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_synthesizeLoop():
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]],
[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]]]], dtype = np.uint8)
frames1 = (2,3)
frames2 = (1,1)
out1 = [np.array([[[ 2, 2, 2],
[ 2, 2, 2]]], dtype = np.uint8),
np.array([[[ 3, 3, 3],
[ 3, 3, 3]]], dtype = np.uint8)]
out2 = [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)]
for video_volume, frames, true_out in zip((video_volume1, video_volume2),
(frames1, frames2),
(out1, out2)):
print "Input:\n{}\n".format(video_volume)
print "Input Frame Loop:\n{}\n".format(frames)
usr_out = assignment11.synthesizeLoop(video_volume,
frames[0], frames[1])
if type(usr_out) != type(true_out):
print ("Error: synthesizeLoop has type {}. " +
"Expected type is {}.").format(type(usr_out), type(true_out))
return False
if len(usr_out) != len(true_out):
print ("Error: synthesizeLoop has len {}. " +
"Expected len is {}.").format(len(usr_out), len(true_out))
return False
# Test each individual image.
for usr_img, true_img in zip(usr_out, true_out):
if type(usr_img) != type(true_img):
print ("Error: A frame in synthesizeLoop has type {}. " +
"Expected type is {}.").format(type(usr_img),
type(true_img))
return False
if usr_img.shape != true_img.shape:
print ("Error: A frame in synthesizeLoop has shape {}. " +
"Expected shape is {}.").format(usr_img.shape,
true_img.shape)
return False
if usr_img.dtype != true_img.dtype:
print ("Error: A frame in synthesizeLoop has dtype {}. " +
"Expected dtype is {}.").format(usr_img.dtype,
true_img.dtype)
return False
if np.all(usr_img != true_img):
print ("Error: synthesizeLoop has value:\n{}\n" +
"Expected value:\n{}").format(usr_img, true_img)
return False
print "Current input passed."
print "synthesizeLoop passed."
return True
