mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-10-27 07:44:12 +01:00
factor out more methods
This commit is contained in:
Konstantin Baierer
parent
731b8ded6f
commit
cf1d9b9fd0
3 changed files with 158 additions and 153 deletions
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@ -40,8 +40,12 @@ from .utils import (
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filter_contours_area_of_image_tables,
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filter_contours_area_of_image_interiors,
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rotatedRectWithMaxArea,
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rotate_image,
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rotate_max_area_new,
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rotation_image_new,
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crop_image_inside_box,
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otsu_copy,
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otsu_copy_binary,
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)
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@ -101,87 +105,6 @@ class eynollah:
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###self.model_region_dir_p = dir_models +'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_main_home_5_soft_new.h5'#'/model_home_soft_5_all_data.h5' #'/model_main_office_long_soft.h5'#'/model_20_cat_main.h5'
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self.model_textline_dir = dir_models + "/model_textline_newspapers.h5" #'/model_hor_ver_home_trextline_very_good.h5'# '/model_hor_ver_1_great.h5'#'/model_curved_office_works_great.h5'
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def get_one_hot(self, seg, input_height, input_width, n_classes):
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seg = seg[:, :, 0]
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seg_f = np.zeros((input_height, input_width, n_classes))
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for j in range(n_classes):
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seg_f[:, :, j] = (seg == j).astype(int)
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return seg_f
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def color_images(self, seg, n_classes):
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ann_u = range(n_classes)
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if len(np.shape(seg)) == 3:
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seg = seg[:, :, 0]
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seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(np.uint8)
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colors = sns.color_palette("hls", n_classes)
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for c in ann_u:
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c = int(c)
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segl = seg == c
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seg_img[:, :, 0] = segl * c
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seg_img[:, :, 1] = segl * c
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seg_img[:, :, 2] = segl * c
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return seg_img
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def rotate_image(self, img_patch, slope):
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(h, w) = img_patch.shape[:2]
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center = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D(center, slope, 1.0)
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return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
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def rotyate_image_different(self, img, slope):
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# img = cv2.imread('images/input.jpg')
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num_rows, num_cols = img.shape[:2]
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rotation_matrix = cv2.getRotationMatrix2D((num_cols / 2, num_rows / 2), slope, 1)
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img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
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return img_rotation
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def cleaning_probs(self, probs: np.ndarray, sigma: float) -> np.ndarray:
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# Smooth
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if sigma > 0.0:
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return cv2.GaussianBlur(probs, (int(3 * sigma) * 2 + 1, int(3 * sigma) * 2 + 1), sigma)
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elif sigma == 0.0:
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return cv2.fastNlMeansDenoising((probs * 255).astype(np.uint8), h=20) / 255
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else: # Negative sigma, do not do anything
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return probs
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def crop_image_inside_box(self, box, img_org_copy):
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image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]]
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return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]
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def otsu_copy(self, img):
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img_r = np.zeros(img.shape)
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img1 = img[:, :, 0]
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img2 = img[:, :, 1]
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img3 = img[:, :, 2]
