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