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() 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 resize_image(img_in, input_height, input_width): return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) def rotatedRectWithMaxArea(w, h, angle): if w <= 0 or h <= 0: return 0, 0 width_is_longer = w >= h side_long, side_short = (w, h) if width_is_longer else (h, w) # since the solutions for angle, -angle and 180-angle are all the same, # if suffices to look at the first quadrant and the absolute values of sin,cos: sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle)) if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10: # half constrained case: two crop corners touch the longer side, # the other two corners are on the mid-line parallel to the longer line x = 0.5 * side_short wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a) else: # fully constrained case: crop touches all 4 sides cos_2a = cos_a * cos_a - sin_a * sin_a wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a return wr, hr def rotate_max_area_new(image, rotated, angle): wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle)) h, w, _ = rotated.shape y1 = h // 2 - int(hr / 2) y2 = y1 + int(hr) x1 = w // 2 - int(wr / 2) x2 = x1 + int(wr) return rotated[y1:y2, x1:x2] 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 rotate_max_area(image, rotated, rotated_textline, rotated_layout, angle): wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle)) h, w, _ = rotated.shape y1 = h // 2 - int(hr / 2) y2 = y1 + int(hr) x1 = w // 2 - int(wr / 2) x2 = x1 + int(wr) return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2] def rotation_not_90_func(img, textline, text_regions_p_1, thetha): rotated = imutils.rotate(img, thetha) rotated_textline = imutils.rotate(textline, thetha) rotated_layout = imutils.rotate(text_regions_p_1, thetha) return rotate_max_area(img, rotated, rotated_textline, rotated_layout, thetha) def rotation_not_90_func_full_layout(img, textline, text_regions_p_1, text_regions_p_fully, thetha): rotated = imutils.rotate(img, thetha) rotated_textline = imutils.rotate(textline, thetha) rotated_layout = imutils.rotate(text_regions_p_1, thetha) rotated_layout_full = imutils.rotate(text_regions_p_fully, thetha) return rotate_max_area_full_layout(img, rotated, rotated_textline, rotated_layout, rotated_layout_full, thetha) def rotate_max_area_full_layout(image, rotated, rotated_textline, rotated_layout, rotated_layout_full, angle): wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle)) h, w, _ = rotated.shape y1 = h // 2 - int(hr / 2) y2 = y1 + int(hr) x1 = w // 2 - int(wr / 2) x2 = x1 + int(wr) return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2], rotated_layout_full[y1:y2, x1:x2] 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 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 find_features_of_lines(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))]) slope_lines = [] for kk in range(len(contours_main)): [vx, vy, x, y] = cv2.fitLine(contours_main[kk], cv2.DIST_L2, 0, 0.01, 0.01) slope_lines.append(((vy / vx) / np.pi * 180)[0]) slope_lines_org = slope_lines slope_lines = np.array(slope_lines) slope_lines[(slope_lines < 10) & (slope_lines > -10)] = 0 slope_lines[(slope_lines < -200) | (slope_lines > 200)] = 1 slope_lines[(slope_lines != 0) & (slope_lines != 1)] = 2 dis_x = np.abs(x_max_main - x_min_main) return slope_lines, dis_x, x_min_main, x_max_main, np.array(cy_main), np.array(slope_lines_org), y_min_main, y_max_main, np.array(cx_main) def isNaN(num): return num != num 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 boosting_headers_by_longshot_region_segmentation(textregion_pre_p, textregion_pre_np, img_only_text): textregion_pre_p_org = np.copy(textregion_pre_p) # 4 is drop capitals headers_in_longshot = (textregion_pre_np[:, :, 0] == 2) * 1 # headers_in_longshot= ( (textregion_pre_np[:,:,0]==2) | (textregion_pre_np[:,:,0]==1) )*1 textregion_pre_p[:, :, 0][(headers_in_longshot[:, :] == 1) & (textregion_pre_p[:, :, 0] != 4)] = 2 textregion_pre_p[:, :, 0][textregion_pre_p[:, :, 0] == 1] = 0 # textregion_pre_p[:,:,0][( img_only_text[:,:]==1) & (textregion_pre_p[:,:,0]!=7) & (textregion_pre_p[:,:,0]!=2)]=1 # eralier it was so, but by this manner the drop capitals are alse deleted textregion_pre_p[:, :, 0][(img_only_text[:, :] == 1) & (textregion_pre_p[:, :, 0] != 7) & (textregion_pre_p[:, :, 0] != 4) & (textregion_pre_p[:, :, 0] != 2)] = 1 return textregion_pre_p 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 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 seperate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_ind, add_boxes_coor_into_textlines): kernel = np.ones((5, 5), np.uint8) pixel = 255 min_area = 0 max_area = 1 if len(img_patch.shape) == 3: cnts_images = (img_patch[:, :, 0] == pixel) * 1 else: cnts_images = (img_patch[:, :] == 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) cont_final = [] ###print(add_boxes_coor_into_textlines,'ikki') for i in range(len(contours_imgs)): img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3)) img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255)) img_contour = img_contour.astype(np.uint8) img_contour = cv2.dilate(img_contour, kernel, iterations=4) imgrayrot = cv2.cvtColor(img_contour, cv2.COLOR_BGR2GRAY) _, threshrot = cv2.threshold(imgrayrot, 0, 255, 0) contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) ##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[ ##0] ##contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1] ##if add_boxes_coor_into_textlines: ##print(np.shape(contours_text_rot[0]),'sjppo') ##contours_text_rot[0][:, 0, 0]=contours_text_rot[0][:, 0, 0] + box_ind[0] ##contours_text_rot[0][:, 0, 1]=contours_text_rot[0][:, 0, 1] + box_ind[1] cont_final.append(contours_text_rot[0]) ##print(cont_final,'nadizzzz') return None, cont_final def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): (h, w) = img_patch.shape[:2] center = (w // 2, h // 2) M = cv2.getRotationMatrix2D(center, -thetha, 1.0) x_d = M[0, 2] y_d = M[1, 2] thetha = thetha / 180.0 * np.pi rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]]) contour_text_interest_copy = contour_text_interest.copy() x_cont = contour_text_interest[:, 0, 0] y_cont = contour_text_interest[:, 0, 1] x_cont = x_cont - np.min(x_cont) y_cont = y_cont - np.min(y_cont) x_min_cont = 0 x_max_cont = img_patch.shape[1] y_min_cont = 0 y_max_cont = img_patch.shape[0] xv = np.linspace(x_min_cont, x_max_cont, 1000) textline_patch_sum_along_width = img_patch.sum(axis=1) first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None)) y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero] y_padded = np.zeros(len(y) + 40) y_padded[20 : len(y) + 20] = y x = np.array(range(len(y))) peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0) if 1 > 0: try: y_padded_smoothed_e = gaussian_filter1d(y_padded, 2) y_padded_up_to_down_e = -y_padded + np.max(y_padded) y_padded_up_to_down_padded_e = np.zeros(len(y_padded_up_to_down_e) + 40) y_padded_up_to_down_padded_e[20 : len(y_padded_up_to_down_e) + 20] = y_padded_up_to_down_e y_padded_up_to_down_padded_e = gaussian_filter1d(y_padded_up_to_down_padded_e, 2) peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0) peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0) neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e]) arg_neg_must_be_deleted = np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3] diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted))) arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1] peaks_new = peaks_e[:] peaks_neg_new = peaks_neg_e[:] clusters_to_be_deleted = [] if len(arg_diff_cluster) > 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1]) clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] for m in range(len(clusters_to_be_deleted)): min_cluster = np.min(peaks_e[clusters_to_be_deleted[m]]) max_cluster = np.max(peaks_e[clusters_to_be_deleted[m]]) peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0)) for m1 in range(len(clusters_to_be_deleted[m])): peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1] - 1]] peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1]]] peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg_e[clusters_to_be_deleted[m][m1]]] peaks_new_tot = [] for i1 in peaks_new: peaks_new_tot.append(i1) for i1 in peaks_new_extra: peaks_new_tot.append(i1) peaks_new_tot = np.sort(peaks_new_tot) else: peaks_new_tot = peaks_e[:] textline_con, hierachy = return_contours_of_image(img_patch) textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008) y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) # print(sigma_gaus,'sigma_gaus') except: sigma_gaus = 12 if sigma_gaus < 3: sigma_gaus = 3 # print(sigma_gaus,'sigma') y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus) y_padded_up_to_down = -y_padded + np.max(y_padded) y_padded_up_to_down_padded = np.zeros(len(y_padded_up_to_down) + 40) y_padded_up_to_down_padded[20 : len(y_padded_up_to_down) + 20] = y_padded_up_to_down y_padded_up_to_down_padded = gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus) peaks, _ = find_peaks(y_padded_smoothed, height=0) peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0) try: neg_peaks_max = np.max(y_padded_smoothed[peaks]) arg_neg_must_be_deleted = np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42] diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted))) arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1] except: arg_neg_must_be_deleted = [] arg_diff_cluster = [] try: peaks_new = peaks[:] peaks_neg_new = peaks_neg[:] clusters_to_be_deleted = [] if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1]) clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[:]) if len(arg_neg_must_be_deleted) == 1: clusters_to_be_deleted.append(arg_neg_must_be_deleted) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] for m in range(len(clusters_to_be_deleted)): min_cluster = np.min(peaks[clusters_to_be_deleted[m]]) max_cluster = np.max(peaks[clusters_to_be_deleted[m]]) peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0)) for m1 in range(len(clusters_to_be_deleted[m])): peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1] - 1]] peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]] peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]] peaks_new_tot = [] for i1 in peaks_new: peaks_new_tot.append(i1) for i1 in peaks_new_extra: peaks_new_tot.append(i1) peaks_new_tot = np.sort(peaks_new_tot) ##plt.plot(y_padded_up_to_down_padded) ##plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*') ##plt.show() ##plt.plot(y_padded_up_to_down_padded) ##plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*') ##plt.show() ##plt.plot(y_padded_smoothed) ##plt.plot(peaks,y_padded_smoothed[peaks],'*') ##plt.show() ##plt.plot(y_padded_smoothed) ##plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*') ##plt.show() peaks = peaks_new_tot[:] peaks_neg = peaks_neg_new[:] else: peaks_new_tot = peaks[:] peaks = peaks_new_tot[:] peaks_neg = peaks_neg_new[:] except: pass mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) std_value_of_peaks = np.std(y_padded_smoothed[peaks]) peaks_values = y_padded_smoothed[peaks] peaks_neg = peaks_neg - 20 - 20 peaks = peaks - 20 for jj in range(len(peaks_neg)): if peaks_neg[jj] > len(x) - 1: peaks_neg[jj] = len(x) - 1 for jj in range(len(peaks)): if peaks[jj] > len(x) - 1: peaks[jj] = len(x) - 1 textline_boxes = [] textline_boxes_rot = [] if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3: for jj in range(len(peaks)): if jj == (len(peaks) - 1): dis_to_next_up = abs(peaks[jj] - peaks_neg[jj]) dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1]) if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0: point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) point_down = y_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) else: point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) point_down = y_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2) else: dis_to_next_up = abs(peaks[jj] - peaks_neg[jj]) dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1]) if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0: point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) else: point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2) if point_down_narrow >= img_patch.shape[0]: point_down_narrow = img_patch.shape[0] - 2 distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] if len(xvinside) == 0: x_min = x_min_cont x_max = x_max_cont else: x_min = np.min(xvinside) # max(x_min_interest,x_min_cont) x_max = np.max(xvinside) # min(x_max_interest,x_max_cont) p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)]) p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)]) p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)]) p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)]) x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d if x_min_rot1 < 0: x_min_rot1 = 0 if x_min_rot4 < 0: x_min_rot4 = 0 if point_up_rot1 < 0: point_up_rot1 = 0 if point_up_rot2 < 0: point_up_rot2 = 0 x_min_rot1 = x_min_rot1 - x_help x_max_rot2 = x_max_rot2 - x_help x_max_rot3 = x_max_rot3 - x_help x_min_rot4 = x_min_rot4 - x_help point_up_rot1 = point_up_rot1 - y_help point_up_rot2 = point_up_rot2 - y_help point_down_rot3 = point_down_rot3 - y_help point_down_rot4 = point_down_rot4 - y_help textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) elif len(peaks) < 1: pass elif len(peaks) == 1: distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[0] + first_nonzero), True) for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] if len(xvinside) == 0: x_min = x_min_cont x_max = x_max_cont else: x_min = np.min(xvinside) # max(x_min_interest,x_min_cont) x_max = np.max(xvinside) # min(x_max_interest,x_max_cont) # x_min = x_min_cont # x_max = x_max_cont y_min = y_min_cont y_max = y_max_cont p1 = np.dot(rotation_matrix, [int(x_min), int(y_min)]) p2 = np.dot(rotation_matrix, [int(x_max), int(y_min)]) p3 = np.dot(rotation_matrix, [int(x_max), int(y_max)]) p4 = np.dot(rotation_matrix, [int(x_min), int(y_max)]) x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d if x_min_rot1 < 0: x_min_rot1 = 0 if x_min_rot4 < 0: x_min_rot4 = 0 if point_up_rot1 < 0: point_up_rot1 = 0 if point_up_rot2 < 0: point_up_rot2 = 0 x_min_rot1 = x_min_rot1 - x_help x_max_rot2 = x_max_rot2 - x_help x_max_rot3 = x_max_rot3 - x_help x_min_rot4 = x_min_rot4 - x_help point_up_rot1 = point_up_rot1 - y_help point_up_rot2 = point_up_rot2 - y_help point_down_rot3 = point_down_rot3 - y_help point_down_rot4 = point_down_rot4 - y_help textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) textline_boxes.append(np.array([[int(x_min), int(y_min)], [int(x_max), int(y_min)], [int(x_max), int(y_max)], [int(x_min), int(y_max)]])) elif len(peaks) == 2: dis_to_next = np.abs(peaks[1] - peaks[0]) for jj in range(len(peaks)): if jj == 0: point_up = 0 # peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next) if point_up < 0: point_up = 1 point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next) elif jj == 1: point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next) if point_down >= img_patch.shape[0]: point_down = img_patch.shape[0] - 2 point_up = peaks[jj] + first_nonzero - int(1.0 / 1.8 * dis_to_next) distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] if len(xvinside) == 0: x_min = x_min_cont x_max = x_max_cont else: x_min = np.min(xvinside) x_max = np.max(xvinside) p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)]) p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)]) p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)]) p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)]) x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d if x_min_rot1 < 0: x_min_rot1 = 0 if x_min_rot4 < 0: x_min_rot4 = 0 if point_up_rot1 < 0: point_up_rot1 = 0 if point_up_rot2 < 0: point_up_rot2 = 0 x_min_rot1 = x_min_rot1 - x_help x_max_rot2 = x_max_rot2 - x_help x_max_rot3 = x_max_rot3 - x_help x_min_rot4 = x_min_rot4 - x_help point_up_rot1 = point_up_rot1 - y_help point_up_rot2 = point_up_rot2 - y_help point_down_rot3 = point_down_rot3 - y_help point_down_rot4 = point_down_rot4 - y_help textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) else: for jj in range(len(peaks)): if jj == 0: dis_to_next = peaks[jj + 1] - peaks[jj] # point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next) point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next) if point_up < 0: point_up = 1 # point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next) point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next) elif jj == len(peaks) - 1: dis_to_next = peaks[jj] - peaks[jj - 1] # point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next) point_down = peaks[jj] + first_nonzero + int(1.0 / 1.7 * dis_to_next) if point_down >= img_patch.shape[0]: point_down = img_patch.shape[0] - 2 # point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next) point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next) else: dis_to_next_down = peaks[jj + 1] - peaks[jj] dis_to_next_up = peaks[jj] - peaks[jj - 1] point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next_up) point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next_down) distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] if len(xvinside) == 0: x_min = x_min_cont x_max = x_max_cont else: x_min = np.min(xvinside) # max(x_min_interest,x_min_cont) x_max = np.max(xvinside) # min(x_max_interest,x_max_cont) p1 = np.dot(rotation_matrix, [int(x_min), int(point_up)]) p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)]) p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)]) p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)]) x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d if x_min_rot1 < 0: x_min_rot1 = 0 if x_min_rot4 < 0: x_min_rot4 = 0 if point_up_rot1 < 0: point_up_rot1 = 0 if point_up_rot2 < 0: point_up_rot2 = 0 x_min_rot1 = x_min_rot1 - x_help x_max_rot2 = x_max_rot2 - x_help x_max_rot3 = x_max_rot3 - x_help x_min_rot4 = x_min_rot4 - x_help point_up_rot1 = point_up_rot1 - y_help point_up_rot2 = point_up_rot2 - y_help point_down_rot3 = point_down_rot3 - y_help point_down_rot4 = point_down_rot4 - y_help textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) return peaks, textline_boxes_rot def seperate_lines_vertical(img_patch, contour_text_interest, thetha): thetha = thetha + 90 (h, w) = img_patch.shape[:2] center = (w // 2, h // 2) M = cv2.getRotationMatrix2D(center, -thetha, 1.0) x_d = M[0, 2] y_d = M[1, 2] thetha = thetha / 180.0 * np.pi rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]]) contour_text_interest_copy = contour_text_interest.copy() x_cont = contour_text_interest[:, 0, 0] y_cont = contour_text_interest[:, 0, 1] x_cont = x_cont - np.min(x_cont) y_cont = y_cont - np.min(y_cont) x_min_cont = 0 x_max_cont = img_patch.shape[1] y_min_cont = 0 y_max_cont = img_patch.shape[0] xv = np.linspace(x_min_cont, x_max_cont, 1000) textline_patch_sum_along_width = img_patch.sum(axis=0) first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None)) y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero] y_padded = np.zeros(len(y) + 40) y_padded[20 : len(y) + 20] = y x = np.array(range(len(y))) peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0) if 1 > 0: try: y_padded_smoothed_e = gaussian_filter1d(y_padded, 2) y_padded_up_to_down_e = -y_padded + np.max(y_padded) y_padded_up_to_down_padded_e = np.zeros(len(y_padded_up_to_down_e) + 40) y_padded_up_to_down_padded_e[20 : len(y_padded_up_to_down_e) + 20] = y_padded_up_to_down_e y_padded_up_to_down_padded_e = gaussian_filter1d(y_padded_up_to_down_padded_e, 2) peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0) peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0) neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e]) arg_neg_must_be_deleted = np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3] diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted))) arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1] peaks_new = peaks_e[:] peaks_neg_new = peaks_neg_e[:] clusters_to_be_deleted = [] if len(arg_diff_cluster) > 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1]) clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] for m in range(len(clusters_to_be_deleted)): min_cluster = np.min(peaks_e[clusters_to_be_deleted[m]]) max_cluster = np.max(peaks_e[clusters_to_be_deleted[m]]) peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0)) for m1 in range(len(clusters_to_be_deleted[m])): peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1] - 1]] peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1]]] peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg_e[clusters_to_be_deleted[m][m1]]] peaks_new_tot = [] for i1 in peaks_new: peaks_new_tot.append(i1) for i1 in peaks_new_extra: peaks_new_tot.append(i1) peaks_new_tot = np.sort(peaks_new_tot) else: peaks_new_tot = peaks_e[:] textline_con, hierachy = return_contours_of_image(img_patch) textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008) y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) # print(sigma_gaus,'sigma_gaus') except: sigma_gaus = 12 if sigma_gaus < 3: sigma_gaus = 3 # print(sigma_gaus,'sigma') y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus) y_padded_up_to_down = -y_padded + np.max(y_padded) y_padded_up_to_down_padded = np.zeros(len(y_padded_up_to_down) + 40) y_padded_up_to_down_padded[20 : len(y_padded_up_to_down) + 20] = y_padded_up_to_down y_padded_up_to_down_padded = gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus) peaks, _ = find_peaks(y_padded_smoothed, height=0) peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0) # plt.plot(y_padded_up_to_down_padded) # plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*') # plt.title('negs') # plt.show() # plt.plot(y_padded_smoothed) # plt.plot(peaks,y_padded_smoothed[peaks],'*') # plt.title('poss') # plt.show() neg_peaks_max = np.max(y_padded_up_to_down_padded[peaks_neg]) arg_neg_must_be_deleted = np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42] diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted))) arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1] peaks_new = peaks[:] peaks_neg_new = peaks_neg[:] clusters_to_be_deleted = [] if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1]) clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[:]) if len(arg_neg_must_be_deleted) == 1: clusters_to_be_deleted.append(arg_neg_must_be_deleted) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] for m in range(len(clusters_to_be_deleted)): min_cluster = np.min(peaks[clusters_to_be_deleted[m]]) max_cluster = np.max(peaks[clusters_to_be_deleted[m]]) peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0)) for m1 in range(len(clusters_to_be_deleted[m])): peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1] - 1]] peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]] peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]] peaks_new_tot = [] for i1 in peaks_new: peaks_new_tot.append(i1) for i1 in peaks_new_extra: peaks_new_tot.append(i1) peaks_new_tot = np.sort(peaks_new_tot) peaks = peaks_new_tot[:] peaks_neg = peaks_neg_new[:] else: peaks_new_tot = peaks[:] peaks = peaks_new_tot[:] peaks_neg = peaks_neg_new[:] mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) std_value_of_peaks = np.std(y_padded_smoothed[peaks]) peaks_values = y_padded_smoothed[peaks] peaks_neg = peaks_neg - 20 - 20 peaks = peaks - 20 for jj in range(len(peaks_neg)): if peaks_neg[jj] > len(x) - 1: peaks_neg[jj] = len(x) - 1 for jj in range(len(peaks)): if peaks[jj] > len(x) - 1: peaks[jj] = len(x) - 1 textline_boxes = [] textline_boxes_rot = [] if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3: # print('11') for jj in range(len(peaks)): if jj == (len(peaks) - 1): dis_to_next_up = abs(peaks[jj] - peaks_neg[jj]) dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1]) if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0: point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) point_down = x_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) else: point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) point_down = x_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2) else: dis_to_next_up = abs(peaks[jj] - peaks_neg[jj]) dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1]) if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0: point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) else: point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2) if point_down_narrow >= img_patch.shape[0]: point_down_narrow = img_patch.shape[0] - 2 distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] if len(xvinside) == 0: x_min = x_min_cont x_max = x_max_cont else: x_min = np.min(xvinside) # max(x_min_interest,x_min_cont) x_max = np.max(xvinside) # min(x_max_interest,x_max_cont) p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)]) p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)]) p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)]) p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)]) x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d if x_min_rot1 < 0: x_min_rot1 = 0 if x_min_rot4 < 0: x_min_rot4 = 0 if point_up_rot1 < 0: point_up_rot1 = 0 if point_up_rot2 < 0: point_up_rot2 = 0 textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) elif len(peaks) < 1: pass elif len(peaks) == 1: x_min = x_min_cont x_max = x_max_cont y_min = y_min_cont y_max = y_max_cont p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)]) p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)]) p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)]) p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)]) x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d if x_min_rot1 < 0: x_min_rot1 = 0 if x_min_rot4 < 0: x_min_rot4 = 0 if point_up_rot1 < 0: point_up_rot1 = 0 if point_up_rot2 < 0: point_up_rot2 = 0 textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) textline_boxes.append(np.array([[int(x_min), int(y_min)], [int(x_max), int(y_min)], [int(x_max), int(y_max)], [int(x_min), int(y_max)]])) elif len(peaks) == 2: dis_to_next = np.abs(peaks[1] - peaks[0]) for jj in range(len(peaks)): if jj == 0: point_up = 0 # peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next) if point_up < 0: point_up = 1 point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next) elif jj == 1: point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next) if point_down >= img_patch.shape[0]: point_down = img_patch.shape[0] - 2 point_up = peaks[jj] + first_nonzero - int(1.0 / 1.8 * dis_to_next) distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] if len(xvinside) == 0: x_min = x_min_cont x_max = x_max_cont else: x_min = np.min(xvinside) x_max = np.max(xvinside) p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)]) p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)]) p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)]) p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)]) x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d if x_min_rot1 < 0: x_min_rot1 = 0 if x_min_rot4 < 0: x_min_rot4 = 0 if point_up_rot1 < 0: point_up_rot1 = 0 if point_up_rot2 < 0: point_up_rot2 = 0 textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) else: for jj in range(len(peaks)): if jj == 0: dis_to_next = peaks[jj + 1] - peaks[jj] # point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next) point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next) if point_up < 0: point_up = 1 # point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next) point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next) elif jj == len(peaks) - 1: dis_to_next = peaks[jj] - peaks[jj - 1] # point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next) point_down = peaks[jj] + first_nonzero + int(1.0 / 1.7 * dis_to_next) if point_down >= img_patch.shape[0]: point_down = img_patch.shape[0] - 2 # point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next) point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next) else: dis_to_next_down = peaks[jj + 1] - peaks[jj] dis_to_next_up = peaks[jj] - peaks[jj - 1] point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next_up) point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next_down) distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] if len(xvinside) == 0: x_min = x_min_cont x_max = x_max_cont else: x_min = np.min(xvinside) # max(x_min_interest,x_min_cont) x_max = np.max(xvinside) # min(x_max_interest,x_max_cont) p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)]) p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)]) p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)]) p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)]) x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d if x_min_rot1 < 0: x_min_rot1 = 0 if x_min_rot4 < 0: x_min_rot4 = 0 if point_up_rot1 < 0: point_up_rot1 = 0 if point_up_rot2 < 0: point_up_rot2 = 0 textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) return peaks, textline_boxes_rot def seperate_lines_new_inside_teils2(img_patch, thetha): (h, w) = img_patch.shape[:2] center = (w // 2, h // 2) M = cv2.getRotationMatrix2D(center, -thetha, 1.0) x_d = M[0, 2] y_d = M[1, 2] thetha = thetha / 180.0 * np.pi rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]]) # contour_text_interest_copy = contour_text_interest.copy() # x_cont = contour_text_interest[:, 0, 0] # y_cont = contour_text_interest[:, 0, 1] # x_cont = x_cont - np.min(x_cont) # y_cont = y_cont - np.min(y_cont) x_min_cont = 0 x_max_cont = img_patch.shape[1] y_min_cont = 0 y_max_cont = img_patch.shape[0] xv = np.linspace(x_min_cont, x_max_cont, 1000) textline_patch_sum_along_width = img_patch.sum(axis=1) first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None)) y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero] y_padded = np.zeros(len(y) + 40) y_padded[20 : len(y) + 20] = y x = np.array(range(len(y))) peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0) if 1 > 0: try: y_padded_smoothed_e = gaussian_filter1d(y_padded, 2) y_padded_up_to_down_e = -y_padded + np.max(y_padded) y_padded_up_to_down_padded_e = np.zeros(len(y_padded_up_to_down_e) + 40) y_padded_up_to_down_padded_e[20 : len(y_padded_up_to_down_e) + 20] = y_padded_up_to_down_e y_padded_up_to_down_padded_e = gaussian_filter1d(y_padded_up_to_down_padded_e, 2) peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0) peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0) neg_peaks_max = np.max(y_padded_up_to_down_padded_e[peaks_neg_e]) arg_neg_must_be_deleted = np.array(range(len(peaks_neg_e)))[y_padded_up_to_down_padded_e[peaks_neg_e] / float(neg_peaks_max) < 0.3] diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted))) arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1] peaks_new = peaks_e[:] peaks_neg_new = peaks_neg_e[:] clusters_to_be_deleted = [] if len(arg_diff_cluster) > 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1]) clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] for m in range(len(clusters_to_be_deleted)): min_cluster = np.min(peaks_e[clusters_to_be_deleted[m]]) max_cluster = np.max(peaks_e[clusters_to_be_deleted[m]]) peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0)) for m1 in range(len(clusters_to_be_deleted[m])): peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1] - 1]] peaks_new = peaks_new[peaks_new != peaks_e[clusters_to_be_deleted[m][m1]]] peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg_e[clusters_to_be_deleted[m][m1]]] peaks_new_tot = [] for i1 in peaks_new: peaks_new_tot.append(i1) for i1 in peaks_new_extra: peaks_new_tot.append(i1) peaks_new_tot = np.sort(peaks_new_tot) else: peaks_new_tot = peaks_e[:] textline_con, hierachy = return_contours_of_image(img_patch) textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008) y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) # print(sigma_gaus,'sigma_gaus') except: sigma_gaus = 12 if sigma_gaus < 3: sigma_gaus = 3 # print(sigma_gaus,'sigma') y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus) y_padded_up_to_down = -y_padded + np.max(y_padded) y_padded_up_to_down_padded = np.zeros(len(y_padded_up_to_down) + 40) y_padded_up_to_down_padded[20 : len(y_padded_up_to_down) + 20] = y_padded_up_to_down y_padded_up_to_down_padded = gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus) peaks, _ = find_peaks(y_padded_smoothed, height=0) peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0) peaks_new = peaks[:] peaks_neg_new = peaks_neg[:] try: neg_peaks_max = np.max(y_padded_smoothed[peaks]) arg_neg_must_be_deleted = np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.24] diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) arg_diff = np.array(range(len(diff_arg_neg_must_be_deleted))) arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1] clusters_to_be_deleted = [] if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : arg_diff_cluster[i + 1] + 1]) clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[:]) if len(arg_neg_must_be_deleted) == 1: clusters_to_be_deleted.append(arg_neg_must_be_deleted) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] for m in range(len(clusters_to_be_deleted)): min_cluster = np.min(peaks[clusters_to_be_deleted[m]]) max_cluster = np.max(peaks[clusters_to_be_deleted[m]]) peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0)) for m1 in range(len(clusters_to_be_deleted[m])): peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1] - 1]] peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]] peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]] peaks_new_tot = [] for i1 in peaks_new: peaks_new_tot.append(i1) for i1 in peaks_new_extra: peaks_new_tot.append(i1) peaks_new_tot = np.sort(peaks_new_tot) # plt.plot(y_padded_up_to_down_padded) # plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*') # plt.show() # plt.plot(y_padded_up_to_down_padded) # plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*') # plt.show() # plt.plot(y_padded_smoothed) # plt.plot(peaks,y_padded_smoothed[peaks],'*') # plt.show() # plt.plot(y_padded_smoothed) # plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*') # plt.show() peaks = peaks_new_tot[:] peaks_neg = peaks_neg_new[:] except: pass else: peaks_new_tot = peaks[:] peaks = peaks_new_tot[:] peaks_neg = peaks_neg_new[:] mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) std_value_of_peaks = np.std(y_padded_smoothed[peaks]) peaks_values = y_padded_smoothed[peaks] ###peaks_neg = peaks_neg - 20 - 20 ###peaks = peaks - 20 peaks_neg_true = peaks_neg[:] peaks_pos_true = peaks[:] if len(peaks_neg_true) > 0: peaks_neg_true = np.array(peaks_neg_true) peaks_neg_true = peaks_neg_true - 20 - 20 # print(peaks_neg_true) for i in range(len(peaks_neg_true)): img_patch[peaks_neg_true[i] - 6 : peaks_neg_true[i] + 6, :] = 0 else: pass if len(peaks_pos_true) > 0: peaks_pos_true = np.array(peaks_pos_true) peaks_pos_true = peaks_pos_true - 20 for i in range(len(peaks_pos_true)): ##img_patch[peaks_pos_true[i]-8:peaks_pos_true[i]+8,:]=1 img_patch[peaks_pos_true[i] - 6 : peaks_pos_true[i] + 6, :] = 1 else: pass kernel = np.ones((5, 5), np.uint8) # img_patch = cv2.erode(img_patch,kernel,iterations = 3) #######################img_patch = cv2.erode(img_patch,kernel,iterations = 2) img_patch = cv2.erode(img_patch, kernel, iterations=1) return img_patch def filter_small_drop_capitals_from_no_patch_layout(layout_no_patch, layout1): drop_only = (layout_no_patch[:, :, 0] == 4) * 1 contours_drop, hir_on_drop = return_contours_of_image(drop_only) contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop) areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))]) areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1]) contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.001] areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001] contours_drop_parent_final = [] for jj in range(len(contours_drop_parent)): x, y, w, h = cv2.boundingRect(contours_drop_parent[jj]) # boxes.append([int(x), int(y), int(w), int(h)]) iou_of_box_and_contoure = float(drop_only.shape[0] * drop_only.shape[1]) * areas_cnt_text[jj] / float(w * h) * 100 height_to_weight_ratio = h / float(w) weigh_to_height_ratio = w / float(h) if iou_of_box_and_contoure > 60 and weigh_to_height_ratio < 1.2 and height_to_weight_ratio < 2: map_of_drop_contour_bb = np.zeros((layout1.shape[0], layout1.shape[1])) map_of_drop_contour_bb[y : y + h, x : x + w] = layout1[y : y + h, x : x + w] if (((map_of_drop_contour_bb == 1) * 1).sum() / float(((map_of_drop_contour_bb == 5) * 1).sum()) * 100) >= 15: contours_drop_parent_final.append(contours_drop_parent[jj]) layout_no_patch[:, :, 0][layout_no_patch[:, :, 0] == 4] = 0 layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4)) return layout_no_patch def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8): regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=1) meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1] first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0) last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0) last_nonzero = len(regions_without_seperators_0) - last_nonzero y = regions_without_seperators_0 # [first_nonzero:last_nonzero] y_help = np.zeros(len(y) + 20) y_help[10 : len(y) + 10] = y x = np.array(range(len(y))) zneg_rev = -y_help + np.max(y_help) zneg = np.zeros(len(zneg_rev) + 20) zneg[10 : len(zneg_rev) + 10] = zneg_rev z = gaussian_filter1d(y, sigma_) zneg = gaussian_filter1d(zneg, sigma_) peaks_neg, _ = find_peaks(zneg, height=0) peaks, _ = find_peaks(z, height=0) peaks_neg = peaks_neg - 10 - 10 # print(np.std(z),'np.std(z)np.std(z)np.std(z)') ##plt.plot(z) ##plt.show() ##plt.imshow(regions_without_seperators) ##plt.show() """ last_nonzero=last_nonzero-0#100 first_nonzero=first_nonzero+0#+100 peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])] """ interest_pos = z[peaks] interest_pos = interest_pos[interest_pos > 10] interest_neg = z[peaks_neg] min_peaks_pos = np.mean(interest_pos) min_peaks_neg = 0 # np.min(interest_neg) dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier # print(interest_pos) grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0 interest_neg_fin = interest_neg[(interest_neg < grenze)] peaks_neg_fin = peaks_neg[(interest_neg < grenze)] interest_neg_fin = interest_neg[(interest_neg < grenze)] """ if interest_neg[0]<0.1: interest_neg=interest_neg[1:] if interest_neg[len(interest_neg)-1]<0.1: interest_neg=interest_neg[:len(interest_neg)-1] min_peaks_pos=np.min(interest_pos) min_peaks_neg=0#np.min(interest_neg) dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0 """ # interest_neg_fin=interest_neg#[(interest_neg 0)] max_h_neg_n = max_h[max_h > 0] if len(max_h_neg_n) > 0: max_h_neg = [max_h_neg[np.argmin(max_h_neg_n)]] else: max_h_neg = [] if len(min_h_neg) > 0 and len(max_h_neg) > 0: deletions = list(range(min_h_neg[0] + 1, max_h_neg[0])) unique_delets_int = [] # print(deletions,len(deletions),'delii') if len(deletions) > 0: for j in range(len(deletions)): indexes_to_delete.append(deletions[j]) # print(deletions,indexes_to_delete,'badiii') unique_delets = np.unique(indexes_to_delete) # print(min_h_neg[0],unique_delets) unique_delets_int = unique_delets[unique_delets < min_h_neg[0]] indexer_lines_deletions_len.append(len(deletions)) indexr_uniq_ind.append([deletions]) else: indexer_lines_deletions_len.append(0) indexr_uniq_ind.append(-999) index_line_true = min_h_neg[0] - len(unique_delets_int) # print(index_line_true) if index_line_true > 0 and min_h_neg[0] >= 2: index_line_true = index_line_true else: index_line_true = min_h_neg[0] indexer_lines.append(index_line_true) if len(unique_delets_int) > 0: for dd in range(len(unique_delets_int)): indexes_to_delete.append(unique_delets_int[dd]) else: indexer_lines.append(-999) indexer_lines_deletions_len.append(-999) indexr_uniq_ind.append(-999) peaks_true = [] for m in range(len(peaks_neg_fin_t)): if m in indexes_to_delete: pass else: peaks_true.append(peaks_neg_fin_t[m]) return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind 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 find_num_col(regions_without_seperators, multiplier=3.8): regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0) ##plt.plot(regions_without_seperators_0) ##plt.show() sigma_ = 35 # 70#35 meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1] first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0) last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0) # print(last_nonzero) # print(isNaN(last_nonzero)) # last_nonzero=0#halalikh last_nonzero = len(regions_without_seperators_0) - last_nonzero y = regions_without_seperators_0 # [first_nonzero:last_nonzero] y_help = np.zeros(len(y) + 20) y_help[10 : len(y) + 10] = y x = np.array(range(len(y))) zneg_rev = -y_help + np.max(y_help) zneg = np.zeros(len(zneg_rev) + 20) zneg[10 : len(zneg_rev) + 10] = zneg_rev z = gaussian_filter1d(y, sigma_) zneg = gaussian_filter1d(zneg, sigma_) peaks_neg, _ = find_peaks(zneg, height=0) peaks, _ = find_peaks(z, height=0) peaks_neg = peaks_neg - 10 - 10 last_nonzero = last_nonzero - 100 first_nonzero = first_nonzero + 200 peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)] peaks = peaks[(peaks > 0.06 * regions_without_seperators.shape[1]) & (peaks < 0.94 * regions_without_seperators.shape[1])] peaks_neg = peaks_neg[(peaks_neg > 370) & (peaks_neg < (regions_without_seperators.shape[1] - 370))] # print(peaks) interest_pos = z[peaks] interest_pos = interest_pos[interest_pos > 10] # plt.plot(z) # plt.show() interest_neg = z[peaks_neg] min_peaks_pos = np.min(interest_pos) max_peaks_pos = np.max(interest_pos) if max_peaks_pos / min_peaks_pos >= 35: min_peaks_pos = np.mean(interest_pos) min_peaks_neg = 0 # np.min(interest_neg) # print(np.min(interest_pos),np.max(interest_pos),np.max(interest_pos)/np.min(interest_pos),'minmax') # $print(min_peaks_pos) dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier # print(interest_pos) grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0 # print(interest_neg,'interest_neg') # print(grenze,'grenze') # print(min_peaks_pos,'min_peaks_pos') # print(dis_talaei,'dis_talaei') # print(peaks_neg,'peaks_neg') interest_neg_fin = interest_neg[(interest_neg < grenze)] peaks_neg_fin = peaks_neg[(interest_neg < grenze)] # interest_neg_fin=interest_neg[(interest_neg p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or ((peaks_neg_fin[0] + 200) < p_m and peaks_neg_fin[1] < p_m) or ((peaks_neg_fin[0] - 200) > p_m and peaks_neg_fin[1] > p_m): num_col = 1 peaks_neg_fin = [] else: pass if num_col == 2: if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l): num_col = 1 peaks_neg_fin = [] else: pass ##print(len(peaks_neg_fin)) diff_peaks = np.abs(np.diff(peaks_neg_fin)) cut_off = 400 peaks_neg_true = [] forest = [] # print(len(peaks_neg_fin),'len_') for i in range(len(peaks_neg_fin)): if i == 0: forest.append(peaks_neg_fin[i]) if i < (len(peaks_neg_fin) - 1): if diff_peaks[i] <= cut_off: forest.append(peaks_neg_fin[i + 1]) if diff_peaks[i] > cut_off: # print(forest[np.argmin(z[forest]) ] ) if not isNaN(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) forest = [] forest.append(peaks_neg_fin[i + 1]) if i == (len(peaks_neg_fin) - 1): # print(print(forest[np.argmin(z[forest]) ] )) if not isNaN(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) num_col = (len(peaks_neg_true)) + 1 p_l = 0 p_u = len(y) - 1 p_m = int(len(y) / 2.0) p_quarter = int(len(y) / 5.0) p_g_l = int(len(y) / 4.0) p_g_u = len(y) - int(len(y) / 4.0) p_u_quarter = len(y) - p_quarter ##print(num_col,'early') if num_col == 3: if (peaks_neg_true[0] > p_g_u and peaks_neg_true[1] > p_g_u) or (peaks_neg_true[0] < p_g_l and peaks_neg_true[1] < p_g_l) or (peaks_neg_true[0] < p_m and (peaks_neg_true[1] + 200) < p_m) or ((peaks_neg_true[0] - 200) > p_m and peaks_neg_true[1] > p_m): num_col = 1 peaks_neg_true = [] elif (peaks_neg_true[0] < p_g_u and peaks_neg_true[0] > p_g_l) and (peaks_neg_true[1] > p_u_quarter): peaks_neg_true = [peaks_neg_true[0]] elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter): peaks_neg_true = [peaks_neg_true[1]] else: pass if