def test_remap_luminance():
A = np.random.rand(25, 25)
Ap = np.random.rand(25, 25)
B = np.random.rand(30, 30)
A_remap, Ap_remap = remap_luminance(A, Ap, B)
assert(np.isclose(np.mean(B), np.mean(A_remap), atol=0.05))
assert(np.isclose(np.mean(B), np.mean(Ap_remap), atol=0.05))
assert(np.isclose( np.std(B), np.std(A_remap), atol=0.05))
assert(np.isclose( np.std(B), np.std(Ap_remap), atol=0.05))
def test_remap_luminance():
A = np.random.rand(25, 25)
Ap = np.random.rand(25, 25)
B = np.random.rand(30, 30)
A_remap, Ap_remap = remap_luminance(A, Ap, B)
assert (np.isclose(np.mean(B), np.mean(A_remap), atol=0.05))
assert (np.isclose(np.mean(B), np.mean(Ap_remap), atol=0.05))
assert (np.isclose(np.std(B), np.std(A_remap), atol=0.05))
assert (np.isclose(np.std(B), np.std(Ap_remap), atol=0.05))
def img_setup(A_fname, Ap_fname_list, B_fname, out_path, c):
if not os.path.exists(out_path):
os.makedirs(out_path)
A_orig = plt.imread(A_fname)
B_orig = plt.imread(B_fname)
assert(len(A_orig.shape) == len(B_orig.shape)) # same number of channels (for now)
Ap_orig_list = []
for Ap_fname in Ap_fname_list:
Ap_orig = plt.imread(Ap_fname)
assert(A_orig.shape == Ap_orig.shape) # src alignment
Ap_orig_list.append(Ap_orig)
# Make sure all images are floats on 0 to 1 scale
scales = []
for img in [A_orig, B_orig, Ap_orig[0]]:
if np.max(img) > 1.0:
scales.append(255.)
else:
scales.append(1.0)
# Do conversions
if c.convert:
A_yiq = convert_to_YIQ( A_orig/scales[0])
B_yiq = convert_to_YIQ( B_orig/scales[1])
A = A_yiq[:, :, 0]
B = B_yiq[:, :, 0]
Ap_yiq_list = []
Ap_list = []
for Ap_orig in Ap_orig_list:
Ap_yiq_list.append(convert_to_YIQ(Ap_orig/scales[2]))
Ap_list.append(Ap_yiq_list[-1][:, :, 0])
else:
A = A_orig/scales[0]
B = B_orig/scales[1]
Ap_list = []
for Ap_orig in Ap_orig_list:
Ap_list.append(Ap_orig/scales[2])
# Process input images
if c.remap_lum:
A, Ap_list = remap_luminance(A, Ap_list, B)
if not c.init_rand:
B_orig_pyr = compute_gaussian_pyramid(B, c.n_sm)
A, B = compress_values(A, B, c.AB_weight)
c.num_ch, c.padding_sm, c.padding_lg, c.weights = setup_vars(A)
# Create Pyramids
A_pyr = compute_gaussian_pyramid(A, c.n_sm)
B_pyr = compute_gaussian_pyramid(B, c.n_sm)
Ap_pyr_list = []
for Ap in Ap_list:
Ap_pyr_list.append(compute_gaussian_pyramid(Ap, c.n_sm))
if c.convert:
color_pyr_list = [compute_gaussian_pyramid(B_yiq, c.n_sm)]
else:
color_pyr_list = [compute_gaussian_pyramid(Ap_orig, c.n_sm) for Ap_orig in Ap_list]
if len(A_pyr) != len(B_pyr):
c.max_levels = min(len(A_pyr), len(B_pyr))
warnings.warn('Warning: input images are very different sizes! The minimum number of levels will be used.')
else:
c.max_levels = len(B_pyr)
# Create Random Initialization of Bp
if c.init_rand:
Bp_pyr = initialize_Bp(B_pyr, init_rand=True)
else:
Bp_pyr = initialize_Bp(B_orig_pyr, init_rand=False)
return A_pyr, Ap_pyr_list, B_pyr, Bp_pyr, color_pyr_list, c
def img_setup(A_fname, Ap_fname_list, B_fname, out_path, c):
if not os.path.exists(out_path):
os.makedirs(out_path)
A_orig = plt.imread(A_fname)
B_orig = plt.imread(B_fname)
assert (len(A_orig.shape) == len(B_orig.shape)
) # same number of channels (for now)
Ap_orig_list = []
for Ap_fname in Ap_fname_list:
Ap_orig = plt.imread(Ap_fname)
assert (A_orig.shape == Ap_orig.shape) # src alignment
Ap_orig_list.append(Ap_orig)
# Make sure all images are floats on 0 to 1 scale
scales = []
for img in [A_orig, B_orig, Ap_orig[0]]:
if np.max(img) > 1.0:
scales.append(255.)
else:
scales.append(1.0)
# Do conversions
if c.convert:
A_yiq = convert_to_YIQ(A_orig / scales[0])
B_yiq = convert_to_YIQ(B_orig / scales[1])
A = A_yiq[:, :, 0]
B = B_yiq[:, :, 0]
Ap_yiq_list = []
Ap_list = []
for Ap_orig in Ap_orig_list:
Ap_yiq_list.append(convert_to_YIQ(Ap_orig / scales[2]))
Ap_list.append(Ap_yiq_list[-1][:, :, 0])
else:
A = A_orig / scales[0]
B = B_orig / scales[1]
Ap_list = []
for Ap_orig in Ap_orig_list:
Ap_list.append(Ap_orig / scales[2])
# Process input images
if c.remap_lum:
A, Ap_list = remap_luminance(A, Ap_list, B)
if not c.init_rand:
B_orig_pyr = compute_gaussian_pyramid(B, c.n_sm)
A, B = compress_values(A, B, c.AB_weight)
c.num_ch, c.padding_sm, c.padding_lg, c.weights = setup_vars(A)
# Create Pyramids
A_pyr = compute_gaussian_pyramid(A, c.n_sm)
B_pyr = compute_gaussian_pyramid(B, c.n_sm)
Ap_pyr_list = []
for Ap in Ap_list:
Ap_pyr_list.append(compute_gaussian_pyramid(Ap, c.n_sm))
if c.convert:
color_pyr_list = [compute_gaussian_pyramid(B_yiq, c.n_sm)]
else:
color_pyr_list = [
compute_gaussian_pyramid(Ap_orig, c.n_sm) for Ap_orig in Ap_list
]
if len(A_pyr) != len(B_pyr):
c.max_levels = min(len(A_pyr), len(B_pyr))
warnings.warn(
'Warning: input images are very different sizes! The minimum number of levels will be used.'
)
else:
c.max_levels = len(B_pyr)
# Create Random Initialization of Bp
if c.init_rand:
Bp_pyr = initialize_Bp(B_pyr, init_rand=True)
else:
Bp_pyr = initialize_Bp(B_orig_pyr, init_rand=False)
return A_pyr, Ap_pyr_list, B_pyr, Bp_pyr, color_pyr_list, c