Merge branch 'release/2.5' into release2.5

applications/README.md

@@ -57,3 +57,7 @@ PaddleOCR场景应用覆盖通用，制造、金融、交通行业的主要OCR
 </div>
 
 如果您是企业开发者且未在上述场景中找到合适的方案，可以填写[OCR应用合作调研问卷](https://paddle.wjx.cn/vj/QwF7GKw.aspx)，免费与官方团队展开不同层次的合作，包括但不限于问题抽象、确定技术方案、项目答疑、共同研发等。如果您已经使用PaddleOCR落地项目，也可以填写此问卷，与飞桨平台共同宣传推广，提升企业技术品宣。期待您的提交！
+
+<a href="https://trackgit.com">
+<img src="https://us-central1-trackgit-analytics.cloudfunctions.net/token/ping/l63cvzo0w09yxypc7ygl" alt="traffic" />
+</a>

doc/doc_ch/algorithm_det_east.md

@@ -26,8 +26,8 @@
 
 |模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
 | --- | --- | --- | --- | --- | --- | --- |
-|EAST|ResNet50_vd|88.71%|    81.36%|    84.88%|    [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
-|EAST|    MobileNetV3| 78.2%|    79.1%|    78.65%|    [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
+|EAST|ResNet50_vd|[det_r50_vd_east.yml](../../configs/det/det_r50_vd_east.yml)|88.71%|    81.36%|    84.88%|    [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
+|EAST|MobileNetV3|[det_mv3_east.yml](../../configs/det/det_mv3_east.yml)| 78.2%|    79.1%|    78.65%|    [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
 
 
 <a name="2"></a>

doc/doc_ch/inference_ppocr.md

@@ -87,9 +87,9 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.9956803321838379)
 
 ```
 # 下载英文数字识别模型：
-wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar
-tar xf en_PP-OCRv3_det_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt"
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar
+tar xf en_PP-OCRv3_rec_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_rec_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt"
 ```
 
 ![](../imgs_words/en/word_1.png)

doc/doc_ch/quickstart.md

@@ -201,3 +201,7 @@ im_show.save('result.jpg')
 通过本节内容，相信您已经熟练掌握PaddleOCR whl包的使用方法并获得了初步效果。
 
 PaddleOCR是一套丰富领先实用的OCR工具库，打通数据、模型训练、压缩和推理部署全流程，您可以参考[文档教程](../../README_ch.md#文档教程)，正式开启PaddleOCR的应用之旅。
+
+<a href="https://trackgit.com">
+<img src="https://us-central1-trackgit-analytics.cloudfunctions.net/token/ping/l63dyu35d5dpcg6y3ibl" alt="trackgit-views" />
+</a>

tools/infer_kie.py

@@ -89,6 +89,29 @@ def draw_kie_result(batch, node, idx_to_cls, count):
     cv2.imwrite(save_path, vis_img)
     logger.info("The Kie Image saved in {}".format(save_path))
 
+def write_kie_result(fout, node, data):
+    """
+    Write infer result to output file, sorted by the predict label of each line.
+    The format keeps the same as the input with additional score attribute.
+    """
+    import json
+    label = data['label']
+    annotations = json.loads(label)
+    max_value, max_idx = paddle.max(node, -1), paddle.argmax(node, -1)
+    node_pred_label = max_idx.numpy().tolist()
+    node_pred_score = max_value.numpy().tolist()
+    res = []
+    for i, label in enumerate(node_pred_label):
+        pred_score = '{:.2f}'.format(node_pred_score[i])
+        pred_res = {
+                'label': label,
+                'transcription': annotations[i]['transcription'],
+                'score': pred_score,
+                'points': annotations[i]['points'],
+            }
+        res.append(pred_res)
+    res.sort(key=lambda x: x['label'])
+    fout.writelines([json.dumps(res, ensure_ascii=False) + '\n'])
 
 def main():
     global_config = config['Global']
@@ -116,7 +139,7 @@ def main():
 
     warmup_times = 0
     count_t = []
-    with open(save_res_path, "wb") as fout:
+    with open(save_res_path, "w") as fout:
         with open(config['Global']['infer_img'], "rb") as f:
             lines = f.readlines()
             for index, data_line in enumerate(lines):
@@ -141,6 +164,8 @@ def main():
                 node = F.softmax(node, -1)
                 count_t.append(time.time() - st)
                 draw_kie_result(batch, node, idx_to_cls, index)
+                write_kie_result(fout, node, data)
+        fout.close()
     logger.info("success!")
     logger.info("It took {} s for predict {} images.".format(
         np.sum(count_t), len(count_t)))