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# print(img.min())
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# print(img[:,:,0].min())
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# blur = cv2.GaussianBlur(img,(5,5))
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# ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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img_r[:, :, 0] = threshold1
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img_r[:, :, 1] = threshold1
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img_r[:, :, 2] = threshold1
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return img_r
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def otsu_copy_binary(self, img):
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img_r = np.zeros((img.shape[0], img.shape[1], 3))
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img1 = img[:, :, 0]
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retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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img_r[:, :, 0] = threshold1
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img_r[:, :, 1] = threshold1
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img_r[:, :, 2] = threshold1
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img_r = img_r / float(np.max(img_r)) * 255
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return img_r
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def predict_enhancement(self, img):
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model_enhancement, session_enhancemnet = self.start_new_session_and_model(self.model_dir_of_enhancemnet)
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@ -1036,7 +959,7 @@ class eynollah:
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img = img.astype(np.uint8)
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patches = False
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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###img = self.otsu_copy(self.image)
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###img = otsu_copy(self.image)
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for ii in range(1):
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img = cv2.GaussianBlur(img, (5, 5), 0)
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@ -1056,7 +979,7 @@ class eynollah:
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box = [x, y, w, h]
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croped_page, page_coord = self.crop_image_inside_box(box, img)
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croped_page, page_coord = crop_image_inside_box(box, img)
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session_page.close()
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del model_page
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@ -1080,7 +1003,7 @@ class eynollah:
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def extract_page(self):
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patches = False
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model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
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###img = self.otsu_copy(self.image)
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###img = otsu_copy(self.image)
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for ii in range(1):
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img = cv2.GaussianBlur(self.image, (5, 5), 0)
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@ -1112,7 +1035,7 @@ class eynollah:
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box = [x, y, w, h]
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croped_page, page_coord = self.crop_image_inside_box(box, self.image)
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croped_page, page_coord = crop_image_inside_box(box, self.image)
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self.cont_page = []
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self.cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
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@ -1134,7 +1057,7 @@ class eynollah:
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img_width_h = img.shape[1]
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patches = False
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img = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img = otsu_copy_binary(img) # otsu_copy(img)
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img = img.astype(np.uint16)
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model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
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@ -1143,17 +1066,17 @@ class eynollah:
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img_2 = img[int(img.shape[0] / 3.0) : int(2 * img.shape[0] / 3.0), :, :]
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img_3 = img[int(2 * img.shape[0] / 3.0) :, :, :]
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# img_1 = self.otsu_copy_binary(img_1)#self.otsu_copy(img)
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# img_1 = otsu_copy_binary(img_1)#otsu_copy(img)
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# img_1 = img_1.astype(np.uint16)
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plt.imshow(img_1)
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plt.show()
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# img_2 = self.otsu_copy_binary(img_2)#self.otsu_copy(img)
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# img_2 = otsu_copy_binary(img_2)#otsu_copy(img)
