def get_distance_var(lof,width,height,frame_oi):
filtered_frames=[]
print('')
print('Now in get_distance_var')
print(lof)
for f in lof:
print('loop')
print(f)
frames=get_green_frames(f,width,height)
print(type(frames))
filtered_frames.append(filter2_test_j(frames[frame_oi,:,:]))
print "Getting all the distances.."
# Get all the distances using all videos as ref point, thus size of matrix is n^2
list_of_ref = []
for frame_ref in filtered_frames:
list_of_positions = []
res_trials = parmap.map(image_registration.chi2_shift, filtered_frames, frame_ref)
# res_trials is array of trials * [dx, dy, edx, edy]
for res in res_trials:
list_of_positions.append(Position(res[0], res[1]))
#for frame in filtered_frames:
# dx, dy, edx, edy = image_registration.chi2_shift(frame_ref, frame)
# list_of_positions.append(Position(dx, dy))
list_of_ref.append(list_of_positions)
print "Finding the min..."
list_of_positions = find_min_ref(list_of_ref)
return list_of_positions
def get_green_video_frames(lof,width,height):
print('')
print('in get_green_video_frames')
print(lof)
list_of_trial_frames = []
lof_tmp=[]
lof_tmp.append(lof)
print(type(lof_tmp))
for video_file in lof_tmp:
print ("Getting green frames: " + str(video_file))
frames = get_green_frames(video_file,width,height)
frames = frames[:800, :, :]
print np.shape(frames)
list_of_trial_frames.append(frames)
print np.shape(list_of_trial_frames)
return list_of_trial_frames
