def pyrup_hybrid_test():
image = imread('test_materials/MonroeEnstein_AudeOliva2007_small.png')
image = pyrup(image)
image = pyrup(image)
image = pyrup(image)
image = np.uint8(np.clip(image, 0, 255))
correct = imread('test_materials/MonroeEnstein_AudeOliva2007_large.png')
assert (np.abs(image - correct) / 255.0 < 1e-2).all()
def pyrup_hybrid_test():
image = imread('test_materials/MonroeEnstein_AudeOliva2007_small.png')
image = image.astype(np.float32)
image = pyrup(image)
image = pyrup(image)
image = pyrup(image)
image = np.uint8(np.clip(image, 0, 255))
correct = imread('test_materials/MonroeEnstein_AudeOliva2007_large.png')
assertNear(image / 255.0, correct / 255.0, 1e-2)
def pyrup_hybrid_test():
image = imread('test_materials/MonroeEnstein_AudeOliva2007_small.png')
image = image.astype(np.float32)
image = pyrup(image)
image = pyrup(image)
image = pyrup(image)
image = np.uint8(np.clip(image, 0, 255))
correct = imread('test_materials/MonroeEnstein_AudeOliva2007_large.png')
assertNear(image / 255.0, correct / 255.0, 1e-2)
def pyrdown_random_nonsquare_test():
height = 16
width = 31
image = np.random.random((height, width, 3))
up = pyrup(image)
assert up.shape == (2 * height, 2 * width, 3)
def pyrdown_random_nonsquare_test():
height = 16
width = 31
image = np.random.random((height, width, 3))
up = pyrup(image)
assert up.shape == (2 * height, 2 * width, 3)
def pyrdown_ones_nonsquare_test():
height = 16
width = 31
image = np.ones((height, width, 3), dtype=np.float32)
up = pyrup(image)
assert up.shape == (2 * height, 2 * width, 3)
assert (up == 1).all()
def pyrdown_ones_square_test():
height = 16
width = 16
image = np.ones((height, width, 3), dtype=np.float32)
up = pyrup(image)
assert up.shape == (2 * height, 2 * width, 3)
assertNear(up, 1, 1e-6)
def pyrdown_ones_nonsquare_test():
height = 16
width = 31
image = np.ones((height, width, 3), dtype=np.float32)
up = pyrup(image)
assert up.shape == (2 * height, 2 * width, 3)
assertNear(up, 1, 1e-5)
def pyrdown_pyrup_test_single_channel():
height = 16
width = 31
image = np.random.random((height * 2, width * 2, 1))
down = pyrdown(image)
assert down.shape == (height, width, 1)
up = pyrup(down)
assert up.shape == (height * 2, width * 2, 1)
def pyrup_pyrdown_test_single_channel():
height = 16
width = 31
image = np.random.random((height, width, 1))
up = pyrup(image)
assert up.shape == (2 * height, 2 * width, 1)
down = pyrdown(up)
assert down.shape == (height, width, 1)
def pyrup_pyrdown_test():
height = 16
width = 31
image = np.random.random((height, width, 3))
up = pyrup(image)
assert up.shape == (2 * height, 2 * width, 3)
down = pyrdown(up)
assert down.shape == (height, width, 3)
if mode in ('depth', 'both'):
depth = np.load(data.depth_npy)
K_right = data.K_right
depth = cv2.medianBlur(depth, 5)
depth = cv2.medianBlur(depth, 5)
if data.mesh_downscale_factor > data.stereo_downscale_factor:
for i in range(data.mesh_downscale_factor -
data.stereo_downscale_factor):
depth = pyrdown(depth)
elif data.stereo_downscale_factor > data.mesh_downscale_factor:
for i in range(data.stereo_downscale_factor -
data.mesh_downscale_factor):
depth = pyrup(depth)
for i in range(data.mesh_downscale_factor):
K_right[:2, :] /= 2
if mode == 'both':
depth_weight = data.depth_weight
if mode == 'depth':
albedo = data.right[0]
if alpha is not None:
for i in range(data.mesh_downscale_factor):
alpha = pyrdown(alpha)
if normals is not None:
depth = cv2.medianBlur(depth, 5)
depth = cv2.medianBlur(depth, 5)
if data.mesh_downscale_factor > data.stereo_downscale_factor:
for i in range(data.mesh_downscale_factor -
data.stereo_downscale_factor):
# depth is 2D so give it 1 channel and then extract it back out
x = depth[:, :, np.newaxis]
y = pyrdown(x)
depth = y[:, :, 0]
elif data.stereo_downscale_factor > data.mesh_downscale_factor:
for i in range(data.stereo_downscale_factor -
data.mesh_downscale_factor):
x = depth[:, :, np.newaxis]
y = pyrup(x)
depth = y[:, :, 0]
for i in range(data.mesh_downscale_factor):
K_right[:2, :] /= 2
if mode == 'both':
depth_weight = data.depth_weight
if mode == 'depth':
albedo = data.right[0]
if alpha is not None:
for i in range(data.mesh_downscale_factor):
x = alpha[:, :, np.newaxis]
y = pyrdown(x)
if mode in ('depth', 'both'):
depth = np.load(data.depth_npy)
K_right = data.K_right
depth = cv2.medianBlur(depth, 5)
depth = cv2.medianBlur(depth, 5)
if data.mesh_downscale_factor > data.stereo_downscale_factor:
for i in xrange(data.mesh_downscale_factor -
data.stereo_downscale_factor):
depth = pyrdown(depth)
elif data.stereo_downscale_factor > data.mesh_downscale_factor:
for i in xrange(data.stereo_downscale_factor -
data.mesh_downscale_factor):
depth = pyrup(depth)
for i in xrange(data.mesh_downscale_factor):
K_right[:2, :] /= 2
if mode == 'both':
depth_weight = data.depth_weight
if mode == 'depth':
albedo = data.right[0]
if alpha is not None:
for i in xrange(data.mesh_downscale_factor):
alpha = pyrdown(alpha)
if normals is not None:
