# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import re import sys import shapely from shapely.geometry import Polygon import numpy as np from collections import defaultdict import operator from rapidfuzz.distance import Levenshtein import argparse import json import copy def parse_ser_results_fp(fp, fp_type="gt", ignore_background=True): # img/zh_val_0.jpg { # "height": 3508, # "width": 2480, # "ocr_info": [ # {"text": "Maribyrnong", "label": "other", "bbox": [1958, 144, 2184, 198]}, # {"text": "CITYCOUNCIL", "label": "other", "bbox": [2052, 183, 2171, 214]}, # ] assert fp_type in ["gt", "pred"] key = "label" if fp_type == "gt" else "pred" res_dict = dict() with open(fp, "r", encoding='utf-8') as fin: lines = fin.readlines() for _, line in enumerate(lines): img_path, info = line.strip().split("\t") # get key image_name = os.path.basename(img_path) res_dict[image_name] = [] # get infos json_info = json.loads(info) for single_ocr_info in json_info["ocr_info"]: label = single_ocr_info[key].upper() if label in ["O", "OTHERS", "OTHER"]: label = "O" if ignore_background and label == "O": continue single_ocr_info["label"] = label res_dict[image_name].append(copy.deepcopy(single_ocr_info)) return res_dict def polygon_from_str(polygon_points): """ Create a shapely polygon object from gt or dt line. """ polygon_points = np.array(polygon_points).reshape(4, 2) polygon = Polygon(polygon_points).convex_hull return polygon def polygon_iou(poly1, poly2): """ Intersection over union between two shapely polygons. """ if not poly1.intersects( poly2): # this test is fast and can accelerate calculation iou = 0 else: try: inter_area = poly1.intersection(poly2).area union_area = poly1.area + poly2.area - inter_area iou = float(inter_area) / union_area except shapely.geos.TopologicalError: # except Exception as e: # print(e) print('shapely.geos.TopologicalError occurred, iou set to 0') iou = 0 return iou def ed(args, str1, str2): if args.ignore_space: str1 = str1.replace(" ", "") str2 = str2.replace(" ", "") if args.ignore_case: str1 = str1.lower() str2 = str2.lower() return Levenshtein.distance(str1, str2) def convert_bbox_to_polygon(bbox): """ bbox : [x1, y1, x2, y2] output: [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] """ xmin, ymin, xmax, ymax = bbox poly = [[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]] return poly def eval_e2e(args): # gt gt_results = parse_ser_results_fp(args.gt_json_path, "gt", args.ignore_background) # pred dt_results = parse_ser_results_fp(args.pred_json_path, "pred", args.ignore_background) iou_thresh = args.iou_thres num_gt_chars = 0 gt_count = 0 dt_count = 0 hit = 0 ed_sum = 0 for img_name in dt_results: gt_info = gt_results[img_name] gt_count += len(gt_info) dt_info = dt_results[img_name] dt_count += len(dt_info) dt_match = [False] * len(dt_info) gt_match = [False] * len(gt_info) all_ious = defaultdict(tuple) # gt: {text, label, bbox or poly} for index_gt, gt in enumerate(gt_info): if "poly" not in gt: gt["poly"] = convert_bbox_to_polygon(gt["bbox"]) gt_poly = polygon_from_str(gt["poly"]) for index_dt, dt in enumerate(dt_info): if "poly" not in dt: dt["poly"] = convert_bbox_to_polygon(dt["bbox"]) dt_poly = polygon_from_str(dt["poly"]) iou = polygon_iou(dt_poly, gt_poly) if iou >= iou_thresh: all_ious[(index_gt, index_dt)] = iou sorted_ious = sorted( all_ious.items(), key=operator.itemgetter(1), reverse=True) sorted_gt_dt_pairs = [item[0] for item in sorted_ious] # matched gt and dt for gt_dt_pair in sorted_gt_dt_pairs: index_gt, index_dt = gt_dt_pair if gt_match[index_gt] == False and dt_match[index_dt] == False: gt_match[index_gt] = True dt_match[index_dt] = True # ocr rec results gt_text = gt_info[index_gt]["text"] dt_text = dt_info[index_dt]["text"] # ser results gt_label = gt_info[index_gt]["label"] dt_label = dt_info[index_dt]["pred"] if True: # ignore_masks[index_gt] == '0': ed_sum += ed(args, gt_text, dt_text) num_gt_chars += len(gt_text) if gt_text == dt_text: if args.ignore_ser_prediction or gt_label == dt_label: hit += 1 # unmatched dt for tindex, dt_match_flag in enumerate(dt_match): if dt_match_flag == False: dt_text = dt_info[tindex]["text"] gt_text = "" ed_sum += ed(args, dt_text, gt_text) # unmatched gt for tindex, gt_match_flag in enumerate(gt_match): if gt_match_flag == False: dt_text = "" gt_text = gt_info[tindex]["text"] ed_sum += ed(args, gt_text, dt_text) num_gt_chars += len(gt_text) eps = 1e-9 print("config: ", args) print('hit, dt_count, gt_count', hit, dt_count, gt_count) precision = hit / (dt_count + eps) recall = hit / (gt_count + eps) fmeasure = 2.0 * precision * recall / (precision + recall + eps) avg_edit_dist_img = ed_sum / len(gt_results) avg_edit_dist_field = ed_sum / (gt_count + eps) character_acc = 1 - ed_sum / (num_gt_chars + eps) print('character_acc: %.2f' % (character_acc * 100) + "%") print('avg_edit_dist_field: %.2f' % (avg_edit_dist_field)) print('avg_edit_dist_img: %.2f' % (avg_edit_dist_img)) print('precision: %.2f' % (precision * 100) + "%") print('recall: %.2f' % (recall * 100) + "%") print('fmeasure: %.2f' % (fmeasure * 100) + "%") return def parse_args(): """ """ def str2bool(v): return v.lower() in ("true", "t", "1") parser = argparse.ArgumentParser() ## Required parameters parser.add_argument( "--gt_json_path", default=None, type=str, required=True, ) parser.add_argument( "--pred_json_path", default=None, type=str, required=True, ) parser.add_argument("--iou_thres", default=0.5, type=float) parser.add_argument( "--ignore_case", default=False, type=str2bool, help="whether to do lower case for the strs") parser.add_argument( "--ignore_space", default=True, type=str2bool, help="whether to ignore space") parser.add_argument( "--ignore_background", default=True, type=str2bool, help="whether to ignore other label") parser.add_argument( "--ignore_ser_prediction", default=False, type=str2bool, help="whether to ignore ocr pred results") args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() eval_e2e(args)