(lambda p: p['conf']>0, l), pp_by_frame)
+# 				# Computing a single pupil position for the frame by taking mean of all detected pupil positions
+# 				pp_by_frame = map(lambda data: 
+# 					sum(np.array([pp['x'], pp['y']]) for pp in data)/len(data) if data else np.array([-1, -1]), pp_by_frame)
+# 				# Now each nonempty value of pp_by_frame is a tuple of (x, y) for pupil position in that frame		
+				
+# 				# Checking if timestamps, markers per frame and pupil positions per frame are correlated
+# 				assert len(marker_data) == len(wt) == len(pp_by_frame)
+				
+# 				# Good, now we need to find the frame ranges in which marker is not moving, for that we need the marker_data
+# 				# and using the position info per frame, we can compute movement speed and detect when it is it almost zero
+				
+# 				marker_data, mrdata = unifiy_markers_per_frame(marker_data)
+# 				# Smoothing x and y coords
+# 				marker_data[:, 3] = g1d(marker_data[:, 3], sigma=2)
+# 				marker_data[:, 4] = g1d(marker_data[:, 4], sigma=2)
+
+# 				marker_speed = []
+# 				for fn, fnp1 in ((f, f+1) for f in xrange(len(marker_data)-1)):
+# 					if marker_data[fnp1] != [] and marker_data[fn] != []:
+# 						# dx = marker_data[fnp1][0] - marker_data[fn][0]
+# 						# dy = marker_data[fnp1][1] - marker_data[fn][1]
+# 						# dz = marker_data[fnp1][2] - marker_data[fn][2]
+# 						# speed = np.sqrt(dx**2 + dy**2 + dz**2) * 100
+						
+# 						# print fn, fnp1, len(marker_data), marker_data[fnp1], marker_data[fn]
+# 						dx = marker_data[fnp1][3] - marker_data[fn][3]
+# 						dy = marker_data[fnp1][4] - marker_data[fn][4]
+# 						speed = np.sqrt(dx**2 + dy**2)
+						
+# 						# print 'marker speed:', speed
+# 						marker_speed.append(speed)
+# 					else:
+# 						marker_speed.append(marker_speed[-1]) # set speed to last speed if marker could not be detected
+# 				# Performing binary clustering on marker speed
+# 				model = AgglomerativeClustering(n_clusters=2, linkage="ward", affinity="euclidean")
+# 				marker_speed = np.array(marker_speed)
+# 				# Checking for outliers based on "median absolute deviation"
+# 				outliers = is_outlier(marker_speed, thresh=3.5)
+# 				print sum(outliers == True), 'outliers detected'
+				
+# 				# removing outliers
+# 				outlier_inds = [i for i in xrange(outliers.size) if outliers[i]]
+# 				marker_speed = list(np.delete(marker_speed, outlier_inds))
+# 				# replacing removed outliers by average of their neighbours
+# 				outliers_inds = sorted(outlier_inds)
+# 				window = 1
+# 				for ind in outlier_inds:
+# 					start = max(ind-window, 0)
+# 					neighbours = marker_speed[start:ind+window]
+# 					new_val = sum(neighbours)/len(neighbours)
+# 					marker_speed.insert(ind, new_val)
+# 				marker_speed = np.array(marker_speed)
+
+# 				# smoothed_signal = marker_speed[:]
+# 				smoothed_signal = signal.medfilt(marker_speed, 13)
+# 				# smoothed_signal = g1d(marker_speed, sigma=2)
+# 				# smoothed_signal = moving_average(smoothed_signal, 7)
+# 				model.fit(map(lambda e: [e], smoothed_signal))
+# 				labels = fix_labels(model.labels_, window=1, outliers = outliers)
+# 				outliers = map(lambda e: 10 if e else 5, outliers)
+
+# 				mean = np.mean(smoothed_signal)
+
+# 				intervals, nm = find_intervals(labels, mean, smoothed_signal)
+# 				print '>', len(intervals), 'Intervals found in total.', nm, 'gaze intervals.'
+# 				interval_display = []
+# 				for dur in intervals:
+# 					interval_display.extend([dur[2]]*(dur[1]-dur[0]+1))
+# 				interval_display = interval_display[:-1]
+
+# 				print len(interval_display), len(marker_data)-1, intervals[-1][1]-intervals[0][0]
+# 				# print intervals
+# 				# print labels
+# 				# return
+# 				# print len(marker_data), len(marker_speed)
+# 				plt.plot(range(len(marker_data)-1), marker_speed, 'b', 
+# 						 # range(len(marker_data)-1), labels, 'r', 
+# 						 range(len(marker_data)-1), smoothed_signal, 'g',
+# 						 range(len(marker_data)-1), interval_display, 'r')
+# 				# plt.show()
+# 				# plt.clf()
+# 				# return
+# 				# plt.clf()
+
+
+# 				recordings_processed += 1
+# 				intervals_okay = True
+# 				if not nm in [16, 25]:
+# 					intervals_okay = False
+# 					pdb.set_trace()
+# 					print '> Storing odd figure...'
+# 					plt.savefig('./temp/%s-%s__%snm.png' % (d1, d3, str(nm)))
+# 					# print '> Entering manual override mode...'
+# 					# print '> Enter halt to quit.'
+# 					# # set manual_bypass to True in case you wanna discard changes in override mode
+# 					# cmd = raw_input(':')
+# 					# while cmd != 'halt' and cmd != 'pass':
+# 					# 	exec cmd in globals(), locals()
+# 					# 	cmd = raw_input(':')
+
+# 				if intervals_okay:
+# 					print '> Intervals seem okay.'
+# 					plt.savefig(os.path.join(d4, 'marker_motion.png'))
+# 					recordings_successful += 1
+# 					# Store interval information
+# 					# Use pp_by_frame and marker_data to compute gaze and target points corresponding to this interval
+# 					gaze_intervals = intervals[::2] # starting from the first interval, gaze, moving, gaze, moving, gaze, ...
+# 					t2d, t3d, p = [], [], []
+# 					for intv in gaze_intervals:
+# 						s, e = intv[0], intv[1]
+# 						null_gaze, null_marker = 0, 0
+# 						gaze_point = np.array([0, 0])
+# 						marker_3d_position = np.array([0, 0, 0])
+# 						marker_2d_position = np.array([0, 0])
+# 						for fn in xrange(s, e+1):
+# 							if all(pp_by_frame[fn]==np.array([-1, -1])):
+# 								null_gaze += 1
+# 							else:
+# 								gaze_point = gaze_point + pp_by_frame[fn]
+# 							if mrdata[fn] == []:
+# 								null_marker += 1
+# 							else:
+# 								marker_3d_position = marker_3d_position + np.array(mrdata[fn][:3])
+# 								marker_2d_position = marker_2d_position + np.array(mrdata[fn][3:])
+						
+# 						gaze_point = gaze_point/(e-s+1-null_gaze)
+# 						marker_3d_position = marker_3d_position/(e-s+1-null_marker)
+# 						marker_2d_position = marker_2d_position/(e-s+1-null_marker)
+						
+# 						t2d.append(marker_2d_position)
+# 						t3d.append(marker_3d_position)
+# 						p.append(gaze_point)
+# 					print '> Storing intervals, gaze data, and marker data...'
+# 					np.save(intervals_dir, np.array(gaze_intervals))
+# 					np.save(os.path.join(d4, 'p.npy'), np.array(p))
+# 					np.save(os.path.join(d4, 't2d.npy'), np.array(t2d))
+# 					np.save(os.path.join(d4, 't3d.npy'), np.array(t3d))
+# 				print '>', recordings_processed, 'recordings processed.', recordings_successful, 'successful.'
+
+# 				plt.clf()
+
+
+PARTICIPANTS = ['p10', 'p16', 'p13', 'p24', 'p5', 'p14', 'p26', 'p12', 'p20', 'p7', 'p15', 'p11', 'p21', 'p25']
+def main(force=False):
+	recordings_processed = 0
+	recordings_successful = 0
+	for d1 in os.listdir(ROOT_DATA_DIR):
+		if d1.startswith('p'): # every participant
+			if not d1 in PARTICIPANTS:
+				continue
+				
+			d2 = os.path.join(ROOT_DATA_DIR, d1) # .../pi/
+			d2 = os.path.join(d2, os.listdir(d2)[0]) # .../pi/../
+			for d3 in os.listdir(d2): # every recording
+				d4 = os.path.join(d2, d3) # .../pi/../00X/
+				print '> Processing', d4
+				frames_dir = os.path.join(d4, '_aruco_frames.npy')
+				if not os.path.isfile(frames_dir): # the recording is not yet processed for marker tracking 
+					print '> Recording does not contain marker data...'
+					continue
+				intervals_dir = os.path.join(d4, 'gaze_intervals.npy')
+				if os.path.isfile(intervals_dir):
+					print '> Recording already processed...'
+					if force:
+						print '> Processing again...'
+					else:
+						continue
+
+				marker_data = np.load(frames_dir)
+				# marker_data includes data on tracked markers per frame
+				# it's a list with as many entries as the number of video frames, each entry 
+				# has a list of tracked markers, each marker item has marker id, marker corners, Rvec, Tvec
+				wt = np.load(os.path.join(d4, 'world_timestamps.npy'))
+				# Processing pupil positions
+				pp = np.load(os.path.join(d4, 'pupil_positions.npy')) # timestamp	confidence	id	pos_x	pos_y	diameter
+				# pos_x and pos_y are normalized (Origin 0,0 at the bottom left and 1,1 at the top right)
+				# converting each element to dictionary for correlation
+				pp = map(lambda e: dict(zip(['timestamp', 'conf', 'id', 'x', 'y', 'diam'], e)), pp)
+				pp_by_frame = player_methods.correlate_data(pp, wt)
+
+				# Keeping only pupil positions with nonzero confidence
+				pp_by_frame = map(lambda l: filter(lambda p: p['conf']>0, l), pp_by_frame)
+				# Computing a single pupil position for the frame by taking mean of all detected pupil positions
+				pp_by_frame = map(lambda data: 
+					sum(np.array([pp['x'], pp['y']]) for pp in data)/len(data) if data else np.array([-1, -1]), pp_by_frame)
+				# Now each nonempty value of pp_by_frame is a tuple of (x, y) for pupil position in that frame		
+				
+				# Checking if timestamps, markers per frame and pupil positions per frame are correlated
+				assert len(marker_data) == len(wt) == len(pp_by_frame)
+				
+				# Good, now we need to find the frame ranges in which marker is not moving, for that we need the marker_data
+				# and using the position info per frame, we can compute movement speed and detect when it is it almost zero
+				
+				marker_data, mrdata = unifiy_markers_per_frame(marker_data)
+				
+				gaze_intervals = np.load(intervals_dir)
+
+				recordings_processed += 1
+				intervals_okay = True
+
+				if intervals_okay:
+					print '> Intervals seem okay.'
+					recordings_successful += 1
+
+					t2d, t3d, p = [], [], []
+					t2d_med, t3d_med, p_med, p_frames = [], [], [], []
+					for intv in gaze_intervals:
+						s, e = intv[0], intv[1]
+						null_gaze, null_marker = 0, 0
+						gaze_point = np.array([0, 0])
+						marker_3d_position = np.array([0, 0, 0])
+						marker_2d_position = np.array([0, 0])
+						gpts, m3ds, m2ds = [], [], []
+						valid_frames = []
+						for fn in xrange(s, e+1):
+							if all(pp_by_frame[fn]==np.array([-1, -1])) or mrdata[fn] == []:
+								# either pupil detection failed or marker detection
+								# the whole pupil-marker correspondence is invalid
+								# ignore this frame
+								pass
+							else:
+								gpts.append(pp_by_frame[fn])
+
+								marker_3d_position = marker_3d_position + np.array(mrdata[fn][:3])
+								marker_2d_position = marker_2d_position + np.array(mrdata[fn][3:])
+								
+								m3ds.append(np.array(mrdata[fn][:3]))
+								m2ds.append(np.array(mrdata[fn][3:]))
+
+								valid_frames.append(fn)
+						
+						if not len(valid_frames):
+							# this marker-pupil correspondece failed
+							print '> Failed to find reliable correspondece for a marker position...'
+							# In this case participant data should be completely ignored
+							# retrun
+
+						# Computing the median pupil position
+						final_p = np.median(gpts, axis=0)
+						p_med.append(final_p)
+						# Finding the closest pupil position to this median in the valid frames
+						dists = map(lambda pupil_position: (v(pupil_position)-v(final_p)).mag, gpts)
+						dists = zip(range(len(gpts)), dists)
+						closest = min(dists, key=lambda pair:pair[1])
+						# Getting the index for this position
+						ind = closest[0]
+						# Finding the k nearest pupil position to this one
+						k = 3
+						while True:
+							try:
+								nbrs = knn(n_neighbors=k, algorithm='ball_tree').fit(gpts)
+								dists, indices = nbrs.kneighbors(gpts)
+								break
+							except ValueError, err: 
+								k-=1
+						nearest_ind = indices[ind]
+						frames_numbers = map(lambda i: valid_frames[i], nearest_ind)
+						p_frames.append(frames_numbers)
+						# Now we take eye images from these frames
+						# Also the pupil-marker correspondece is now final_p and m2ds[ind] m3d[ind]
+						t2d_med.append(m2ds[ind])
+						t3d_med.append(m3ds[ind])
+						# t2d_med.append(np.median(m2ds, axis=0))
+						# t3d_med.append(np.median(m3ds, axis=0))
+
+
+					print '> gaze and marker data...'
+					# np.save(intervals_dir, np.array(gaze_intervals))
+					np.save(os.path.join(d4, 'p_frames.npy'), np.array(p_frames))
+					# np.save(os.path.join(d4, 'p.npy'), np.array(p_med))
+					# np.save(os.path.join(d4, 't2d.npy'), np.array(t2d_med))
+					# np.save(os.path.join(d4, 't3d.npy'), np.array(t3d_med))
+				print '>', recordings_processed, 'recordings processed.', recordings_successful, 'successful.'
+if __name__ == '__main__':
+	main(force=True)