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# img_2 = img_2.astype(np.uint16)
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plt.imshow(img_2)
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plt.show()
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# img_3 = self.otsu_copy_binary(img_3)#self.otsu_copy(img)
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# img_3 = otsu_copy_binary(img_3)#otsu_copy(img)
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# img_3 = img_3.astype(np.uint16)
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plt.imshow(img_3)
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@ -1208,12 +1131,12 @@ class eynollah:
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###if patches and cols>=2:
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###img = self.otsu_copy_binary(img)#self.otsu_copy(img)
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###img = otsu_copy_binary(img)#otsu_copy(img)
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###img = img.astype(np.uint8)
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###if patches and cols==1:
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###img = self.otsu_copy_binary(img)#self.otsu_copy(img)
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###img = otsu_copy_binary(img)#otsu_copy(img)
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###img = img.astype(np.uint8)
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###img= resize_image(img, int(img_height_h*1), int(img_width_h*1) )
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@ -1223,7 +1146,7 @@ class eynollah:
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model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully_np)
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if patches and cols == 1:
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img2 = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img2 = otsu_copy_binary(img) # otsu_copy(img)
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img2 = img2.astype(np.uint8)
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img2 = resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7))
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@ -1232,7 +1155,7 @@ class eynollah:
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prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
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if patches and cols == 2:
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img2 = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img2 = otsu_copy_binary(img) # otsu_copy(img)
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img2 = img2.astype(np.uint8)
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img2 = resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4))
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@ -1240,7 +1163,7 @@ class eynollah:
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prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
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prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
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elif patches and cols > 2:
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img2 = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img2 = otsu_copy_binary(img) # otsu_copy(img)
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img2 = img2.astype(np.uint8)
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img2 = resize_image(img2, int(img_height_h * 0.3), int(img_width_h * 0.3))
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@ -1249,7 +1172,7 @@ class eynollah:
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prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
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if patches and cols == 2:
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img = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img = otsu_copy_binary(img) # otsu_copy(img)
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img = img.astype(np.uint8)
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@ -1260,7 +1183,7 @@ class eynollah:
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img = img.astype(np.uint8)
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if patches and cols == 1:
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img = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img = otsu_copy_binary(img) # otsu_copy(img)
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img = img.astype(np.uint8)
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img = resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5))
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@ -1268,24 +1191,24 @@ class eynollah:
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if patches and cols == 3:
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img = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img = otsu_copy_binary(img) # otsu_copy(img)
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img = img.astype(np.uint8)
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# img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
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if patches and cols == 4:
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img = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img = otsu_copy_binary(img) # otsu_copy(img)
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img = img.astype(np.uint8)
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# img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
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if patches and cols >= 5:
