def runTexture(img_list):
""" This function administrates the extraction of a video texture from the
given frames.
"""
video_volume = assignment11.videoVolume(img_list)
ssd_diff = assignment11.sumSquaredDifferences(video_volume)
transition_diff = assignment11.transitionDifference(ssd_diff)
alpha = 1.5*10**6
idxs = assignment11.findBiggestLoop(transition_diff, alpha)
diff3 = np.zeros(transition_diff.shape, float)
for i in range(transition_diff.shape[0]):
for j in range(transition_diff.shape[1]):
diff3[i,j] = alpha*(i-j) - transition_diff[i,j]
return vizDifference(ssd_diff), \
vizDifference(transition_diff), \
vizDifference(diff3), \
assignment11.synthesizeLoop(video_volume, idxs[0]+2, idxs[1]+2)
def test_sumSquaredDifferences():
video_volume1 = np.array([[[[ 0, 0, 0], [ 0, 0, 0]]],
[[[ 1, 1, 1], [ 1, 1, 1]]],
[[[ 2, 2, 2], [ 2, 2, 2]]],
[[[ 3, 3, 3], [ 3, 3, 3]]]], dtype = np.uint8)
video_volume2 = np.array([[[[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2]],
[[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2]]],
[[[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]],
[[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]]],
[[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255]]]], dtype = np.uint8)
diff1 = np.array([[ 0., 6., 24., 54.],
[ 6., 0., 6., 24.],
[ 24., 6., 0., 6.],
[ 54., 24., 6., 0.]], dtype = np.float)
diff2 = np.array([[ 0., 24., 768156.],
[ 24., 0., 774204.],
[ 768156., 774204., 0.]], dtype = np.float)
for vid_volume, true_out in zip((video_volume1, video_volume2),
(diff1, diff2)):
print "Input:\n{}\n".format(vid_volume)
usr_out = assignment11.sumSquaredDifferences(vid_volume)
if type(usr_out) != type(true_out):
print ("Error: sumSquaredDifferences has type {}." +
"Expected type is {}.").format(type(usr_out), type(true_out))
return False
if usr_out.shape != true_out.shape:
print ("Error: sumSquaredDifferences has shape {}." +
"Expected shape is {}.").format(usr_out.shape,
true_out.shape)
return False
if usr_out.dtype != true_out.dtype:
print ("Error: sumSquaredDifferences has dtype {}." +
"Expected dtype is {}.").format(usr_out.dtype,
true_out.dtype)
return False
if not np.all(np.abs(usr_out - true_out) < 1.):
print ("Error: sumSquaredDifferences has value:\n{}\n" +
"Expected value:\n{}").format(usr_out, true_out)
return False
print "Passed current input."
print "sumSquaredDifferences passed."
return True