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