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img = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img = otsu_copy_binary(img) # otsu_copy(img)
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img = img.astype(np.uint8)
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# img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
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if not patches:
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img = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img = otsu_copy_binary(img) # otsu_copy(img)
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img = img.astype(np.uint8)
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prediction_regions2 = None
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@ -1303,7 +1226,7 @@ class eynollah:
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def extract_only_text_regions(self, img, patches):
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model_region, session_region = self.start_new_session_and_model(self.model_only_text)
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img = self.otsu_copy_binary(img) # self.otsu_copy(img)
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img = otsu_copy_binary(img) # otsu_copy(img)
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img = img.astype(np.uint8)
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img_org = np.copy(img)
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@ -1540,7 +1463,7 @@ class eynollah:
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index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
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crop_img, crop_coor = self.crop_image_inside_box(boxes_text[mv], image_page_rotated)
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crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
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if abs(slope_for_all) < 45:
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# all_box_coord.append(crop_coor)
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@ -1632,7 +1555,7 @@ class eynollah:
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for mv in range(len(boxes_text)):
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crop_img, crop_coor = self.crop_image_inside_box(boxes_text[mv], image_page_rotated)
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crop_img, crop_coor = crop_image_inside_box(boxes_text[mv], image_page_rotated)
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# all_box_coord.append(crop_coor)
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@ -1768,7 +1691,7 @@ class eynollah:
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def get_all_image_patches_coordination(self, image_page):
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self.all_box_coord = []
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for jk in range(len(self.boxes)):
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_, crop_coor = self.crop_image_inside_box(self.boxes[jk], image_page)
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_, crop_coor = crop_image_inside_box(self.boxes[jk], image_page)
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self.all_box_coord.append(crop_coor)
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def textline_contours(self, img, patches, scaler_h, scaler_w):
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@ -1778,7 +1701,7 @@ class eynollah:
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if not patches:
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model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir_np)
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##img = self.otsu_copy(img)
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##img = otsu_copy(img)
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img = img.astype(np.uint8)
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img_org = np.copy(img)
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@ -1824,7 +1747,7 @@ class eynollah:
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self.area_of_cropped = []
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self.all_text_region_raw = []
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for jk in range(len(boxes)):
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crop_img, crop_coor = self.crop_image_inside_box(boxes[jk], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
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crop_img, crop_coor = crop_image_inside_box(boxes[jk], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
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crop_img = crop_img.astype(np.uint8)
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self.all_text_region_raw.append(crop_img[:, :, 0])
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self.area_of_cropped.append(crop_img.shape[0] * crop_img.shape[1])
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@ -2337,7 +2260,7 @@ class eynollah:
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slope_xline = 0
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slopes_tile_wise.append(slope_xline)
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# print(slope_xline,'xlineeee')
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img_line_rotated = self.rotate_image(img_xline, slope_xline)
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img_line_rotated = rotate_image(img_xline, slope_xline)
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img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
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"""
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@ -2359,7 +2282,7 @@ class eynollah:
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slope_xline=0
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slopes_tile_wise.append(slope_xline)
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print(slope_xline,'xlineeee')
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img_line_rotated=self.rotate_image(img_xline,slope_xline)
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img_line_rotated=rotate_image(img_xline,slope_xline)
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##plt.imshow(img_line_rotated)
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##plt.show()
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@ -2394,14 +2317,14 @@ class eynollah:
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img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]] = img_int[:, :]
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##plt.imshow(img_xline)
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##plt.show()
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img_line_rotated = self.rotate_image(img_resized, slopes_tile_wise[i])
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img_line_rotated = rotate_image(img_resized, slopes_tile_wise[i])
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img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
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img_patch_seperated = self.seperate_lines_new_inside_teils(img_line_rotated, 0)
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##plt.imshow(img_patch_seperated)
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##plt.show()
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img_patch_seperated_returned = self.rotate_image(img_patch_seperated, -slopes_tile_wise[i])
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img_patch_seperated_returned = rotate_image(img_patch_seperated, -slopes_tile_wise[i])
|
||||
img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1
|
||||
|
||||
img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]
|
||||
|
|
@ -2422,14 +2345,14 @@ class eynollah:
|
|||
img_resized[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(1)):int( img_int.shape[1]*(1))+img_int.shape[1] ]=img_int[:,:]
|
||||
##plt.imshow(img_xline)
|
||||
##plt.show()
|
||||
img_line_rotated=self.rotate_image(img_resized,slopes_tile_wise[ui])
|
||||
img_line_rotated=rotate_image(img_resized,slopes_tile_wise[ui])
|
||||
|
||||
|
||||
#img_patch_seperated=self.seperate_lines_new_inside_teils(img_line_rotated,0)
|
||||
|
||||
img_patch_seperated=self.seperate_lines_new_inside_teils(img_line_rotated,0)
|
||||
|
||||
img_patch_seperated_returned=self.rotate_image(img_patch_seperated,-slopes_tile_wise[ui])
|
||||
img_patch_seperated_returned=rotate_image(img_patch_seperated,-slopes_tile_wise[ui])
|
||||
##plt.imshow(img_patch_seperated)
|
||||
##plt.show()
|
||||
print(img_patch_seperated_returned.shape)
|
||||
|
|
@ -2507,7 +2430,7 @@ class eynollah:
|
|||
# slope_xline=[slope_region][0]
|
||||
slopes_tile_wise.append(slope_xline)
|
||||
# print(slope_xline,'xlineeee')
|
||||
img_line_rotated = self.rotate_image(img_xline, slope_xline)
|
||||
img_line_rotated = rotate_image(img_xline, slope_xline)
|
||||
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
|
||||
|
||||
# print(slopes_tile_wise,'slopes_tile_wise')
|
||||
|
|
@ -2537,12 +2460,12 @@ class eynollah:
|
|||
img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]] = img_int[:, :]
|
||||
# plt.imshow(img_xline)
|
||||
# plt.show()
|
||||
img_line_rotated = self.rotate_image(img_resized, slopes_tile_wise[i])
|
||||
img_line_rotated = rotate_image(img_resized, slopes_tile_wise[i])
|
||||
img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
|
||||
|
||||
img_patch_seperated = self.seperate_lines_new_inside_teils2(img_line_rotated, 0)
|
||||
|
||||
img_patch_seperated_returned = self.rotate_image(img_patch_seperated, -slopes_tile_wise[i])
|
||||
img_patch_seperated_returned = rotate_image(img_patch_seperated, -slopes_tile_wise[i])
|
||||
img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1
|
||||
|
||||
img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]
|
||||
|
|
@ -3017,7 +2940,7 @@ class eynollah:
|
|||
return peaks, textline_boxes_rot
|
||||
|
||||
def return_rotated_contours(self, slope, img_patch):
|
||||
dst = self.rotate_image(img_patch, slope)
|
||||
dst = rotate_image(img_patch, slope)
|
||||
dst = dst.astype(np.uint8)
|
||||
dst = dst[:, :, 0]
|
||||
dst[dst != 0] = 1
|
||||
|
|
@ -3501,7 +3424,7 @@ class eynollah:
|
|||
textline_mask_help = np.zeros((textline_mask.shape[0] + int(2 * y_help), textline_mask.shape[1] + int(2 * x_help), 3))
|
||||
textline_mask_help[y_help : y_help + textline_mask.shape[0], x_help : x_help + textline_mask.shape[1], :] = np.copy(textline_mask[:, :, :])
|
||||
|
||||
dst = self.rotate_image(textline_mask_help, slope)
|
||||
dst = rotate_image(textline_mask_help, slope)
|
||||
dst = dst[:, :, 0]
|
||||
dst[dst != 0] = 1
|
||||
|
||||
|
|
@ -3525,7 +3448,7 @@ class eynollah:
|
|||
|
||||
img_contour_help[y_help : y_help + img_contour.shape[0], x_help : x_help + img_contour.shape[1], :] = np.copy(img_contour[:, :, :])
|
||||
|
||||
img_contour_rot = self.rotate_image(img_contour_help, slope)
|
||||
img_contour_rot = rotate_image(img_contour_help, slope)
|
||||
|
||||
# plt.imshow(img_contour_rot_help)
|
||||
# plt.show()
|
||||
|
|
@ -3867,7 +3790,7 @@ class eynollah:
|
|||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = self.rotate_image(img_resized, rot)
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
# plt.imshow(img_rot)
|
||||
# plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
|
|
@ -3914,7 +3837,7 @@ class eynollah:
|
|||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = self.rotate_image(img_resized, rot)
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
|
|
@ -3962,7 +3885,7 @@ class eynollah:
|
|||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = self.rotate_image(img_resized, rot)
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
# plt.imshow(img_rot)
|
||||
# plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
|
|
@ -4024,7 +3947,7 @@ class eynollah:
|
|||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = self.rotate_image(img_resized, rot)
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
|
|
@ -4070,7 +3993,7 @@ class eynollah:
|
|||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = self.rotate_image(img_resized, rot)
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
|
|
@ -4115,7 +4038,7 @@ class eynollah:
|
|||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = self.rotate_image(img_resized, rot)
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
# plt.imshow(img_rot)
|
||||
# plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
|
|
@ -4167,7 +4090,7 @@ class eynollah:
|
|||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = self.rotate_image(img_resized, rot)
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
|
|
@ -4213,7 +4136,7 @@ class eynollah:
|
|||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = self.rotate_image(img_resized, rot)
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
|
|
@ -4278,7 +4201,7 @@ class eynollah:
|
|||
indexer = 0
|
||||
for rot in angles:
|
||||
# print(rot,'rot')
|
||||
img_rotated = self.rotate_image(img_patch_padded, rot)
|
||||
img_rotated = rotate_image(img_patch_padded, rot)
|
||||
img_rotated[img_rotated != 0] = 1
|
||||
|
||||
# plt.imshow(img_rotated)
|
||||
|
|
@ -4326,7 +4249,7 @@ class eynollah:
|
|||
indexer = 0
|
||||
for rot in angles:
|
||||
# print(rot,'rot')
|
||||
img_rotated = self.rotate_image(img_patch_padded, rot)
|
||||
img_rotated = rotate_image(img_patch_padded, rot)
|
||||
img_rotated[img_rotated != 0] = 1
|
||||
|
||||
# plt.imshow(img_rotated)
|
||||
|
|
@ -4371,7 +4294,7 @@ class eynollah:
|
|||
poly_sub = []
|
||||
for mv in range(len(boxes_per_process)):
|
||||
|
||||
crop_img, _ = self.crop_image_inside_box(boxes_per_process[mv], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
|
||||
crop_img, _ = crop_image_inside_box(boxes_per_process[mv], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
|
||||
crop_img = crop_img[:, :, 0]
|
||||
crop_img = cv2.erode(crop_img, self.kernel, iterations=2)
|
||||
|
||||
|
|
@ -5636,7 +5559,7 @@ class eynollah:
|
|||
for ind in np.unique(regions_prediction[:, :]):
|
||||
interest_reg = (regions_prediction[:, :] == ind) * 1
|
||||
interest_reg = interest_reg.astype(np.uint8)
|
||||
deskewed_new = self.rotate_image(interest_reg, slope)
|
||||
deskewed_new = rotate_image(interest_reg, slope)
|
||||
deskewed_new = deskewed_new[:, :]
|
||||
deskewed_new[deskewed_new != 0] = ind
|
||||
|
||||
|
|
@ -5734,14 +5657,14 @@ class eynollah:
|
|||
# if abs(slope)>=1:
|
||||
# slope=0
|
||||
|
||||
# dst=self.rotate_image(textline_mask,slope_true)
|
||||
# dst=rotate_image(textline_mask,slope_true)
|
||||
# dst=dst[:,:,0]
|
||||
# dst[dst!=0]=1
|
||||
image_regions_deskewd = np.zeros(textline_mask_org[:, :].shape)
|
||||
for ind in np.unique(textline_mask_org[:, :]):
|
||||
interest_reg = (textline_mask_org[:, :] == ind) * 1
|
||||
interest_reg = interest_reg.astype(np.uint8)
|
||||
deskewed_new = self.rotate_image(interest_reg, slope_true)
|
||||
deskewed_new = rotate_image(interest_reg, slope_true)
|
||||
deskewed_new = deskewed_new[:, :]
|
||||
deskewed_new[deskewed_new != 0] = ind
|
||||
|
||||
|
|
@ -8596,7 +8519,7 @@ class eynollah:
|
|||
regions_without_seperators = self.return_regions_without_seperators(region_pre_p)
|
||||
|
||||
##print(args_big_parts,'args_big_parts')
|
||||
# image_page_otsu=self.otsu_copy(image_page_deskewd)
|
||||
# image_page_otsu=otsu_copy(image_page_deskewd)
|
||||
# print(np.unique(image_page_otsu[:,:,0]))
|
||||
# image_page_background_zero=self.image_change_background_pixels_to_zero(image_page_otsu)
|
||||
|
||||
|
|
@ -8698,7 +8621,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -8726,7 +8649,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -8775,7 +8698,7 @@ class eynollah:
|
|||
img = img[0:one_third_upper_ny, :, :]
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -8792,7 +8715,7 @@ class eynollah:
|
|||
img = img[one_third_upper_ny : int(2 * one_third_upper_ny), :, :]
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -8809,7 +8732,7 @@ class eynollah:
|
|||
img = img[int(2 * one_third_upper_ny) :, :, :]
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -8849,7 +8772,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -8871,7 +8794,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -8984,7 +8907,7 @@ class eynollah:
|
|||
median_blur = False
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -9012,7 +8935,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -9032,7 +8955,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -9146,7 +9069,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -9172,7 +9095,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -9220,7 +9143,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -9250,7 +9173,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -9287,7 +9210,7 @@ class eynollah:
|
|||
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
|
||||
|
||||
if binary:
|
||||
img = self.otsu_copy_binary(img) # self.otsu_copy(img)
|
||||
img = otsu_copy_binary(img) # otsu_copy(img)
|
||||
img = img.astype(np.uint16)
|
||||
|
||||
if median_blur:
|
||||
|
|
