mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-08-29 03:49:54 +02:00
ocr for curved lines
This commit is contained in:
vahidrezanezhad
parent
848156dd9d
commit
c0835665a9
1 changed files with 146 additions and 11 deletions
|
|
@ -5263,7 +5263,7 @@ class Eynollah_ocr:
|
|||
self.b_s = int(batch_size)
|
||||
|
||||
else:
|
||||
self.model_ocr_dir = dir_models + "/model_step_600000_ocr"#"/model_step_125000_ocr"#"/model_step_25000_ocr"#"/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
|
||||
self.model_ocr_dir = dir_models + "/model_step_750000_ocr"#"/model_step_125000_ocr"#"/model_step_25000_ocr"#"/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
|
||||
model_ocr = load_model(self.model_ocr_dir , compile=False)
|
||||
|
||||
self.prediction_model = tf.keras.models.Model(
|
||||
|
|
@ -5464,7 +5464,7 @@ class Eynollah_ocr:
|
|||
|
||||
return cropped_textline
|
||||
|
||||
def rotate_image_with_padding(self, image, angle):
|
||||
def rotate_image_with_padding(self, image, angle, border_value=(0,0,0)):
|
||||
# Get image dimensions
|
||||
(h, w) = image.shape[:2]
|
||||
|
||||
|
|
@ -5485,7 +5485,7 @@ class Eynollah_ocr:
|
|||
rotation_matrix[1, 2] += (new_h / 2) - center[1]
|
||||
|
||||
# Perform the rotation
|
||||
rotated_image = cv2.warpAffine(image, rotation_matrix, (new_w, new_h), borderValue=(0, 0, 0))
|
||||
rotated_image = cv2.warpAffine(image, rotation_matrix, (new_w, new_h), borderValue=border_value)
|
||||
|
||||
return rotated_image
|
||||
|
||||
|
|
@ -5496,6 +5496,21 @@ class Eynollah_ocr:
|
|||
else:
|
||||
angle = 0.5 * np.arctan2(2 * moments["mu11"], moments["mu20"] - moments["mu02"])
|
||||
return np.degrees(angle) # Convert radians to degrees
|
||||
|
||||
|
||||
def get_orientation_moments_of_mask(self, mask):
|
||||
mask=mask.astype('uint8')
|
||||
print(mask.shape)
|
||||
contours, _ = cv2.findContours(mask[:,:,0], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
largest_contour = max(contours, key=cv2.contourArea) if contours else None
|
||||
|
||||
moments = cv2.moments(largest_contour)
|
||||
if moments["mu20"] - moments["mu02"] == 0: # Avoid division by zero
|
||||
return 90 if moments["mu11"] > 0 else -90
|
||||
else:
|
||||
angle = 0.5 * np.arctan2(2 * moments["mu11"], moments["mu20"] - moments["mu02"])
|
||||
return np.degrees(angle) # Convert radians to degrees
|
||||
|
||||
def get_contours_and_bounding_boxes(self, mask):
|
||||
# Find contours in the binary mask
|
||||
|
|
@ -5508,6 +5523,121 @@ class Eynollah_ocr:
|
|||
#bounding_boxes.append((x, y, w, h))
|
||||
|
||||
return x, y, w, h
|
||||
|
||||
def return_splitting_point_of_image(self, image_to_spliited):
|
||||
width = np.shape(image_to_spliited)[1]
|
||||
height = np.shape(image_to_spliited)[0]
|
||||
common_window = int(0.03*width)
|
||||
|
||||
width1 = int ( common_window)
|
||||
width2 = int ( width - common_window )
|
||||
|
||||
img_sum = np.sum(image_to_spliited[:,:,0], axis=0)
|
||||
sum_smoothed = gaussian_filter1d(img_sum, 3)
|
||||
|
||||
peaks_real, _ = find_peaks(sum_smoothed, height=0)
|
||||
|
||||
peaks_real = peaks_real[(peaks_real<width2) & (peaks_real>width1)]
|
||||
|
||||
arg_sort = np.argsort(sum_smoothed[peaks_real])
|
||||
arg_sort4 =arg_sort[::-1][:4]
|
||||
peaks_sort_4 = peaks_real[arg_sort][::-1][:4]
|
||||
|
||||
return np.sort(peaks_sort_4)
|
||||
|
||||
def break_curved_line_into_small_pieces_and_then_merge(self, img_curved, mask_curved):
|
||||
peaks_4 = self.return_splitting_point_of_image(img_curved)
|
||||
|
||||
|
||||
|
||||
img_0 = img_curved[:, :peaks_4[0], :]
|
||||
img_1 = img_curved[:, peaks_4[0]:peaks_4[1], :]
|
||||
img_2 = img_curved[:, peaks_4[1]:peaks_4[2], :]
|
||||
img_3 = img_curved[:, peaks_4[2]:peaks_4[3], :]
|
||||
img_4 = img_curved[:, peaks_4[3]:, :]
|
||||
|
||||
|
||||
mask_0 = mask_curved[:, :peaks_4[0], :]
|
||||
mask_1 = mask_curved[:, peaks_4[0]:peaks_4[1], :]
|
||||
mask_2 = mask_curved[:, peaks_4[1]:peaks_4[2], :]
|
||||
mask_3 = mask_curved[:, peaks_4[2]:peaks_4[3], :]
|
||||
mask_4 = mask_curved[:, peaks_4[3]:, :]
|
||||
|
||||
cv2.imwrite("split0.png", img_0)
|
||||
cv2.imwrite("split1.png", img_1)
|
||||
cv2.imwrite("split2.png", img_2)
|
||||
cv2.imwrite("split3.png", img_3)
|
||||
|
||||
or_ma_0 = self.get_orientation_moments_of_mask(mask_0)
|
||||
or_ma_1 = self.get_orientation_moments_of_mask(mask_1)
|
||||
or_ma_2 = self.get_orientation_moments_of_mask(mask_2)
|
||||
or_ma_3 = self.get_orientation_moments_of_mask(mask_3)
|
||||
or_ma_4 = self.get_orientation_moments_of_mask(mask_4)
|
||||
|
||||
imgs_tot = []
|
||||
imgs_tot.append([img_0, mask_0, or_ma_0] )
|
||||
imgs_tot.append([img_1, mask_1, or_ma_1])
|
||||
imgs_tot.append([img_2, mask_2, or_ma_2])
|
||||
imgs_tot.append([img_3, mask_3, or_ma_3])
|
||||
imgs_tot.append([img_4, mask_4, or_ma_4])
|
||||
|
||||
w_tot_des_list = []
|
||||
w_tot_des = 0
|
||||
imgs_deskewed_list = []
|
||||
for ind in range(len(imgs_tot)):
|
||||
img_in = imgs_tot[ind][0]
|
||||
mask_in = imgs_tot[ind][1]
|
||||
ori_in = imgs_tot[ind][2]
|
||||
|
||||
if abs(ori_in)<45:
|
||||
img_in_des = self.rotate_image_with_padding(img_in, ori_in, border_value=(255,255,255) )
|
||||
mask_in_des = self.rotate_image_with_padding(mask_in, ori_in)
|
||||
mask_in_des = mask_in_des.astype('uint8')
|
||||
|
||||
#new bounding box
|
||||
x_n, y_n, w_n, h_n = self.get_contours_and_bounding_boxes(mask_in_des[:,:,0])
|
||||
|
||||
mask_in_des = mask_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
img_in_des = img_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
|
||||
w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) )
|
||||
img_in_des = resize_image(img_in_des, 32, w_relative)
|
||||
|
||||
|
||||
else:
|
||||
img_in_des = np.copy(img_in)
|
||||
w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) )
|
||||
img_in_des = resize_image(img_in_des, 32, w_relative)
|
||||
|
||||
w_tot_des+=img_in_des.shape[1]
|
||||
w_tot_des_list.append(img_in_des.shape[1])
|
||||
