diff --git a/tools/end2end/convert_ppocr_label.py b/tools/end2end/convert_ppocr_label.py new file mode 100644 index 0000000000000000000000000000000000000000..8084cac785125f23885399931f98531326b6fb20 --- /dev/null +++ b/tools/end2end/convert_ppocr_label.py @@ -0,0 +1,94 @@ +# Copyright (c) 2022 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 numpy as np +import json +import os + + +def poly_to_string(poly): + if len(poly.shape) > 1: + poly = np.array(poly).flatten() + + string = "\t".join(str(i) for i in poly) + return string + + +def convert_label(label_dir, mode="gt", save_dir="./save_results/"): + if not os.path.exists(label_dir): + raise ValueError(f"The file {label_dir} does not exist!") + + assert label_dir != save_dir, "hahahhaha" + + label_file = open(label_dir, 'r') + data = label_file.readlines() + + gt_dict = {} + + for line in data: + try: + tmp = line.split('\t') + assert len(tmp) == 2, "" + except: + tmp = line.strip().split(' ') + + gt_lists = [] + + if tmp[0].split('/')[0] is not None: + img_path = tmp[0] + anno = json.loads(tmp[1]) + gt_collect = [] + for dic in anno: + #txt = dic['transcription'].replace(' ', '') # ignore blank + txt = dic['transcription'] + if 'score' in dic and float(dic['score']) < 0.5: + continue + if u'\u3000' in txt: txt = txt.replace(u'\u3000', u' ') + #while ' ' in txt: + # txt = txt.replace(' ', '') + poly = np.array(dic['points']).flatten() + if txt == "###": + txt_tag = 1 ## ignore 1 + else: + txt_tag = 0 + if mode == "gt": + gt_label = poly_to_string(poly) + "\t" + str( + txt_tag) + "\t" + txt + "\n" + else: + gt_label = poly_to_string(poly) + "\t" + txt + "\n" + + gt_lists.append(gt_label) + + gt_dict[img_path] = gt_lists + else: + continue + + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + for img_name in gt_dict.keys(): + save_name = img_name.split("/")[-1] + save_file = os.path.join(save_dir, save_name + ".txt") + with open(save_file, "w") as f: + f.writelines(gt_dict[img_name]) + + print("The convert label saved in {}".format(save_dir)) + + +if __name__ == "__main__": + + ppocr_label_gt = "/paddle/Datasets/chinese/test_set/Label_refine_310_V2.txt" + convert_label(ppocr_label_gt, "gt", "./save_gt_310_V2/") + + ppocr_label_gt = "./infer_results/ch_PPOCRV2_infer.txt" + convert_label(ppocr_label_gt_en, "pred", "./save_PPOCRV2_infer/") diff --git a/tools/end2end/draw_html.py b/tools/end2end/draw_html.py new file mode 100644 index 0000000000000000000000000000000000000000..7b094c01a04fdc8a8a73ce55aff479eca5ee2f0f --- /dev/null +++ b/tools/end2end/draw_html.py @@ -0,0 +1,62 @@ +# Copyright (c) 2022 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 + + +def draw_debug_img(html_path): + + err_cnt = 0 + with open(html_path, 'w') as html: + html.write('\n\n') + html.write('\n') + html.write( + "" + ) + image_list = [] + path = "./det_results/310_gt/" + for i, filename in enumerate(sorted(os.listdir(path))): + if filename.endswith("txt"): continue + print(filename) + # The image path + base = "{}/{}".format(path, filename) + base_2 = "../PaddleOCR/det_results/ch_PPOCRV2_infer/{}".format( + filename) + base_3 = "../PaddleOCR/det_results/ch_ppocr_mobile_infer/{}".format( + filename) + + html.write("\n") + html.write(f'' % (base)) + html.write('' % (base_2)) + html.write('' % + (base_3)) + + html.write("\n") + html.write('\n') + html.write('
{filename}\n GT') + html.write('GT\nPPOCRV2\nppocr_mobile\n
\n') + html.write('\n\n') + print("ok") + return + + +if __name__ == "__main__": + + html_path = "sys_visual_iou_310.html" + + draw_debug_img() diff --git a/tools/end2end/eval_end2end.py b/tools/end2end/eval_end2end.py new file mode 100644 index 0000000000000000000000000000000000000000..6e7573ca472503e5d2216723c056ddf42c77e0aa --- /dev/null +++ b/tools/end2end/eval_end2end.py @@ -0,0 +1,193 @@ +# Copyright (c) 2022 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 +import editdistance + + +def strQ2B(ustring): + rstring = "" + for uchar in ustring: + inside_code = ord(uchar) + if inside_code == 12288: + inside_code = 32 + elif (inside_code >= 65281 and inside_code <= 65374): + inside_code -= 65248 + rstring += chr(inside_code) + return rstring + + +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 occured, iou set to 0') + iou = 0 + return iou + + +def ed(str1, str2): + return editdistance.eval(str1, str2) + + +def e2e_eval(gt_dir, res_dir, ignore_blank=False): + print('start testing...') + iou_thresh = 0.5 + val_names = os.listdir(gt_dir) + num_gt_chars = 0 + gt_count = 0 + dt_count = 0 + hit = 0 + ed_sum = 0 + + for i, val_name in enumerate(val_names): + with open(os.path.join(gt_dir, val_name), encoding='utf-8') as f: + gt_lines = [o.strip() for o in f.readlines()] + gts = [] + ignore_masks = [] + for line in gt_lines: + parts = line.strip().split('\t') + # ignore illegal data + if len(parts) < 9: + continue + assert (len(parts) < 11) + if len(parts) == 9: + gts.append(parts[:8] + ['']) + else: + gts.append(parts[:8] + [parts[-1]]) + + ignore_masks.append(parts[8]) + + val_path = os.path.join(res_dir, val_name) + if not os.path.exists(val_path): + dt_lines = [] + else: + with open(val_path, encoding='utf-8') as f: + dt_lines = [o.strip() for o in f.readlines()] + dts = [] + for line in dt_lines: + # print(line) + parts = line.strip().split("\t") + assert (len(parts) < 10), "line error: {}".format(line) + if len(parts) == 8: + dts.append(parts + ['']) + else: + dts.append(parts) + + dt_match = [False] * len(dts) + gt_match = [False] * len(gts) + all_ious = defaultdict(tuple) + for index_gt, gt in enumerate(gts): + gt_coors = [float(gt_coor) for gt_coor in gt[0:8]] + gt_poly = polygon_from_str(gt_coors) + for index_dt, dt in enumerate(dts): + dt_coors = [float(dt_coor) for dt_coor in dt[0:8]] + dt_poly = polygon_from_str(dt_coors) + 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 + if ignore_blank: + gt_str = strQ2B(gts[index_gt][8]).replace(" ", "") + dt_str = strQ2B(dts[index_dt][8]).replace(" ", "") + else: + gt_str = strQ2B(gts[index_gt][8]) + dt_str = strQ2B(dts[index_dt][8]) + if ignore_masks[index_gt] == '0': + ed_sum += ed(gt_str, dt_str) + num_gt_chars += len(gt_str) + if gt_str == dt_str: + hit += 1 + gt_count += 1 + dt_count += 1 + + # unmatched dt + for tindex, dt_match_flag in enumerate(dt_match): + if dt_match_flag == False: + dt_str = dts[tindex][8] + gt_str = '' + ed_sum += ed(dt_str, gt_str) + dt_count += 1 + + # unmatched gt + for tindex, gt_match_flag in enumerate(gt_match): + if gt_match_flag == False and ignore_masks[tindex] == '0': + dt_str = '' + gt_str = gts[tindex][8] + ed_sum += ed(gt_str, dt_str) + num_gt_chars += len(gt_str) + gt_count += 1 + + eps = 1e-9 + 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(val_names) + 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) + "%") + + +if __name__ == '__main__': + # if len(sys.argv) != 3: + # print("python3 ocr_e2e_eval.py gt_dir res_dir") + # exit(-1) + # gt_folder = sys.argv[1] + # pred_folder = sys.argv[2] + gt_folder = sys.argv[1] + pred_folder = sys.argv[2] + e2e_eval(gt_folder, pred_folder) diff --git a/tools/end2end/readme.md b/tools/end2end/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..69da06dcdabc92c0b6f1831341e592e674ea7473 --- /dev/null +++ b/tools/end2end/readme.md @@ -0,0 +1,69 @@ + +# 简介 + +`tools/end2end`目录下存放了文本检测+文本识别pipeline串联预测的指标评测代码以及可视化工具。本节介绍文本检测+文本识别的端对端指标评估方式。 + + +## 端对端评测步骤 + +**步骤一：** + +运行`tools/infer/predict_system.py`，得到保存的结果: + +``` +python3 tools/infer/predict_system.py --det_model_dir=./ch_PP-OCRv2_det_infer/ --rec_model_dir=./ch_PP-OCRv2_rec_infer/ --image_dir=./datasets/img_dir/ --draw_img_save_dir=./ch_PP-OCRv2_results/ --is_visualize=True +``` + +文本检测识别可视化图默认保存在`./ch_PP-OCRv2_results/`目录下，预测结果默认保存在`./ch_PP-OCRv2_results/system_results.txt`中，格式如下： +``` +all-sum-510/00224225.jpg [{"transcription": "超赞", "points": [[8.0, 48.0], [157.0, 44.0], [159.0, 115.0], [10.0, 119.0]], "score": "0.99396634"}, {"transcription": "中", "points": [[202.0, 152.0], [230.0, 152.0], [230.0, 163.0], [202.0, 163.0]], "score": "0.09310734"}, {"transcription": "58.0m", "points": [[196.0, 192.0], [444.0, 192.0], [444.0, 240.0], [196.0, 240.0]], "score": "0.44041982"}, {"transcription": "汽配", "points": [[55.0, 263.0], [95.0, 263.0], [95.0, 281.0], [55.0, 281.0]], "score": "0.9986651"}, {"transcription": "成总店", "points": [[120.0, 262.0], [176.0, 262.0], [176.0, 283.0], [120.0, 283.0]], "score": "0.9929402"}, {"transcription": "K", "points": [[237.0, 286.0], [311.0, 286.0], [311.0, 345.0], [237.0, 345.0]], "score": "0.6074794"}, {"transcription": "88：-8", "points": [[203.0, 405.0], [477.0, 414.0], [475.0, 459.0], [201.0, 450.0]], "score": "0.7106863"}] +``` + + +**步骤二：** + +将步骤一保存的数据转换为端对端评测需要的数据格式： +修改 `tools/convert_ppocr_label.py`中的代码，convert_label函数中设置输入标签路径，Mode，保存标签路径等，对预测数据的GTlabel和预测结果的label格式进行转换。 + +``` +ppocr_label_gt = "gt_label.txt" +convert_label(ppocr_label_gt, "gt", "./save_gt_label/") + +ppocr_label_gt = "./ch_PP-OCRv2_results/system_results.txt" +convert_label(ppocr_label_gt_en, "pred", "./save_PPOCRV2_infer/") +``` + +运行`convert_ppocr_label.py`: +``` +python3 tools/convert_ppocr_label.py +``` + +得到如下结果： +``` +├── ./save_gt_label/ +├── ./save_PPOCRV2_infer/ +``` + +**步骤三：** + +执行端对端评测，运行`tools/eval_end2end.py`计算端对端指标，运行方式如下： + +``` +python3 tools/eval_end2end.py "gt_label_dir" "predict_label_dir" +``` + +比如： + +``` +python3 tools/eval_end2end.py ./save_gt_label/ ./save_PPOCRV2_infer/ +``` +将得到如下结果，fmeasure为主要关注的指标： +``` +hit, dt_count, gt_count 1557 2693 3283 +character_acc: 61.77% +avg_edit_dist_field: 3.08 +avg_edit_dist_img: 51.82 +precision: 57.82% +recall: 47.43% +fmeasure: 52.11% +```