@ -9352,7 +9275,7 @@ class eynollah:
|
|||
# img= resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
|
||||
|
||||
# if binary:
|
||||
# img = self.otsu_copy_binary(img)#self.otsu_copy(img)
|
||||
# img = otsu_copy_binary(img)#otsu_copy(img)
|
||||
# img = img.astype(np.uint16)
|
||||
|
||||
# if median_blur:
|
||||
|
|
@ -9486,7 +9409,7 @@ class eynollah:
|
|||
|
||||
x, y, w, h = cv2.boundingRect(cont_ind)
|
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box = [x, y, w, h]
|
||||
croped_page, page_coord = self.crop_image_inside_box(box, image_page)
|
||||
croped_page, page_coord = crop_image_inside_box(box, image_page)
|
||||
|
||||
croped_page = resize_image(croped_page, int(croped_page.shape[0] / self.scale_y), int(croped_page.shape[1] / self.scale_x))
|
||||
|
||||
|
|
@ -9613,7 +9536,7 @@ class eynollah:
|
|||
mask_marginals[:, :] = 1
|
||||
|
||||
# print(mask_marginals.shape,point_left,point_right,'nadosh')
|
||||
mask_marginals_rotated = self.rotate_image(mask_marginals, -slope_deskew)
|
||||
mask_marginals_rotated = rotate_image(mask_marginals, -slope_deskew)
|
||||
|
||||
# print(mask_marginals_rotated.shape,'nadosh')
|
||||
mask_marginals_rotated_sum = mask_marginals_rotated.sum(axis=0)
|
||||
|
|
@ -9715,7 +9638,7 @@ class eynollah:
|
|||
polygons_per_par_process_per_process = []
|
||||
textline_cnt_seperated = np.zeros(textline_mask_tot.shape)
|
||||
for iiii in range(len(polygons_per_process)):
|
||||
# crop_img,crop_coor=self.crop_image_inside_box(boxes_text[mv],image_page_rotated)
|
||||
# crop_img,crop_coor=crop_image_inside_box(boxes_text[mv],image_page_rotated)
|
||||
# arg_max=np.argmax(areas_cnt_only_text)
|
||||
textregions_cnt_tot_per_process.append(polygons_per_process[iiii] / scale_par)
|
||||
textline_region_in_image = np.zeros(textline_mask_tot.shape)
|
||||
|
|
@ -11051,7 +10974,7 @@ class eynollah:
|
|||
regions_without_seperators = (text_regions_p[:, :] == 1) * 1
|
||||
regions_without_seperators = regions_without_seperators.astype(np.uint8)
|
||||
|
||||
text_regions_p = self.get_marginals(self.rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew)
|
||||
text_regions_p = self.get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew)
|
||||
|
||||
except:
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -49,3 +49,35 @@ def resize_ann(seg_in, input_height, input_width):
|
|||
"""
|
||||
return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
|
||||
|
||||
def get_one_hot(seg, input_height, input_width, n_classes):
|
||||
seg = seg[:, :, 0]
|
||||
seg_f = np.zeros((input_height, input_width, n_classes))
|
||||
for j in range(n_classes):
|
||||
seg_f[:, :, j] = (seg == j).astype(int)
|
||||
return seg_f
|
||||
|
||||
def color_images(seg, n_classes):
|
||||
ann_u = range(n_classes)
|
||||
if len(np.shape(seg)) == 3:
|
||||
seg = seg[:, :, 0]
|
||||
|
||||
seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(np.uint8)
|
||||
colors = sns.color_palette("hls", n_classes)
|
||||
|
||||
for c in ann_u:
|
||||
c = int(c)
|
||||
segl = seg == c
|
||||
seg_img[:, :, 0] = segl * c
|
||||
seg_img[:, :, 1] = segl * c
|
||||
seg_img[:, :, 2] = segl * c
|
||||
return seg_img
|
||||
|
||||
def cleaning_probs(self, probs: np.ndarray, sigma: float) -> np.ndarray:
|
||||
# Smooth
|
||||
if sigma > 0.0:
|
||||
return cv2.GaussianBlur(probs, (int(3 * sigma) * 2 + 1, int(3 * sigma) * 2 + 1), sigma)
|
||||
elif sigma == 0.0:
|
||||
return cv2.fastNlMeansDenoising((probs * 255).astype(np.uint8), h=20) / 255
|
||||
else: # Negative sigma, do not do anything
|
||||
return probs
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import numpy as np
|
||||
from shapely import geometry
|
||||
import cv2
|
||||
import imutils
|
||||
|
||||
def filter_contours_area_of_image(image, contours, hirarchy, max_area, min_area):
|
||||
found_polygons_early = list()
|
||||
|
|
@ -92,3 +93,52 @@ def rotation_image_new(img, thetha):
|
|||
rotated = imutils.rotate(img, thetha)
|
||||
return rotate_max_area_new(img, rotated, thetha)
|
||||
|
||||
def rotate_image(img_patch, slope):
|
||||
(h, w) = img_patch.shape[:2]
|
||||
center = (w // 2, h // 2)
|
||||
M = cv2.getRotationMatrix2D(center, slope, 1.0)
|
||||
return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
|
||||
|
||||
def rotyate_image_different( img, slope):
|
||||
# img = cv2.imread('images/input.jpg')
|
||||
num_rows, num_cols = img.shape[:2]
|
||||
|
||||
rotation_matrix = cv2.getRotationMatrix2D((num_cols / 2, num_rows / 2), slope, 1)
|
||||
img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
|
||||
return img_rotation
|
||||
|
||||
def crop_image_inside_box(box, img_org_copy):
|
||||
image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]]
|
||||
return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]
|
||||
|
||||
def otsu_copy(img):
|
||||
img_r = np.zeros(img.shape)
|
||||
img1 = img[:, :, 0]
|
||||
img2 = img[:, :, 1]
|
||||
img3 = img[:, :, 2]
|
||||
# print(img.min())
|
||||
# print(img[:,:,0].min())
|
||||
# blur = cv2.GaussianBlur(img,(5,5))
|
||||
# ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
|
||||
retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
|
||||
img_r[:, :, 0] = threshold1
|
||||
img_r[:, :, 1] = threshold1
|
||||
img_r[:, :, 2] = threshold1
|
||||
return img_r
|
||||
|
||||
def otsu_copy_binary(img):
|
||||
img_r = np.zeros((img.shape[0], img.shape[1], 3))
|
||||
img1 = img[:, :, 0]
|
||||
|
||||
retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
|
||||
img_r[:, :, 0] = threshold1
|
||||
img_r[:, :, 1] = threshold1
|
||||
img_r[:, :, 2] = threshold1
|
||||
|
||||
img_r = img_r / float(np.max(img_r)) * 255
|
||||
return img_r
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue