def test_converts():
np.random.seed(0xba5eba11)
img = np.random.rand(25, 25, 3)
assert np.allclose(convert_to_RGB(convert_to_YIQ(img)), img, atol=0.05)
img = np.ones((25, 25, 3))
assert np.allclose(convert_to_RGB(convert_to_YIQ(img)), img)
img = np.zeros((25, 25, 3))
assert np.allclose(convert_to_RGB(convert_to_YIQ(img)), img)
def test_converts():
np.random.seed(0xba5eba11)
img = np.random.rand(25, 25, 3)
assert np.allclose(convert_to_RGB(convert_to_YIQ(img)), img, atol=0.05)
img = np.ones((25, 25, 3))
assert np.allclose(convert_to_RGB(convert_to_YIQ(img)), img)
img = np.zeros((25, 25, 3))
assert np.allclose(convert_to_RGB(convert_to_YIQ(img)), img)
def test_best_coherence_match():
# make A_pd, Ap_pd, BBp_feat, s
A_orig = plt.imread('./test_images/test_best_coherence_match_A.jpg')
Ap_orig = plt.imread('./test_images/test_best_coherence_match_Ap.jpg')
A = convert_to_YIQ( A_orig/255.)[:, :, 0]
Ap = convert_to_YIQ(Ap_orig/255.)[:, :, 0]
A_pyr = compute_gaussian_pyramid( A, min_size=3)
Ap_pyr = compute_gaussian_pyramid(Ap, min_size=3)
imh, imw = A.shape[:2]
c.num_ch, c.padding_sm, c.padding_lg, c.weights = c.setup_vars(A)
c.max_levels = len(A_pyr)
A_pd = pad_img_pair( A_pyr[-2], A_pyr[-1], c)
Ap_pd = pad_img_pair(Ap_pyr[-2], Ap_pyr[-1], c)
flann, flann_params, As, As_size = create_index(A_pyr, Ap_pyr, c)
# BBp_feat cases: all corners and middle
indices = [(1, 1),
(1, imw - 1),
(imh - 1, 1),
(imh - 1, imw - 1),
(np.floor(imh/2.).astype(int), np.floor(imw/2.).astype(int))]
for row, col in indices:
num_px = row * imw + col
s_rows = np.random.random_integers(num_px, size=num_px) - 1
s_cols = np.random.random_integers(num_px, size=num_px) - 1
s = [(rr, cc) for rr, cc in zip(s_rows, s_cols)]
s[(row - 1) * imw + col - 1] = (row - 1, col - 1)
Bs_feat = np.hstack([extract_pixel_feature( A_pd, (row, col), c, full_feat=True),
extract_pixel_feature(Ap_pd, (row, col), c, full_feat=False)])
p_coh_orig, r_star_orig = best_coherence_match_orig(A_pd, Ap_pd, Bs_feat, s, (row, col, imw), c)
p_coh_new, r_star_new = best_coherence_match(As[-1], A.shape, Bs_feat, s, (row, col, imw), c)
try:
assert(p_coh_orig == (row, col))
assert(p_coh_new == (row, col))
except:
print('row, col, p_coh_orig, p_coh_new, s', row, col, p_coh_orig, p_coh_new, s)
As_feat = np.hstack([extract_pixel_feature( A_pd, p_coh_orig, p_coh_new, c, full_feat=True),
extract_pixel_feature(Ap_pd, p_coh_orig, p_coh_new, c, full_feat=False)])
print('As_feat', As_feat)
print('Bs_feat', Bs_feat)
assert(False)
def test_best_coherence_match():
# make A_pd, Ap_pd, BBp_feat, s
A_orig = plt.imread('./test_images/test_best_coherence_match_A.jpg')
Ap_orig = plt.imread('./test_images/test_best_coherence_match_Ap.jpg')
A = convert_to_YIQ(A_orig / 255.)[:, :, 0]
Ap = convert_to_YIQ(Ap_orig / 255.)[:, :, 0]
A_pyr = compute_gaussian_pyramid(A, min_size=3)
Ap_pyr = compute_gaussian_pyramid(Ap, min_size=3)
imh, imw = A.shape[:2]
c.num_ch, c.padding_sm, c.padding_lg, c.weights = c.setup_vars(A)
c.max_levels = len(A_pyr)
A_pd = pad_img_pair(A_pyr[-2], A_pyr[-1], c)
Ap_pd = pad_img_pair(Ap_pyr[-2], Ap_pyr[-1], c)
flann, flann_params, As, As_size = create_index(A_pyr, Ap_pyr, c)
# BBp_feat cases: all corners and middle
indices = [(1, 1), (1, imw - 1), (imh - 1, 1), (imh - 1, imw - 1),
(np.floor(imh / 2.).astype(int), np.floor(imw / 2.).astype(int))
]
for row, col in indices:
num_px = row * imw + col
s_rows = np.random.random_integers(num_px, size=num_px) - 1
s_cols = np.random.random_integers(num_px, size=num_px) - 1
s = [(rr, cc) for rr, cc in zip(s_rows, s_cols)]
s[(row - 1) * imw + col - 1] = (row - 1, col - 1)
Bs_feat = np.hstack([
extract_pixel_feature(A_pd, (row, col), c, full_feat=True),
extract_pixel_feature(Ap_pd, (row, col), c, full_feat=False)
])
p_coh_orig, r_star_orig = best_coherence_match_orig(
A_pd, Ap_pd, Bs_feat, s, (row, col, imw), c)
p_coh_new, r_star_new = best_coherence_match(As[-1], A.shape, Bs_feat,
s, (row, col, imw), c)
try:
assert (p_coh_orig == (row, col))
assert (p_coh_new == (row, col))
except:
print('row, col, p_coh_orig, p_coh_new, s', row, col, p_coh_orig,
p_coh_new, s)
As_feat = np.hstack([
extract_pixel_feature(A_pd,
p_coh_orig,
p_coh_new,
c,
full_feat=True),
extract_pixel_feature(Ap_pd,
p_coh_orig,
p_coh_new,
c,
full_feat=False)
])
print('As_feat', As_feat)
print('Bs_feat', Bs_feat)
assert (False)
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
