Merge branch 'dygraph' into dygraph

configs/e2e/e2e_r50_vd_pg.yml

@@ -60,8 +60,10 @@ PostProcess:
   name: PGPostProcess
   score_thresh: 0.5
   mode: fast   # fast or slow two ways
+
 Metric:
   name: E2EMetric
+  mode: A   # two ways for eval, A: label from txt,  B: label from gt_mat
   gt_mat_dir:  ./train_data/total_text/gt  # the dir of gt_mat
   character_dict_path: ppocr/utils/ic15_dict.txt
   main_indicator: f_score_e2e
@@ -70,13 +72,13 @@ Train:
   dataset:
     name: PGDataSet
     data_dir: ./train_data/total_text/train
-    label_file_list: [./train_data/total_text/train/]
+    label_file_list: [./train_data/total_text/train/train.txt]
     ratio_list: [1.0]
     transforms:
       - DecodeImage: # load image
           img_mode: BGR
           channel_first: False
-      - E2ELabelEncode:
+      - E2ELabelEncodeTrain:
       - PGProcessTrain:
           batch_size: 14  # same as loader: batch_size_per_card
           min_crop_size: 24
@@ -94,11 +96,12 @@ Eval:
   dataset:
     name: PGDataSet
     data_dir: ./train_data/total_text/test
-    label_file_list: [./train_data/total_text/test/]
+    label_file_list: [./train_data/total_text/test/test.txt]
     transforms:
       - DecodeImage: # load image
           img_mode: RGB
           channel_first: False
+      - E2ELabelEncodeTest:
       - E2EResizeForTest:
           max_side_len: 768
       - NormalizeImage:
@@ -108,7 +111,7 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'image', 'shape', 'img_id']
+          keep_keys: [ 'image', 'shape', 'polys', 'texts', 'ignore_tags', 'img_id']
   loader:
     shuffle: False
     drop_last: False

configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_common_v2.0.txt
 
 
 Optimizer:

configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
 
 
 Optimizer:

configs/rec/rec_icdar15_train.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_ic15.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_mv3_none_bilstm_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_none_bilstm_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_mv3_none_none_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_none_none_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_mv3_tps_bilstm_att.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_tps_bilstm_att.txt
 
 
 Optimizer:

configs/rec/rec_mv3_tps_bilstm_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_tps_bilstm_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_r34_vd_none_bilstm_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_r34_vd_none_bilstm_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_r34_vd_none_none_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_r34_vd_none_none_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_r34_vd_tps_bilstm_att.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_b3_rare_r34_none_gru.txt
 
 
 Optimizer:

configs/rec/rec_r34_vd_tps_bilstm_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_r34_vd_tps_bilstm_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_r50_fpn_srn.yml

@@ -20,6 +20,7 @@ Global:
   num_heads: 8
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_srn.txt
 
 
 Optimizer:

deploy/cpp_infer/include/postprocess_op.h

@@ -51,6 +51,7 @@ public:
                                                float &ssid);
 
   float BoxScoreFast(std::vector<std::vector<float>> box_array, cv::Mat pred);
+  float PolygonScoreAcc(std::vector<cv::Point> contour, cv::Mat pred);
 
   std::vector<std::vector<std::vector<int>>>
   BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,

deploy/cpp_infer/src/postprocess_op.cpp

@@ -159,6 +159,39 @@ std::vector<std::vector<float>> PostProcessor::GetMiniBoxes(cv::RotatedRect box,
   return array;
 }
 
+float PostProcessor::PolygonScoreAcc(std::vector<cv::Point> contour,
+		                  cv::Mat pred){
+  int width = pred.cols;
+  int height = pred.rows;
+  std::vector<float> box_x;
+  std::vector<float> box_y;
+  for(int i=0; i<contour.size(); ++i){
+    box_x.push_back(contour[i].x);
+    box_y.push_back(contour[i].y);
+  }
+
+  int xmin = clamp(int(std::floor(*(std::min_element(box_x.begin(), box_x.end())))), 0, width - 1);
+  int xmax = clamp(int(std::ceil(*(std::max_element(box_x.begin(), box_x.end())))), 0, width - 1);
+  int ymin = clamp(int(std::floor(*(std::min_element(box_y.begin(), box_y.end())))), 0, height - 1);
+  int ymax = clamp(int(std::ceil(*(std::max_element(box_y.begin(), box_y.end())))), 0, height - 1);
+
+  cv::Mat mask;
+  mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
+
+  cv::Point rook_point[contour.size()];
+  for(int i=0; i<contour.size(); ++i){
+    rook_point[i] = cv::Point(int(box_x[i]) - xmin, int(box_y[i]) - ymin);
+  }
+  const cv::Point *ppt[1] = {rook_point};
+  int npt[] = {int(contour.size())};
+  cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
+
+  cv::Mat croppedImg;
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)).copyTo(croppedImg);
+  float score = cv::mean(croppedImg, mask)[0];
+  return score;
+}
+
 float PostProcessor::BoxScoreFast(std::vector<std::vector<float>> box_array,
                                   cv::Mat pred) {
   auto array = box_array;
@@ -235,6 +268,8 @@ PostProcessor::BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
 
     float score;
     score = BoxScoreFast(array, pred);
+    /* compute using polygon*/ 
+    // score = PolygonScoreAcc(contours[_i], pred);
     if (score < box_thresh)
       continue;
 

deploy/cpp_infer/src/preprocess_op.cpp

@@ -77,19 +77,10 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
 
   int resize_h = int(float(h) * ratio);
   int resize_w = int(float(w) * ratio);
-  if (resize_h % 32 == 0)
-    resize_h = resize_h;
-  else if (resize_h / 32 < 1 + 1e-5)
-    resize_h = 32;
-  else
-    resize_h = (resize_h / 32) * 32;
+  
+  resize_h = max(int(round(float(resize_h) / 32) * 32), 32);
+  resize_w = max(int(round(float(resize_w) / 32) * 32), 32);
 
-  if (resize_w % 32 == 0)
-    resize_w = resize_w;
-  else if (resize_w / 32 < 1 + 1e-5)
-    resize_w = 32;
-  else
-    resize_w = (resize_w / 32) * 32;
   if (!use_tensorrt) {
     cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
     ratio_h = float(resize_h) / float(h);

deploy/hubserving/ocr_cls/module.py

@@ -6,6 +6,7 @@ from __future__ import print_function
 import os
 import sys
 sys.path.insert(0, ".")
+import copy
 
 from paddlehub.common.logger import logger
 from paddlehub.module.module import moduleinfo, runnable, serving
@@ -14,6 +15,8 @@ import paddlehub as hub
 
 from tools.infer.utility import base64_to_cv2
 from tools.infer.predict_cls import TextClassifier
+from tools.infer.utility import parse_args
+from deploy.hubserving.ocr_cls.params import read_params
 
 
 @moduleinfo(
@@ -28,8 +31,7 @@ class OCRCls(hub.Module):
         """
         initialize with the necessary elements
         """
-        from ocr_cls.params import read_params
-        cfg = read_params()
+        cfg = self.merge_configs()
 
         cfg.use_gpu = use_gpu
         if use_gpu:
@@ -48,6 +50,20 @@ class OCRCls(hub.Module):
 
         self.text_classifier = TextClassifier(cfg)
 
+    def merge_configs(self, ):
+        # deafult cfg
+        backup_argv = copy.deepcopy(sys.argv)
+        sys.argv = sys.argv[:1]
+        cfg = parse_args()
+
+        update_cfg_map = vars(read_params())
+
+        for key in update_cfg_map:
+            cfg.__setattr__(key, update_cfg_map[key])
+
+        sys.argv = copy.deepcopy(backup_argv)
+        return cfg
+
     def read_images(self, paths=[]):
         images = []
         for img_path in paths:

deploy/hubserving/ocr_det/module.py

@@ -7,6 +7,8 @@ import os
 import sys
 sys.path.insert(0, ".")
 
+import copy
+
 from paddlehub.common.logger import logger
 from paddlehub.module.module import moduleinfo, runnable, serving
 import cv2
@@ -15,6 +17,8 @@ import paddlehub as hub
 
 from tools.infer.utility import base64_to_cv2
 from tools.infer.predict_det import TextDetector
+from tools.infer.utility import parse_args
+from deploy.hubserving.ocr_system.params import read_params
 
 
 @moduleinfo(
@@ -29,8 +33,7 @@ class OCRDet(hub.Module):
         """
         initialize with the necessary elements
         """
-        from ocr_det.params import read_params
-        cfg = read_params()
+        cfg = self.merge_configs()
 
         cfg.use_gpu = use_gpu
         if use_gpu:
@@ -49,6 +52,20 @@ class OCRDet(hub.Module):
 
         self.text_detector = TextDetector(cfg)
 
+    def merge_configs(self, ):
+        # deafult cfg
+        backup_argv = copy.deepcopy(sys.argv)
+        sys.argv = sys.argv[:1]
+        cfg = parse_args()
+
+        update_cfg_map = vars(read_params())
+
+        for key in update_cfg_map:
+            cfg.__setattr__(key, update_cfg_map[key])
+
+        sys.argv = copy.deepcopy(backup_argv)
+        return cfg
+
     def read_images(self, paths=[]):
         images = []
         for img_path in paths:

deploy/hubserving/ocr_det/params.py

@@ -22,6 +22,7 @@ def read_params():
     cfg.det_db_box_thresh = 0.5
     cfg.det_db_unclip_ratio = 1.6
     cfg.use_dilation = False
+    cfg.det_db_score_mode = "fast"
 
     # #EAST parmas
     # cfg.det_east_score_thresh = 0.8

deploy/hubserving/ocr_rec/module.py

@@ -6,6 +6,7 @@ from __future__ import print_function
 import os
 import sys
 sys.path.insert(0, ".")
+import copy
 
 from paddlehub.common.logger import logger
 from paddlehub.module.module import moduleinfo, runnable, serving
@@ -14,6 +15,8 @@ import paddlehub as hub
 
 from tools.infer.utility import base64_to_cv2
 from tools.infer.predict_rec import TextRecognizer
+from tools.infer.utility import parse_args
+from deploy.hubserving.ocr_rec.params import read_params
 
 
 @moduleinfo(
@@ -28,8 +31,7 @@ class OCRRec(hub.Module):
         """
         initialize with the necessary elements
         """
-        from ocr_rec.params import read_params
-        cfg = read_params()
+        cfg = self.merge_configs()
 
         cfg.use_gpu = use_gpu
         if use_gpu:
@@ -48,6 +50,20 @@ class OCRRec(hub.Module):
 
         self.text_recognizer = TextRecognizer(cfg)
 
+    def merge_configs(self, ):
+        # deafult cfg
+        backup_argv = copy.deepcopy(sys.argv)
+        sys.argv = sys.argv[:1]
+        cfg = parse_args()
+
+        update_cfg_map = vars(read_params())
+
+        for key in update_cfg_map:
+            cfg.__setattr__(key, update_cfg_map[key])
+
+        sys.argv = copy.deepcopy(backup_argv)
+        return cfg
+
     def read_images(self, paths=[]):
         images = []
         for img_path in paths:

deploy/hubserving/ocr_system/module.py

@@ -6,6 +6,7 @@ from __future__ import print_function
 import os
 import sys
 sys.path.insert(0, ".")
+import copy
 
 import time
 
@@ -17,6 +18,8 @@ import paddlehub as hub
 
 from tools.infer.utility import base64_to_cv2
 from tools.infer.predict_system import TextSystem
+from tools.infer.utility import parse_args
+from deploy.hubserving.ocr_system.params import read_params
 
 
 @moduleinfo(
@@ -31,8 +34,7 @@ class OCRSystem(hub.Module):
         """
         initialize with the necessary elements
         """
-        from ocr_system.params import read_params
-        cfg = read_params()
+        cfg = self.merge_configs()
 
         cfg.use_gpu = use_gpu
         if use_gpu:
@@ -51,6 +53,20 @@ class OCRSystem(hub.Module):
 
         self.text_sys = TextSystem(cfg)
 
+    def merge_configs(self, ):
+        # deafult cfg
+        backup_argv = copy.deepcopy(sys.argv)
+        sys.argv = sys.argv[:1]
+        cfg = parse_args()
+
+        update_cfg_map = vars(read_params())
+
+        for key in update_cfg_map:
+            cfg.__setattr__(key, update_cfg_map[key])
+
+        sys.argv = copy.deepcopy(backup_argv)
+        return cfg
+
     def read_images(self, paths=[]):
         images = []
         for img_path in paths:

deploy/hubserving/ocr_system/params.py

@@ -22,6 +22,7 @@ def read_params():
     cfg.det_db_box_thresh = 0.5
     cfg.det_db_unclip_ratio = 1.6
     cfg.use_dilation = False
+    cfg.det_db_score_mode = "fast"
 
     #EAST parmas
     cfg.det_east_score_thresh = 0.8

doc/doc_ch/pgnet.md

@@ -83,19 +83,19 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
 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://paddleocr.bj.bcebos.com/dataset/total_text.tar) 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
-      |- gt_0.png
+      |- img11.jpg
       | ...  
-  |- total_text.txt  # total_text数据集的训练标注
+  |- train.txt       # total_text数据集的训练标注
 ```
 
 total_text.txt标注文件格式如下，文件名和标注信息中间用"\t"分隔：
 ```
 " 图像文件名                    json.dumps编码的图像标注信息"
-rgb/gt_0.png    [{"transcription": "EST", "points": [[1004.0,689.0],[1019.0,698.0],[1034.0,708.0],[1049.0,718.0],[1064.0,728.0],[1079.0,738.0],[1095.0,748.0],[1094.0,774.0],[1079.0,765.0],[1065.0,756.0],[1050.0,747.0],[1036.0,738.0],[1021.0,729.0],[1007.0,721.0]]}, {...}]
+rgb/img11.jpg    [{"transcription": "ASRAMA", "points": [[214.0, 325.0], [235.0, 308.0], [259.0, 296.0], [286.0, 291.0], [313.0, 295.0], [338.0, 305.0], [362.0, 320.0], [349.0, 347.0], [330.0, 337.0], [310.0, 329.0], [290.0, 324.0], [269.0, 328.0], [249.0, 336.0], [231.0, 346.0]]}, {...}]
 ```
 json.dumps编码前的图像标注信息是包含多个字典的list，字典中的 `points` 表示文本框的四个点的坐标(x, y)，从左上角的点开始顺时针排列。
 `transcription` 表示当前文本框的文字，**当其内容为“###”时，表示该文本框无效，在训练时会跳过。**

doc/doc_en/pgnet_en.md

@@ -76,19 +76,19 @@ The visualized end-to-end results are saved to the `./inference_results` folder
 This section takes the totaltext dataset as an example to introduce the training, evaluation and testing of the end-to-end model in PaddleOCR.
 
 ###  Data Preparation
-Download and unzip [totaltext](https://github.com/cs-chan/Total-Text-Dataset/blob/master/Dataset/README.md) dataset to PaddleOCR/train_data/, dataset organization structure is as follow：
+Download and unzip [totaltext](https://paddleocr.bj.bcebos.com/dataset/total_text.tar) dataset to PaddleOCR/train_data/, dataset organization structure is as follow：
 ```
 /PaddleOCR/train_data/total_text/train/
   |- rgb/            # total_text training data of dataset
-      |- gt_0.png
+      |- img11.png
       | ...  
-  |- total_text.txt  # total_text training annotation of dataset
+  |- train.txt       # total_text training annotation of dataset
 ```
 
 total_text.txt: the format of dimension file is as follows，the file name and annotation information are separated by "\t"：
 ```
 " Image file name             Image annotation information encoded by json.dumps"
-rgb/gt_0.png    [{"transcription": "EST", "points": [[1004.0,689.0],[1019.0,698.0],[1034.0,708.0],[1049.0,718.0],[1064.0,728.0],[1079.0,738.0],[1095.0,748.0],[1094.0,774.0],[1079.0,765.0],[1065.0,756.0],[1050.0,747.0],[1036.0,738.0],[1021.0,729.0],[1007.0,721.0]]}, {...}]
+rgb/img11.jpg    [{"transcription": "ASRAMA", "points": [[214.0, 325.0], [235.0, 308.0], [259.0, 296.0], [286.0, 291.0], [313.0, 295.0], [338.0, 305.0], [362.0, 320.0], [349.0, 347.0], [330.0, 337.0], [310.0, 329.0], [290.0, 324.0], [269.0, 328.0], [249.0, 336.0], [231.0, 346.0]]}, {...}]
 ```
 The image annotation after **json.dumps()** encoding is a list containing multiple dictionaries.
 

paddleocr.py

@@ -193,6 +193,7 @@ def parse_args(mMain=True, add_help=True):
         parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
         parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
         parser.add_argument("--use_dilation", type=bool, default=False)
+        parser.add_argument("--det_db_score_mode", type=str, default="fast")
 
         # EAST parmas
         parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
@@ -241,6 +242,7 @@ def parse_args(mMain=True, add_help=True):
             det_db_box_thresh=0.5,
             det_db_unclip_ratio=1.6,
             use_dilation=False,
+            det_db_score_mode="fast",
             det_east_score_thresh=0.8,
             det_east_cover_thresh=0.1,
             det_east_nms_thresh=0.2,

ppocr/data/imaug/label_ops.py

@@ -187,7 +187,51 @@ class CTCLabelEncode(BaseRecLabelEncode):
         return dict_character
 
 
-class E2ELabelEncode(object):
+class E2ELabelEncodeTest(BaseRecLabelEncode):
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path=None,
+                 character_type='EN',
+                 use_space_char=False,
+                 **kwargs):
+        super(E2ELabelEncodeTest,
+              self).__init__(max_text_length, character_dict_path,
+                             character_type, use_space_char)
+
+    def __call__(self, data):
+        import json
+        padnum = len(self.dict)
+        label = data['label']
+        label = json.loads(label)
+        nBox = len(label)
+        boxes, txts, txt_tags = [], [], []
+        for bno in range(0, nBox):
+            box = label[bno]['points']
+            txt = label[bno]['transcription']
+            boxes.append(box)
+            txts.append(txt)
+            if txt in ['*', '###']:
+                txt_tags.append(True)
+            else:
+                txt_tags.append(False)
+        boxes = np.array(boxes, dtype=np.float32)
+        txt_tags = np.array(txt_tags, dtype=np.bool)
+        data['polys'] = boxes
+        data['ignore_tags'] = txt_tags
+        temp_texts = []
+        for text in txts:
+            text = text.lower()
+            text = self.encode(text)
+            if text is None:
+                return None
+            text = text + [padnum] * (self.max_text_len - len(text)
+                                      )  # use 36 to pad
+            temp_texts.append(text)
+        data['texts'] = np.array(temp_texts)
+        return data
+
+
+class E2ELabelEncodeTrain(object):
     def __init__(self, **kwargs):
         pass
 

ppocr/data/pgnet_dataset.py

@@ -72,6 +72,7 @@ class PGDataSet(Dataset):
     def __getitem__(self, idx):
         file_idx = self.data_idx_order_list[idx]
         data_line = self.data_lines[file_idx]
+        img_id = 0
         try:
             data_line = data_line.decode('utf-8')
             substr = data_line.strip("\n").split(self.delimiter)
@@ -79,9 +80,10 @@ class PGDataSet(Dataset):
             label = substr[1]
             img_path = os.path.join(self.data_dir, file_name)
             if self.mode.lower() == 'eval':
-                img_id = int(data_line.split(".")[0][7:])
-            else:
-                img_id = 0
+                try:
+                    img_id = int(data_line.split(".")[0][7:])
+                except:
+                    img_id = 0
             data = {'img_path': img_path, 'label': label, 'img_id': img_id}
             if not os.path.exists(img_path):
                 raise Exception("{} does not exist!".format(img_path))

ppocr/metrics/e2e_metric.py

@@ -18,16 +18,18 @@ from __future__ import print_function
 
 __all__ = ['E2EMetric']
 
-from ppocr.utils.e2e_metric.Deteval import get_socre, combine_results
+from ppocr.utils.e2e_metric.Deteval import get_socre_A, get_socre_B, combine_results
 from ppocr.utils.e2e_utils.extract_textpoint_slow import get_dict
 
 
 class E2EMetric(object):
     def __init__(self,
+                 mode,
                  gt_mat_dir,
                  character_dict_path,
                  main_indicator='f_score_e2e',
                  **kwargs):
+        self.mode = mode
         self.gt_mat_dir = gt_mat_dir
         self.label_list = get_dict(character_dict_path)
         self.max_index = len(self.label_list)
@@ -35,13 +37,45 @@ class E2EMetric(object):
         self.reset()
 
     def __call__(self, preds, batch, **kwargs):
-        img_id = batch[2][0]
-        e2e_info_list = [{
-            'points': det_polyon,
-            'texts': pred_str
-        } for det_polyon, pred_str in zip(preds['points'], preds['texts'])]
-        result = get_socre(self.gt_mat_dir, img_id, e2e_info_list)
-        self.results.append(result)
+        if self.mode == 'A':
+            gt_polyons_batch = batch[2]
+            temp_gt_strs_batch = batch[3][0]
+            ignore_tags_batch = batch[4]
+            gt_strs_batch = []
+
+            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