num_col == 2: if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l): num_col = 1 peaks_neg_true = [] else: pass diff_peaks_annormal = diff_peaks[diff_peaks < 360] if len(diff_peaks_annormal) > 0: arg_help = np.array(range(len(diff_peaks))) arg_help_ann = arg_help[diff_peaks < 360] peaks_neg_fin_new = [] for ii in range(len(peaks_neg_fin)): if ii in arg_help_ann: arg_min = np.argmin([interest_neg_fin[ii], interest_neg_fin[ii + 1]]) if arg_min == 0: peaks_neg_fin_new.append(peaks_neg_fin[ii]) else: peaks_neg_fin_new.append(peaks_neg_fin[ii + 1]) elif (ii - 1) in arg_help_ann: pass else: peaks_neg_fin_new.append(peaks_neg_fin[ii]) else: peaks_neg_fin_new = peaks_neg_fin # plt.plot(gaussian_filter1d(y, sigma_)) # plt.plot(peaks_neg_true,z[peaks_neg_true],'*') # plt.plot([0,len(y)], [grenze,grenze]) # plt.show() ##print(len(peaks_neg_true)) return len(peaks_neg_true), peaks_neg_true def find_num_col_only_image(regions_without_seperators, multiplier=3.8): regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0) ##plt.plot(regions_without_seperators_0) ##plt.show() sigma_ = 15 meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1] first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0) last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0) last_nonzero = len(regions_without_seperators_0) - last_nonzero y = regions_without_seperators_0 # [first_nonzero:last_nonzero] y_help = np.zeros(len(y) + 20) y_help[10 : len(y) + 10] = y x = np.array(range(len(y))) zneg_rev = -y_help + np.max(y_help) zneg = np.zeros(len(zneg_rev) + 20) zneg[10 : len(zneg_rev) + 10] = zneg_rev z = gaussian_filter1d(y, sigma_) zneg = gaussian_filter1d(zneg, sigma_) peaks_neg, _ = find_peaks(zneg, height=0) peaks, _ = find_peaks(z, height=0) peaks_neg = peaks_neg - 10 - 10 peaks_neg_org = np.copy(peaks_neg) peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)] peaks = peaks[(peaks > 0.09 * regions_without_seperators.shape[1]) & (peaks < 0.91 * regions_without_seperators.shape[1])] peaks_neg = peaks_neg[(peaks_neg > 500) & (peaks_neg < (regions_without_seperators.shape[1] - 500))] # print(peaks) interest_pos = z[peaks] interest_pos = interest_pos[interest_pos > 10] interest_neg = z[peaks_neg] min_peaks_pos = np.mean(interest_pos) # np.min(interest_pos) min_peaks_neg = 0 # np.min(interest_neg) # $print(min_peaks_pos) dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier # print(interest_pos) grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0 interest_neg_fin = interest_neg[(interest_neg < grenze)] peaks_neg_fin = peaks_neg[(interest_neg < grenze)] num_col = (len(interest_neg_fin)) + 1 p_l = 0 p_u = len(y) - 1 p_m = int(len(y) / 2.0) p_g_l = int(len(y) / 3.0) p_g_u = len(y) - int(len(y) / 3.0) if num_col == 3: if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or (peaks_neg_fin[0] < p_m and peaks_neg_fin[1] < p_m) or (peaks_neg_fin[0] > p_m and peaks_neg_fin[1] > p_m): num_col = 1 else: pass if num_col == 2: if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l): num_col = 1 else: pass diff_peaks = np.abs(np.diff(peaks_neg_fin)) cut_off = 400 peaks_neg_true = [] forest = [] for i in range(len(peaks_neg_fin)): if i == 0: forest.append(peaks_neg_fin[i]) if i < (len(peaks_neg_fin) - 1): if diff_peaks[i] <= cut_off: forest.append(peaks_neg_fin[i + 1]) if diff_peaks[i] > cut_off: # print(forest[np.argmin(z[forest]) ] ) if not isNaN(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) forest = [] forest.append(peaks_neg_fin[i + 1]) if i == (len(peaks_neg_fin) - 1): # print(print(forest[np.argmin(z[forest]) ] )) if not isNaN(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) num_col = (len(peaks_neg_true)) + 1 p_l = 0 p_u = len(y) - 1 p_m = int(len(y) / 2.0) p_quarter = int(len(y) / 4.0) p_g_l = int(len(y) / 3.0) p_g_u = len(y) - int(len(y) / 3.0) p_u_quarter = len(y) - p_quarter if num_col == 3: if (peaks_neg_true[0] > p_g_u and peaks_neg_true[1] > p_g_u) or (peaks_neg_true[0] < p_g_l and peaks_neg_true[1] < p_g_l) or (peaks_neg_true[0] < p_m and peaks_neg_true[1] < p_m) or (peaks_neg_true[0] > p_m and peaks_neg_true[1] > p_m): num_col = 1 peaks_neg_true = [] elif (peaks_neg_true[0] < p_g_u and peaks_neg_true[0] > p_g_l) and (peaks_neg_true[1] > p_u_quarter): peaks_neg_true = [peaks_neg_true[0]] elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter): peaks_neg_true = [peaks_neg_true[1]] else: pass if num_col == 2: if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l): num_col = 1 peaks_neg_true = [] if num_col == 4: if len(np.array(peaks_neg_true)[np.array(peaks_neg_true) < p_g_l]) == 2 or len(np.array(peaks_neg_true)[np.array(peaks_neg_true) > (len(y) - p_g_l)]) == 2: num_col = 1 peaks_neg_true = [] else: pass # no deeper hill around found hills peaks_fin_true = [] for i in range(len(peaks_neg_true)): hill_main = peaks_neg_true[i] # deep_depth=z[peaks_neg] hills_around = peaks_neg_org[((peaks_neg_org > hill_main) & (peaks_neg_org <= hill_main + 400)) | ((peaks_neg_org < hill_main) & (peaks_neg_org >= hill_main - 400))] deep_depth_around = z[hills_around] # print(hill_main,z[hill_main],hills_around,deep_depth_around,'manoooo') try: if np.min(deep_depth_around) < z[hill_main]: pass else: peaks_fin_true.append(hill_main) except: pass diff_peaks_annormal = diff_peaks[diff_peaks < 360] if len(diff_peaks_annormal) > 0: arg_help = np.array(range(len(diff_peaks))) arg_help_ann = arg_help[diff_peaks < 360] peaks_neg_fin_new = [] for ii in range(len(peaks_neg_fin)): if ii in arg_help_ann: arg_min = np.argmin([interest_neg_fin[ii], interest_neg_fin[ii + 1]]) if arg_min == 0: peaks_neg_fin_new.append(peaks_neg_fin[ii]) else: peaks_neg_fin_new.append(peaks_neg_fin[ii + 1]) elif (ii - 1) in arg_help_ann: pass else: peaks_neg_fin_new.append(peaks_neg_fin[ii]) else: peaks_neg_fin_new = peaks_neg_fin # sometime pages with one columns gives also some negative peaks. delete those peaks param = z[peaks_neg_true] / float(min_peaks_pos) * 100 if len(param[param <= 41]) == 0: peaks_neg_true = [] return len(peaks_fin_true), peaks_fin_true def find_num_col_by_vertical_lines(regions_without_seperators, multiplier=3.8): regions_without_seperators_0 = regions_without_seperators[:, :, 0].sum(axis=0) ##plt.plot(regions_without_seperators_0) ##plt.show() sigma_ = 35 # 70#35 z = gaussian_filter1d(regions_without_seperators_0, sigma_) peaks, _ = find_peaks(z, height=0) # print(peaks,'peaksnew') return peaks 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 return_contours_of_interested_region_and_bounding_box(region_pre_p, pixel): # pixels of images are identified by 5 cnts_images = (region_pre_p[:, :, 0] == 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=0.0003) boxes = [] for jj in range(len(contours_imgs)): x, y, w, h = cv2.boundingRect(contours_imgs[jj]) boxes.append([int(x), int(y), int(w), int(h)]) return contours_imgs, boxes 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 seperate_lines_new_inside_teils(img_path, thetha): (h, w) = img_path.shape[:2] center = (w // 2, h // 2) M = cv2.getRotationMatrix2D(center, -thetha, 1.0) x_d = M[0, 2] y_d = M[1, 2] thetha = thetha / 180.0 * np.pi rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]]) x_min_cont = 0 x_max_cont = img_path.shape[1] y_min_cont = 0 y_max_cont = img_path.shape[0] xv = np.linspace(x_min_cont, x_max_cont, 1000) mada_n = img_path.sum(axis=1) ##plt.plot(mada_n) ##plt.show() first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None)) y = mada_n[:] # [first_nonzero:last_nonzero] y_help = np.zeros(len(y) + 40) y_help[20 : len(y) + 20] = y x = np.array(range(len(y))) peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0) if len(peaks_real) <= 2 and len(peaks_real) > 1: sigma_gaus = 10 else: sigma_gaus = 5 z = gaussian_filter1d(y_help, sigma_gaus) zneg_rev = -y_help + np.max(y_help) zneg = np.zeros(len(zneg_rev) + 40) zneg[20 : len(zneg_rev) + 20] = zneg_rev zneg = gaussian_filter1d(zneg, sigma_gaus) peaks, _ = find_peaks(z, height=0) peaks_neg, _ = find_peaks(zneg, height=0) for nn in range(len(peaks_neg)): if peaks_neg[nn] > len(z) - 1: peaks_neg[nn] = len(z) - 1 if peaks_neg[nn] < 0: peaks_neg[nn] = 0 diff_peaks = np.abs(np.diff(peaks_neg)) cut_off = 20 peaks_neg_true = [] forest = [] for i in range(len(peaks_neg)): if i == 0: forest.append(peaks_neg[i]) if i < (len(peaks_neg) - 1): if diff_peaks[i] <= cut_off: forest.append(peaks_neg[i + 1]) if diff_peaks[i] > cut_off: # print(forest[np.argmin(z[forest]) ] ) if not isNaN(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) forest = [] forest.append(peaks_neg[i + 1]) if i == (len(peaks_neg) - 1): # print(print(forest[np.argmin(z[forest]) ] )) if not isNaN(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) diff_peaks_pos = np.abs(np.diff(peaks)) cut_off = 20 peaks_pos_true = [] forest = [] for i in range(len(peaks)): if i == 0: forest.append(peaks[i]) if i < (len(peaks) - 1): if diff_peaks_pos[i] <= cut_off: forest.append(peaks[i + 1]) if diff_peaks_pos[i] > cut_off: # print(forest[np.argmin(z[forest]) ] ) if not isNaN(forest[np.argmax(z[forest])]): peaks_pos_true.append(forest[np.argmax(z[forest])]) forest = [] forest.append(peaks[i + 1]) if i == (len(peaks) - 1): # print(print(forest[np.argmin(z[forest]) ] )) if not isNaN(forest[np.argmax(z[forest])]): peaks_pos_true.append(forest[np.argmax(z[forest])]) # print(len(peaks_neg_true) ,len(peaks_pos_true) ,'lensss') if len(peaks_neg_true) > 0: peaks_neg_true = np.array(peaks_neg_true) """ #plt.figure(figsize=(40,40)) #plt.subplot(1,2,1) #plt.title('Textline segmentation von Textregion') #plt.imshow(img_path) #plt.xlabel('X') #plt.ylabel('Y') #plt.subplot(1,2,2) #plt.title('Dichte entlang X') #base = pyplot.gca().transData #rot = transforms.Affine2D().rotate_deg(90) #plt.plot(zneg,np.array(range(len(zneg)))) #plt.plot(zneg[peaks_neg_true],peaks_neg_true,'*') #plt.gca().invert_yaxis() #plt.xlabel('Dichte') #plt.ylabel('Y') ##plt.plot([0,len(y)], [grenze,grenze]) #plt.show() """ peaks_neg_true = peaks_neg_true - 20 - 20 # print(peaks_neg_true) for i in range(len(peaks_neg_true)): img_path[peaks_neg_true[i] - 6 : peaks_neg_true[i] + 6, :] = 0 else: pass if len(peaks_pos_true) > 0: peaks_pos_true = np.array(peaks_pos_true) peaks_pos_true = peaks_pos_true - 20 for i in range(len(peaks_pos_true)): img_path[peaks_pos_true[i] - 8 : peaks_pos_true[i] + 8, :] = 1 else: pass kernel = np.ones((5, 5), np.uint8) # img_path = cv2.erode(img_path,kernel,iterations = 3) img_path = cv2.erode(img_path, kernel, iterations=2) return img_path def delete_seperator_around(spliter_y, peaks_neg, image_by_region): # format of subboxes box=[x1, x2 , y1, y2] if len(image_by_region.shape) == 3: for i in range(len(spliter_y) - 1): for j in range(1, len(peaks_neg[i]) - 1): image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0] == 6] = 0 image_by_region[spliter_y[i] : spliter_y[i + 1], peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 1] == 6] = 0 image_by_region[spliter_y[i] : spliter_y[i + 1], peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 2] == 6] = 0 image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0] == 7] = 0 image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 1] == 7] = 0 image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 2] == 7] = 0 else: for i in range(len(spliter_y) - 1): for j in range(1, len(peaks_neg[i]) - 1): image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])] == 6] = 0 image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])] == 7] = 0 return image_by_region def return_regions_without_seperators(regions_pre): kernel = np.ones((5, 5), np.uint8) regions_without_seperators = ((regions_pre[:, :] != 6) & (regions_pre[:, :] != 0)) * 1 # regions_without_seperators=( (image_regions_eraly_p[:,:,:]!=6) & (image_regions_eraly_p[:,:,:]!=0) & (image_regions_eraly_p[:,:,:]!=5) & (image_regions_eraly_p[:,:,:]!=8) & (image_regions_eraly_p[:,:,:]!=7))*1 regions_without_seperators = regions_without_seperators.astype(np.uint8) regions_without_seperators = cv2.erode(regions_without_seperators, kernel, iterations=6) return regions_without_seperators def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=None, f_name=None): if main_page and dir_of_all is not None: plt.figure(figsize=(70, 40)) plt.rcParams["font.size"] = "50" plt.subplot(1, 2, 1) plt.imshow(img_patch_org) plt.subplot(1, 2, 2) plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3), np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))), linewidth=8) plt.xlabel("Density of textline prediction in direction of X axis", fontsize=60) plt.ylabel("Height", fontsize=60) plt.yticks([0, len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))]) plt.gca().invert_yaxis() plt.savefig(os.path.join(dir_of_all, f_name + "_density_of_textline.png")) # print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar') # img_patch_org=resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5)) # print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2') img_int = np.zeros((img_patch_org.shape[0], img_patch_org.shape[1])) img_int[:, :] = img_patch_org[:, :] # img_patch_org[:,:,0] img_resized = np.zeros((int(img_int.shape[0] * (1.8)), int(img_int.shape[1] * (2.6)))) img_resized[int(img_int.shape[0] * (0.4)) : int(img_int.shape[0] * (0.4)) + img_int.shape[0], int(img_int.shape[1] * (0.8)) : int(img_int.shape[1] * (0.8)) + img_int.shape[1]] = img_int[:, :] if main_page and img_patch_org.shape[1] > img_patch_org.shape[0]: # plt.imshow(img_resized) # plt.show() angels = np.array( [ -45, 0, 45, 90, ] ) # np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45]) res = [] num_of_peaks = [] index_cor = [] var_res = [] indexer = 0 for rot in angels: img_rot = rotate_image(img_resized, rot) # plt.imshow(img_rot) # plt.show() img_rot[img_rot != 0] = 1 # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 )) # neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 ) # print(var_spectrum,'var_spectrum') try: neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3) # print(rot,var_spectrum,'var_spectrum') res_me = np.mean(neg_peaks) if res_me == 0: res_me = VERY_LARGE_NUMBER else: pass res_num = len(neg_peaks) except: res_me = VERY_LARGE_NUMBER res_num = 0 var_spectrum = 0 if isNaN(res_me): pass else: res.append(res_me) var_res.append(var_spectrum) num_of_peaks.append(res_num) index_cor.append(indexer) indexer = indexer + 1 try: var_res = np.array(var_res) ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] except: ang_int = 0 angels = np.linspace(ang_int - 22.5, ang_int + 22.5, 100) res = [] num_of_peaks = [] index_cor = [] var_res = [] indexer = 0 for rot in angels: img_rot = rotate_image(img_resized, rot) ##plt.imshow(img_rot) ##plt.show() img_rot[img_rot != 0] = 1 # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 )) try: neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3) # print(indexer,'indexer') res_me = np.mean(neg_peaks) if res_me == 0: res_me = VERY_LARGE_NUMBER else: pass res_num = len(neg_peaks) except: res_me = VERY_LARGE_NUMBER res_num = 0 var_spectrum = 0 if isNaN(res_me): pass else: res.append(res_me) var_res.append(var_spectrum) num_of_peaks.append(res_num) index_cor.append(indexer) indexer = indexer + 1 try: var_res = np.array(var_res) ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] except: ang_int = 0 elif main_page and img_patch_org.shape[1] <= img_patch_org.shape[0]: # plt.imshow(img_resized) # plt.show() angels = np.linspace(-12, 12, 100) # np.array([0 , 45 , 90 , -45]) res = [] num_of_peaks = [] index_cor = [] var_res = [] indexer = 0 for rot in angels: img_rot = rotate_image(img_resized, rot) # plt.imshow(img_rot) # plt.show() img_rot[img_rot != 0] = 1 # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 )) # neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 ) # print(var_spectrum,'var_spectrum') try: neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3) # print(rot,var_spectrum,'var_spectrum') res_me = np.mean(neg_peaks) if res_me == 0: res_me = VERY_LARGE_NUMBER else: pass res_num = len(neg_peaks) except: res_me = VERY_LARGE_NUMBER res_num = 0 var_spectrum = 0 if isNaN(res_me): pass else: res.append(res_me) var_res.append(var_spectrum) num_of_peaks.append(res_num) index_cor.append(indexer) indexer = indexer + 1 if dir_of_all is not None: print("galdi?") plt.figure(figsize=(60, 30)) plt.rcParams["font.size"] = "50" plt.plot(angels, np.array(var_res), "-o", markersize=25, linewidth=4) plt.xlabel("angle", fontsize=50) plt.ylabel("variance of sum of rotated textline in direction of x axis", fontsize=50) plt.plot(angels[np.argmax(var_res)], var_res[np.argmax(np.array(var_res))], "*", markersize=50, label="Angle of deskewing=" + str("{:.2f}".format(angels[np.argmax(var_res)])) + r"$\degree$") plt.legend(loc="best") plt.savefig(os.path.join(dir_of_all, f_name + "_rotation_angle.png")) try: var_res = np.array(var_res) ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] except: ang_int = 0 early_slope_edge = 11 if abs(ang_int) > early_slope_edge and ang_int < 0: angels = np.linspace(-90, -12, 100) res = [] num_of_peaks = [] index_cor = [] var_res = [] indexer = 0 for rot in angels: img_rot = rotate_image(img_resized, rot) ##plt.imshow(img_rot) ##plt.show() img_rot[img_rot != 0] = 1 # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 )) try: neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3) # print(indexer,'indexer') res_me = np.mean(neg_peaks) if res_me == 0: res_me = VERY_LARGE_NUMBER else: pass res_num = len(neg_peaks) except: res_me = VERY_LARGE_NUMBER res_num = 0 var_spectrum = 0 if isNaN(res_me): pass else: res.append(res_me) var_res.append(var_spectrum) num_of_peaks.append(res_num) index_cor.append(indexer) indexer = indexer + 1 try: var_res = np.array(var_res) ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] except: ang_int = 0 elif abs(ang_int) > early_slope_edge and ang_int > 0: angels = np.linspace(90, 12, 100) res = [] num_of_peaks = [] index_cor = [] var_res = [] indexer = 0 for rot in angels: img_rot = rotate_image(img_resized, rot) ##plt.imshow(img_rot) ##plt.show() img_rot[img_rot != 0] = 1 # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 )) try: neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3) # print(indexer,'indexer') res_me = np.mean(neg_peaks) if res_me == 0: res_me = VERY_LARGE_NUMBER else: pass res_num = len(neg_peaks) except: res_me = VERY_LARGE_NUMBER res_num = 0 var_spectrum = 0 if isNaN(res_me): pass else: res.append(res_me) var_res.append(var_spectrum) num_of_peaks.append(res_num) index_cor.append(indexer) indexer = indexer + 1 try: var_res = np.array(var_res) ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] except: ang_int = 0 else: angels = np.linspace(-25, 25, 60) res = [] num_of_peaks = [] index_cor = [] var_res = [] indexer = 0 for rot in angels: img_rot = rotate_image(img_resized, rot) # plt.imshow(img_rot) # plt.show() img_rot[img_rot != 0] = 1 # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 )) # neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 ) # print(var_spectrum,'var_spectrum') try: neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3) # print(rot,var_spectrum,'var_spectrum') res_me = np.mean(neg_peaks) if res_me == 0: res_me = VERY_LARGE_NUMBER else: pass res_num = len(neg_peaks) except: res_me = VERY_LARGE_NUMBER res_num = 0 var_spectrum = 0 if isNaN(res_me): pass else: res.append(res_me) var_res.append(var_spectrum) num_of_peaks.append(res_num) index_cor.append(indexer) indexer = indexer + 1 try: var_res = np.array(var_res) ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] except: ang_int = 0 # print(ang_int,'ang_int') early_slope_edge = 22 if abs(ang_int) > early_slope_edge and ang_int < 0: angels = np.linspace(-90, -25, 60) res = [] num_of_peaks = [] index_cor = [] var_res = [] indexer = 0 for rot in angels: img_rot = rotate_image(img_resized, rot) ##plt.imshow(img_rot) ##plt.show() img_rot[img_rot != 0] = 1 # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 )) try: neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3) # print(indexer,'indexer') res_me = np.mean(neg_peaks) if res_me == 0: res_me = VERY_LARGE_NUMBER else: pass res_num = len(neg_peaks) except: res_me = VERY_LARGE_NUMBER res_num = 0 var_spectrum = 0 if isNaN(res_me): pass else: res.append(res_me) var_res.append(var_spectrum) num_of_peaks.append(res_num) index_cor.append(indexer) indexer = indexer + 1 try: var_res = np.array(var_res) ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] except: ang_int = 0 elif abs(ang_int) > early_slope_edge and ang_int > 0: angels = np.linspace(90, 25, 60) res = [] num_of_peaks = [] index_cor = [] var_res = [] indexer = 0 for rot in angels: img_rot = rotate_image(img_resized, rot) ##plt.imshow(img_rot) ##plt.show() img_rot[img_rot != 0] = 1 # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 )) try: neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3) # print(indexer,'indexer') res_me = np.mean(neg_peaks) if res_me == 0: res_me = VERY_LARGE_NUMBER else: pass res_num = len(neg_peaks) except: res_me = VERY_LARGE_NUMBER res_num = 0 var_spectrum = 0 if isNaN(res_me): pass else: res.append(res_me) var_res.append(var_spectrum) num_of_peaks.append(res_num) index_cor.append(indexer) indexer = indexer + 1 try: var_res = np.array(var_res) ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] except: ang_int = 0 return ang_int def put_drop_out_from_only_drop_model(layout_no_patch, layout1): drop_only = (layout_no_patch[:, :, 0] == 4) * 1 contours_drop, hir_on_drop = return_contours_of_image(drop_only) contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop) areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))]) areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1]) contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001] areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.00001] contours_drop_parent_final = [] for jj in range(len(contours_drop_parent)): x, y, w, h = cv2.boundingRect(contours_drop_parent[jj]) # boxes.append([int(x), int(y), int(w), int(h)]) map_of_drop_contour_bb = np.zeros((layout1.shape[0], layout1.shape[1])) map_of_drop_contour_bb[y : y + h, x : x + w] = layout1[y : y + h, x : x + w] if (((map_of_drop_contour_bb == 1) * 1).sum() / float(((map_of_drop_contour_bb == 5) * 1).sum()) * 100) >= 15: contours_drop_parent_final.append(contours_drop_parent[jj]) layout_no_patch[:, :, 0][layout_no_patch[:, :, 0] == 4] = 0 layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4)) return layout_no_patch def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch): drop_only = (layout_in_patch[:, :, 0] == 4) * 1 contours_drop, hir_on_drop = return_contours_of_image(drop_only) contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop) areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))]) areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1]) contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.00001] areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001] contours_drop_parent_final = [] for jj in range(len(contours_drop_parent)): x, y, w, h = cv2.boundingRect(contours_drop_parent[jj]) layout_in_patch[y : y + h, x : x + w, 0] = 4 return layout_in_patch def check_any_text_region_in_model_one_is_main_or_header(regions_model_1, regions_model_full, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered): text_only = (regions_model_1[:, :] == 1) * 1 contours_only_text, hir_on_text = return_contours_of_image(text_only) """ contours_only_text_parent=return_parent_contours( contours_only_text,hir_on_text) areas_cnt_text=np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))]) areas_cnt_text=areas_cnt_text/float(text_only.shape[0]*text_only.shape[1]) ###areas_cnt_text_h=np.array([cv2.contourArea(contours_only_text_parent_h[j]) for j in range(len(contours_only_text_parent_h))]) ###areas_cnt_text_h=areas_cnt_text_h/float(text_only_h.shape[0]*text_only_h.shape[1]) ###contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.0002] contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.00001] """ cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin = find_new_features_of_contoures(contours_only_text_parent) length_con = x_max_main - x_min_main height_con = y_max_main - y_min_main all_found_texline_polygons_main = [] all_found_texline_polygons_head = [] all_box_coord_main = [] all_box_coord_head = [] slopes_main = [] slopes_head = [] contours_only_text_parent_main = [] contours_only_text_parent_head = [] contours_only_text_parent_main_d = [] contours_only_text_parent_head_d = [] for ii in range(len(contours_only_text_parent)): con = contours_only_text_parent[ii] img = np.zeros((regions_model_1.shape[0], regions_model_1.shape[1], 3)) img = cv2.fillPoly(img, pts=[con], color=(255, 255, 255)) all_pixels = ((img[:, :, 0] == 255) * 1).sum() pixels_header = (((img[:, :, 0] == 255) & (regions_model_full[:, :, 0] == 2)) * 1).sum() pixels_main = all_pixels - pixels_header if (pixels_header >= pixels_main) and ((length_con[ii] / float(height_con[ii])) >= 1.3): regions_model_1[:, :][(regions_model_1[:, :] == 1) & (img[:, :, 0] == 255)] = 2 contours_only_text_parent_head.append(con) if contours_only_text_parent_d_ordered is not None: contours_only_text_parent_head_d.append(contours_only_text_parent_d_ordered[ii]) all_box_coord_head.append(all_box_coord[ii]) slopes_head.append(slopes[ii]) all_found_texline_polygons_head.append(all_found_texline_polygons[ii]) else: regions_model_1[:, :][(regions_model_1[:, :] == 1) & (img[:, :, 0] == 255)] = 1 contours_only_text_parent_main.append(con) if contours_only_text_parent_d_ordered is not None: contours_only_text_parent_main_d.append(contours_only_text_parent_d_ordered[ii]) all_box_coord_main.append(all_box_coord[ii]) slopes_main.append(slopes[ii]) all_found_texline_polygons_main.append(all_found_texline_polygons[ii]) # print(all_pixels,pixels_main,pixels_header) # plt.imshow(img[:,:,0]) # plt.show() return regions_model_1, contours_only_text_parent_main, contours_only_text_parent_head, all_box_coord_main, all_box_coord_head, all_found_texline_polygons_main, all_found_texline_polygons_head, slopes_main, slopes_head, contours_only_text_parent_main_d, contours_only_text_parent_head_d def small_textlines_to_parent_adherence2(textlines_con, textline_iamge, num_col): # print(textlines_con) # textlines_con=textlines_con.astype(np.uint32) textlines_con_changed = [] for m1 in range(len(textlines_con)): # textlines_tot=textlines_con[m1] # textlines_tot=textlines_tot.astype() textlines_tot = [] textlines_tot_org_form = [] # print(textlines_tot) for nn in range(len(textlines_con[m1])): textlines_tot.append(np.array(textlines_con[m1][nn], dtype=np.int32)) textlines_tot_org_form.append(textlines_con[m1][nn]) ##img_text_all=np.zeros((textline_iamge.shape[0],textline_iamge.shape[1])) ##img_text_all=cv2.fillPoly(img_text_all, pts =textlines_tot , color=(1,1,1)) ##plt.imshow(img_text_all) ##plt.show() areas_cnt_text = np.array([cv2.contourArea(textlines_tot[j]) for j in range(len(textlines_tot))]) areas_cnt_text = areas_cnt_text / float(textline_iamge.shape[0] * textline_iamge.shape[1]) indexes_textlines = np.array(range(len(textlines_tot))) # print(areas_cnt_text,np.min(areas_cnt_text),np.max(areas_cnt_text)) if num_col == 0: min_area = 0.0004 elif num_col == 1: min_area = 0.0003 else: min_area = 0.0001 indexes_textlines_small = indexes_textlines[areas_cnt_text < min_area] # print(indexes_textlines) textlines_small = [] textlines_small_org_form = [] for i in indexes_textlines_small: textlines_small.append(textlines_tot[i]) textlines_small_org_form.append(textlines_tot_org_form[i]) textlines_big = [] textlines_big_org_form = [] for i in list(set(indexes_textlines) - set(indexes_textlines_small)): textlines_big.append(textlines_tot[i]) textlines_big_org_form.append(textlines_tot_org_form[i]) img_textline_s = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1])) img_textline_s = cv2.fillPoly(img_textline_s, pts=textlines_small, color=(1, 1, 1)) img_textline_b = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1])) img_textline_b = cv2.fillPoly(img_textline_b, pts=textlines_big, color=(1, 1, 1)) sum_small_big_all = img_textline_s + img_textline_b sum_small_big_all2 = (sum_small_big_all[:, :] == 2) * 1 sum_intersection_sb = sum_small_big_all2.sum(axis=1).sum() if sum_intersection_sb > 0: dis_small_from_bigs_tot = [] for z1 in range(len(textlines_small)): # print(len(textlines_small),'small') intersections = [] for z2 in range(len(textlines_big)): img_text = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1])) img_text = cv2.fillPoly(img_text, pts=[textlines_small[z1]], color=(1, 1, 1)) img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1])) img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z2]], color=(1, 1, 1)) sum_small_big = img_text2 + img_text sum_small_big_2 = (sum_small_big[:, :] == 2) * 1 sum_intersection = sum_small_big_2.sum(axis=1).sum() # print(sum_intersection) intersections.append(sum_intersection) if len(np.array(intersections)[np.array(intersections) > 0]) == 0: intersections = [] try: dis_small_from_bigs_tot.append(np.argmax(intersections)) except: dis_small_from_bigs_tot.append(-1) smalls_list = np.array(dis_small_from_bigs_tot)[np.array(dis_small_from_bigs_tot) >= 0] # index_small_textlines_rest=list( set(indexes_textlines_small)-set(smalls_list) ) textlines_big_with_change = [] textlines_big_with_change_con = [] textlines_small_with_change = [] for z in list(set(smalls_list)): index_small_textlines = list(np.where(np.array(dis_small_from_bigs_tot) == z)[0]) # print(z,index_small_textlines) img_text2 = np.zeros((textline_iamge.shape[0], textline_iamge.shape[1], 3)) img_text2 = cv2.fillPoly(img_text2, pts=[textlines_big[z]], color=(255, 255, 255)) textlines_big_with_change.append(z) for k in index_small_textlines: img_text2 = cv2.fillPoly(img_text2, pts=[textlines_small[k]], color=(255, 255, 255)) textlines_small_with_change.append(k) img_text2 = img_text2.astype(np.uint8) imgray = cv2.cvtColor(img_text2, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) cont, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # print(cont[0],type(cont)) textlines_big_with_change_con.append(cont) textlines_big_org_form[z] = cont[0] # plt.imshow(img_text2) # plt.show() # print(textlines_big_with_change,'textlines_big_with_change') # print(textlines_small_with_change,'textlines_small_with_change') # print(textlines_big) textlines_con_changed.append(textlines_big_org_form) else: textlines_con_changed.append(textlines_big_org_form) return textlines_con_changed 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] def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region_h, matrix_of_orders, indexes_sorted, index_of_types, kind_of_texts, ref_point): indexes_sorted = np.array(indexes_sorted) index_of_types = np.array(index_of_types) kind_of_texts = np.array(kind_of_texts) id_of_texts = [] order_of_texts = [] index_of_types_1 = index_of_types[kind_of_texts == 1] indexes_sorted_1 = indexes_sorted[kind_of_texts == 1] index_of_types_2 = index_of_types[kind_of_texts == 2] indexes_sorted_2 = indexes_sorted[kind_of_texts == 2] ##print(index_of_types,'index_of_types') ##print(kind_of_texts,'kind_of_texts') ##print(len(found_polygons_text_region),'found_polygons_text_region') ##print(index_of_types_1,'index_of_types_1') ##print(indexes_sorted_1,'indexes_sorted_1') index_b = 0 + ref_point for mm in range(len(found_polygons_text_region)): id_of_texts.append("r" + str(index_b)) interest = indexes_sorted_1[indexes_sorted_1 == index_of_types_1[mm]] if len(interest) > 0: order_of_texts.append(interest[0]) index_b += 1 else: pass for mm in range(len(found_polygons_text_region_h)): id_of_texts.append("r" + str(index_b)) interest = indexes_sorted_2[index_of_types_2[mm]] order_of_texts.append(interest) index_b += 1 return order_of_texts, id_of_texts def order_of_regions(textline_mask, contours_main, contours_header, y_ref): ##plt.imshow(textline_mask) ##plt.show() """ print(len(contours_main),'contours_main') mada_n=textline_mask.sum(axis=1) y=mada_n[:] y_help=np.zeros(len(y)+40) y_help[20:len(y)+20]=y x=np.array( range(len(y)) ) peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0) ##plt.imshow(textline_mask[:,:]) ##plt.show() sigma_gaus=8 z= gaussian_filter1d(y_help, sigma_gaus) zneg_rev=-y_help+np.max(y_help) zneg=np.zeros(len(zneg_rev)+40) zneg[20:len(zneg_rev)+20]=zneg_rev zneg= gaussian_filter1d(zneg, sigma_gaus) peaks, _ = find_peaks(z, height=0) peaks_neg, _ = find_peaks(zneg, height=0) peaks_neg=peaks_neg-20-20 peaks=peaks-20 """ textline_sum_along_width = textline_mask.sum(axis=1) y = textline_sum_along_width[:] y_padded = np.zeros(len(y) + 40) y_padded[20 : len(y) + 20] = y x = np.array(range(len(y))) peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0) sigma_gaus = 8 z = gaussian_filter1d(y_padded, sigma_gaus) zneg_rev = -y_padded + np.max(y_padded) zneg = np.zeros(len(zneg_rev) + 40) zneg[20 : len(zneg_rev) + 20] = zneg_rev zneg = gaussian_filter1d(zneg, sigma_gaus) peaks, _ = find_peaks(z, height=0) peaks_neg, _ = find_peaks(zneg, height=0) peaks_neg = peaks_neg - 20 - 20 peaks = peaks - 20 ##plt.plot(z) ##plt.show() if contours_main != None: 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))]) if len(contours_header) != None: areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))]) M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))] cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))] cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))] x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))]) x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))]) y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))]) y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))]) # print(cy_main,'mainy') peaks_neg_new = [] peaks_neg_new.append(0 + y_ref) for iii in range(len(peaks_neg)): peaks_neg_new.append(peaks_neg[iii] + y_ref) peaks_neg_new.append(textline_mask.shape[0] + y_ref) if len(cy_main) > 0 and np.max(cy_main) > np.max(peaks_neg_new): cy_main = np.array(cy_main) * (np.max(peaks_neg_new) / np.max(cy_main)) - 10 if contours_main != None: indexer_main = np.array(range(len(contours_main))) if contours_main != None: len_main = len(contours_main) else: len_main = 0 matrix_of_orders = np.zeros((len(contours_main) + len(contours_header), 5)) matrix_of_orders[:, 0] = np.array(range(len(contours_main) + len(contours_header))) matrix_of_orders[: len(contours_main), 1] = 1 matrix_of_orders[len(contours_main) :, 1] = 2 matrix_of_orders[: len(contours_main), 2] = cx_main matrix_of_orders[len(contours_main) :, 2] = cx_header matrix_of_orders[: len(contours_main), 3] = cy_main matrix_of_orders[len(contours_main) :, 3] = cy_header matrix_of_orders[: len(contours_main), 4] = np.array(range(len(contours_main))) matrix_of_orders[len(contours_main) :, 4] = np.array(range(len(contours_header))) # print(peaks_neg_new,'peaks_neg_new') # print(matrix_of_orders,'matrix_of_orders') # print(peaks_neg_new,np.max(peaks_neg_new)) final_indexers_sorted = [] final_types = [] final_index_type = [] for i in range(len(peaks_neg_new) - 1): top = peaks_neg_new[i] down = peaks_neg_new[i + 1] # print(top,down,'topdown') indexes_in = matrix_of_orders[:, 0][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))] cxs_in = matrix_of_orders[:, 2][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))] cys_in = matrix_of_orders[:, 3][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))] types_of_text = matrix_of_orders[:, 1][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))] index_types_of_text = matrix_of_orders[:, 4][(matrix_of_orders[:, 3] >= top) & ((matrix_of_orders[:, 3] < down))] # print(top,down) # print(cys_in,'cyyyins') # print(indexes_in,'indexes') sorted_inside = np.argsort(cxs_in) ind_in_int = indexes_in[sorted_inside] ind_in_type = types_of_text[sorted_inside] ind_ind_type = index_types_of_text[sorted_inside] for j in range(len(ind_in_int)): final_indexers_sorted.append(int(ind_in_int[j])) final_types.append(int(ind_in_type[j])) final_index_type.append(int(ind_ind_type[j])) ##matrix_of_orders[:len_main,4]=final_indexers_sorted[:] # print(peaks_neg_new,'peaks') # print(final_indexers_sorted,'indexsorted') # print(final_types,'types') # print(final_index_type,'final_index_type') return final_indexers_sorted, matrix_of_orders, final_types, final_index_type def implent_law_head_main_not_parallel(text_regions): # print(text_regions.shape) text_indexes = [1, 2] # 1: main text , 2: header , 3: comments for t_i in text_indexes: textline_mask = text_regions[:, :] == t_i textline_mask = textline_mask * 255.0 textline_mask = textline_mask.astype(np.uint8) textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) kernel = np.ones((5, 5), np.uint8) # print(type(textline_mask),np.unique(textline_mask),textline_mask.shape) imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) if t_i == 1: contours_main, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # print(type(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))]) # print(contours_main[0],np.shape(contours_main[0]),contours_main[0][:,0,0]) elif t_i == 2: contours_header, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # print(type(contours_header)) areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))]) M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))] cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))] cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))] x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))]) x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))]) y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))]) y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))]) args = np.array(range(1, len(cy_header) + 1)) args_main = np.array(range(1, len(cy_main) + 1)) for jj in range(len(contours_main)): headers_in_main = [(cy_header > y_min_main[jj]) & ((cy_header < y_max_main[jj]))] mains_in_main = [(cy_main > y_min_main[jj]) & ((cy_main < y_max_main[jj]))] args_log = args * headers_in_main res = args_log[args_log > 0] res_true = res - 1 args_log_main = args_main * mains_in_main res_main = args_log_main[args_log_main > 0] res_true_main = res_main - 1 if len(res_true) > 0: sum_header = np.sum(areas_header[res_true]) sum_main = np.sum(areas_main[res_true_main]) if sum_main > sum_header: cnt_int = [contours_header[j] for j in res_true] text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(1, 1, 1)) else: cnt_int = [contours_main[j] for j in res_true_main] text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(2, 2, 2)) for jj in range(len(contours_header)): main_in_header = [(cy_main > y_min_header[jj]) & ((cy_main < y_max_header[jj]))] header_in_header = [(cy_header > y_min_header[jj]) & ((cy_header < y_max_header[jj]))] args_log = args_main * main_in_header res = args_log[args_log > 0] res_true = res - 1 args_log_header = args * header_in_header res_header = args_log_header[args_log_header > 0] res_true_header = res_header - 1 if len(res_true) > 0: sum_header = np.sum(areas_header[res_true_header]) sum_main = np.sum(areas_main[res_true]) if sum_main > sum_header: cnt_int = [contours_header[j] for j in res_true_header] text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(1, 1, 1)) else: cnt_int = [contours_main[j] for j in res_true] text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(2, 2, 2)) return text_regions def return_hor_spliter_by_index(peaks_neg_fin_t, x_min_hor_some, x_max_hor_some): arg_min_hor_sort = np.argsort(x_min_hor_some) x_min_hor_some_sort = np.sort(x_min_hor_some) x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort] arg_minmax = np.array(range(len(peaks_neg_fin_t))) indexer_lines = [] indexes_to_delete = [] indexer_lines_deletions_len = [] indexr_uniq_ind = [] for i in range(len(x_min_hor_some_sort)): min_h = peaks_neg_fin_t - x_min_hor_some_sort[i] max_h = peaks_neg_fin_t - x_max_hor_some_sort[i] min_h[0] = min_h[0] # +20 max_h[len(max_h) - 1] = max_h[len(max_h) - 1] ##-20 min_h_neg = arg_minmax[(min_h < 0) & (np.abs(min_h) < 360)] max_h_neg = arg_minmax[(max_h >= 0) & (np.abs(max_h) < 360)] if len(min_h_neg) > 0 and len(max_h_neg) > 0: deletions = list(range(min_h_neg[0] + 1, max_h_neg[0])) unique_delets_int = [] # print(deletions,len(deletions),'delii') if len(deletions) > 0: # print(deletions,len(deletions),'delii2') for j in range(len(deletions)): indexes_to_delete.append(deletions[j]) # print(deletions,indexes_to_delete,'badiii') unique_delets = np.unique(indexes_to_delete) # print(min_h_neg[0],unique_delets) unique_delets_int = unique_delets[unique_delets < min_h_neg[0]] indexer_lines_deletions_len.append(len(deletions)) indexr_uniq_ind.append([deletions]) else: indexer_lines_deletions_len.append(0) indexr_uniq_ind.append(-999) index_line_true = min_h_neg[0] - len(unique_delets_int) # print(index_line_true) if index_line_true > 0 and min_h_neg[0] >= 2: index_line_true = index_line_true else: index_line_true = min_h_neg[0] indexer_lines.append(index_line_true) if len(unique_delets_int) > 0: for dd in range(len(unique_delets_int)): indexes_to_delete.append(unique_delets_int[dd]) else: indexer_lines.append(-999) indexer_lines_deletions_len.append(-999) indexr_uniq_ind.append(-999) peaks_true = [] for m in range(len(peaks_neg_fin_t)): if m in indexes_to_delete: pass else: peaks_true.append(peaks_neg_fin_t[m]) return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(img_p_in_ver, img_in_hor): # plt.imshow(img_in_hor) # plt.show() # img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2) img_p_in_ver = img_p_in_ver.astype(np.uint8) img_p_in_ver = np.repeat(img_p_in_ver[:, :, np.newaxis], 3, axis=2) imgray = cv2.cvtColor(img_p_in_ver, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_lines_ver, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) slope_lines_ver, dist_x_ver, x_min_main_ver, x_max_main_ver, cy_main_ver, slope_lines_org_ver, y_min_main_ver, y_max_main_ver, cx_main_ver = find_features_of_lines(contours_lines_ver) for i in range(len(x_min_main_ver)): img_p_in_ver[int(y_min_main_ver[i]) : int(y_min_main_ver[i]) + 30, int(cx_main_ver[i]) - 25 : int(cx_main_ver[i]) + 25, 0] = 0 img_p_in_ver[int(y_max_main_ver[i]) - 30 : int(y_max_main_ver[i]), int(cx_main_ver[i]) - 25 : int(cx_main_ver[i]) + 25, 0] = 0 # plt.imshow(img_p_in_ver[:,:,0]) # plt.show() img_in_hor = img_in_hor.astype(np.uint8) img_in_hor = np.repeat(img_in_hor[:, :, np.newaxis], 3, axis=2) imgray = cv2.cvtColor(img_in_hor, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_lines_hor, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) slope_lines_hor, dist_x_hor, x_min_main_hor, x_max_main_hor, cy_main_hor, slope_lines_org_hor, y_min_main_hor, y_max_main_hor, cx_main_hor = find_features_of_lines(contours_lines_hor) args_hor = np.array(range(len(slope_lines_hor))) all_args_uniq = contours_in_same_horizon(cy_main_hor) # print(all_args_uniq,'all_args_uniq') if len(all_args_uniq) > 0: if type(all_args_uniq[0]) is list: special_seperators = [] contours_new = [] for dd in range(len(all_args_uniq)): merged_all = None some_args = args_hor[all_args_uniq[dd]] some_cy = cy_main_hor[all_args_uniq[dd]] some_x_min = x_min_main_hor[all_args_uniq[dd]] some_x_max = x_max_main_hor[all_args_uniq[dd]] # img_in=np.zeros(seperators_closeup_n[:,:,2].shape) for jv in range(len(some_args)): img_p_in = cv2.fillPoly(img_in_hor, pts=[contours_lines_hor[some_args[jv]]], color=(1, 1, 1)) img_p_in[int(np.mean(some_cy)) - 5 : int(np.mean(some_cy)) + 5, int(np.min(some_x_min)) : int(np.max(some_x_max))] = 1 sum_dis = dist_x_hor[some_args].sum() diff_max_min_uniques = np.max(x_max_main_hor[some_args]) - np.min(x_min_main_hor[some_args]) # print( sum_dis/float(diff_max_min_uniques) ,diff_max_min_uniques/float(img_p_in_ver.shape[1]),dist_x_hor[some_args].sum(),diff_max_min_uniques,np.mean( dist_x_hor[some_args]),np.std( dist_x_hor[some_args]) ) if diff_max_min_uniques > sum_dis and ((sum_dis / float(diff_max_min_uniques)) > 0.85) and ((diff_max_min_uniques / float(img_p_in_ver.shape[1])) > 0.85) and np.std(dist_x_hor[some_args]) < (0.55 * np.mean(dist_x_hor[some_args])): # print(dist_x_hor[some_args],dist_x_hor[some_args].sum(),np.min(x_min_main_hor[some_args]) ,np.max(x_max_main_hor[some_args]),'jalibdi') # print(np.mean( dist_x_hor[some_args] ),np.std( dist_x_hor[some_args] ),np.var( dist_x_hor[some_args] ),'jalibdiha') special_seperators.append(np.mean(cy_main_hor[some_args])) else: img_p_in = img_in_hor special_seperators = [] else: img_p_in = img_in_hor special_seperators = [] img_p_in_ver[:, :, 0][img_p_in_ver[:, :, 0] == 255] = 1 # print(img_p_in_ver.shape,np.unique(img_p_in_ver[:,:,0])) # plt.imshow(img_p_in[:,:,0]) # plt.show() # plt.imshow(img_p_in_ver[:,:,0]) # plt.show() sep_ver_hor = img_p_in + img_p_in_ver # print(sep_ver_hor.shape,np.unique(sep_ver_hor[:,:,0]),'sep_ver_horsep_ver_horsep_ver_hor') # plt.imshow(sep_ver_hor[:,:,0]) # plt.show() sep_ver_hor_cross = (sep_ver_hor[:, :, 0] == 2) * 1 sep_ver_hor_cross = np.repeat(sep_ver_hor_cross[:, :, np.newaxis], 3, axis=2) sep_ver_hor_cross = sep_ver_hor_cross.astype(np.uint8) imgray = cv2.cvtColor(sep_ver_hor_cross, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_cross, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cx_cross, cy_cross, _, _, _, _, _ = find_new_features_of_contoures(contours_cross) for ii in range(len(cx_cross)): img_p_in[int(cy_cross[ii]) - 30 : int(cy_cross[ii]) + 30, int(cx_cross[ii]) + 5 : int(cx_cross[ii]) + 40, 0] = 0 img_p_in[int(cy_cross[ii]) - 30 : int(cy_cross[ii]) + 30, int(cx_cross[ii]) - 40 : int(cx_cross[ii]) - 4, 0] = 0 # plt.imshow(img_p_in[:,:,0]) # plt.show() return img_p_in[:, :, 0], special_seperators def return_points_with_boundies(peaks_neg_fin, first_point, last_point): peaks_neg_tot = [] peaks_neg_tot.append(first_point) for ii in range(len(peaks_neg_fin)): peaks_neg_tot.append(peaks_neg_fin[ii]) peaks_neg_tot.append(last_point) return peaks_neg_tot def textline_contours_postprocessing(textline_mask, slope, contour_text_interest, box_ind, slope_first, add_boxes_coor_into_textlines=False): textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255 textline_mask = textline_mask.astype(np.uint8) kernel = np.ones((5, 5), np.uint8) textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, kernel) textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, kernel) textline_mask = cv2.erode(textline_mask, kernel, iterations=2) # textline_mask = cv2.erode(textline_mask, kernel, iterations=1) # print(textline_mask.shape[0]/float(textline_mask.shape[1]),'miz') try: # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: # plt.imshow(textline_mask) # plt.show() # if abs(slope)>1: # x_help=30 # y_help=2 # else: # x_help=2 # y_help=2 x_help = 30 y_help = 2 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 = rotate_image(textline_mask_help, slope) dst = dst[:, :, 0] dst[dst != 0] = 1 # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: # plt.imshow(dst) # plt.show() contour_text_copy = contour_text_interest.copy() contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[0] contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1] img_contour = np.zeros((box_ind[3], box_ind[2], 3)) img_contour = cv2.fillPoly(img_contour, pts=[contour_text_copy], color=(255, 255, 255)) # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: # plt.imshow(img_contour) # plt.show() img_contour_help = np.zeros((img_contour.shape[0] + int(2 * y_help), img_contour.shape[1] + int(2 * x_help), 3)) 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 = rotate_image(img_contour_help, slope) # plt.imshow(img_contour_rot_help) # plt.show() # plt.imshow(dst_help) # plt.show() # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: # plt.imshow(img_contour_rot_help) # plt.show() # plt.imshow(dst_help) # plt.show() img_contour_rot = img_contour_rot.astype(np.uint8) # dst_help = dst_help.astype(np.uint8) imgrayrot = cv2.cvtColor(img_contour_rot, cv2.COLOR_BGR2GRAY) _, threshrot = cv2.threshold(imgrayrot, 0, 255, 0) contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) len_con_text_rot = [len(contours_text_rot[ib]) for ib in range(len(contours_text_rot))] ind_big_con = np.argmax(len_con_text_rot) # print('juzaa') if abs(slope) > 45: # print(add_boxes_coor_into_textlines,'avval') _, contours_rotated_clean = seperate_lines_vertical_cont(textline_mask, contours_text_rot[ind_big_con], box_ind, slope, add_boxes_coor_into_textlines=add_boxes_coor_into_textlines) else: _, contours_rotated_clean = seperate_lines(dst, contours_text_rot[ind_big_con], slope, x_help, y_help) except: contours_rotated_clean = [] return contours_rotated_clean def find_number_of_columns_in_document(region_pre_p, num_col_classifier, pixel_lines, contours_h=None): seperators_closeup = ((region_pre_p[:, :, :] == pixel_lines)) * 1 seperators_closeup[0:110, :, :] = 0 seperators_closeup[seperators_closeup.shape[0] - 150 :, :, :] = 0 kernel = np.ones((5, 5), np.uint8) seperators_closeup = seperators_closeup.astype(np.uint8) seperators_closeup = cv2.dilate(seperators_closeup, kernel, iterations=1) seperators_closeup = cv2.erode(seperators_closeup, kernel, iterations=1) ##plt.imshow(seperators_closeup[:,:,0]) ##plt.show() seperators_closeup_new = np.zeros((seperators_closeup.shape[0], seperators_closeup.shape[1])) ##_,seperators_closeup_n=self.combine_hor_lines_and_delete_cross_points_and_get_lines_features_back(region_pre_p[:,:,0]) seperators_closeup_n = np.copy(seperators_closeup) seperators_closeup_n = seperators_closeup_n.astype(np.uint8) ##plt.imshow(seperators_closeup_n[:,:,0]) ##plt.show() seperators_closeup_n_binary = np.zeros((seperators_closeup_n.shape[0], seperators_closeup_n.shape[1])) seperators_closeup_n_binary[:, :] = seperators_closeup_n[:, :, 0] seperators_closeup_n_binary[:, :][seperators_closeup_n_binary[:, :] != 0] = 1 # seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==0]=255 # seperators_closeup_n_binary[:,:][seperators_closeup_n_binary[:,:]==-255]=0 # seperators_closeup_n_binary=(seperators_closeup_n_binary[:,:]==2)*1 # gray = cv2.cvtColor(seperators_closeup_n, cv2.COLOR_BGR2GRAY) # print(np.unique(seperators_closeup_n_binary)) ##plt.imshow(seperators_closeup_n_binary) ##plt.show() # print( np.unique(gray),np.unique(seperators_closeup_n[:,:,1]) ) gray = cv2.bitwise_not(seperators_closeup_n_binary) gray = gray.astype(np.uint8) ##plt.imshow(gray) ##plt.show() bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 15, -2) ##plt.imshow(bw[:,:]) ##plt.show() horizontal = np.copy(bw) vertical = np.copy(bw) cols = horizontal.shape[1] horizontal_size = cols // 30 # Create structure element for extracting horizontal lines through morphology operations horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1)) # Apply morphology operations horizontal = cv2.erode(horizontal, horizontalStructure) horizontal = cv2.dilate(horizontal, horizontalStructure) kernel = np.ones((5, 5), np.uint8) horizontal = cv2.dilate(horizontal, kernel, iterations=2) horizontal = cv2.erode(horizontal, kernel, iterations=2) # plt.imshow(horizontal) # plt.show() rows = vertical.shape[0] verticalsize = rows // 30 # Create structure element for extracting vertical lines through morphology operations verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize)) # Apply morphology operations vertical = cv2.erode(vertical, verticalStructure) vertical = cv2.dilate(vertical, verticalStructure) vertical = cv2.dilate(vertical, kernel, iterations=1) # Show extracted vertical lines horizontal, special_seperators = combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(vertical, horizontal) ##plt.imshow(vertical) ##plt.show() # print(vertical.shape,np.unique(vertical),'verticalvertical') seperators_closeup_new[:, :][vertical[:, :] != 0] = 1 seperators_closeup_new[:, :][horizontal[:, :] != 0] = 1 ##plt.imshow(seperators_closeup_new) ##plt.show() ##seperators_closeup_n vertical = np.repeat(vertical[:, :, np.newaxis], 3, axis=2) vertical = vertical.astype(np.uint8) ##plt.plot(vertical[:,:,0].sum(axis=0)) ##plt.show() # plt.plot(vertical[:,:,0].sum(axis=1)) # plt.show() imgray = cv2.cvtColor(vertical, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_line_vers, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = find_features_of_lines(contours_line_vers) # print(slope_lines,'vertical') args = np.array(range(len(slope_lines))) args_ver = args[slope_lines == 1] dist_x_ver = dist_x[slope_lines == 1] y_min_main_ver = y_min_main[slope_lines == 1] y_max_main_ver = y_max_main[slope_lines == 1] x_min_main_ver = x_min_main[slope_lines == 1] x_max_main_ver = x_max_main[slope_lines == 1] cx_main_ver = cx_main[slope_lines == 1] dist_y_ver = y_max_main_ver - y_min_main_ver len_y = seperators_closeup.shape[0] / 3.0 # plt.imshow(horizontal) # plt.show() horizontal = np.repeat(horizontal[:, :, np.newaxis], 3, axis=2) horizontal = horizontal.astype(np.uint8) imgray = cv2.cvtColor(horizontal, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_line_hors, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = find_features_of_lines(contours_line_hors) slope_lines_org_hor = slope_lines_org[slope_lines == 0] args = np.array(range(len(slope_lines))) len_x = seperators_closeup.shape[1] / 5.0 dist_y = np.abs(y_max_main - y_min_main) args_hor = args[slope_lines == 0] dist_x_hor = dist_x[slope_lines == 0] y_min_main_hor = y_min_main[slope_lines == 0] y_max_main_hor = y_max_main[slope_lines == 0] x_min_main_hor = x_min_main[slope_lines == 0] x_max_main_hor = x_max_main[slope_lines == 0] dist_y_hor = dist_y[slope_lines == 0] cy_main_hor = cy_main[slope_lines == 0] args_hor = args_hor[dist_x_hor >= len_x / 2.0] x_max_main_hor = x_max_main_hor[dist_x_hor >= len_x / 2.0] x_min_main_hor = x_min_main_hor[dist_x_hor >= len_x / 2.0] cy_main_hor = cy_main_hor[dist_x_hor >= len_x / 2.0] y_min_main_hor = y_min_main_hor[dist_x_hor >= len_x / 2.0] y_max_main_hor = y_max_main_hor[dist_x_hor >= len_x / 2.0] dist_y_hor = dist_y_hor[dist_x_hor >= len_x / 2.0] slope_lines_org_hor = slope_lines_org_hor[dist_x_hor >= len_x / 2.0] dist_x_hor = dist_x_hor[dist_x_hor >= len_x / 2.0] matrix_of_lines_ch = np.zeros((len(cy_main_hor) + len(cx_main_ver), 10)) matrix_of_lines_ch[: len(cy_main_hor), 0] = args_hor matrix_of_lines_ch[len(cy_main_hor) :, 0] = args_ver matrix_of_lines_ch[len(cy_main_hor) :, 1] = cx_main_ver matrix_of_lines_ch[: len(cy_main_hor), 2] = x_min_main_hor + 50 # x_min_main_hor+150 matrix_of_lines_ch[len(cy_main_hor) :, 2] = x_min_main_ver matrix_of_lines_ch[: len(cy_main_hor), 3] = x_max_main_hor - 50 # x_max_main_hor-150 matrix_of_lines_ch[len(cy_main_hor) :, 3] = x_max_main_ver matrix_of_lines_ch[: len(cy_main_hor), 4] = dist_x_hor matrix_of_lines_ch[len(cy_main_hor) :, 4] = dist_x_ver matrix_of_lines_ch[: len(cy_main_hor), 5] = cy_main_hor matrix_of_lines_ch[: len(cy_main_hor), 6] = y_min_main_hor matrix_of_lines_ch[len(cy_main_hor) :, 6] = y_min_main_ver matrix_of_lines_ch[: len(cy_main_hor), 7] = y_max_main_hor matrix_of_lines_ch[len(cy_main_hor) :, 7] = y_max_main_ver matrix_of_lines_ch[: len(cy_main_hor), 8] = dist_y_hor matrix_of_lines_ch[len(cy_main_hor) :, 8] = dist_y_ver matrix_of_lines_ch[len(cy_main_hor) :, 9] = 1 if contours_h is not None: slope_lines_head, dist_x_head, x_min_main_head, x_max_main_head, cy_main_head, slope_lines_org_head, y_min_main_head, y_max_main_head, cx_main_head = find_features_of_lines(contours_h) matrix_l_n = np.zeros((matrix_of_lines_ch.shape[0] + len(cy_main_head), matrix_of_lines_ch.shape[1])) matrix_l_n[: matrix_of_lines_ch.shape[0], :] = np.copy(matrix_of_lines_ch[:, :]) args_head = np.array(range(len(cy_main_head))) + len(cy_main_hor) matrix_l_n[matrix_of_lines_ch.shape[0] :, 0] = args_head matrix_l_n[matrix_of_lines_ch.shape[0] :, 2] = x_min_main_head + 30 matrix_l_n[matrix_of_lines_ch.shape[0] :, 3] = x_max_main_head - 30 matrix_l_n[matrix_of_lines_ch.shape[0] :, 4] = dist_x_head matrix_l_n[matrix_of_lines_ch.shape[0] :, 5] = y_min_main_head - 3 - 8 matrix_l_n[matrix_of_lines_ch.shape[0] :, 6] = y_min_main_head - 5 - 8 matrix_l_n[matrix_of_lines_ch.shape[0] :, 7] = y_min_main_head + 1 - 8 matrix_l_n[matrix_of_lines_ch.shape[0] :, 8] = 4 matrix_of_lines_ch = np.copy(matrix_l_n) # print(matrix_of_lines_ch) """ seperators_closeup=seperators_closeup.astype(np.uint8) imgray = cv2.cvtColor(seperators_closeup, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(imgray, 0, 255, 0) contours_lines,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) slope_lines,dist_x, x_min_main ,x_max_main ,cy_main,slope_lines_org,y_min_main, y_max_main, cx_main=find_features_of_lines(contours_lines) slope_lines_org_hor=slope_lines_org[slope_lines==0] args=np.array( range(len(slope_lines) )) len_x=seperators_closeup.shape[1]/4.0 args_hor=args[slope_lines==0] dist_x_hor=dist_x[slope_lines==0] x_min_main_hor=x_min_main[slope_lines==0] x_max_main_hor=x_max_main[slope_lines==0] cy_main_hor=cy_main[slope_lines==0] args_hor=args_hor[dist_x_hor>=len_x/2.0] x_max_main_hor=x_max_main_hor[dist_x_hor>=len_x/2.0] x_min_main_hor=x_min_main_hor[dist_x_hor>=len_x/2.0] cy_main_hor=cy_main_hor[dist_x_hor>=len_x/2.0] slope_lines_org_hor=slope_lines_org_hor[dist_x_hor>=len_x/2.0] slope_lines_org_hor=slope_lines_org_hor[np.abs(slope_lines_org_hor)<1.2] slope_mean_hor=np.mean(slope_lines_org_hor) args_ver=args[slope_lines==1] y_min_main_ver=y_min_main[slope_lines==1] y_max_main_ver=y_max_main[slope_lines==1] x_min_main_ver=x_min_main[slope_lines==1] x_max_main_ver=x_max_main[slope_lines==1] cx_main_ver=cx_main[slope_lines==1] dist_y_ver=y_max_main_ver-y_min_main_ver len_y=seperators_closeup.shape[0]/3.0 print(matrix_of_lines_ch[:,8][matrix_of_lines_ch[:,9]==0],'khatlarrrr') args_main_spliters=matrix_of_lines_ch[:,0][ (matrix_of_lines_ch[:,9]==0) & ((matrix_of_lines_ch[:,8]<=290)) & ((matrix_of_lines_ch[:,2]<=.16*region_pre_p.shape[1])) & ((matrix_of_lines_ch[:,3]>=.84*region_pre_p.shape[1]))] cy_main_spliters=matrix_of_lines_ch[:,5][ (matrix_of_lines_ch[:,9]==0) & ((matrix_of_lines_ch[:,8]<=290)) & ((matrix_of_lines_ch[:,2]<=.16*region_pre_p.shape[1])) & ((matrix_of_lines_ch[:,3]>=.84*region_pre_p.shape[1]))] """ cy_main_spliters = cy_main_hor[(x_min_main_hor <= 0.16 * region_pre_p.shape[1]) & (x_max_main_hor >= 0.84 * region_pre_p.shape[1])] cy_main_spliters = np.array(list(cy_main_spliters) + list(special_seperators)) if contours_h is not None: try: cy_main_spliters_head = cy_main_head[(x_min_main_head <= 0.16 * region_pre_p.shape[1]) & (x_max_main_head >= 0.84 * region_pre_p.shape[1])] cy_main_spliters = np.array(list(cy_main_spliters) + list(cy_main_spliters_head)) except: pass args_cy_spliter = np.argsort(cy_main_spliters) cy_main_spliters_sort = cy_main_spliters[args_cy_spliter] spliter_y_new = [] spliter_y_new.append(0) for i in range(len(cy_main_spliters_sort)): spliter_y_new.append(cy_main_spliters_sort[i]) spliter_y_new.append(region_pre_p.shape[0]) spliter_y_new_diff = np.diff(spliter_y_new) / float(region_pre_p.shape[0]) * 100 args_big_parts = np.array(range(len(spliter_y_new_diff)))[spliter_y_new_diff > 22] regions_without_seperators = return_regions_without_seperators(region_pre_p) ##print(args_big_parts,'args_big_parts') # 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) length_y_threshold = regions_without_seperators.shape[0] / 4.0 num_col_fin = 0 peaks_neg_fin_fin = [] for iteils in args_big_parts: regions_without_seperators_teil = regions_without_seperators[int(spliter_y_new[iteils]) : int(spliter_y_new[iteils + 1]), :, 0] # image_page_background_zero_teil=image_page_background_zero[int(spliter_y_new[iteils]):int(spliter_y_new[iteils+1]),:] # print(regions_without_seperators_teil.shape) ##plt.imshow(regions_without_seperators_teil) ##plt.show() # num_col, peaks_neg_fin=find_num_col(regions_without_seperators_teil,multiplier=6.0) # regions_without_seperators_teil=cv2.erode(regions_without_seperators_teil,kernel,iterations = 3) # num_col, peaks_neg_fin = find_num_col(regions_without_seperators_teil, multiplier=7.0) if num_col > num_col_fin: num_col_fin = num_col peaks_neg_fin_fin = peaks_neg_fin """ #print(length_y_vertical_lines,length_y_threshold,'x_center_of_ver_linesx_center_of_ver_linesx_center_of_ver_lines') if len(cx_main_ver)>0 and len( dist_y_ver[dist_y_ver>=length_y_threshold] ) >=1: num_col, peaks_neg_fin=find_num_col(regions_without_seperators_teil,multiplier=6.0) else: #plt.imshow(image_page_background_zero_teil) #plt.show() #num_col, peaks_neg_fin=find_num_col_only_image(image_page_background_zero,multiplier=2.4)#2.3) num_col, peaks_neg_fin=find_num_col_only_image(image_page_background_zero_teil,multiplier=3.4)#2.3) print(num_col,'birda') if num_col>0: pass elif num_col==0: print(num_col,'birda2222') num_col_regions, peaks_neg_fin_regions=find_num_col(regions_without_seperators_teil,multiplier=10.0) if num_col_regions==0: pass else: num_col=num_col_regions peaks_neg_fin=peaks_neg_fin_regions[:] """ # print(num_col+1,'num colmsssssssss') if len(args_big_parts) == 1 and (len(peaks_neg_fin_fin) + 1) < num_col_classifier: peaks_neg_fin = find_num_col_by_vertical_lines(vertical) peaks_neg_fin = peaks_neg_fin[peaks_neg_fin >= 500] peaks_neg_fin = peaks_neg_fin[peaks_neg_fin <= (vertical.shape[1] - 500)] peaks_neg_fin_fin = peaks_neg_fin[:] # print(peaks_neg_fin_fin,'peaks_neg_fin_fintaza') return num_col_fin, peaks_neg_fin_fin, matrix_of_lines_ch, spliter_y_new, seperators_closeup_n def return_boxes_of_images_by_order_of_reading_new(spliter_y_new, regions_without_seperators, matrix_of_lines_ch): boxes = [] # here I go through main spliters and i do check whether a vertical seperator there is. If so i am searching for \ # holes in the text and also finding spliter which covers more than one columns. for i in range(len(spliter_y_new) - 1): # print(spliter_y_new[i],spliter_y_new[i+1]) matrix_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 6] > spliter_y_new[i]) & (matrix_of_lines_ch[:, 7] < spliter_y_new[i + 1])] # print(len( matrix_new[:,9][matrix_new[:,9]==1] )) # print(matrix_new[:,8][matrix_new[:,9]==1],'gaddaaa') # check to see is there any vertical seperator to find holes. if 1 > 0: # len( matrix_new[:,9][matrix_new[:,9]==1] )>0 and np.max(matrix_new[:,8][matrix_new[:,9]==1])>=0.1*(np.abs(spliter_y_new[i+1]-spliter_y_new[i] )): # org_img_dichte=-gaussian_filter1d(( image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,0]/255.).sum(axis=0) ,30) # org_img_dichte=org_img_dichte-np.min(org_img_dichte) ##plt.figure(figsize=(20,20)) ##plt.plot(org_img_dichte) ##plt.show() ###find_num_col_both_layout_and_org(regions_without_seperators,image_page[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:,:],7.) # print(int(spliter_y_new[i]),int(spliter_y_new[i+1]),'firssst') # plt.imshow(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:]) # plt.show() try: num_col, peaks_neg_fin = find_num_col(regions_without_seperators[int(spliter_y_new[i]) : int(spliter_y_new[i + 1]), :], multiplier=7.0) except: peaks_neg_fin = [] # print(peaks_neg_fin,'peaks_neg_fin') # num_col, peaks_neg_fin=find_num_col(regions_without_seperators[int(spliter_y_new[i]):int(spliter_y_new[i+1]),:],multiplier=7.0) x_min_hor_some = matrix_new[:, 2][(matrix_new[:, 9] == 0)] x_max_hor_some = matrix_new[:, 3][(matrix_new[:, 9] == 0)] cy_hor_some = matrix_new[:, 5][(matrix_new[:, 9] == 0)] arg_org_hor_some = matrix_new[:, 0][(matrix_new[:, 9] == 0)] peaks_neg_tot = return_points_with_boundies(peaks_neg_fin, 0, regions_without_seperators[:, :].shape[1]) start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot, x_min_hor_some, x_max_hor_some) arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort] start_index_of_hor_with_subset = [start_index_of_hor[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] # start_index_of_hor[lines_length_dels>0] arg_min_hor_sort_with_subset = [arg_min_hor_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] lines_indexes_deleted_with_subset = [lines_indexes_deleted[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] lines_length_dels_with_subset = [lines_length_dels[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] arg_org_hor_some_sort_subset = [arg_org_hor_some_sort[vij] for vij in range(len(start_index_of_hor)) if lines_length_dels[vij] > 0] # arg_min_hor_sort_with_subset=arg_min_hor_sort[lines_length_dels>0] # lines_indexes_deleted_with_subset=lines_indexes_deleted[lines_length_dels>0] # lines_length_dels_with_subset=lines_length_dels[lines_length_dels>0] vahid_subset = np.zeros((len(start_index_of_hor_with_subset), len(start_index_of_hor_with_subset))) - 1 for kkk1 in range(len(start_index_of_hor_with_subset)): index_del_sub = np.unique(lines_indexes_deleted_with_subset[kkk1]) for kkk2 in range(len(start_index_of_hor_with_subset)): if set(lines_indexes_deleted_with_subset[kkk2][0]) < set(lines_indexes_deleted_with_subset[kkk1][0]): vahid_subset[kkk1, kkk2] = kkk1 else: pass # print(set(lines_indexes_deleted[kkk2][0]), set(lines_indexes_deleted[kkk1][0])) # check the len of matrix if it has no length means that there is no spliter at all if len(vahid_subset > 0): # print('hihoo') # find parenets args line_int = np.zeros(vahid_subset.shape[0]) childs_id = [] arg_child = [] for li in range(vahid_subset.shape[0]): # print(vahid_subset[:,li]) if np.all(vahid_subset[:, li] == -1): line_int[li] = -1 else: line_int[li] = 1 # childs_args_in=[ idd for idd in range(vahid_subset.shape[0]) if vahid_subset[idd,li]!=-1] # helpi=[] # for nad in range(len(childs_args_in)): # helpi.append(arg_min_hor_sort_with_subset[childs_args_in[nad]]) arg_child.append(arg_min_hor_sort_with_subset[li]) # line_int=vahid_subset[0,:] arg_parent = [arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1] start_index_of_hor_parent = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] == -1] # arg_parent=[lines_indexes_deleted_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1] # arg_parent=[lines_length_dels_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]==-1] # arg_child=[arg_min_hor_sort_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij]!=-1] start_index_of_hor_child = [start_index_of_hor_with_subset[vij] for vij in range(len(arg_min_hor_sort_with_subset)) if line_int[vij] != -1] cy_hor_some_sort = cy_hor_some[arg_parent] # print(start_index_of_hor, lines_length_dels ,lines_indexes_deleted,'zartt') # args_indexes=np.array(range(len(start_index_of_hor) )) newest_y_spliter_tot = [] for tj in range(len(newest_peaks) - 1): newest_y_spliter = [] newest_y_spliter.append(spliter_y_new[i]) if tj in np.unique(start_index_of_hor_parent): # print(cy_hor_some_sort) cy_help = np.array(cy_hor_some_sort)[np.array(start_index_of_hor_parent) == tj] cy_help_sort = np.sort(cy_help) # print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha') for mj in range(len(cy_help_sort)): newest_y_spliter.append(cy_help_sort[mj]) newest_y_spliter.append(spliter_y_new[i + 1]) newest_y_spliter_tot.append(newest_y_spliter) else: line_int = [] newest_y_spliter_tot = [] for tj in range(len(newest_peaks) - 1): newest_y_spliter = [] newest_y_spliter.append(spliter_y_new[i]) newest_y_spliter.append(spliter_y_new[i + 1]) newest_y_spliter_tot.append(newest_y_spliter) # if line_int is all -1 means that big spliters have no child and we can easily go through if np.all(np.array(line_int) == -1): for j in range(len(newest_peaks) - 1): newest_y_spliter = newest_y_spliter_tot[j] for n in range(len(newest_y_spliter) - 1): # print(j,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'maaaa') ##plt.imshow(regions_without_seperators[int(newest_y_spliter[n]):int(newest_y_spliter[n+1]),newest_peaks[j]:newest_peaks[j+1]]) ##plt.show() # print(matrix_new[:,0][ (matrix_new[:,9]==1 )]) for jvt in matrix_new[:, 0][(matrix_new[:, 9] == 1) & (matrix_new[:, 6] > newest_y_spliter[n]) & (matrix_new[:, 7] < newest_y_spliter[n + 1]) & ((matrix_new[:, 1]) < newest_peaks[j + 1]) & ((matrix_new[:, 1]) > newest_peaks[j])]: pass ###plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)]) # print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1]) matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])] # print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada') if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )): # print( int(newest_y_spliter[n]),int(newest_y_spliter[n+1]),newest_peaks[j],newest_peaks[j+1] ) try: num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0) except: peaks_neg_fin_sub = [] else: peaks_neg_fin_sub = [] peaks_sub = [] peaks_sub.append(newest_peaks[j]) for kj in range(len(peaks_neg_fin_sub)): peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j]) peaks_sub.append(newest_peaks[j + 1]) # peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1]) for kh in range(len(peaks_sub) - 1): boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]]) else: for j in range(len(newest_peaks) - 1): newest_y_spliter = newest_y_spliter_tot[j] if j in start_index_of_hor_parent: x_min_ch = x_min_hor_some[arg_child] x_max_ch = x_max_hor_some[arg_child] cy_hor_some_sort_child = cy_hor_some[arg_child] cy_hor_some_sort_child = np.sort(cy_hor_some_sort_child) for n in range(len(newest_y_spliter) - 1): cy_child_in = cy_hor_some_sort_child[(cy_hor_some_sort_child > newest_y_spliter[n]) & (cy_hor_some_sort_child < newest_y_spliter[n + 1])] if len(cy_child_in) > 0: try: num_col_ch, peaks_neg_ch = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0) except: peaks_neg_ch = [] # print(peaks_neg_ch,'mizzzz') # peaks_neg_ch=[] # for djh in range(len(peaks_neg_ch)): # peaks_neg_ch.append( peaks_neg_ch[djh]+newest_peaks[j] ) peaks_neg_ch_tot = return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1]) ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot, x_min_ch, x_max_ch) newest_y_spliter_ch_tot = [] for tjj in range(len(nst_p_ch) - 1): newest_y_spliter_new = [] newest_y_spliter_new.append(newest_y_spliter[n]) if tjj in np.unique(ss_in_ch): # print(tj,cy_hor_some_sort,start_index_of_hor,cy_help,'maashhaha') for mjj in range(len(cy_child_in)): newest_y_spliter_new.append(cy_child_in[mjj]) newest_y_spliter_new.append(newest_y_spliter[n + 1]) newest_y_spliter_ch_tot.append(newest_y_spliter_new) for jn in range(len(nst_p_ch) - 1): newest_y_spliter_h = newest_y_spliter_ch_tot[jn] for nd in range(len(newest_y_spliter_h) - 1): matrix_new_new2 = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter_h[nd]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter_h[nd + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < nst_p_ch[jn + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > nst_p_ch[jn])] # print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada') if 1 > 0: # len( matrix_new_new2[:,9][matrix_new_new2[:,9]==1] )>0 and np.max(matrix_new_new2[:,8][matrix_new_new2[:,9]==1])>=0.2*(np.abs(newest_y_spliter_h[nd+1]-newest_y_spliter_h[nd] )): try: num_col_sub_ch, peaks_neg_fin_sub_ch = find_num_col(regions_without_seperators[int(newest_y_spliter_h[nd]) : int(newest_y_spliter_h[nd + 1]), nst_p_ch[jn] : nst_p_ch[jn + 1]], multiplier=7.0) except: peaks_neg_fin_sub_ch = [] else: peaks_neg_fin_sub_ch = [] peaks_sub_ch = [] peaks_sub_ch.append(nst_p_ch[jn]) for kjj in range(len(peaks_neg_fin_sub_ch)): peaks_sub_ch.append(peaks_neg_fin_sub_ch[kjj] + nst_p_ch[jn]) peaks_sub_ch.append(nst_p_ch[jn + 1]) # peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1]) for khh in range(len(peaks_sub_ch) - 1): boxes.append([peaks_sub_ch[khh], peaks_sub_ch[khh + 1], newest_y_spliter_h[nd], newest_y_spliter_h[nd + 1]]) else: matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])] # print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada') if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )): try: num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=7.0) except: peaks_neg_fin_sub = [] else: peaks_neg_fin_sub = [] peaks_sub = [] peaks_sub.append(newest_peaks[j]) for kj in range(len(peaks_neg_fin_sub)): peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j]) peaks_sub.append(newest_peaks[j + 1]) # peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1]) for kh in range(len(peaks_sub) - 1): boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]]) else: for n in range(len(newest_y_spliter) - 1): # plot_contour(regions_without_seperators.shape[0],regions_without_seperators.shape[1], contours_lines[int(jvt)]) # print(matrix_of_lines_ch[matrix_of_lines_ch[:,9]==1]) matrix_new_new = matrix_of_lines_ch[:, :][(matrix_of_lines_ch[:, 9] == 1) & (matrix_of_lines_ch[:, 6] > newest_y_spliter[n]) & (matrix_of_lines_ch[:, 7] < newest_y_spliter[n + 1]) & ((matrix_of_lines_ch[:, 1] + 500) < newest_peaks[j + 1]) & ((matrix_of_lines_ch[:, 1] - 500) > newest_peaks[j])] # print(matrix_new_new,newest_y_spliter[n],newest_y_spliter[n+1],newest_peaks[j],newest_peaks[j+1],'gada') if 1 > 0: # len( matrix_new_new[:,9][matrix_new_new[:,9]==1] )>0 and np.max(matrix_new_new[:,8][matrix_new_new[:,9]==1])>=0.2*(np.abs(newest_y_spliter[n+1]-newest_y_spliter[n] )): try: num_col_sub, peaks_neg_fin_sub = find_num_col(regions_without_seperators[int(newest_y_spliter[n]) : int(newest_y_spliter[n + 1]), newest_peaks[j] : newest_peaks[j + 1]], multiplier=5.0) except: peaks_neg_fin_sub = [] else: peaks_neg_fin_sub = [] peaks_sub = [] peaks_sub.append(newest_peaks[j]) for kj in range(len(peaks_neg_fin_sub)): peaks_sub.append(peaks_neg_fin_sub[kj] + newest_peaks[j]) peaks_sub.append(newest_peaks[j + 1]) # peaks_sub=return_points_with_boundies(peaks_neg_fin_sub+newest_peaks[j],newest_peaks[j], newest_peaks[j+1]) for kh in range(len(peaks_sub) - 1): boxes.append([peaks_sub[kh], peaks_sub[kh + 1], newest_y_spliter[n], newest_y_spliter[n + 1]]) else: boxes.append([0, regions_without_seperators[:, :].shape[1], spliter_y_new[i], spliter_y_new[i + 1]]) return boxes