def get_AH_lines(im):
all_letters = algorithm.all_letters(im)
# AH == average character height
AH = algorithm.dominant_char_height(im, letters=all_letters)
if lib.debug: print('AH =', AH)
letters = algorithm.filter_size(AH, im, letters=all_letters)
all_lines = collate.collate_lines(AH, letters)
# From up to down
all_lines.sort(key=lambda l: l[0].y)
combined = algorithm.combine_underlined(AH, im, all_lines, all_letters)
filtered = algorithm.remove_stroke_outliers(im, combined, k=2.0)
# filtered = algorithm.filter_spacing_deviation(im, AH, filtered)
lines = remove_outliers(im, AH, filtered)
# lines = combined
# if lib.debug:
# debug = cv2.cvtColor(bw, cv2.COLOR_GRAY2BGR)
# for l in all_lines:
# for l1, l2 in zip(l, l[1:]):
# cv2.line(debug, tuple(l1.base_point().astype(int)),
# tuple(l2.base_point().astype(int)), RED, 2)
# lib.debug_imwrite('all_lines.png', debug)
return AH, lines, all_lines
def crop(im, bw, split=True):
im_h, im_w = im.shape[:2]
all_letters = algorithm.all_letters(bw)
AH = algorithm.dominant_char_height(bw, letters=all_letters)
letters = algorithm.filter_size(AH, bw, letters=all_letters)
all_lines = collate.collate_lines(AH, letters)
combined = algorithm.combine_underlined(AH, bw, all_lines, all_letters)
lines = algorithm.remove_stroke_outliers(bw, combined)
if not lines:
print('WARNING: no lines in image.')
return AH, []
lines = filter_position(AH, bw, lines, split)
lines = [
line for line in lines if not np.all(line.crop().apply(bw) == 255)
]
if not lines:
print('WARNING: eliminated all lines.')
return AH, []
if split and im_w > im_h: # two pages
line_sets = split_lines(lines)
else:
line_sets = [lines]
return AH, line_sets
def test(models_dir, image_path):
im = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
bw = binarize.binarize(im, algorithm=binarize.ntirogiannis2014)
AH = algorithm.dominant_char_height(bw)
print('AH =', AH)
possible_AHs = np.array([int(d) for d in os.listdir(models_dir) if d.isdigit()])
size = possible_AHs[np.abs(possible_AHs - AH).argmin()]
model_dir = os.path.join(models_dir, str(size))
P_l_T, P_h_T, pca, A_l_T, Q_T, A_h_T = load_model(model_dir)
assert np.all(segments[:, 0] < segments[:, 1])
theta_p = np.zeros((len(ellipses), ))
s_p = np.full((len(ellipses), ), 5)
def V_pq_sites(p1, p2, l1, l2):
s_1, theta_1 = unpack_label(l1)
s_2, theta_2 = unpack_label(l2)
centroid_1, centroid_2 = centroids[(p1, p2), :]
d_pq_sq = np.square(centroid_1 - centroid_2).sum()
scale = np.exp(-k * d_pq_sq / (s_values[s_1]**2 + s_values[s_2]**2))
f_diff = abs(s_1 - s_2) + abs(theta_1 - theta_2)
mu = 0 if l1 == l2 else (lam_1 if f_diff <= 3 else lam_2)
return mu * scale
# graph.set_smooth_cost_function(V_pq_sites)
# graph.expansion()
if __name__ == '__main__':
lib.debug = True
orig = cv2.imread(sys.argv[1])
bw = binarize.binarize(orig, algorithm=binarize.sauvola)
lib.debug_imwrite('retinex.png', bw)
AH = algorithm.dominant_char_height(bw)
koo2010(bw, AH)
def upscale(path, data_dir, factor):
assert factor == 2
im = cv2.imread(path, cv2.IMREAD_UNCHANGED)
im_h, im_w = im.shape
bw = binarize.binarize(im, algorithm=binarize.ntirogiannis2014)
AH = dominant_char_height(bw)
print('AH =', AH)
possible_AHs = np.array(
[int(d) for d in os.listdir(data_dir) if d.isdigit()])
size = possible_AHs[np.abs(possible_AHs - AH).argmin()]
D_T_coupled = np.load(os.path.join(data_dir, str(size), 'dict.npy'))
W_l = int(size / 3) | 1
W_h = 2 * W_l
step = 3
assert D_T_coupled.shape[1] == W_l * W_l + W_h * W_h
D_T = D_T_coupled[:, :W_l * W_l]
K = D_T.shape[0]
lam = 0.2 # weight of sparsity. TODO: confirm same as training data.
lo_patches = patches(im, W_l, step)
struct = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
gradient = cv2.morphologyEx(im, cv2.MORPH_GRADIENT, struct)
gradient_means, _ = binarize.mean_std(gradient, W_l)
patch_gradient = gradient_means[W_l // 2:-W_l // 2 + 1:step,
W_l // 2:-W_l // 2 + 1:step]
assert patch_gradient.shape == (lo_patches.shape[0], lo_patches.shape[1])
patch_mask = patch_gradient > np.percentile(patch_gradient, 50)
good_patches = np.nonzero(patch_mask)
cv2.imwrite('spp.png', -patch_mask.astype(np.uint8))
lo_patches_vec = lo_patches[good_patches].reshape(-1, W_l * W_l).astype(
np.float64)
means = lo_patches_vec.mean(axis=1)
X_T = lo_patches_vec - means[:, newaxis]
t = X_T.shape[0]
Z_T = np.zeros((t, K), dtype=np.float64)
last_objective = None
while True:
training.feature_sign_search_vec(X_T, Z_T, D_T, lam)
objective = np.square(X_T - Z_T.dot(D_T)).sum()
print('\ncurrent objective:', objective)
if last_objective is not None:
relative_err = abs(last_objective - objective) / last_objective
print('relative error:', relative_err)
if relative_err < 1e-4:
break
last_objective = objective
hi_patches = Z_T.dot(D_T_coupled)[:, W_l * W_l:] + means[:, newaxis]
hi_float = np.zeros((factor * im_h, factor * im_w), dtype=np.float64)
dest_patches = patches(hi_float, W_h, 2 * step)
patch_count = np.zeros((factor * im_h, factor * im_w), dtype=int)
count_patches = patches(patch_count, W_h, 2 * step)
for i, patch in enumerate(hi_patches):
dest_patches[good_patches[i]] += patch
count_patches[good_patches[i]] += 1
np.divide(hi_float, patch_count, hi_float, where=patch_count > 0)
hi_lanczos = cv2.resize(im, (0, 0),
None,
2.,
2.,
interpolation=cv2.INTER_LANCZOS4)
return np.where(patch_count > 0, hi_float,
hi_lanczos).clip(0, 255).astype(np.uint8)