code/recording/retrieve.py

@@ -0,0 +1,150 @@
+import sys
+import numpy as np 
+import cv2
+import matplotlib.pyplot as plt
+
+sys.path.append('..') # so we can import from pupil
+from pupil import player_methods
+
+# from tracker import processFrame
+
+DATA_DIR = '/home/mmbrian/HiWi/pupil_clone/pupil/recordings/2015_09_10/007/'
+OUTPUT_DIR = DATA_DIR + 'pp.npy'
+
+
+def capture(frame_number):
+	cap = cv2.VideoCapture(DATA_DIR + "world.mp4")
+	fc = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
+	assert frame_number<fc
+	frame = 1
+	status, img = cap.read() # extract the first frame
+	while status:
+		if frame == frame_number:
+			save_dir = DATA_DIR + "frames/frame_%d.jpg" % frame_number
+			cv2.imwrite(save_dir, img)	
+			break
+		frame+=1
+		status, img = cap.read()
+
+def main():
+	p2d, t3d = [], [] # 2D-3D pupil position to target position correspondences
+
+	cap = cv2.VideoCapture(DATA_DIR + "world.mp4")
+	fc = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
+	
+	wt = np.load(DATA_DIR + "world_timestamps.npy")
+
+	# time is measured in seconds (floating point) since the epoch.
+	# world timestamps correspond to each frame, we have to correlate timestamp information from
+	# pupil positions and world timestamps to find a 1-to-1 mapping between pupil positions and 
+	# marker positions in the video
+	print fc, len(wt)
+	assert fc == len(wt)
+
+	########################################################################################################
+	# Processing markers
+	# print 'Processing markers...'
+
+	## Processing frame by frame does not work as ArUco process opens a Gtk which cannot be terminated
+	## automatically, therefore we better process all frames before and work with the data here
+
+	# frame = 1
+	# status, img = cap.read() # extract the first frame
+	# while status:
+	# 	# cv2.imwrite(DATA_DIR + "frames/frame-%d.jpg" % frame, img)
+	# 	save_dir = DATA_DIR + "frames/current_frame.jpg"
+	# 	cv2.imwrite(save_dir, img)
+
+	# 	processFrame(save_dir)
+
+	# 	frame+=1
+	# 	status, img = cap.read()
+	# print "Processed %d frames." % (frame-1)
+
+	
+	########################################################################################################
+	# Processing pupil positions
+	print 'Processing pupil positions...'
+	pp = np.load(DATA_DIR + "pupil_positions.npy") # timestamp	confidence	id	pos_x	pos_y	diameter
+	# pos_x and pos_y are normalized (Origin 0,0 at the bottom left and 1,1 at the top right)
+	# converting each element to dictionary for correlation
+	pp = map(lambda e: dict(zip(['timestamp', 'conf', 'id', 'x', 'y', 'diam'], e)), pp)
+	pp_by_frame = player_methods.correlate_data(pp, wt)
+
+	# Keeping only pupil positions with nonzero confidence
+	pp_by_frame = map(lambda l: filter(lambda p: p['conf']>0, l), pp_by_frame)
+	# Computing a single pupil position for the frame by taking mean of all detected pupil positions
+	pp_by_frame = map(lambda data: 
+		sum(np.array([pp['x'], pp['y']]) for pp in data)/len(data) if data else np.array([-1, -1]), pp_by_frame)
+	# Now each nonempty value of pp_by_frame is a tuple of (x, y) for pupil position in that frame
+	
+	# Next we need to associate each frame to a detected marker and by taking mean pupil point and
+	# mean 3D marker position over a series of frames corresponding to that marker find a 2D-3D 
+	# mapping for calibration/test
+
+	tdiff = map(lambda e: e-wt[0], wt)
+	# This time correspondence to each marker was coordinated using the GazeHelper android application
+	# for 005 > starting from 00:56, 3 seconds gaze, 1 second for saccade
+	# These parameters are specific to the experiment
+	# 005 > 56, 3, 1
+	# 006 > 3, 3, 1
+	# 007 > 7, 3, 1 (or 8)
+	# 010 > 3, 3, 1
+	starting_point, gaze_duration, saccade_duration = 56, 3, 1 # these are specific to the experiment
+	# finding the starting frame
+	ind = 0
+	while tdiff[ind] < starting_point:
+		ind+=1
+	print ind
+
+	data = []
+	tstart = wt[ind]
+	for i in xrange(9):
+		print i
+		while ind<len(wt) and wt[ind] - tstart < saccade_duration:
+			ind+=1
+		if ind<len(wt):
+			tstart = wt[ind]
+		starting_ind = ind
+
+		while ind<len(wt) and wt[ind] - tstart < gaze_duration:
+			ind+=1
+
+		# all frames from starting_ind to ind-1 correspond to currently gazed marker
+		c = 0
+		cp = np.array([0, 0])
+		all_corresponding_points = []
+		for j in xrange(starting_ind, ind):
+			if pp_by_frame[j][0] >= 0:
+				c+=1
+				cp = cp + pp_by_frame[j]
+				all_corresponding_points.append(pp_by_frame[j])
+		# print c
+		if c>0:
+			ret = cp/c
+		else:
+			ret = np.array([-1, -1])  # no detected pupil for this marker
+		p2d.append(ret)
+		data.append([
+			np.array([starting_ind, ind-1]), # frame range
+			ret, # mean pupil position
+			all_corresponding_points]) # all pupil positions in range
+
+		if ind<len(wt):
+			tstart = wt[ind]
+
+	# p2d is now the list of detected pupil positions (not always 9 points)
+	print 'Saving data...'
+	np.save(OUTPUT_DIR, data)
+
+	# plt.plot([x[0] if x!=None else 0 for x in pp_by_frame], [y[1] if y!=None else 0 for y in pp_by_frame], 'ro')
+	# plt.show()
+	########################################################################################################
+
+	print len(p2d), 'gaze points'
+	print p2d
+	print len(wt), 'frames...'
+
+if __name__ == '__main__':
+	main()
+

code/recording/samples/frame_1_details.txt

@@ -0,0 +1,27 @@
+25 markers detected
+[-10.2193  -4.3434  31.3079] [ 364.02288846  279.34265322]
+[  4.45719   -0.890039  30.1925  ] [ 738.90737229  366.01616286]
+[ -0.241048  -0.781436  29.4198  ] [ 615.79477403  368.42821006]
+[  9.71565   2.76641  28.2099 ] [ 896.58718475  466.75202885]
+[ -9.72456   -0.578541  28.3965  ] [ 351.64670069  372.91016582]
+[  3.95976  -4.62243  33.3605 ] [ 716.20539106  279.93901788]
+[  8.71796  -4.73768  34.2061 ] [ 824.90658662  279.37948968]
+[-15.4225   -7.95261  33.4987 ] [ 257.11533368  200.38492012]
+[-10.759    -8.10442  34.3725 ] [ 374.21786998  202.19909734]
+[ -9.16547   3.18029  25.2879 ] [ 335.82956957  488.18787966]
+[  8.2166   -8.50777  37.2677 ] [ 797.87302812  207.99485459]
+[  0.251393   2.96439   26.5568  ] [ 629.74093457  476.94503839]
+[  4.95556   2.84818  27.3074 ] [ 765.81823876  471.39805837]
+[  9.2531    -0.996382  31.2532  ] [ 857.61665601  363.81620258]
+[ -0.751971  -4.49805   32.4702  ] [ 604.03285282  280.22127036]
+[ -4.4535    3.07115  26.0102 ] [ 487.44771208  481.98387392]
+[ -1.26526  -8.25379  35.5274 ] [ 594.23607203  205.79036006]
+[-14.1847   -0.48957  27.1355 ] [ 208.39215781  374.30642197]
+[ -4.96301   -0.668829  28.9992  ] [ 487.46065075  371.08870379]
+[-14.8024   -4.22344  30.2936 ] [ 234.96053223  278.18514623]
+[-15.4051   -4.40168  31.5249 ] [ 234.93398339  278.01955418]
+[ -5.99238  -8.19846  34.9627 ] [ 486.77529469  203.86450319]
+[ -5.48118  -4.44441  32.0198 ] [ 487.21960875  279.85135589]
+[-13.2512    3.09461  23.3448 ] [ 174.4254351   492.96000757]
+[  3.45682  -8.37202  36.4481 ] [ 697.60290572  207.58466561]
+

code/recording/tracker.py

@@ -0,0 +1,221 @@
+import os, signal, subprocess
+import cv2
+import numpy as np
+import json
+
+LOG_DIR = "/home/mmbrian/HiWi/etra2016_mohsen/code/recording/data/log.log"
+FRAMES_DIR = "/home/mmbrian/HiWi/etra2016_mohsen/code/recording/data/frames_012.npy"
+ARUCO_EXECUTABLE = "/home/mmbrian/temp/aruco-1.3.0/build/utils/aruco_test "
+ARUCO_SIMPLE_EXECUTABLE = "/home/mmbrian/temp/aruco-1.3.0/build/utils/aruco_simple "
+RECORDING_VIDEO = "/home/mmbrian/HiWi/pupil_clone/pupil/recordings/2015_09_10/012/world.avi "
+## convert videos to avi using:
+## avconv -i world.mp4 -c:a copy -c:v copy world.avi
+CAMERA_MATRIX = "/home/mmbrian/Pictures/chessboard_shots_new/camera.yml "
+SQUARE_SIZE = "0.029"
+
+class MyEncoder(json.JSONEncoder):
+	def default(self, o):
+		if '__dict__' in dir(o):
+			return o.__dict__
+		else:
+			return str(o)
+
+
+class Marker(object):
+	def __init__(self, _id, p, Rvec, Tvec):
+		'''
+		p is the array of marker corners in 2D detected in target image
+		'''
+		self.id = _id
+		self.p = p
+		self.Rvec = Rvec
+		self.Tvec = Tvec
+
+	def getCenter(self):
+		'''
+		Returns 3D position of the marker's center in camera coordinate system
+		'''
+		ret = []
+		# Constructing 4x4 intrinsic matrix
+		R = cv2.Rodrigues(self.Rvec)[0]
+		for i, row in enumerate(R):
+			ret.append(np.concatenate((row, [self.Tvec[i]])))	
+		ret.append([0, 0, 0, 1])
+		mat = np.array(ret)
+		# Applying the intrinsic matrix to marker center (wrt marker coordinate system)
+		return mat.dot(np.array([0, 0, 0, 1]))[:-1] # removing the 4th coordinate
+
+	def getCenterInImage(self, camera_matrix, dist_coeffs):
+		ret = cv2.projectPoints(np.array([(0.0, 0.0, 0.0)]), self.Rvec, self.Tvec, camera_matrix, dist_coeffs)
+		return ret[0][0][0]
+
+	@classmethod
+	def fromList(cls, data):
+		# np.array([np.array([curr_marker.id]), curr_marker.p, curr_marker.Rvec, curr_marker.Tvec])
+		return Marker(data[0][0], data[1], data[2], data[3])
+
+# def processFrame(frame_path):
+# 	cmd = ARUCO_SIMPLE_EXECUTABLE + frame_path + ' ' + CAMERA_MATRIX + SQUARE_SIZE
+# 	p = subprocess.Popen('exec ' + cmd, shell=True, stdout=subprocess.PIPE)
+# 	print '########################################################'
+# 	print 'Begin Output'
+# 	while True:
+# 		line = p.stdout.readline()
+# 		if line != '':
+# 			#the real code does filtering here
+# 			print line.rstrip()
+# 		else:
+# 			break
+# 	print 'End Output'
+# 	print '########################################################'
+# 	# p.terminate()
+# 	p.kill()
+# 	# os.killpg(p.pid, signal.SIGTERM)
+
+def readCameraParams(cam_mat = None):
+	'''
+	Reads an openCV camera.yml file and returns camera_matrix and distortion_coefficients
+	'''
+	if not cam_mat:
+		cam_mat = CAMERA_MATRIX
+
+	data = ''.join(open(cam_mat.strip(), 'r').readlines()).replace('\n', '').lower()
+	try:
+		ind1 = data.index('[', data.index('camera_matrix'))
+		ind2 = data.index(']', ind1)
+		camera_matrix = eval(data[ind1:ind2+1])
+		camera_matrix = np.array([camera_matrix[:3],
+								  camera_matrix[3:6],
+								  camera_matrix[6:]])
+		ind1 = data.index('[', data.index('distortion_coefficients'))
+		ind2 = data.index(']', ind1)
+		dist_coeffs = np.array(eval(data[ind1:ind2+1]))
+		return camera_matrix, dist_coeffs
+	except Exception:
+		print 'Could not load camera parameters'
+		print 'Invalid camera.yml file.'
+
+
+def performMarkerTracking(read_from_log = False, log = False, ret = False, 
+							log_dir = None, recording_video = None, frames_dir = None, 
+							square_size = None, cam_mat = None):
+	if not log_dir:
+		log_dir = LOG_DIR
+	if not frames_dir:
+		frames_dir = FRAMES_DIR
+	if not recording_video:
+		recording_video = RECORDING_VIDEO
+	if not cam_mat:
+		cam_mat = CAMERA_MATRIX
+	if not square_size:
+		square_size = SQUARE_SIZE
+	square_size = str(square_size)
+
+	data = []
+	if read_from_log:
+		if log:
+			print '> Reading data from log file:', log_dir
+		with open(log_dir, 'r') as f:
+			for l in f:
+				if l.strip():
+					data.append(l)
+	else:
+		if log:	
+			print '> Performing marker tracking on file:', recording_video
+			print '> Writing data to log file:', log_dir
+		with open(log_dir, 'w') as f:
+			for l in os.popen(ARUCO_EXECUTABLE + recording_video + cam_mat + square_size):
+				l = l.strip()
+				if '\r' in l:
+					for line in l.split('\r'):
+						if line:
+							f.write(line + "\n") # logging to file
+							data.append(line)
+				else:
+					if l:
+						f.write(l + "\n") # logging to file
+						data.append(l)
+	
+	if log:
+		print '> Parsing marker data...'
+	frame_count = 0
+	curr_frame = 0
+	markers = {}
+	frames, frame = [], []
+	visited_first_frame = False
+	for line in data:
+		line = line.strip().lower()
+		# print
+		# print repr(line)
+		# print
+		if line.startswith('time'):
+			if visited_first_frame:
+				frames.append(frame)
+			visited_first_frame = True
+			frame = []
+			# if frame:
+			# 	frames.append(frame)
+			# 	frame = []
+
+			curr_frame=curr_frame+1
+			nmarkers = int(line[line.index('nmarkers')+9:])
+			
+		if 'txyz' in line: # This line holds information of a detected marker
+			ind = line.index('=')
+			_id = int(line[:ind])
+			p = []
+			for i in xrange(4):
+				pind = ind
+				ind = line.index(' ', pind+1)
+				p.append(line[pind+1:ind])
+			pind = ind
+			ind = line.index('rxyz', pind)
+			T = line[pind+1+5:ind]
+			R = line[ind+5:]
+			
+			if not _id in markers:
+				markers[_id] = []
+			curr_marker = Marker(_id, 
+								map(lambda pstr: np.array(eval(pstr)), p), 
+								np.array(eval('(' + R.strip().replace(' ', ',') + ')')),
+								np.array(eval('(' + T.strip().replace(' ', ',') + ')')))
+			markers[_id].append(curr_marker)
+			frame.append(np.array([np.array([curr_marker.id]), curr_marker.p, curr_marker.Rvec, curr_marker.Tvec]))
+	# Last frame data
+	frames.append(frame)
+
+	frames = np.array(frames)
+	if log:
+		print '> Saving marker data for all frames in:', frames_dir
+	np.save(frames_dir, frames)
+	if log:
+		print '> Successfully processed %s frames.' % curr_frame
+	if ret:
+		return markers
+
+
+
+if __name__ == '__main__':
+	camera_matrix, dist_coeffs = readCameraParams()
+
+	markers = performMarkerTracking()
+	print '################################################'
+	print len(markers), 'markers detected'
+	for m in markers:
+		# print '################################################'
+		# print markers[m][0].__dict__
+		# print '################################################'
+		print m, ':', len(markers[m]), 'instances'
+		c = markers[m][0].getCenter()
+		print c * 100, markers[m][0].getCenterInImage(camera_matrix, dist_coeffs) 
+
+
+	# # TODO: investigate if this is how to get projections of unit vectors 
+	# # originating from the camera onto the image plane
+	# # the trick is that we consider a marker with no R and zero T
+	# v = [(0.0, 0.0, 0.0), (1, 0, 0), (0, 1, 0), (0, 0, 1)]
+	# ret = cv2.projectPoints(np.array(v), np.eye(3), np.zeros(3), camera_matrix, dist_coeffs)
+	# p = ret[0]
+
+	# for i, t in enumerate(v):
+	# 	print t, 'P->', p[i][0]

code/recording/util/GazeHelper/.gitignore

@@ -0,0 +1,41 @@
+.gradle
+/local.properties
+/.idea/workspace.xml
+/.idea/libraries
+.DS_Store
+/build
+/captures
+
+### Android ###
+# Built application files
+*.apk
+*.ap_
+
+# Files for the Dalvik VM
+*.dex
+
+# Java class files
+*.class
+
+# Generated files
+bin/
+gen/
+
+# Gradle files
+.gradle/
+build/
+
+# Local configuration file (sdk path, etc)
+local.properties
+
+# Proguard folder generated by Eclipse
+proguard/
+
+# Log Files
+*.log
+
+# Android Studio Navigation editor temp files
+.navigation/
+
+### Android Patch ###
+gen-external-apklibs

code/recording/util/GazeHelper/.idea/.name

@@ -0,0 +1 @@
+GazeHelper

code/recording/util/GazeHelper/.idea/compiler.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CompilerConfiguration">
+    <resourceExtensions />
+    <wildcardResourcePatterns>
+      <entry name="!?*.java" />
+      <entry name="!?*.form" />
+      <entry name="!?*.class" />
+      <entry name="!?*.groovy" />
+      <entry name="!?*.scala" />
+      <entry name="!?*.flex" />
+      <entry name="!?*.kt" />
+      <entry name="!?*.clj" />
+      <entry name="!?*.aj" />
+    </wildcardResourcePatterns>
+    <annotationProcessing>
+      <profile default="true" name="Default" enabled="false">
+        <processorPath useClasspath="true" />
+      </profile>
+    </annotationProcessing>
+  </component>
+</project>

code/recording/util/GazeHelper/.idea/copyright/profiles_settings.xml

@@ -0,0 +1,3 @@
+<component name="CopyrightManager">
+  <settings default="" />
+</component>

code/recording/util/GazeHelper/.idea/gradle.xml

@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="GradleSettings">
+    <option name="linkedExternalProjectsSettings">
+      <GradleProjectSettings>
+        <option name="distributionType" value="LOCAL" />
+        <option name="externalProjectPath" value="$PROJECT_DIR$" />
+        <option name="gradleHome" value="$APPLICATION_HOME_DIR$/gradle/gradle-2.2.1" />
+        <option name="gradleJvm" value="1.8" />
+        <option name="modules">
+          <set>
+            <option value="$PROJECT_DIR$" />
+            <option value="$PROJECT_DIR$/app" />
+          </set>
+        </option>
+      </GradleProjectSettings>
+    </option>
+  </component>
+</project>

code/recording/util/GazeHelper/.idea/misc.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="EntryPointsManager">
+    <entry_points version="2.0" />
+  </component>
+  <component name="ProjectLevelVcsManager" settingsEditedManually="false">
+    <OptionsSetting value="true" id="Add" />
+    <OptionsSetting value="true" id="Remove" />
+    <OptionsSetting value="true" id="Checkout" />
+    <OptionsSetting value="true" id="Update" />
+    <OptionsSetting value="true" id="Status" />
+    <OptionsSetting value="true" id="Edit" />
+    <ConfirmationsSetting value="0" id="Add" />
+    <ConfirmationsSetting value="0" id="Remove" />
+  </component>
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_1_7" default="true" assert-keyword="true" jdk-15="true" project-jdk-name="1.8" project-jdk-type="JavaSDK">
+    <output url="file://$PROJECT_DIR$/build/classes" />
+  </component>
+  <component name="ProjectType">
+    <option name="id" value="Android" />
+  </component>
+</project>

code/recording/util/GazeHelper/.idea/modules.xml

@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/GazeHelper.iml" filepath="$PROJECT_DIR$/GazeHelper.iml" />
+      <module fileurl="file://$PROJECT_DIR$/app/app.iml" filepath="$PROJECT_DIR$/app/app.iml" />
+    </modules>
+  </component>
+</project>

code/recording/util/GazeHelper/.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="" />
+  </component>
+</project>

code/recording/util/GazeHelper/GazeHelper.iml

@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module external.linked.project.id="GazeHelper" external.linked.project.path="$MODULE_DIR$" external.root.project.path="$MODULE_DIR$" external.system.id="GRADLE" external.system.module.group="" external.system.module.version="unspecified" type="JAVA_MODULE" version="4">
+  <component name="FacetManager">
+    <facet type="java-gradle" name="Java-Gradle">
+      <configuration>
+        <option name="BUILD_FOLDER_PATH" value="$MODULE_DIR$/build" />
+        <option name="BUILDABLE" value="false" />
+      </configuration>
+    </facet>
+  </component>
+  <component name="NewModuleRootManager" inherit-compiler-output="true">
+    <exclude-output />
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.gradle" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

code/recording/util/GazeHelper/app/.gitignore

@@ -0,0 +1 @@
+/build

code/recording/util/GazeHelper/app/app.iml

@@ -0,0 +1,95 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module external.linked.project.id=":app" external.linked.project.path="$MODULE_DIR$" external.root.project.path="$MODULE_DIR$/.." external.system.id="GRADLE" external.system.module.group="GazeHelper" external.system.module.version="unspecified" type="JAVA_MODULE" version="4">
+  <component name="FacetManager">
+    <facet type="android-gradle" name="Android-Gradle">
+      <configuration>
+        <option name="GRADLE_PROJECT_PATH" value=":app" />
+      </configuration>
+    </facet>
+    <facet type="android" name="Android">
+      <configuration>
+        <option name="SELECTED_BUILD_VARIANT" value="debug" />
+        <option name="SELECTED_TEST_ARTIFACT" value="_android_test_" />
+        <option name="ASSEMBLE_TASK_NAME" value="assembleDebug" />
+        <option name="COMPILE_JAVA_TASK_NAME" value="compileDebugSources" />
+        <option name="SOURCE_GEN_TASK_NAME" value="generateDebugSources" />
+        <option name="ASSEMBLE_TEST_TASK_NAME" value="assembleDebugAndroidTest" />
+        <option name="COMPILE_JAVA_TEST_TASK_NAME" value="compileDebugAndroidTestSources" />
+        <option name="TEST_SOURCE_GEN_TASK_NAME" value="generateDebugAndroidTestSources" />
+        <option name="ALLOW_USER_CONFIGURATION" value="false" />
+        <option name="MANIFEST_FILE_RELATIVE_PATH" value="/src/main/AndroidManifest.xml" />
+        <option name="RES_FOLDER_RELATIVE_PATH" value="/src/main/res" />
+        <option name="RES_FOLDERS_RELATIVE_PATH" value="file://$MODULE_DIR$/src/main/res" />
+        <option name="ASSETS_FOLDER_RELATIVE_PATH" value="/src/main/assets" />
+      </configuration>
+    </facet>
+  </component>
+  <component name="NewModuleRootManager" inherit-compiler-output="false">
+    <output url="file://$MODULE_DIR$/build/intermediates/classes/debug" />
+    <output-test url="file://$MODULE_DIR$/build/intermediates/classes/androidTest/debug" />
+    <exclude-output />
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/r/debug" isTestSource="false" generated="true" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/aidl/debug" isTestSource="false" generated="true" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/buildConfig/debug" isTestSource="false" generated="true" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/rs/debug" isTestSource="false" generated="true" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/res/rs/debug" type="java-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/res/generated/debug" type="java-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/r/androidTest/debug" isTestSource="true" generated="true" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/aidl/androidTest/debug" isTestSource="true" generated="true" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/buildConfig/androidTest/debug" isTestSource="true" generated="true" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/rs/androidTest/debug" isTestSource="true" generated="true" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/res/rs/androidTest/debug" type="java-test-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/build/generated/res/generated/androidTest/debug" type="java-test-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/debug/res" type="java-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/debug/resources" type="java-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/debug/assets" type="java-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/debug/aidl" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/src/debug/java" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/src/debug/jni" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/src/debug/rs" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/src/main/res" type="java-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/main/resources" type="java-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/main/assets" type="java-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/main/aidl" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/src/main/java" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/src/main/jni" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/src/main/rs" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/res" type="java-test-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/resources" type="java-test-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/assets" type="java-test-resource" />
+      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/aidl" isTestSource="true" />
+      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/java" isTestSource="true" />
+      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/jni" isTestSource="true" />
+      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/rs" isTestSource="true" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/assets" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/bundles" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/classes" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/coverage-instrumented-classes" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/dependency-cache" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/dex" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/dex-cache" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/exploded-aar/com.android.support/appcompat-v7/23.0.1/jars" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/exploded-aar/com.android.support/support-v4/23.0.1/jars" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/incremental" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/jacoco" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/javaResources" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/libs" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/lint" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/manifests" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/ndk" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/pre-dexed" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/proguard" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/res" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/rs" />
+      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/symbols" />
+      <excludeFolder url="file://$MODULE_DIR$/build/outputs" />
+      <excludeFolder url="file://$MODULE_DIR$/build/tmp" />
+    </content>
+    <orderEntry type="jdk" jdkName="Android API 23 Platform" jdkType="Android SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+    <orderEntry type="library" exported="" name="support-v4-23.0.1" level="project" />
+    <orderEntry type="library" exported="" name="appcompat-v7-23.0.1" level="project" />
+    <orderEntry type="library" exported="" name="support-annotations-23.0.1" level="project" />
+  </component>
+</module>

code/recording/util/GazeHelper/app/build.gradle

@@ -0,0 +1,25 @@
+apply plugin: 'com.android.application'
+
+android {
+    compileSdkVersion 23
+    buildToolsVersion "22.0.1"
+
+    defaultConfig {
+        applicationId "gazehelper.android.mmbrian.com.gazehelper"
+        minSdkVersion 14
+        targetSdkVersion 23
+        versionCode 1
+        versionName "1.0"
+    }
+    buildTypes {
+        release {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'
+        }
+    }
+}
+
+dependencies {
+    compile fileTree(dir: 'libs', include: ['*.jar'])
+    compile 'com.android.support:appcompat-v7:23.0.1'
+}

code/recording/util/GazeHelper/app/proguard-rules.pro

@@ -0,0 +1,17 @@
+# Add project specific ProGuard rules here.
+# By default, the flags in this file are appended to flags specified
+# in /home/mmbrian/Android/Sdk/tools/proguard/proguard-android.txt
+# You can edit the include path and order by changing the proguardFiles
+# directive in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# Add any project specific keep options here:
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}

code/recording/util/GazeHelper/app/src/main/AndroidManifest.xml

@@ -0,0 +1,21 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="gazehelper.android.mmbrian.com.gazehelper" >
+
+    <application
+        android:allowBackup="true"
+        android:icon="@mipmap/ic_launcher"
+        android:label="@string/app_name"
+        android:theme="@style/AppTheme" >
+        <activity
+            android:name=".MainActivity"
+            android:label="@string/app_name" >
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN" />
+
+                <category android:name="android.intent.category.LAUNCHER" />
+            </intent-filter>
+        </activity>
+    </application>
+
+</manifest>

code/recording/util/GazeHelper/app/src/main/java/gazehelper/android/mmbrian/com/gazehelper/MainActivity.java

@@ -0,0 +1,108 @@
+package gazehelper.android.mmbrian.com.gazehelper;
+
+import android.app.Activity;
+import android.media.AudioManager;
+import android.media.ToneGenerator;
+import android.os.Bundle;
+import android.os.Handler;
+import android.util.Log;
+import android.view.View;
+import android.widget.Button;
+import android.widget.EditText;
+import android.widget.TextView;
+import android.widget.ViewFlipper;
+
+import java.util.Timer;
+import java.util.TimerTask;
+
+
+public class MainActivity extends Activity {
+
+    public static final String CLASS_TAG = "GazeHelper";
+
+    ViewFlipper flipper;
+    Button btn_start;
+    EditText txt_gaze_dur, txt_bgaze_dur, txt_ngaze;
+    TextView txt_stats;
+
+    int gaze_dur, bgaze_dur, ngaze, cgaze;
+    long total_dur, start_time;
+
+    static Timer timer;
+    ToneGenerator toneG;
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_main);
+
+        flipper = (ViewFlipper)findViewById(R.id.flipper);
+
+        txt_gaze_dur = (EditText)findViewById(R.id.txt_gaze_dur);
+        txt_bgaze_dur = (EditText)findViewById(R.id.txt_bgaze_dur);
+        txt_ngaze = (EditText)findViewById(R.id.txt_ngaze);
+
+        txt_stats = (TextView)findViewById(R.id.txt_stats);
+
+        timer = new Timer("Gaze Timer");
+        toneG = new ToneGenerator(AudioManager.STREAM_MUSIC, 100);
+
+        btn_start = (Button)findViewById(R.id.btn_start);
+        btn_start.setOnClickListener(new View.OnClickListener() {
+            @Override
+            public void onClick(View v) {
+                txt_stats.setText("Starting...");
+
+                gaze_dur = Integer.valueOf(txt_gaze_dur.getText().toString());
+                bgaze_dur = Integer.valueOf(txt_bgaze_dur.getText().toString());
+                ngaze = Integer.valueOf(txt_ngaze.getText().toString());
+                total_dur = (ngaze * gaze_dur + (ngaze - 1) * bgaze_dur) * 1000; // in milliseconds
+
+                flipper.showNext();
+
+                cgaze = 0;
+                start_time = -1;
+                Log.d(CLASS_TAG, "Started...");
+                timer = new Timer("Gaze Timer");
+                timer.schedule(new TimerTask() {
+                    @Override
+                    public void run() {
+                        runOnUiThread(new Runnable() {
+                            @Override
+                            public void run() {
+                                updateUI();
+                            }
+                        });
+                    }
+                }, bgaze_dur * 1000, (gaze_dur + bgaze_dur) * 1000); // initial delay, recall delay
+                // first gaze is after 2 bgaze durations, later gazes start after a bgaze
+            }
+        });
+    }
+
+    public void updateUI() {
+        Log.d(CLASS_TAG, cgaze + "/" + ngaze);
+        if (cgaze++ >= ngaze) {
+            txt_stats.setText("Finished :)");
+            new Handler().postDelayed(new Runnable() {
+                @Override
+                public void run() {
+                    runOnUiThread(new Runnable() {
+                        @Override
+                        public void run() {
+                            flipper.showPrevious();
+                        }
+                    });
+                }
+            }, gaze_dur * 1000);
+            timer.cancel();
+            return;
+        }
+        toneG.startTone(ToneGenerator.TONE_CDMA_ALERT_CALL_GUARD, 200);
+
+//        if (start_time < 0)
+//            start_time = System.currentTimeMillis();
+//        txt_stats.setText((System.currentTimeMillis()-start_time)/1000 + "");
+        txt_stats.setText("Gaze at Target #" + cgaze);
+    }
+}

code/recording/util/GazeHelper/app/src/main/res/layout/activity_main.xml

@@ -0,0 +1,59 @@
+<RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:tools="http://schemas.android.com/tools" android:layout_width="match_parent"
+    android:layout_height="match_parent" android:paddingLeft="@dimen/activity_horizontal_margin"
+    android:paddingRight="@dimen/activity_horizontal_margin"
+    android:paddingTop="@dimen/activity_vertical_margin"
+    android:paddingBottom="@dimen/activity_vertical_margin" tools:context=".MainActivity">
+
+    <ViewFlipper
+        android:id="@+id/flipper"
+        android:layout_width="match_parent"
+        android:layout_height="wrap_content"
+        android:layout_centerInParent="true">
+
+        <LinearLayout
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content"
+            android:orientation="vertical"
+            android:layout_centerInParent="true">
+
+            <EditText
+                android:id="@+id/txt_gaze_dur"
+                android:layout_width="match_parent"
+                android:layout_height="wrap_content"
+                android:hint="Gaze Duration (Seconds)"
+                android:inputType="number"/>
+
+            <EditText
+                android:id="@+id/txt_bgaze_dur"
+                android:layout_width="match_parent"
+                android:layout_height="wrap_content"
+                android:hint="Between-Gaze Duration (Seconds)"
+                android:inputType="number"/>
+            <EditText
+                android:id="@+id/txt_ngaze"
+                android:layout_width="match_parent"
+                android:layout_height="wrap_content"
+                android:hint="Number of Targets"
+                android:inputType="number"/>
+
+            <Button
+                android:id="@+id/btn_start"
+                style="@android:style/TextAppearance.DeviceDefault.Medium"
+                android:layout_width="match_parent"
+                android:layout_height="wrap_content"
+                android:text="Start"/>
+        </LinearLayout>
+
+        <TextView
+            android:id="@+id/txt_stats"
+            style="@android:style/TextAppearance.DeviceDefault.Large"
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content"
+            android:layout_gravity="center_vertical"
+            android:gravity="center"
+            android:text=""/>
+    </ViewFlipper>
+
+
+</RelativeLayout>

code/recording/util/GazeHelper/app/src/main/res/values-w820dp/dimens.xml

@@ -0,0 +1,6 @@
+<resources>
+    <!-- Example customization of dimensions originally defined in res/values/dimens.xml
+         (such as screen margins) for screens with more than 820dp of available width. This
+         would include 7" and 10" devices in landscape (~960dp and ~1280dp respectively). -->
+    <dimen name="activity_horizontal_margin">64dp</dimen>
+</resources>

code/recording/util/GazeHelper/app/src/main/res/values/dimens.xml

@@ -0,0 +1,5 @@
+<resources>
+    <!-- Default screen margins, per the Android Design guidelines. -->
+    <dimen name="activity_horizontal_margin">16dp</dimen>
+    <dimen name="activity_vertical_margin">16dp</dimen>
+</resources>

code/recording/util/GazeHelper/app/src/main/res/values/strings.xml

@@ -0,0 +1,6 @@
+<resources>
+    <string name="app_name">GazeHelper</string>
+
+    <string name="hello_world">Hello world!</string>
+    <string name="action_settings">Settings</string>
+</resources>

code/recording/util/GazeHelper/app/src/main/res/values/styles.xml

@@ -0,0 +1,8 @@
+<resources>
+
+    <!-- Base application theme. -->
+    <style name="AppTheme" parent="Theme.AppCompat.Light.DarkActionBar">
+        <!-- Customize your theme here. -->
+    </style>
+
+</resources>

code/recording/util/GazeHelper/build.gradle

@@ -0,0 +1,19 @@
+// Top-level build file where you can add configuration options common to all sub-projects/modules.
+
+buildscript {
+    repositories {
+        jcenter()
+    }
+    dependencies {
+        classpath 'com.android.tools.build:gradle:1.2.3'
+
+        // NOTE: Do not place your application dependencies here; they belong
+        // in the individual module build.gradle files
+    }
+}
+
+allprojects {
+    repositories {
+        jcenter()
+    }
+}

code/recording/util/GazeHelper/gradle.properties

@@ -0,0 +1,18 @@
+# Project-wide Gradle settings.
+
+# IDE (e.g. Android Studio) users:
+# Gradle settings configured through the IDE *will override*
+# any settings specified in this file.
+
+# For more details on how to configure your build environment visit
+# http://www.gradle.org/docs/current/userguide/build_environment.html
+
+# Specifies the JVM arguments used for the daemon process.
+# The setting is particularly useful for tweaking memory settings.
+# Default value: -Xmx10248m -XX:MaxPermSize=256m
+# org.gradle.jvmargs=-Xmx2048m -XX:MaxPermSize=512m -XX:+HeapDumpOnOutOfMemoryError -Dfile.encoding=UTF-8
+
+# When configured, Gradle will run in incubating parallel mode.
+# This option should only be used with decoupled projects. More details, visit
+# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
+# org.gradle.parallel=true

code/recording/util/GazeHelper/gradle/wrapper/gradle-wrapper.properties

@@ -0,0 +1,6 @@
+#Wed Apr 10 15:27:10 PDT 2013
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-2.2.1-all.zip

code/recording/util/GazeHelper/gradlew

@@ -0,0 +1,164 @@
+#!/usr/bin/env bash
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS=""
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn ( ) {
+    echo "$*"
+}
+
+die ( ) {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+esac
+
+# For Cygwin, ensure paths are in UNIX format before anything is touched.
+if $cygwin ; then
+    [ -n "$JAVA_HOME" ] && JAVA_HOME=`cygpath --unix "$JAVA_HOME"`
+fi
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >&-
+APP_HOME="`pwd -P`"
+cd "$SAVED" >&-
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin, switch paths to Windows format before running java
+if $cygwin ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=$((i+1))
+    done
+    case $i in
+        (0) set -- ;;
+        (1) set -- "$args0" ;;
+        (2) set -- "$args0" "$args1" ;;
+        (3) set -- "$args0" "$args1" "$args2" ;;
+        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Split up the JVM_OPTS And GRADLE_OPTS values into an array, following the shell quoting and substitution rules
+function splitJvmOpts() {
+    JVM_OPTS=("$@")
+}
+eval splitJvmOpts $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS
+JVM_OPTS[${#JVM_OPTS[*]}]="-Dorg.gradle.appname=$APP_BASE_NAME"
+
+exec "$JAVACMD" "${JVM_OPTS[@]}" -classpath "$CLASSPATH" org.gradle.wrapper.GradleWrapperMain "$@"

code/recording/util/GazeHelper/gradlew.bat

@@ -0,0 +1,90 @@
+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS=
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto init
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto init
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:init
+@rem Get command-line arguments, handling Windowz variants
+
+if not "%OS%" == "Windows_NT" goto win9xME_args
+if "%@eval[2+2]" == "4" goto 4NT_args
+
+:win9xME_args
+@rem Slurp the command line arguments.
+set CMD_LINE_ARGS=
+set _SKIP=2
+
+:win9xME_args_slurp
+if "x%~1" == "x" goto execute
+
+set CMD_LINE_ARGS=%*
+goto execute
+
+:4NT_args
+@rem Get arguments from the 4NT Shell from JP Software
+set CMD_LINE_ARGS=%$
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

code/recording/util/GazeHelper/settings.gradle

@@ -0,0 +1 @@
+include ':app'

code/recording/util/SingleMarkerVisualizer/CalibrationBoard.pde

@@ -0,0 +1,102 @@
+
+float marker_image_width;
+float screen_border_width;
+
+float mark_pause; // seconds
+
+// Represents a 9-point grid calibration board
+class CalibrationBoard {
+  PVector[] marks;
+  float xgap, ygap, xOffset, yOffset;
+  
+  // standard order for 9point grid, starting from center and then clockwise
+  int[] inds = new int[] {1, 2, 3, 8, 0, 4, 7, 6, 5};
+  public void shuffleCalibrationOrder() { // Fisher–Yates shuffle
+    for (int i = inds.length - 1; i > 0; i--)
+    {
+      int index = int(random(0, i + 1));
+      int tmp = inds[index];
+      inds[index] = inds[i];
+      inds[i] = tmp;
+    }
+  }
+  public void generateInds(int grid_width) {
+    inds = new int[grid_width*grid_width];
+    for (int i=0; i<inds.length; i++) {
+      inds[i] = i;
+    }
+  }
+  
+  public CalibrationBoard(int mode, int grid_width, float xOffset, float yOffset, float xgap, float ygap) {
+    if (mode == 1) {
+      this.xOffset = xOffset;
+      this.yOffset = yOffset;
+      generateInds(grid_width);
+      shuffleCalibrationOrder();
+      
+      if (xgap == -1 || ygap == -1) {
+        computeGridMarks(grid_width, false);
+      } else {
+        this.xgap = xgap;
+        this.ygap = ygap;
+        computeGridMarks(grid_width, true);
+      }
+    } else {
+      int num = grid_width; // num of calibration points
+      inds = new int[num];
+      for (int i=0; i<inds.length; i++)
+        inds[i] = i;
+      generateRandomMarks();
+    }
+  }
+  
+  public void generateRandomMarks() {
+    float w = width - 2*screen_border_width - marker_image_width;
+    float xOffset = screen_border_width + marker_image_width/2.;
+    float h = height - 2*screen_border_width - marker_image_width;
+    float yOffset = screen_border_width + marker_image_width/2.;
+    float x, y;
+    marks = new PVector[inds.length];
+    for (int i=0; i<inds.length; i++) {
+      x = random(0, w);
+      y = random(0, h);
+      marks[inds[i]] = new PVector(x + xOffset, y + yOffset); 
+    }
+  }
+  
+  public void computeGridMarks(int grid_width, boolean custom_gap_size) {
+    float w = width - 2*screen_border_width;
+    float h = height - 2*screen_border_width;
+    if (!custom_gap_size) {
+      xgap = (w - grid_width*marker_image_width)/float(grid_width-1);
+      ygap = (h - grid_width*marker_image_width)/float(grid_width-1);
+    }
+    marks = new PVector[grid_width*grid_width];
+    float x, y;
+    int c = 0;
+    for (int i=0; i<grid_width; i++) {
+      y = i * (ygap + marker_image_width) + screen_border_width + yOffset;
+      y += marker_image_width/2.;      
+      for (int j=0; j<grid_width; j++) {
+        x = j * (xgap + marker_image_width) + screen_border_width + xOffset;
+        x += marker_image_width/2.;
+        marks[inds[c++]] = new PVector(x, y);  
+      }
+    }
+  }
+
+  public void drawMarks() {
+    for (int i=0; i<marks.length; i++) {
+      drawMark(marks[i]);
+      fill(0);
+      //text(i + "", marks[i].x + 3, marks[i].y - 5);
+    }
+  }
+  
+  private void drawMark(PVector pos) {
+    int w = 13;
+    stroke(0);
+    line(pos.x - w, pos.y, pos.x + w, pos.y);
+    line(pos.x, pos.y - w, pos.x, pos.y + w);
+  }
+}

code/recording/util/SingleMarkerVisualizer/Marker.pde

@@ -0,0 +1,69 @@
+
+int marker_stroke_weight;
+int marker_stroke_width;
+float marker_move_step;
+
+class Marker {
+ PVector p, c;
+ float w, h;
+ PImage image;
+ 
+ PVector dest;
+ boolean onDest;
+ 
+ public Marker(PImage img) {
+   this.image = img;
+   this.w = img.width;
+   this.h = img.height;
+   this.c = new PVector(width/2., height/2.);
+   this.p = new PVector(c.x - w/2., c.y - h/2.);
+   this.dest = this.p;
+   this.onDest = true;
+ }
+  
+ public void draw() {
+   image(image, p.x, p.y);
+   stroke(255, 0, 0);
+   strokeWeight(marker_stroke_weight);
+   line(c.x - marker_stroke_width, c.y, c.x + marker_stroke_width, c.y);
+   line(c.x, c.y - marker_stroke_width, c.x, c.y + marker_stroke_width);
+ }
+ 
+ public void update() {
+   if (p.x != dest.x || p.y != dest.y) {
+     onDest = false;
+     PVector diff = dest.get();
+     diff.sub(p);
+     if (diff.mag() > marker_move_step) {
+       diff.normalize();
+       moveX(diff.x * marker_move_step);
+       moveY(diff.y * marker_move_step);
+     } else {
+       setX(dest.x);
+       setY(dest.y);
+     }
+   } else {
+     onDest = true;
+   }
+ }
+ 
+ public void moveX(float step) {
+    this.p.x += step;
+    this.c.x += step;
+ }
+ 
+ public void moveY(float step) {
+    this.p.y += step;
+    this.c.y += step;
+ }
+ 
+ public void setX(float x) {
+   this.p.x = x;
+   this.c.x = x + w/2.;
+ }
+ 
+ public void setY(float y) {
+   this.p.y = y;
+   this.c.y = y + h/2.;
+ }
+}

code/recording/util/SingleMarkerVisualizer/SingleMarkerVisualizer.pde

@@ -0,0 +1,177 @@
+Marker marker;
+CalibrationBoard gc, gc_test;
+String status = "not started";
+
+int mark_pause_start = -1;
+int curr_mark = 0;
+boolean pause = true;
+boolean finished = false;
+boolean display_marks = true;
+boolean calibration_mode = true;
+boolean display_stats = true;
+
+float start_delay = 3;
+int count_down_begin;
+
+void setup() {
+  fullScreen();
+  //size(640, 480);
+  frameRate(60);
+  screen_border_width = 0;
+  mark_pause = 2.0; // in seconds
+  marker_stroke_weight = 4;
+  marker_stroke_width = 13;
+  marker_move_step = 7;
+
+
+  PImage img = loadImage("1023.png");
+  marker_image_width = img.width;
+  marker = new Marker(img);
+
+  gc = new CalibrationBoard(1, 5, 0, 0, -1, -1); // means 5-point grid
+  gc_test = new CalibrationBoard(1, 4, (gc.xgap + marker_image_width)/2., (gc.ygap + marker_image_width)/2., gc.xgap, gc.ygap);
+
+  resetMarker();
+}
+
+void reset() {
+  mark_pause_start = -1;
+  curr_mark = 0;
+  pause = true;
+  finished = false;
+  display_marks = true;
+  calibration_mode = true;
+
+  gc = new CalibrationBoard(1, 5, 0, 0, -1, -1); // means 5-point grid
+  gc_test = new CalibrationBoard(1, 4, (gc.xgap + marker_image_width)/2., (gc.ygap + marker_image_width)/2., gc.xgap, gc.ygap);
+  resetMarker();
+}
+
+void resetMarker() {
+  float x, y;
+  if (calibration_mode) {
+    x = gc.marks[curr_mark].x - marker_image_width/2.;
+    y = gc.marks[curr_mark].y - marker_image_width/2.;
+  } else {
+    x = gc_test.marks[curr_mark].x - marker_image_width/2.;
+    y = gc_test.marks[curr_mark].y - marker_image_width/2.;
+  }
+  marker.dest = new PVector(x, y);
+  marker.setX(x);
+  marker.setY(y);
+}
+
+
+void draw() {
+  background(255);
+  if (!finished) {
+    marker.draw();
+    marker.update();
+  }
+
+  if (!pause) {
+    if (frameCount - count_down_begin > start_delay * frameRate) {
+      status = "started"; 
+      if (marker.onDest) {
+        if (mark_pause_start < 0) {
+          mark_pause_start = frameCount;
+        } else {
+          status = ((frameCount - mark_pause_start) / frameRate) + "";
+          if (frameCount - mark_pause_start > mark_pause * frameRate) {
+            if ((calibration_mode && curr_mark < gc.inds.length-1) || (!calibration_mode && curr_mark < gc_test.inds.length-1)) {
+              PVector destC;
+              if (calibration_mode) {
+                destC = gc.marks[++curr_mark];
+              } else {
+                destC = gc_test.marks[++curr_mark];
+              }
+              marker.dest = new PVector(destC.x - marker_image_width/2., destC.y - marker_image_width/2.);
+            } else {
+              status = "finished";
+              finished = true;
+            }
+          }
+        }
+      } else {
+        status = "moving";
+        mark_pause_start = -1;
+      }
+    } else {
+      text(((frameCount - count_down_begin)/frameRate) + "/" + start_delay, width/2, height-7);
+    }
+  } else {
+    status = "paused";
+  }
+
+  if (display_marks) {
+    gc.drawMarks();
+    gc_test.drawMarks();
+  }
+
+  //fill(255, 0, 0);
+  //noStroke();
+  //ellipse(marker.dest.x, marker.dest.y, 5, 5);
+
+  //fill(0, 0, 255);
+  //for (PVector m: gc.marks) {
+  //  ellipse(m.x - marker_image_width/2., m.y - marker_image_width/2., 5, 5);  
+  //}
+
+
+  if (finished) {
+    fill(255, 0, 0);
+  } else if (pause) {
+    fill(0);
+  } else {
+    fill(0, 0, 255);
+  }
+
+  if (display_stats) {
+    text("status: " + status, 3, 13);
+    String progress;
+    if (calibration_mode) {
+      progress = (curr_mark+1) + "/" + gc.inds.length;
+    } else {
+      progress = (curr_mark+1) + "/" + gc_test.inds.length;
+    }
+    text(progress, 3, height-7);
+  }
+}
+
+void keyPressed() {
+  println(keyCode);
+  switch (keyCode) {
+  case 32: // Space Button
+    pause = !pause;
+    display_marks = false;
+    if (!pause)
+      count_down_begin = frameCount;
+    break;
+  case 77: // M key
+    display_marks = !display_marks;
+    break;
+  case 67: // C key
+    curr_mark = 0;
+    calibration_mode = true;
+    println("calibration mode");
+    resetMarker();
+    break;
+  case 84: // T key
+    curr_mark = 0;
+    calibration_mode = false;
+    println("test mode");
+    resetMarker();
+    break;
+  case 82: // R key
+    reset();
+    break;
+  case 83: // S key
+    saveFrame("screenshot_###.png");
+    break;
+  case 72: // H key
+    display_stats = !display_stats;
+    break;
+  default:
+    break;
+  }
+}

code/recording/util/SingleMarkerVisualizer/sketch.properties

@@ -0,0 +1,2 @@
+mode.id=processing.mode.java.JavaMode
+mode=Java

code/recording/util/check_pupil_positions.py

@@ -0,0 +1,18 @@
+from __future__ import division
+import numpy as np
+import os, sys
+
+if __name__ == '__main__':
+	try:
+		path = sys.argv[1]
+		time_stamps = np.load(os.path.join(path, 'world_timestamps.npy'))
+		pts = np.load(os.path.join(path, 'pupil_positions.npy'))
+		valid = filter(lambda e: e[1] > 0, pts) # filter all with zero confidence
+		csum = sum(e[1] for e in pts)
+		print 'Ratio: %s' % round(len(valid)*100/len(pts), 2) + '%'
+		if len(valid):
+			print 'Average Confidence:', csum/len(valid)
+		print 'Frames: %s' % len(time_stamps)
+	except Exception, err:
+		print 'Something went wrong.'
+		print err

code/recording/util/markers/marker.py

@@ -0,0 +1,201 @@
+import os, sys
+import Image
+from random import sample
+
+'''
+IMPORTANT:
+- When printing images generated with this code you HAVE to ignore page margins! (use GIMP)
+- Markers on a board must be separated by white borders or ArUco won't detect them
+'''
+
+ARUCO_MARKER_CREATOR = '/home/mmbrian/temp/aruco-1.3.0/build/utils/aruco_create_marker '
+DESTINATION_DIR = '/home/mmbrian/HiWi/etra2016_mohsen/code/recording/util/markers/'
+DEFAULT_MARKER_SIZE_IN_PIXELS = 64
+DEFAULT_MARKER_BORDER_IN_PIXELS = 7 # only applies for random boards
+DEFAULT_MARKER_SIZE_IN_MM = 40
+A4_PADDING_IN_PIXELS = 50
+
+
+def createMarker(marker_id, size_in_pixels, format = 'png'):
+	assert marker_id >= 0 and marker_id < 1024, 'Invalid Marker ID. Must be in Range 0-1023'
+	cmd = ARUCO_MARKER_CREATOR + '{0} ' + DESTINATION_DIR + '{0}.' + format + ' {1} 0'
+	os.popen(cmd.format(marker_id, size_in_pixels))
+
+def handleMarkers():
+	'''
+	Usage: 
+		marker.py num_of_markers size_in_pixels
+		marker.py -i marker_id size_in_pixels
+	'''
+	if len(sys.argv) > 3:
+		try:
+			createMarker(int(sys.argv[2]), int(sys.argv[3]))
+		except:
+			print 'Usage: marker.py -i marker_id size_in_pixels'
+	else:
+		try:
+			num = int(sys.argv[1])
+			assert num>0 and num<=1024
+		except:
+			print 'Invalid number of markers. please specify a number between 1 and 1024'
+			return
+
+		size = DEFAULT_MARKER_SIZE_IN_PIXELS
+		try:
+			size = int(sys.argv[2])
+			print 'Marker size set to', size, 'px'
+		except:
+			print 'Marker size set to default size', size, 'px'
+
+		print 'Creating', num, 'random markers...'
+		
+		generateRandomMarkers(num, size)
+
+def generateRandomMarkers(num, size):
+	# for i in range(num):
+	for i, _id in enumerate(sorted(sample(range(1024), num))):
+		createMarker(_id, size)
+		print i+1, '/', num
+	print 'Finished.'
+
+def computeDimensions():
+	A4W, A4H = 210, 297 # A4 dimension in millimeters
+	MM2IN = 25.4 # millimeters per inch
+	dpi = -1 # Must be given (use xdpyinfo under linux)
+	marker_width = -1 # Arbitrary value which must be given by user (in millimeters)
+
+	try:
+		dpi = int(sys.argv[2])
+		assert dpi>0
+	except:
+		print sys.argv
+		print 'Invalid dpi.'
+		return
+
+	marker_width = DEFAULT_MARKER_SIZE_IN_MM
+	try:
+		marker_width = int(sys.argv[3])
+		print 'Marker size set to', marker_width, 'mm'
+	except:
+		print 'Marker size set to default size', marker_width, 'mm'
+
+	# Width and Height of A4 image in pixels
+	xWidth = A4W / MM2IN * dpi
+	yWidth = A4H / MM2IN * dpi
+	# Marker width in pixels
+	mWidth = marker_width / MM2IN * dpi
+
+	e = mWidth - int(mWidth) # subpixel error in marker width
+	eMM = e / dpi * MM2IN # subpixel error in millimeters (report to user)
+	print 'Marker size finally set to', marker_width - eMM, 'mm'
+
+	# Converting pixel values to real integers
+	xWidth, yWidth, mWidth = int(xWidth), int(yWidth), int(mWidth)
+	return xWidth, yWidth, mWidth
+
+def removeMarkers():
+	print 'Removing old markers...'
+	os.popen('rm ' + DESTINATION_DIR + '*.png') # Removing previous markers
+
+def generateGrid():
+	'''
+	Generates a picture of a 9-point calibration grid using random markers, the size of which is 
+	calculated such that after printing the marker size is of a specified length in millimeters
+
+	Usage: marker.py dpi marker_size_in_mm
+	'''
+	xWidth, yWidth, mWidth = computeDimensions()
+
+	# Create markers with this pixel size
+	removeMarkers()
+	print 'Generating new markers...'
+	generateRandomMarkers(9, mWidth)
+
+	# Stitch markers and generate grid
+	print 'Creating grid...'
+	markers = []
+	for f in os.listdir(DESTINATION_DIR):
+		if f.endswith('.png'):
+			markers.append(Image.open(f))
+
+	grid = Image.new("RGB", (xWidth, yWidth), (255, 255, 255))
+	for row in range(3):
+		if row == 0:
+			y = A4_PADDING_IN_PIXELS
+		elif row == 1:
+			y = (yWidth - mWidth) / 2
+		else:
+			y = yWidth - A4_PADDING_IN_PIXELS - mWidth
+		for col in range(3):
+			if col == 0:
+				x = A4_PADDING_IN_PIXELS
+			elif col == 1:
+				x = (xWidth - mWidth) / 2
+			else:
+				x = xWidth - A4_PADDING_IN_PIXELS - mWidth
+
+			grid.paste(markers[row*3+col], (x, y))
+
+	grid.save(DESTINATION_DIR + 'grid_board/grid.jpg')
+	print 'Finished.'
+
+def generateRandomBoard():
+	xWidth, yWidth, mWidth = computeDimensions()
+	# Get number of random markers
+	try:
+		n = int(sys.argv[4])
+		assert n>0 and n<150 # no more than 150 markers allowed
+	except:
+		print 'Please choose between 1 to 20 markers'
+		return
+
+	# Create markers with this pixel size
+	removeMarkers()
+	print 'Generating new markers...'
+	generateRandomMarkers(n, mWidth)
+
+	# Stitch markers and generate board
+	print 'Creating board...'
+	markers = []
+	for f in os.listdir(DESTINATION_DIR):
+		if f.endswith('.png'):
+			markers.append(Image.open(f))
+
+	board = Image.new("RGB", (xWidth, yWidth), (255, 255, 255))
+	# Now we have to randomly append markers to board in a way that they do not collide
+	# To simplify this, we gridify the image based on marker size and choose a random sample 
+	# of grids (not entirely random but in case markers are small enough it gives a good sample)
+	w = (xWidth - 2*A4_PADDING_IN_PIXELS) / mWidth
+	h = (yWidth - 2*A4_PADDING_IN_PIXELS) / mWidth
+	w, h = int(w), int(h) # w and h are maximum row and column size for the grid
+	# Now markers would take w*mWidth pixels in a row with no borders, applying border size:
+	w = ((xWidth - 2*A4_PADDING_IN_PIXELS) - (w-1)*DEFAULT_MARKER_BORDER_IN_PIXELS) / mWidth
+	h = ((yWidth - 2*A4_PADDING_IN_PIXELS) - (h-1)*DEFAULT_MARKER_BORDER_IN_PIXELS) / mWidth
+	w, h = int(w), int(h) # w and h now also count for marker borders
+
+	xOffset = A4_PADDING_IN_PIXELS + ((xWidth - 2*A4_PADDING_IN_PIXELS) - w*mWidth - (w-1)*DEFAULT_MARKER_BORDER_IN_PIXELS)/2
+	yOffset = A4_PADDING_IN_PIXELS + ((yWidth - 2*A4_PADDING_IN_PIXELS) - h*mWidth - (h-1)*DEFAULT_MARKER_BORDER_IN_PIXELS)/2
+	grids = [(xOffset + c*mWidth + c*DEFAULT_MARKER_BORDER_IN_PIXELS, 
+			  yOffset + r*mWidth + r*DEFAULT_MARKER_BORDER_IN_PIXELS) for r in xrange(h) for c in xrange(w)]
+	print len(grids), 'possible positions exist for markers...'
+
+	n = min(n, len(grids))
+	for i, pos in enumerate(sample(grids, n)):
+		board.paste(markers[i], pos)		
+
+	board.save(DESTINATION_DIR + 'random_board/board.jpg')
+	print 'Finished.'
+
+def main():
+	assert len(sys.argv) > 1, 'Invalid number of arguments.'
+
+	if sys.argv[1] == 'grid':
+		generateGrid()
+	elif sys.argv[1] == 'rand':
+		generateRandomBoard()
+	else:
+		handleMarkers()
+
+
+if __name__ == '__main__':
+	main()

code/recording/util/tools.py

@@ -0,0 +1,44 @@
+from __future__ import division
+import numpy as np
+
+'''
+Reference: https://stackoverflow.com/questions/22354094/pythonic-way-of-detecting-outliers-in-one-dimensional-observation-data
+'''
+
+def is_outlier(points, thresh=3.5):
+    """
+    Returns a boolean array with True if points are outliers and False 
+    otherwise.
+
+    Parameters:
+    -----------
+        points : An numobservations by numdimensions array of observations
+        thresh : The modified z-score to use as a threshold. Observations with
+            a modified z-score (based on the median absolute deviation) greater
+            than this value will be classified as outliers.
+
+    Returns:
+    --------
+        mask : A numobservations-length boolean array.
+
+    References:
+    ----------
+        Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and
+        Handle Outliers", The ASQC Basic References in Quality Control:
+        Statistical Techniques, Edward F. Mykytka, Ph.D., Editor. 
+    """
+    if len(points.shape) == 1:
+        points = points[:,None]
+    median = np.median(points, axis=0)
+    diff = np.sum((points - median)**2, axis=-1)
+    diff = np.sqrt(diff)
+    med_abs_deviation = np.median(diff)
+
+    modified_z_score = 0.6745 * diff / med_abs_deviation
+
+    return modified_z_score > thresh
+
+
+def moving_average(interval, window_size):
+	window = np.ones(int(window_size))/float(window_size)
+	return np.convolve(interval, window, 'same')

code/recording_experiment.py

@@ -0,0 +1,196 @@
+from __future__ import division
+import numpy as np
+from numpy import linalg as LA
+from minimize import findInitialW, _q, g, minimizeEnergy
+
+from time import time
+
+from geom import getSphericalCoords, getAngularDiff
+from recording.tracker import Marker
+
+# from visual import vector as v # for vector operations
+from vector import Vector as v
+
+DATA_DIR = './recording/data/'
+# DATA_DIR = '.\\recording\\data\\'
+
+EYE_CAMERA_IMAGE_WIDTH = 640
+EYE_CAMERA_IMAGE_HEIGHT = 360
+
+curr_calibration_experiment = '006'
+curr_test_experiment = '010'
+# markers in order of being targeted:
+experiments = {'005': [130, 608, 456, 399, 659, 301, 351, 707, 18],
+			   '006': [130, 608, 456, 399, 659, 301, 351, 707, 18],
+			   '007': [449, 914, 735, 842, 347, 660, 392, 782],
+			   '010': [449, 914, 554, 243, 347, 173, 664, 399]}
+
+def denormalize(p):
+	# return p * np.array([EYE_CAMERA_IMAGE_WIDTH, EYE_CAMERA_IMAGE_HEIGHT])
+	return p * np.array([EYE_CAMERA_IMAGE_WIDTH, EYE_CAMERA_IMAGE_HEIGHT]) - \
+			   (np.array([EYE_CAMERA_IMAGE_WIDTH, EYE_CAMERA_IMAGE_HEIGHT]) / 2)
+
+def main():
+	single_point = False
+	__p, _t = [], []
+	p, t = [], []
+
+	# Fetching marker position wrt camera t from calibration data
+	marker_data = np.load(DATA_DIR + 'frames_%s.npy' % curr_calibration_experiment)
+	# marker_data includes data on tracked markers per frame
+	# it's a list with as many entries as the number of video frames, each entry 
+	# has a list of tracked markers, each marker item has marker id, marker corners, Rvec, Tvec
+	# TODO (remember the unit)
+
+	# Fetching pupil positions p from calibration data
+	pupil_data = np.load(DATA_DIR + 'pp_%s.npy' % curr_calibration_experiment)
+	# pupil_data is a list of tracked pupil positions, each entry has 3 elements
+	# array: frame range (start, end)
+	# array: mean pupil position
+	# list: all pupil positions in the range
+	# TODO (also remember to denormalize)
+	for i, pos in enumerate(pupil_data):
+		corresponding_marker_id = experiments[curr_calibration_experiment][i]
+		# print corresponding_marker_id
+
+		start, end = pos[0]
+		if len(pos[2]) == end-start+1: # all samples for this points are reliable
+			# add all corresponding pupil-3d points as mappings
+			# print start, end, len(pos[2])
+			for i, _p in enumerate(pos[2]):
+				frame_number = start + i
+				if frame_number >= len(marker_data): continue # TODO: investigate
+				for marker in marker_data[frame_number]:
+					if marker[0][0] == corresponding_marker_id:
+						if single_point:
+							__p.append(denormalize(_p))
+							_t.append(Marker.fromList(marker).getCenter())
+						else:
+							p.append(denormalize(_p))
+							t.append(Marker.fromList(marker).getCenter())
+			if single_point and len(__p):
+				p.append(sum(__p)/len(__p))
+				t.append(sum(_t)/len(_t))
+				__p, _t = [], []
+
+		else: # if pos[2] is nonempty consider the mean 
+			if len(pos[2]): # TODO: here we can still map the corresponding pupil points to their detected marker given
+									# we have the frame correspondence (investigate)
+				# map pos[1] to corresponding markers
+				for frame_number in xrange(start, end+1):
+					if frame_number >= len(marker_data): continue # TODO: investigate
+					for marker in marker_data[frame_number]:
+						if marker[0][0] == corresponding_marker_id:
+							if single_point:
+								__p.append(denormalize(pos[1]))
+								_t.append(Marker.fromList(marker).getCenter())
+							else:
+								p.append(denormalize(pos[1]))
+								t.append(Marker.fromList(marker).getCenter())
+				if single_point and len(__p):
+					p.append(sum(__p)/len(__p))
+					t.append(sum(_t)/len(_t))
+					__p, _t = [], []
+			else:
+				pass
+				# No mapping here
+	
+	print len(p), len(t)
+	# print p[0], t[0]
+
+	# we have to denormalize pupil points and correlated the two data streams (frame correspondence)
+
+	print 'Successfully loaded calibration data...'
+	# return
+
+	print 'Performing minimization...'
+	## Finding the optimal transformation matrix by minimizing the nonlinear energy
+	# w0 is the initial w by solving the leastsq with e=(0,0,0)
+	# w is by solving the leastsq again optimizing for both e and w
+	start = time()
+	w, e, w0 = minimizeEnergy(p, t)
+	minimizationTime = time() - start
+	print 'minimization time:', minimizationTime
+
+	p, t = [], []
+	marker_data = np.load(DATA_DIR + 'frames_%s.npy' % curr_test_experiment)
+	pupil_data = np.load(DATA_DIR + 'pp_%s.npy' % curr_test_experiment)
+	print len(pupil_data), len(experiments[curr_test_experiment])
+	for i, pos in enumerate(pupil_data):
+		corresponding_marker_id = experiments[curr_test_experiment][i]
+		# print corresponding_marker_id
+
+		start, end = pos[0]
+		if len(pos[2]) == end-start+1: # all samples for this points are reliable
+			# add all corresponding pupil-3d points as mappings
+			# print start, end, len(pos[2])
+			for i, _p in enumerate(pos[2]):
+				frame_number = start + i
+				if frame_number >= len(marker_data): continue # TODO: investigate
+				for marker in marker_data[frame_number]:
+					if marker[0][0] == corresponding_marker_id:
+						if single_point:
+							__p.append(denormalize(_p))
+							_t.append(Marker.fromList(marker).getCenter())
+						else:
+							p.append(denormalize(_p))
+							t.append(Marker.fromList(marker).getCenter())
+		if single_point and len(__p):
+			p.append(sum(__p)/len(__p))
+			t.append(sum(_t)/len(_t))
+			__p, _t = [], []
+
+		else: # if pos[2] is nonempty consider the mean 
+			if len(pos[2]): # TODO: here we can still map the corresponding pupil points to their detected marker given
+									# we have the frame correspondence (investigate)
+				# map pos[1] to corresponding markers
+				for frame_number in xrange(start, end+1):
+					if frame_number >= len(marker_data): continue # TODO: investigate
+					for marker in marker_data[frame_number]:
+						if marker[0][0] == corresponding_marker_id:
+							if single_point:
+								__p.append(denormalize(pos[1]))
+								_t.append(Marker.fromList(marker).getCenter())
+							else:
+								p.append(denormalize(pos[1]))
+								t.append(Marker.fromList(marker).getCenter())
+				if single_point and len(__p):
+					p.append(sum(__p)/len(__p))
+					t.append(sum(_t)/len(_t))
+					__p, _t = [], []
+			else:
+				pass
+	print 'Successfully loaded test data...'
+
+	# closest point distance to scene camera
+	cDist = min(v(pt).mag for pt in t)
+	# farthest point distance to scene camera
+	fDist = max(v(pt).mag for pt in t)
+	# average point distance to scene camera
+	avgDist = sum(v(pt).mag for pt in t)/len(t) 
+
+	qi = map(_q, p) # computing feature vectors from raw pupil coordinates in 2D
+	# computing unit gaze vectors corresponding to pupil positions
+	# here we use the computed mapping matrix w
+	gis = map(lambda q: g(q, w), qi) 
+	gis0 = map(lambda q: g(q, w0), qi) 
+
+	# now we can compare unit gaze vectors with their corresponding gaze rays t
+	# normalizing gaze rays first
+	t = np.array(map(lambda vec: v(vec).norm(), t))
+	# TODO: compare spherical coordinates instead
+
+
+	AE = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gis, t))
+	N = len(t)
+	AAE = sum(AE)/N
+	VAR = sum((ae - AAE)**2 for ae in AE)/N
+	print 'AAE:', AAE, '\nVariance:', VAR, 'STD:', np.sqrt(VAR), '\nMin:', min(AE), 'Max:', max(AE), '(N=' + str(N) + ')'
+	print 'Target Distances: m=%s M=%s Avg=%s' % (cDist, fDist, avgDist)
+
+	AE0 = list(np.degrees(np.arctan((v(p[0]).cross(p[1])/(v(p[0]).dot(p[1]))).mag)) for p in zip(gis0, t))
+	AAE0 = sum(AE0)/N
+	print 'AAE (only optimizing W for e=(0,0,0)):', AAE0
+
+if __name__ == '__main__':
+	main()

code/results/2d2d/2_calibration_depths/p10.csv

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