import cv2 import numpy as np from shapely import geometry from .rotate import rotate_image, rotation_image_new def contours_in_same_horizon(cy_main_hor): X1 = np.zeros((len(cy_main_hor), len(cy_main_hor))) X2 = np.zeros((len(cy_main_hor), len(cy_main_hor))) X1[0::1, :] = cy_main_hor[:] X2 = X1.T X_dif = np.abs(X2 - X1) args_help = np.array(range(len(cy_main_hor))) all_args = [] for i in range(len(cy_main_hor)): list_h = list(args_help[X_dif[i, :] <= 20]) list_h.append(i) if len(list_h) > 1: all_args.append(list(set(list_h))) return np.unique(all_args) def find_contours_mean_y_diff(contours_main): M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))] cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] return np.mean(np.diff(np.sort(np.array(cy_main)))) def find_features_of_contours(contours_main): areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))]) M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))] cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))]) x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))]) y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))]) y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))]) return y_min_main, y_max_main, areas_main def get_text_region_boxes_by_given_contours(contours): kernel = np.ones((5, 5), np.uint8) boxes = [] contours_new = [] for jj in range(len(contours)): x, y, w, h = cv2.boundingRect(contours[jj]) boxes.append([x, y, w, h]) contours_new.append(contours[jj]) del contours return boxes, contours_new def filter_contours_area_of_image(image, contours, hirarchy, max_area, min_area): found_polygons_early = list() jv = 0 for c in contours: if len(c) < 3: # A polygon cannot have less than 3 points continue polygon = geometry.Polygon([point[0] for point in c]) area = polygon.area if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] == -1: # and hirarchy[0][jv][3]==-1 : found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint)) jv += 1 return found_polygons_early def filter_contours_area_of_image_interiors(image, contours, hirarchy, max_area, min_area): found_polygons_early = list() jv = 0 for c in contours: if len(c) < 3: # A polygon cannot have less than 3 points continue polygon = geometry.Polygon([point[0] for point in c]) area = polygon.area if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] != -1: # print(c[0][0][1]) found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint)) jv += 1 return found_polygons_early def filter_contours_area_of_image_tables(image, contours, hirarchy, max_area, min_area): found_polygons_early = list() jv = 0 for c in contours: if len(c) < 3: # A polygon cannot have less than 3 points continue polygon = geometry.Polygon([point[0] for point in c]) # area = cv2.contourArea(c) area = polygon.area ##print(np.prod(thresh.shape[:2])) # Check that polygon has area greater than minimal area # print(hirarchy[0][jv][3],hirarchy ) if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hirarchy[0][jv][3]==-1 : # print(c[0][0][1]) found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.int32)) jv += 1 return found_polygons_early def find_new_features_of_contoures(contours_main): areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))]) M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))] cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] try: x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))]) argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0]) for j in range(len(contours_main))]) x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0] for j in range(len(contours_main))]) y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1] for j in range(len(contours_main))]) x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))]) y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))]) y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))]) except: x_min_main = np.array([np.min(contours_main[j][:, 0]) for j in range(len(contours_main))]) argmin_x_main = np.array([np.argmin(contours_main[j][:, 0]) for j in range(len(contours_main))]) x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0] for j in range(len(contours_main))]) y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1] for j in range(len(contours_main))]) x_max_main = np.array([np.max(contours_main[j][:, 0]) for j in range(len(contours_main))]) y_min_main = np.array([np.min(contours_main[j][:, 1]) for j in range(len(contours_main))]) y_max_main = np.array([np.max(contours_main[j][:, 1]) for j in range(len(contours_main))]) # dis_x=np.abs(x_max_main-x_min_main) return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin def return_parent_contours(contours, hierarchy): contours_parent = [contours[i] for i in range(len(contours)) if hierarchy[0][i][3] == -1] return contours_parent def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002): # pixels of images are identified by 5 if len(region_pre_p.shape) == 3: cnts_images = (region_pre_p[:, :, 0] == pixel) * 1 else: cnts_images = (region_pre_p[:, :] == pixel) * 1 cnts_images = cnts_images.astype(np.uint8) cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2) imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_imgs = return_parent_contours(contours_imgs, hiearchy) contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_area) return contours_imgs def get_textregion_contours_in_org_image(cnts, img, slope_first): cnts_org = [] # print(cnts,'cnts') for i in range(len(cnts)): img_copy = np.zeros(img.shape) img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1)) # plt.imshow(img_copy) # plt.show() # print(img.shape,'img') img_copy = rotation_image_new(img_copy, -slope_first) ##print(img_copy.shape,'img_copy') # plt.imshow(img_copy) # plt.show() img_copy = img_copy.astype(np.uint8) imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1]) cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0]) # print(np.shape(cont_int[0])) cnts_org.append(cont_int[0]) # print(cnts_org,'cnts_org') # sys.exit() # self.y_shift = np.abs(img_copy.shape[0] - img.shape[0]) # self.x_shift = np.abs(img_copy.shape[1] - img.shape[1]) return cnts_org def return_contours_of_interested_textline(region_pre_p, pixel): # pixels of images are identified by 5 if len(region_pre_p.shape) == 3: cnts_images = (region_pre_p[:, :, 0] == pixel) * 1 else: cnts_images = (region_pre_p[:, :] == pixel) * 1 cnts_images = cnts_images.astype(np.uint8) cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2) imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_imgs = return_parent_contours(contours_imgs, hiearchy) contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.000000003) return contours_imgs def return_bonding_box_of_contours(cnts): boxes_tot = [] for i in range(len(cnts)): x, y, w, h = cv2.boundingRect(cnts[i]) box = [x, y, w, h] boxes_tot.append(box) return boxes_tot def return_contours_of_image(image): if len(image.shape) == 2: image = np.repeat(image[:, :, np.newaxis], 3, axis=2) image = image.astype(np.uint8) else: image = image.astype(np.uint8) imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) return contours, hierachy def return_contours_of_interested_region_by_min_size(region_pre_p, pixel, min_size=0.00003): # pixels of images are identified by 5 if len(region_pre_p.shape) == 3: cnts_images = (region_pre_p[:, :, 0] == pixel) * 1 else: cnts_images = (region_pre_p[:, :] == pixel) * 1 cnts_images = cnts_images.astype(np.uint8) cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2) imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_imgs = return_parent_contours(contours_imgs, hiearchy) contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_size) return contours_imgs def return_contours_of_interested_region_by_size(region_pre_p, pixel, min_area, max_area): # pixels of images are identified by 5 if len(region_pre_p.shape) == 3: cnts_images = (region_pre_p[:, :, 0] == pixel) * 1 else: cnts_images = (region_pre_p[:, :] == pixel) * 1 cnts_images = cnts_images.astype(np.uint8) cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2) imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_imgs = return_parent_contours(contours_imgs, hiearchy) contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area) img_ret = np.zeros((region_pre_p.shape[0], region_pre_p.shape[1], 3)) img_ret = cv2.fillPoly(img_ret, pts=contours_imgs, color=(1, 1, 1)) return img_ret[:, :, 0]