+                e2e_info_list = [{
+                    'points': det_polyon,
+                    'texts': pred_str
+                } for det_polyon, pred_str in
+                                 zip(pred['points'], pred['texts'])]
+
+                result = get_socre_A(gt_info_list, e2e_info_list)
+                self.results.append(result)
+        else:
+            img_id = batch[5][0]
+            e2e_info_list = [{
+                'points': det_polyon,
+                'texts': pred_str
+            } for det_polyon, pred_str in zip(preds['points'], preds['texts'])]
+            result = get_socre_B(self.gt_mat_dir, img_id, e2e_info_list)
+            self.results.append(result)
 
     def get_metric(self):
         metircs = combine_results(self.results)

ppocr/postprocess/db_postprocess.py

@@ -34,12 +34,18 @@ class DBPostProcess(object):
                  max_candidates=1000,
                  unclip_ratio=2.0,
                  use_dilation=False,
+                 score_mode="fast",
                  **kwargs):
         self.thresh = thresh
         self.box_thresh = box_thresh
         self.max_candidates = max_candidates
         self.unclip_ratio = unclip_ratio
         self.min_size = 3
+        self.score_mode = score_mode
+        assert score_mode in [
+            "slow", "fast"
+        ], "Score mode must be in [slow, fast] but got: {}".format(score_mode)
+
         self.dilation_kernel = None if not use_dilation else np.array(
             [[1, 1], [1, 1]])
 
@@ -69,7 +75,10 @@ class DBPostProcess(object):
             if sside < self.min_size:
                 continue
             points = np.array(points)
-            score = self.box_score_fast(pred, points.reshape(-1, 2))
+            if self.score_mode == "fast":
+                score = self.box_score_fast(pred, points.reshape(-1, 2))
+            else:
+                score = self.box_score_slow(pred, contour)
             if self.box_thresh > score:
                 continue
 
@@ -120,6 +129,9 @@ class DBPostProcess(object):
         return box, min(bounding_box[1])
 
     def box_score_fast(self, bitmap, _box):
+        '''
+        box_score_fast: use bbox mean score as the mean score
+        '''
         h, w = bitmap.shape[:2]
         box = _box.copy()
         xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
@@ -133,6 +145,27 @@ class DBPostProcess(object):
         cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
         return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
 
+    def box_score_slow(self, bitmap, contour):
+        '''
+        box_score_slow: use polyon mean score as the mean score
+        '''
+        h, w = bitmap.shape[:2]
+        contour = contour.copy()
+        contour = np.reshape(contour, (-1, 2))
+
+        xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
+        xmax = np.clip(np.max(contour[:, 0]), 0, w - 1)
+        ymin = np.clip(np.min(contour[:, 1]), 0, h - 1)
+        ymax = np.clip(np.max(contour[:, 1]), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+
+        contour[:, 0] = contour[:, 0] - xmin
+        contour[:, 1] = contour[:, 1] - ymin
+
+        cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
     def __call__(self, outs_dict, shape_list):
         pred = outs_dict['maps']
         if isinstance(pred, paddle.Tensor):

ppocr/utils/e2e_metric/Deteval.py

@@ -17,7 +17,144 @@ import scipy.io as io
 from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
 
 
-def get_socre(gt_dir, img_id, pred_dict):
+def get_socre_A(gt_dir, pred_dict):
+    allInputs = 1
+
+    def input_reading_mod(pred_dict):
+        """This helper reads input from txt files"""
+        det = []
+        n = len(pred_dict)
+        for i in range(n):
+            points = pred_dict[i]['points']
+            text = pred_dict[i]['texts']
+            point = ",".join(map(str, points.reshape(-1, )))
+            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'].tolist()
+            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 != "":
+                xx[4] = np.array([text], dtype='U{}'.format(len(text)))
+                xx[5] = np.array(['c'], dtype='<U1')
+            gt.append(xx)
+        return gt
+
+    def detection_filtering(detections, groundtruths, threshold=0.5):
+        for gt_id, gt in enumerate(groundtruths):
