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