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 runTexture(img_list, alpha):
""" This function administrates the extraction of a video texture from the
given frames, and generates the three viewable difference matrices.
"""
video_volume = a11.videoVolume(img_list)
ssd_diff = a11.computeSimilarityMetric(video_volume)
transition_diff = a11.transitionDifference(ssd_diff)
print "Alpha is {}".format(alpha)
idxs = a11.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),
a11.synthesizeLoop(video_volume, idxs[0] + 2, idxs[1] + 2))
def test_videoVolume():
image_list1 = [np.array([[[0, 0, 0], [0, 0, 0]]], dtype = np.uint8),
np.array([[[1, 1, 1], [1, 1, 1]]], dtype = np.uint8),
np.array([[[2, 2, 2], [2, 2, 2]]], dtype = np.uint8),
np.array([[[3, 3, 3], [3, 3, 3]]], dtype = np.uint8)]
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)
image_list2 = [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]]], dtype = np.uint8),
np.array([[[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]],
[[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]]], dtype = np.uint8),
np.array([[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]]], 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]],
[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]]]], dtype = np.uint8)
print "Evaluating videoVolume:"
for img_list, true_out in zip((image_list1, image_list2),
(video_volume1, video_volume2)):
print "Input:\n{}\n".format(img_list)
usr_out = assignment11.videoVolume(img_list)
if type(usr_out) != type(true_out):
print "Error: videoVolume has type {}. Expected type is {}.".format(
type(usr_out), type(true_out))
return False
if usr_out.shape != true_out.shape:
print ("Error: videoVolume has shape {}. " +
"Expected shape is {}.").format(usr_out.shape,
true_out.shape)
return False
if usr_out.dtype != true_out.dtype:
print ("Error: videoVolume has dtype {}." +
" Expected dtype is {}.").format(usr_out.dtype,
true_out.dtype)
return False
if not np.all(usr_out == true_out):
print ("Error: videoVolume has value:\n{}\n" +
"Expected value:\n{}").format(usr_out, true_out)
return False
print "Passed current input."
print "videoVolume passed."
return True