+            if (gt[5] == '#') and (gt[1].shape[1] > 1):
+                gt_x = list(map(int, np.squeeze(gt[1])))
+                gt_y = list(map(int, np.squeeze(gt[3])))
+                for det_id, detection in enumerate(detections):
+                    detection_orig = detection
+                    detection = [float(x) for x in detection[0].split(',')]
+                    detection = list(map(int, detection))
+                    det_x = detection[0::2]
+                    det_y = detection[1::2]
+                    det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
+                    if det_gt_iou > threshold:
+                        detections[det_id] = []
+
+                detections[:] = [item for item in detections if item != []]
+        return detections
+
+    def sigma_calculation(det_x, det_y, gt_x, gt_y):
+        """
+        sigma = inter_area / gt_area
+        """
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(gt_x, gt_y)), 2)
+
+    def tau_calculation(det_x, det_y, gt_x, gt_y):
+        if area(det_x, det_y) == 0.0:
+            return 0
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(det_x, det_y)), 2)
+
+    ##############################Initialization###################################
+    # global_sigma = []
+    # global_tau = []
+    # global_pred_str = []
+    # global_gt_str = []
+    ###############################################################################
+
+    for input_id in range(allInputs):
+        if (input_id != '.DS_Store') and (input_id != 'Pascal_result.txt') and (
+                input_id != 'Pascal_result_curved.txt') and (input_id != 'Pascal_result_non_curved.txt') and (
+                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_dir)
+            detections = detection_filtering(
+                detections,
+                groundtruths)  # filters detections overlapping with DC area
+            dc_id = []
+            for i in range(len(groundtruths)):
+                if groundtruths[i][5] == '#':
+                    dc_id.append(i)
+            cnt = 0
+            for a in dc_id:
+                num = a - cnt
+                del groundtruths[num]
+                cnt += 1
+
+            local_sigma_table = np.zeros((len(groundtruths), len(detections)))
+            local_tau_table = np.zeros((len(groundtruths), len(detections)))
+            local_pred_str = {}
+            local_gt_str = {}
+
+            for gt_id, gt in enumerate(groundtruths):
+                if len(detections) > 0:
+                    for det_id, detection in enumerate(detections):
+                        detection_orig = detection
+                        detection = [float(x) for x in detection[0].split(',')]
+                        detection = list(map(int, detection))
+                        pred_seq_str = detection_orig[1].strip()
+                        det_x = detection[0::2]
+                        det_y = detection[1::2]
+                        gt_x = list(map(int, np.squeeze(gt[1])))
+                        gt_y = list(map(int, np.squeeze(gt[3])))
+                        gt_seq_str = str(gt[4].tolist()[0])
+
+                        local_sigma_table[gt_id, det_id] = sigma_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_tau_table[gt_id, det_id] = tau_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_pred_str[det_id] = pred_seq_str
+                        local_gt_str[gt_id] = gt_seq_str
+
+            global_sigma = local_sigma_table
+            global_tau = local_tau_table
+            global_pred_str = local_pred_str
+            global_gt_str = local_gt_str
+
+    single_data = {}
+    single_data['sigma'] = global_sigma
+    single_data['global_tau'] = global_tau
+    single_data['global_pred_str'] = global_pred_str
+    single_data['global_gt_str'] = global_gt_str
+    return single_data
+
+
+def get_socre_B(gt_dir, img_id, pred_dict):
     allInputs = 1
 
     def input_reading_mod(pred_dict):

tools/infer/predict_det.py

@@ -39,7 +39,10 @@ class TextDetector(object):
         self.args = args
         self.det_algorithm = args.det_algorithm
         pre_process_list = [{
-            'DetResizeForTest': None
+            'DetResizeForTest': {
+                'limit_side_len': args.det_limit_side_len,
+                'limit_type': args.det_limit_type
+            }
         }, {
             'NormalizeImage': {
                 'std': [0.229, 0.224, 0.225],
@@ -62,6 +65,7 @@ class TextDetector(object):
             postprocess_params["max_candidates"] = 1000
             postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
             postprocess_params["use_dilation"] = args.use_dilation
+            postprocess_params["score_mode"] = args.det_db_score_mode
         elif self.det_algorithm == "EAST":
             postprocess_params['name'] = 'EASTPostProcess'
             postprocess_params["score_thresh"] = args.det_east_score_thresh

tools/infer/utility.py

@@ -48,6 +48,7 @@ def parse_args():
     parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
     parser.add_argument("--max_batch_size", type=int, default=10)
     parser.add_argument("--use_dilation", type=bool, default=False)
+    parser.add_argument("--det_db_score_mode", type=str, default="fast")
     # EAST parmas
     parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
     parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)

tools/infer_rec.py

@@ -73,35 +73,45 @@ def main():
     global_config['infer_mode'] = True
     ops = create_operators(transforms, global_config)
 
+    save_res_path = config['Global'].get('save_res_path',
+                                         "./output/rec/predicts_rec.txt")
+    if not os.path.exists(os.path.dirname(save_res_path)):
+        os.makedirs(os.path.dirname(save_res_path))
+
     model.eval()
-    for file in get_image_file_list(config['Global']['infer_img']):
-        logger.info("infer_img: {}".format(file))
-        with open(file, 'rb') as f:
-            img = f.read()
-            data = {'image': img}
-        batch = transform(data, ops)
-        if config['Architecture']['algorithm'] == "SRN":
-            encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
-            gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
-            gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
-            gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
-
-            others = [
-                paddle.to_tensor(encoder_word_pos_list),
-                paddle.to_tensor(gsrm_word_pos_list),
-                paddle.to_tensor(gsrm_slf_attn_bias1_list),
-                paddle.to_tensor(gsrm_slf_attn_bias2_list)
-            ]
-
-        images = np.expand_dims(batch[0], axis=0)
-        images = paddle.to_tensor(images)
-        if config['Architecture']['algorithm'] == "SRN":
-            preds = model(images, others)
-        else:
-            preds = model(images)
-        post_result = post_process_class(preds)
-        for rec_reuslt in post_result:
-            logger.info('\t result: {}'.format(rec_reuslt))
+
+    with open(save_res_path, "w") as fout:
+        for file in get_image_file_list(config['Global']['infer_img']):
+            logger.info("infer_img: {}".format(file))
+            with open(file, 'rb') as f:
+                img = f.read()
+                data = {'image': img}
+            batch = transform(data, ops)
+            if config['Architecture']['algorithm'] == "SRN":
+                encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
+                gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
+                gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
+                gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
+
+                others = [
+                    paddle.to_tensor(encoder_word_pos_list),
+                    paddle.to_tensor(gsrm_word_pos_list),
+                    paddle.to_tensor(gsrm_slf_attn_bias1_list),
+                    paddle.to_tensor(gsrm_slf_attn_bias2_list)
+                ]
+
+            images = np.expand_dims(batch[0], axis=0)
+            images = paddle.to_tensor(images)
+            if config['Architecture']['algorithm'] == "SRN":
+                preds = model(images, others)
+            else:
+                preds = model(images)
+            post_result = post_process_class(preds)
+            for rec_reuslt in post_result:
+                logger.info('\t result: {}'.format(rec_reuslt))
+                if len(rec_reuslt) >= 2:
+                    fout.write(file + "\t" + rec_reuslt[0] + "\t" + str(
+                        rec_reuslt[1]) + "\n")
     logger.info("success!")