imgs_deskewed_list.append(img_in_des)
|
||||
|
||||
|
||||
|
||||
|
||||
img_final_deskewed = np.zeros((32, w_tot_des, 3))+255
|
||||
|
||||
w_indexer = 0
|
||||
for ind in range(len(w_tot_des_list)):
|
||||
img_final_deskewed[:,w_indexer:w_indexer+w_tot_des_list[ind],:] = imgs_deskewed_list[ind][:,:,:]
|
||||
w_indexer = w_indexer+w_tot_des_list[ind]
|
||||
|
||||
#cv2.imwrite('final.png', img_final_deskewed)
|
||||
#print(or_ma_0, or_ma_1, or_ma_2, or_ma_3, or_ma_4, 'orients')
|
||||
|
||||
##cv2.imwrite("split4.png", img_curved[:, peaks_4[3]:peaks_4[4], :])
|
||||
##cv2.imwrite("split5.png", img_curved[:, peaks_4[4]:peaks_4[5], :])
|
||||
##cv2.imwrite("split6.png", img_curved[:, peaks_4[5]:peaks_4[6], :])
|
||||
|
||||
##cv2.imwrite("split7.png", img_curved[:, peaks_4[6]:peaks_4[7], :])
|
||||
##cv2.imwrite("split8.png", img_curved[:, peaks_4[7]:peaks_4[8], :])
|
||||
##cv2.imwrite("split9.png", img_curved[:, peaks_4[8]:peaks_4[9], :])
|
||||
|
||||
|
||||
#cv2.imwrite("split4.png", img_4)
|
||||
#sys.exit()
|
||||
return img_final_deskewed
|
||||
|
||||
def run(self):
|
||||
ls_imgs = os.listdir(self.dir_in)
|
||||
|
|
@ -5754,11 +5884,9 @@ class Eynollah_ocr:
|
|||
mask_poly = mask_poly[y:y+h, x:x+w, :]
|
||||
img_crop = img_poly_on_img[y:y+h, x:x+w, :]
|
||||
|
||||
if angle_degrees<=15:
|
||||
if mask_poly[:,:,0].sum() /float(w*h) < 0.6 and w_scaled > 520:
|
||||
cv2.imwrite(file_name+'_desk.png', img_crop)
|
||||
|
||||
|
||||
print(file_name, angle_degrees,w*h , mask_poly[:,:,0].sum(), mask_poly[:,:,0].sum() /float(w*h) , 'didi')
|
||||
#print(file_name, angle_degrees,w*h , mask_poly[:,:,0].sum(), mask_poly[:,:,0].sum() /float(w*h) , 'didi')
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
if angle_degrees > 15:
|
||||
better_des_slope = self.get_orientation_moments(textline_coords)
|
||||
|
|
@ -5773,12 +5901,19 @@ class Eynollah_ocr:
|
|||
mask_poly = mask_poly[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
img_crop = img_crop[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
|
||||
if mask_poly[:,:,0].sum() /float(w_n*h_n) < 0.6 and w_scaled > 520:
|
||||
cv2.imwrite(file_name+'_desk.png', img_crop)
|
||||
img_crop[mask_poly==0] = 255
|
||||
|
||||
print(file_name,w_n*h_n , mask_poly[:,:,0].sum(), mask_poly[:,:,0].sum() /float(w_n*h_n) , 'ikiiiiii')
|
||||
if mask_poly[:,:,0].sum() /float(w_n*h_n) < 0.50 and w_scaled > 100:
|
||||
img_crop = self.break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly)
|
||||
|
||||
img_crop[mask_poly==0] = 255
|
||||
#print(file_name,w_n*h_n , mask_poly[:,:,0].sum(), mask_poly[:,:,0].sum() /float(w_n*h_n) , 'ikiiiiii')
|
||||
else:
|
||||
img_crop[mask_poly==0] = 255
|
||||
if mask_poly[:,:,0].sum() /float(w*h) < 0.50 and w_scaled > 100:
|
||||
img_crop = self.break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly)
|
||||
|
||||
|
||||
|
||||
|
||||
if self.prediction_with_both_of_rgb_and_bin:
|
||||
img_crop_bin[mask_poly==0] = 255
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue