Merge pull request #2437 from JetHong/pgnet-readme

fix eval score

configs/e2e/e2e_r50_vd_pg.yml

@@ -61,6 +61,7 @@ PostProcess:
   score_thresh: 0.5
 Metric:
   name: E2EMetric
+  gt_mat_dir:    # the dir of gt_mat
   character_dict_path: ppocr/utils/ic15_dict.txt
   main_indicator: f_score_e2e
 
@@ -106,7 +107,7 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags' ]
+          keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags', 'img_id']
   loader:
     shuffle: False
     drop_last: False

doc/doc_ch/pgnet.md

@@ -2,7 +2,7 @@
 - [一、简介](#简介)
 - [二、环境配置](#环境配置)
 - [三、快速使用](#快速使用)
-- [四、模型训练、评估、推理](#快速训练)
+- [四、模型训练、评估、推理](#模型训练、评估、推理)
 
 <a name="简介"></a>
 ## 一、简介
@@ -20,7 +20,9 @@ PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.Wang
 ![](../pgnet_framework.png)
 输入图像经过特征提取送入四个分支，分别是：文本边缘偏移量预测TBO模块，文本中心线预测TCL模块，文本方向偏移量预测TDO模块，以及文本字符分类图预测TCC模块。
 其中TBO以及TCL的输出经过后处理后可以得到文本的检测结果，TCL、TDO、TCC负责文本识别。
+
 其检测识别效果图如下：
+
 ![](../imgs_results/e2e_res_img293_pgnet.png)
 ![](../imgs_results/e2e_res_img295_pgnet.png)
 
@@ -61,12 +63,12 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
 可视化文本检测结果默认保存到./inference_results文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
 ![](../imgs_results/e2e_res_img623_pgnet.jpg)
 
-<a name="快速训练"></a>
+<a name="模型训练、评估、推理"></a>
 ## 四、模型训练、评估、推理
 本节以totaltext数据集为例，介绍PaddleOCR中端到端模型的训练、评估与测试。
 
 ###  准备数据
-下载解压[totaltext](https://github.com/cs-chan/Total-Text-Dataset/blob/master/Dataset/README.md)数据集到PaddleOCR/train_data/目录，数据集组织结构：
+下载解压[totaltext](https://github.com/cs-chan/Total-Text-Dataset/blob/master/Dataset/README.md) 数据集到PaddleOCR/train_data/目录，数据集组织结构：
 ```
 /PaddleOCR/train_data/total_text/train/
   |- rgb/            # total_text数据集的训练数据

ppocr/data/pgnet_dataset.py

@@ -64,9 +64,6 @@ class PGDataSet(Dataset):
             for line in f.readlines():
                 poly_str, txt = line.strip().split('\t')
                 poly = list(map(float, poly_str.split(',')))
-                if self.mode.lower() == "eval":
-                    while len(poly) < 100:
-                        poly.append(-1)
                 text_polys.append(
                     np.array(
                         poly, dtype=np.float32).reshape(-1, 2))
@@ -139,10 +136,6 @@ class PGDataSet(Dataset):
         try:
             if self.data_format == 'icdar':
                 im_path = os.path.join(data_path, 'rgb', data_line)
-                if self.mode.lower() == "eval":
-                    poly_path = os.path.join(data_path, 'poly_gt',
-                                             data_line.split('.')[0] + '.txt')
-                else:
                 poly_path = os.path.join(data_path, 'poly',
                                          data_line.split('.')[0] + '.txt')
                 text_polys, text_tags, text_strs = self.extract_polys(poly_path)
@@ -150,12 +143,14 @@ class PGDataSet(Dataset):
                 image_dir = os.path.join(os.path.dirname(data_path), 'image')
                 im_path, text_polys, text_tags, text_strs = self.extract_info_textnet(
                     data_line, image_dir)
+            img_id = int(data_line.split(".")[0][3:])
 
             data = {
                 'img_path': im_path,
                 'polys': text_polys,
                 'tags': text_tags,
-                'strs': text_strs
+                'strs': text_strs,
+                'img_id': img_id
             }
             with open(data['img_path'], 'rb') as f:
                 img = f.read()

ppocr/metrics/e2e_metric.py

@@ -24,52 +24,23 @@ from ppocr.utils.e2e_utils.extract_textpoint import get_dict
 
 class E2EMetric(object):
     def __init__(self,
+                 gt_mat_dir,
                  character_dict_path,
                  main_indicator='f_score_e2e',
                  **kwargs):
+        self.gt_mat_dir = gt_mat_dir
         self.label_list = get_dict(character_dict_path)
         self.max_index = len(self.label_list)
         self.main_indicator = main_indicator
         self.reset()
 
     def __call__(self, preds, batch, **kwargs):
-        temp_gt_polyons_batch = batch[2]
-        temp_gt_strs_batch = batch[3]
-        ignore_tags_batch = batch[4]
-        gt_polyons_batch = []
-        gt_strs_batch = []
-
-        temp_gt_polyons_batch = temp_gt_polyons_batch[0].tolist()
-        for temp_list in temp_gt_polyons_batch:
-            t = []
-            for index in temp_list:
-                if index[0] != -1 and index[1] != -1:
-                    t.append(index)
-            gt_polyons_batch.append(t)
-
-        temp_gt_strs_batch = temp_gt_strs_batch[0].tolist()
-        for temp_list in temp_gt_strs_batch:
-            t = ""
-            for index in temp_list:
-                if index < self.max_index:
-                    t += self.label_list[index]
-            gt_strs_batch.append(t)
-
-        for pred, gt_polyons, gt_strs, ignore_tags in zip(
-            [preds], [gt_polyons_batch], [gt_strs_batch], ignore_tags_batch):
-            # prepare gt
-            gt_info_list = [{
-                'points': gt_polyon,
-                'text': gt_str,
-                'ignore': ignore_tag
-            } for gt_polyon, gt_str, ignore_tag in
-                            zip(gt_polyons, gt_strs, ignore_tags)]
-            # prepare det
+        img_id = batch[5][0]
         e2e_info_list = [{
             'points': det_polyon,
             'text': pred_str
-            } for det_polyon, pred_str in zip(pred['points'], pred['strs'])]
-            result = get_socre(gt_info_list, e2e_info_list)
+        } for det_polyon, pred_str in zip(preds['points'], preds['strs'])]
+        result = get_socre(self.gt_mat_dir, img_id, e2e_info_list)
         self.results.append(result)
 
     def get_metric(self):

ppocr/postprocess/pg_postprocess.py

@@ -138,6 +138,7 @@ class PGPostProcess(object):
                 continue
 
             keep_str_list.append(keep_str)
+            detected_poly = np.round(detected_poly).astype('int32')
             if self.valid_set == 'partvgg':
                 middle_point = len(detected_poly) // 2
                 detected_poly = detected_poly[

ppocr/utils/e2e_metric/Deteval.py

@@ -13,10 +13,11 @@
 # limitations under the License.
 
 import numpy as np
+import scipy.io as io
 from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
 
 
-def get_socre(gt_dict, pred_dict):
+def get_socre(gt_dir, img_id, pred_dict):
     allInputs = 1
 
     def input_reading_mod(pred_dict):
@@ -30,31 +31,9 @@ def get_socre(gt_dict, pred_dict):
             det.append([point, text])
         return det
 
-    def gt_reading_mod(gt_dict):
-        """This helper reads groundtruths from mat files"""
-        gt = []
-        n = len(gt_dict)
-        for i in range(n):
-            points = gt_dict[i]['points']
-            h = len(points)
-            text = gt_dict[i]['text']
-            xx = [
-                np.array(
-                    ['x:'], dtype='<U2'), 0, np.array(
-                        ['y:'], dtype='<U2'), 0, np.array(
-                            ['#'], dtype='<U1'), np.array(
-                                ['#'], dtype='<U1')
-            ]
-            t_x, t_y = [], []
-            for j in range(h):
-                t_x.append(points[j][0])
-                t_y.append(points[j][1])
-            xx[1] = np.array([t_x], dtype='int16')
-            xx[3] = np.array([t_y], dtype='int16')
-            if text != "" and "#" not in text:
-                xx[4] = np.array([text], dtype='U{}'.format(len(text)))
-                xx[5] = np.array(['c'], dtype='<U1')
-            gt.append(xx)
+    def gt_reading_mod(gt_dir, gt_id):
+        gt = io.loadmat('%s/poly_gt_img%s.mat' % (gt_dir, gt_id))
+        gt = gt['polygt']
         return gt
 
     def detection_filtering(detections, groundtruths, threshold=0.5):
@@ -101,7 +80,7 @@ def get_socre(gt_dict, pred_dict):
                 input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
                 and (input_id != 'Deteval_result_non_curved.txt'):
             detections = input_reading_mod(pred_dict)
-            groundtruths = gt_reading_mod(gt_dict)
+            groundtruths = gt_reading_mod(gt_dir, img_id).tolist()
             detections = detection_filtering(
                 detections,
                 groundtruths)  # filters detections overlapping with DC area