diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index 9d94523..40d7a48 100644 --- a/qurator/eynollah/eynollah.py +++ b/qurator/eynollah/eynollah.py @@ -158,6 +158,9 @@ class Eynollah: if uint8: key += '_uint8' return self._imgs[key].copy() + + def isNaN(self, num): + return num != num def predict_enhancement(self, img): @@ -920,16 +923,16 @@ class Eynollah: textline_con, hierarchy = return_contours_of_image(img_int_p) textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.0008) y_diff_mean = find_contours_mean_y_diff(textline_con_fil) - sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0))) + if self.isNaN(y_diff_mean): + slope_for_all = MAX_SLOPE + else: + sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0))) + img_int_p[img_int_p > 0] = 1 + slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter) - img_int_p[img_int_p > 0] = 1 - slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter) + if abs(slope_for_all) < 0.5: + slope_for_all = [slope_deskew][0] - if abs(slope_for_all) < 0.5: - slope_for_all = [slope_deskew][0] - # old method - # slope_for_all=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv]) - # text_patch_processed=textline_contours_postprocessing(gada) except Exception as why: self.logger.error(why) slope_for_all = MAX_SLOPE @@ -1031,13 +1034,16 @@ class Eynollah: textline_con, hierarchy = return_contours_of_image(img_int_p) textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.00008) y_diff_mean = find_contours_mean_y_diff(textline_con_fil) - sigma_des = int(y_diff_mean * (4.0 / 40.0)) - if sigma_des < 1: - sigma_des = 1 - img_int_p[img_int_p > 0] = 1 - slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter) - if abs(slope_for_all) <= 0.5: - slope_for_all = [slope_deskew][0] + if self.isNaN(y_diff_mean): + slope_for_all = MAX_SLOPE + else: + sigma_des = int(y_diff_mean * (4.0 / 40.0)) + if sigma_des < 1: + sigma_des = 1 + img_int_p[img_int_p > 0] = 1 + slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter) + if abs(slope_for_all) <= 0.5: + slope_for_all = [slope_deskew][0] except Exception as why: self.logger.error(why) slope_for_all = MAX_SLOPE @@ -1890,53 +1896,60 @@ class Eynollah: areas_cnt_text_d = np.array([cv2.contourArea(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))]) areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1]) + + if len(areas_cnt_text_d)>0: + contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)] + index_con_parents_d=np.argsort(areas_cnt_text_d) + contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] ) + areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] ) + + cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest_d]) + cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_d) + try: + if len(cx_bigest_d) >= 5: + cx_bigest_d_last5 = cx_bigest_d[-5:] + cy_biggest_d_last5 = cy_biggest_d[-5:] + dists_d = [math.sqrt((cx_bigest_big[0] - cx_bigest_d_last5[j]) ** 2 + (cy_biggest_big[0] - cy_biggest_d_last5[j]) ** 2) for j in range(len(cy_biggest_d_last5))] + ind_largest = len(cx_bigest_d) -5 + np.argmin(dists_d) + else: + cx_bigest_d_last5 = cx_bigest_d[-len(cx_bigest_d):] + cy_biggest_d_last5 = cy_biggest_d[-len(cx_bigest_d):] + dists_d = [math.sqrt((cx_bigest_big[0]-cx_bigest_d_last5[j])**2 + (cy_biggest_big[0]-cy_biggest_d_last5[j])**2) for j in range(len(cy_biggest_d_last5))] + ind_largest = len(cx_bigest_d) - len(cx_bigest_d) + np.argmin(dists_d) + + cx_bigest_d_big[0] = cx_bigest_d[ind_largest] + cy_biggest_d_big[0] = cy_biggest_d[ind_largest] + except Exception as why: + self.logger.error(why) - contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)] - index_con_parents_d=np.argsort(areas_cnt_text_d) - contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] ) - areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] ) - - cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest_d]) - cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_d) - try: - if len(cx_bigest_d) >= 5: - cx_bigest_d_last5 = cx_bigest_d[-5:] - cy_biggest_d_last5 = cy_biggest_d[-5:] - dists_d = [math.sqrt((cx_bigest_big[0] - cx_bigest_d_last5[j]) ** 2 + (cy_biggest_big[0] - cy_biggest_d_last5[j]) ** 2) for j in range(len(cy_biggest_d_last5))] - ind_largest = len(cx_bigest_d) -5 + np.argmin(dists_d) - else: - cx_bigest_d_last5 = cx_bigest_d[-len(cx_bigest_d):] - cy_biggest_d_last5 = cy_biggest_d[-len(cx_bigest_d):] - dists_d = [math.sqrt((cx_bigest_big[0]-cx_bigest_d_last5[j])**2 + (cy_biggest_big[0]-cy_biggest_d_last5[j])**2) for j in range(len(cy_biggest_d_last5))] - ind_largest = len(cx_bigest_d) - len(cx_bigest_d) + np.argmin(dists_d) - - cx_bigest_d_big[0] = cx_bigest_d[ind_largest] - cy_biggest_d_big[0] = cy_biggest_d[ind_largest] - except Exception as why: - self.logger.error(why) - - (h, w) = text_only.shape[:2] - center = (w // 2.0, h // 2.0) - M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0) - M_22 = np.array(M)[:2, :2] - p_big = np.dot(M_22, [cx_bigest_big, cy_biggest_big]) - x_diff = p_big[0] - cx_bigest_d_big - y_diff = p_big[1] - cy_biggest_d_big - - contours_only_text_parent_d_ordered = [] - for i in range(len(contours_only_text_parent)): - p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]]) - p[0] = p[0] - x_diff[0] - p[1] = p[1] - y_diff[0] - dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + (p[1] - cy_biggest_d[j]) ** 2) for j in range(len(cx_bigest_d))] - contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)]) - # img2=np.zeros((text_only.shape[0],text_only.shape[1],3)) - # img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1)) - # plt.imshow(img2[:,:,0]) - # plt.show() + (h, w) = text_only.shape[:2] + center = (w // 2.0, h // 2.0) + M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0) + M_22 = np.array(M)[:2, :2] + p_big = np.dot(M_22, [cx_bigest_big, cy_biggest_big]) + x_diff = p_big[0] - cx_bigest_d_big + y_diff = p_big[1] - cy_biggest_d_big + + contours_only_text_parent_d_ordered = [] + for i in range(len(contours_only_text_parent)): + p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]]) + p[0] = p[0] - x_diff[0] + p[1] = p[1] - y_diff[0] + dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + (p[1] - cy_biggest_d[j]) ** 2) for j in range(len(cx_bigest_d))] + contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)]) + # img2=np.zeros((text_only.shape[0],text_only.shape[1],3)) + # img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1)) + # plt.imshow(img2[:,:,0]) + # plt.show() + else: + contours_only_text_parent_d_ordered = [] + contours_only_text_parent_d = [] + contours_only_text_parent = [] + else: contours_only_text_parent_d_ordered = [] contours_only_text_parent_d = [] + contours_only_text_parent = [] else: contours_only_text, hir_on_text = return_contours_of_image(text_only) @@ -1964,11 +1977,10 @@ class Eynollah: txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first) boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent) boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals) - + if not self.curved_line: slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew) slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew) - else: scale_param = 1