Merge pull request #3 from LDOUBLEV/fixinfer

optimize PaddleOCR 

configs/det/det_db_mv3.yml

@@ -12,7 +12,9 @@ Global:
   image_shape: [3, 640, 640]
   reader_yml: ./configs/det/det_db_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/MobileNetV3_pretrained/MobileNetV3_large_x0_5_pretrained/
+  checkpoints:
   save_res_path: ./output/predicts_db.txt
+  save_inference_dir:
   
 Architecture:
   function: ppocr.modeling.architectures.det_model,DetModel

doc/README.md

+
+# 简介
+PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
+
+## 特性：
+- 超轻量级模型
+    - (检测模型4.1M + 识别模型4.5M = 8.6M)
+- 支持竖排文字识别
+    - (单模型同时支持横排和竖排文字识别)
+- 支持长文本识别
+- 支持中英文数字组合识别
+- 提供训练代码
+- 支持模型部署
+
+
+## 文档教程
+- [快速安装](./doc/installation.md)
+- [文本识别模型训练/评估/预测](./doc/detection.md)
+- [文本预测模型训练/评估/预测](./doc/recognition.md)
+- [基于inference model预测](./doc/)
+
+### **快速开始**
+
+下载inference模型
+```
+# 创建inference模型保存目录
+mkdir inference && cd inference && mkdir det && mkdir rec
+# 下载检测inference模型
+wget -P ./inference/det 检测inference模型链接
+# 下载识别inference模型
+wget -P ./inferencee/rec 识别inference模型链接
+```
+
+实现文本检测、识别串联推理，预测$image_dir$指定的单张图像：
+```
+export PYTHONPATH=.
+python tools/infer/predict_eval.py --image_dir="/Demo.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```
+在执行预测时，通过参数det_model_dir以及rec_model_dir设置存储inference 模型的路径。
+
+实现文本检测、识别串联推理，预测$image_dir$指指定文件夹下的所有图像：
+```
+python tools/infer/predict_eval.py --image_dir="/test_imgs/" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```
+
+
+
+## 文本检测算法:
+
+PaddleOCR开源的文本检测算法列表：
+- [x]  [EAST](https://arxiv.org/abs/1704.03155)
+- [x]  [DB](https://arxiv.org/abs/1911.08947)
+- [ ]  [SAST](https://arxiv.org/abs/1908.05498)
+
+
+算法效果：
+|模型|骨干网络|Hmean|
+|-|-|-|
+|EAST|ResNet50_vd|85.85%|
+|EAST|MobileNetV3|79.08%|
+|DB|ResNet50_vd|83.30%|
+|DB|MobileNetV3|73.00%|
+
+PaddleOCR文本检测算法的训练与使用请参考[文档](./doc/detection.md)。
+
+## 文本识别算法:
+
+PaddleOCR开源的文本识别算法列表：
+- [x]  [CRNN](https://arxiv.org/abs/1507.05717)
+- [x]  [DTRB](https://arxiv.org/abs/1904.01906)
+- [ ]  [Rosetta](https://arxiv.org/abs/1910.05085)
+- [ ]  [STAR-Net](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)
+- [ ]  [RARE](https://arxiv.org/abs/1603.03915v1)
+- [ ]  [SRN]((https://arxiv.org/abs/2003.12294))(百度自研)
+
+算法效果如下表所示，精度指标是在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上的评测结果的平均值。
+
+|模型|骨干网络|ACC|
+|-|-|-|
+|Rosetta|Resnet34_vd|80.24%|
+|Rosetta|MobileNetV3|78.16%|
+|CRNN|Resnet34_vd|82.20%|
+|CRNN|MobileNetV3|79.37%|
+|STAR-Net|Resnet34_vd|83.93%|
+|STAR-Net|MobileNetV3|81.56%|
+|RARE|Resnet34_vd|84.90%|
+|RARE|MobileNetV3|83.32%|
+
+PaddleOCR文本识别算法的训练与使用请参考[文档](./doc/recognition.md)。
+
+## TODO
+**端到端OCR算法**
+PaddleOCR即将开源百度自研端对端OCR模型[End2End-PSL](https://arxiv.org/abs/1909.07808)，敬请关注。
+- [ ]  End2End-PSL (comming soon)
+
+
+
+# 参考文献
+```
+1. EAST:
+@inproceedings{zhou2017east,
+  title={EAST: an efficient and accurate scene text detector},
+  author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
+  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
+  pages={5551--5560},
+  year={2017}
+}
+
+2. DB:
+@article{liao2019real,
+  title={Real-time Scene Text Detection with Differentiable Binarization},
+  author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
+  journal={arXiv preprint arXiv:1911.08947},
+  year={2019}
+}
+
+3. DTRB:
+@inproceedings{baek2019wrong,
+  title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
+  author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={4715--4723},
+  year={2019}
+}
+
+4. SAST:
+@inproceedings{wang2019single,
+  title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
+  author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
+  booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
+  pages={1277--1285},
+  year={2019}
+}
+
+5. SRN:
+@article{yu2020towards,
+  title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
+  author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
+  journal={arXiv preprint arXiv:2003.12294},
+  year={2020}
+}
+
+6. end2end-psl:
+@inproceedings{sun2019chinese,
+  title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
+  author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={9086--9095},
+  year={2019}
+}
+```

doc/detection.md

+# 文字检测
+
+本节以icdar15数据集为例，介绍PaddleOCR中检测模型的训练、评估与测试。
+
+## 数据准备
+icdar2015数据集可以从[官网](https://rrc.cvc.uab.es/?ch=4&com=downloads)下载到，首次下载需注册。
+
+将下载到的数据集解压到工作目录下，假设解压在/PaddleOCR/train_data/ 下。另外，PaddleOCR将零散的标注文件整理成单独的标注文件
+，您可以通过wget的方式进行下载。
+```
+wget -P /PaddleOCR/train_data/  训练标注文件链接
+wget -P /PaddleOCR/train_data/  测试标注文件链接
+```
+
+解压数据集和下载标注文件后，/PaddleOCR/train_data/ 有两个文件夹和两个文件，分别是：
+```
+/PaddleOCR/train_data/  
+  └─ icdar_c4_train_imgs/         icdar数据集的训练数据
+  └─ ch4_test_images/             icdar数据集的测试数据
+  └─ train_icdar2015_label.txt    icdar数据集的训练标注
+  └─ test_icdar2015_label.txt     icdar数据集的测试标注
+```
+
+提供的标注文件格式为：
+```
+" 图像文件名                    json.dumps编码的图像标注信息"
+ch4_test_images/img_61.jpg    [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]], ...}]
+```
+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，在文本检测任务中并不需要这个信息。
+如果您想在其他数据集上训练PaddleOCR，可以按照上述形式构建标注文件。
+
+
+## 快速启动训练
+
+首先下载pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet50_vd，
+您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。
+```
+cd PaddleOCR/
+# 下载MobileNetV3的预训练模型
+wget -P /PaddleOCR/pretrain_models/ 模型链接
+# 下载ResNet50的预训练模型
+wget -P /PaddleOCR/pretrain_models/ 模型链接
+```
+
+**启动训练**
+```
+python3 tools/train.py -c configs/det/det_db_mv3.yml
+```
+
+上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。
+有关配置文件的详细解释，请参考[链接]()。
+
+您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
+```
+python3 tools/train.py -c configs/det/det_db_mv3.yml -o Optimizer.base_lr=0.0001
+```
+
+## 指标评估
+
+PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean。
+
+运行如下代码，根据配置文件det_db_mv3.yml中save_res_path指定的测试集检测结果文件，计算评估指标。
+
+```
+python3 tools/eval.py -c configs/det/det_db_mv3.yml  -o Gloabl.checkpoints="./output/best_accuracy"
+```
+
+## 测试检测效果
+
+测试单张图像的检测效果
+```
+python3 tools/infer_det.py -c config/det/det_db_mv3.yml -o TestReader.single_img_path="./demo.jpg"
+```
+
+测试文件夹下所有图像的检测效果
+```
+python3 tools/infer_det.py -c config/det/det_db_mv3.yml -o TestReader.single_img_path="./demo_img/"
+```

doc/inference.md

+
+# 基于inference model的推理
+
+inference 模型（fluid.io.save_inference_model保存的模型）
+一般是模型训练完成后保存的固化模型，多用于预测部署。
+训练过程中保存的模型是checkpoints模型，保存的是模型的参数，多用于恢复训练等。
+与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越。
+
+PaddleOCR提供了将checkpoints转换成inference model的实现。
+
+
+## 文本检测模型推理
+
+将文本检测模型训练过程中保存的模型，转换成inference model，可以使用如下命令：
+
+```
+python tools/export_model.py -c configs/det/det_db_mv3.yml -o Global.checkpoints="./output/best_accuracy" \
+        Global.save_inference_dir="./inference/det/"
+```
+
+推理模型保存在$./inference/det/model$, $./inference/det/params$
+
+使用保存的inference model实现在单张图像上的预测：
+
+```
+python  tools/infer/predict_det.py --image_dir="/demo.jpg" --det_model_dir="./inference/det/"
+```
+
+
+## 文本识别模型推理
+
+将文本识别模型训练过程中保存的模型，转换成inference model，可以使用如下命令：
+
+```
+python tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints="./output/best_accuracy" \
+        Global.save_inference_dir="./inference/rec/"
+```
+
+推理模型保存在$./inference/rec/model$, $./inference/rec/params$
+
+使用保存的inference model实现在单张图像上的预测：
+
+```
+python  tools/infer/predict_rec.py --image_dir="/demo.jpg" --rec_model_dir="./inference/rec/"
+```
+
+## 文本检测、识别串联推理
+
+实现文本检测、识别串联推理，预测$image_dir$指定的单张图像：
+```
+python tools/infer/predict_eval.py --image_dir="/Demo.jpg" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```
+
+实现文本检测、识别串联推理，预测$image_dir$指指定文件夹下的所有图像：
+
+```
+python tools/infer/predict_eval.py --image_dir="/test_imgs/" --det_model_dir="./inference/det/"  --rec_model_dir="./inference/rec/"
+```

doc/installation.md

+## 快速安装
+
+建议使用我们提供的docker运行PaddleOCR，有关docker使用请参考[链接](https://docs.docker.com/get-started/)。
+1. 准备docker环境。第一次使用这个镜像，会自动下载该镜像，请耐心等待。
+```
+# 切换到工作目录下
+cd /home/Projects
+# 首次运行需创建一个docker容器，再次运行时不需要运行当前命令
+# 创建一个名字为pdocr的docker容器，并将当前目录映射到容器的/data目录下
+sudo nvidia-docker run --name pdocr -v $PWD:/data --network=host -it  hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev  /bin/bash
+
+# ctrl+P+Q可退出docker，重新进入docker使用如下命令
+sudo nvidia-docker container exec -it pdocr /bin/bash
+
+```
+
+2. 克隆PaddleOCR repo代码
+```
+git clone https://github.com/PaddlePaddle/PaddleOCR
+```
+
+3. 安装第三方库
+```
+cd PaddleOCR
+pip3 install --upgrade pip
+pip3 install -r requirements.txt
+```

ppocr/data/det/dataset_traversal.py

@@ -22,6 +22,7 @@ import string
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
 from ppocr.utils.utility import create_module
+from ppocr.utils.utility import get_image_file_list
 import time
 
 
@@ -72,16 +73,8 @@ class EvalTestReader(object):
             self.params)
         batch_size = self.params['test_batch_size_per_card']
 
-        flag_test_single_img = False
-        if mode == "test":
-            single_img_path = self.params['single_img_path']
-            if single_img_path is not None:
-                flag_test_single_img = True
-
         img_list = []
-        if flag_test_single_img:
-            img_list.append([single_img_path, single_img_path])
-        else:
+        if mode != "test":
             img_set_dir = self.params['img_set_dir']
             img_name_list_path = self.params['label_file_path']
             with open(img_name_list_path, "rb") as fin:
@@ -90,6 +83,9 @@ class EvalTestReader(object):
                     img_name = line.decode().strip("\n").split("\t")[0]
                     img_path = img_set_dir + "/" + img_name
                     img_list.append([img_path, img_name])
+        else:
+            img_path = self.params['single_img_path']
+            img_list = get_image_file_list(img_path)
 
         def batch_iter_reader():
             batch_outs = []

ppocr/data/det/db_process.py

@@ -124,9 +124,6 @@ class DBProcessTest(object):
     def resize_image_type0(self, im):
         """
         resize image to a size multiple of 32 which is required by the network
-        :param im: the resized image
-        :param max_side_len: limit of max image size to avoid out of memory in gpu
-        :return: the resized image and the resize ratio
         """
         max_side_len = self.max_side_len
         h, w, _ = im.shape

ppocr/data/reader_main.py

@@ -73,9 +73,3 @@ def reader_main(config=None, mode=None):
         return paddle.reader.multiprocess_reader(readers, False)
     else:
         return function(mode)
-
-
-def test_reader(image_shape, img_path):
-    img = cv2.imread(img_path)
-    norm_img = process_image(img, image_shape)
-    return norm_img

ppocr/utils/utility.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import logging
+import os
 
 
 def initial_logger():
@@ -55,6 +56,23 @@ def get_check_reader_params(mode):
     return check_params
 
 
+def get_image_file_list(img_file):
+    imgs_lists = []
+    if img_file is None or not os.path.exists(img_file):
+        raise Exception("not found any img file in {}".format(img_file))
+
+    img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
+    if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
+        imgs_lists.append(img_file)
+    elif os.path.isdir(img_file):
+        for single_file in os.listdir(img_file):
+            if single_file.split('.')[-1] in img_end:
+                imgs_lists.append(os.path.join(img_file, single_file))
+    if len(imgs_lists) == 0:
+        raise Exception("not found any img file in {}".format(img_file))
+    return imgs_lists
+
+
 from paddle import fluid
 
 

requirments.txt

+shapely
+imgaug
+pyclipper
+lmdb
\ No newline at end of file

tools/eval_utils/eval_det_iou.py

@@ -3,6 +3,10 @@
 from collections import namedtuple
 import numpy as np
 from shapely.geometry import Polygon
+"""
+reference from :
+https://github.com/MhLiao/DB/blob/3c32b808d4412680310d3d28eeb6a2d5bf1566c5/concern/icdar2015_eval/detection/iou.py#L8
+"""
 
 
 class DetectionIoUEvaluator(object):

tools/eval_utils/eval_det_utils.py

@@ -98,6 +98,14 @@ def load_label_infor(label_file_path, do_ignore=False):
 
 
 def cal_det_metrics(gt_label_path, save_res_path):
+    """
+    calculate the detection metrics
+    Args:
+        gt_label_path(string): The groundtruth detection label file path
+        save_res_path(string): The saved predicted detection label path
+    return:
+        claculated metrics including Hmean、precision and recall
+    """
     evaluator = DetectionIoUEvaluator()
     gt_label_infor = load_label_infor(gt_label_path, do_ignore=True)
     dt_label_infor = load_label_infor(save_res_path)

tools/export_model.py

@@ -71,14 +71,19 @@ def main():
 
     init_model(config, eval_program, exe)
 
+    save_inference_dir = config['Global']['save_inference_dir']
+    if not os.path.exists(save_inference_dir):
+        os.makedirs(save_inference_dir)
     fluid.io.save_inference_model(
-        dirname="./output/",
+        dirname=save_inference_dir,
         feeded_var_names=feeded_var_names,
         main_program=eval_program,
         target_vars=target_vars,
         executor=exe,
         model_filename='model',
         params_filename='params')
+    print("inference model saved in {}/model and {}/params".format(
+        save_inference_dir, save_inference_dir))
     print("save success, output_name_list:", fetches_var_name)
 
 

tools/infer/predict_system.py

@@ -64,15 +64,39 @@ class TextSystem(object):
         if dt_boxes is None:
             return None, None
         img_crop_list = []
+
+        dt_boxes = sorted_boxes(dt_boxes)
+
         for bno in range(len(dt_boxes)):
             tmp_box = copy.deepcopy(dt_boxes[bno])
             img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
         rec_res, elapse = self.text_recognizer(img_crop_list)
-        #         self.print_draw_crop_rec_res(img_crop_list, rec_res)
+        # self.print_draw_crop_rec_res(img_crop_list, rec_res)
         return dt_boxes, rec_res
 
 
+def sorted_boxes(dt_boxes):
+    """
+    Sort text boxes in order from top to bottom, left to right
+    args:
+        dt_boxes(array)：detected text boxes with shape [4, 2]
+    return:
+        sorted boxes(array) with shape [4, 2]
+    """
+    num_boxes = dt_boxes.shape[0]
+    sorted_boxes = sorted(dt_boxes, key=lambda x: x[0][1])
+    _boxes = list(sorted_boxes)
+
+    for i in range(num_boxes - 1):
+        if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \
+            (_boxes[i + 1][0][0] < _boxes[i][0][0]):
+            tmp = _boxes[i]
+            _boxes[i] = _boxes[i + 1]
+            _boxes[i + 1] = tmp
+    return _boxes
+
+
 if __name__ == "__main__":
     args = utility.parse_args()
     image_file_list = utility.get_image_file_list(args.image_dir)

tools/infer/utility.py

@@ -61,18 +61,6 @@ def parse_args():
     return parser.parse_args()
 
 
-def get_image_file_list(image_dir):
-    image_file_list = []
-    if image_dir is None:
-        return image_file_list
-    if os.path.isfile(image_dir):
-        image_file_list = [image_dir]
-    elif os.path.isdir(image_dir):
-        for single_file in os.listdir(image_dir):
-            image_file_list.append(os.path.join(image_dir, single_file))
-    return image_file_list
-
-
 def create_predictor(args, mode):
     if mode == "det":
         model_dir = args.det_model_dir
@@ -99,14 +87,7 @@ def create_predictor(args, mode):
         config.disable_gpu()
 
     config.disable_glog_info()
-    config.switch_ir_optim(args.ir_optim)
-    #     if args.use_tensorrt:
-    #         config.enable_tensorrt_engine(
-    #             precision_mode=AnalysisConfig.Precision.Half
-    #             if args.use_fp16 else AnalysisConfig.Precision.Float32,
-    #             max_batch_size=args.batch_size)
-
-    config.enable_memory_optim()
+
     # use zero copy
     config.switch_use_feed_fetch_ops(False)
     predictor = create_paddle_predictor(config)
@@ -127,21 +108,3 @@ def draw_text_det_res(dt_boxes, img_path):
         cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
     img_name_pure = img_path.split("/")[-1]
     cv2.imwrite("./output/%s" % img_name_pure, src_im)
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    args.use_gpu = False
-    root_path = "/Users/liuweiwei06/Desktop/TEST_CODES/icode/baidu/personal-code/PaddleOCR/"
-    args.det_model_dir = root_path + "test_models/public_v1/ch_det_mv3_db"
-
-    predictor, input_tensor, output_tensors = create_predictor(args, mode='det')
-    print(predictor.get_input_names())
-    print(predictor.get_output_names())
-    print(predictor.program(), file=open("det_program.txt", 'w'))
-
-    args.rec_model_dir = root_path + "test_models/public_v1/ch_rec_mv3_crnn/"
-    rec_predictor, input_tensor, output_tensors = create_predictor(
-        args, mode='rec')
-    print(rec_predictor.get_input_names())
-    print(rec_predictor.get_output_names())

tools/infer_det.py

+# Copyright (c) 2020 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import time
+import numpy as np
+from copy import deepcopy
+import json
+
+# from paddle.fluid.contrib.model_stat import summary
+
+
+def set_paddle_flags(**kwargs):
+    for key, value in kwargs.items():
+        if os.environ.get(key, None) is None:
+            os.environ[key] = str(value)
+
+
+# NOTE(paddle-dev): All of these flags should be
+# set before `import paddle`. Otherwise, it would
+# not take any effect.
+set_paddle_flags(
+    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
+)
+
+from paddle import fluid
+from ppocr.utils.utility import create_module
+import program
+from ppocr.utils.save_load import init_model
+from ppocr.data.reader_main import reader_main
+import cv2
+
+from ppocr.utils.utility import initial_logger
+logger = initial_logger()
+
+
+def draw_det_res(dt_boxes, config, img_name, ino):
+    if len(dt_boxes) > 0:
+        img_set_path = config['TestReader']['img_set_dir']
+        img_path = img_set_path + img_name
+        import cv2
+        src_im = cv2.imread(img_path)
+        for box in dt_boxes:
+            box = box.astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
+        save_det_path = os.path.basename(config['Global'][
+            'save_res_path']) + "/det_results/"
+        if not os.path.exists(save_det_path):
+            os.makedirs(save_det_path)
+        save_path = os.path.join(save_det_path, "det_{}.jpg".format(img_name))
+        cv2.imwrite(save_path, src_im)
+        logger.info("The detected Image saved in {}".format(save_path))
+
+
+def main():
+    config = program.load_config(FLAGS.config)
+    program.merge_config(FLAGS.opt)
+    print(config)
+
+    # check if set use_gpu=True in paddlepaddle cpu version
+    use_gpu = config['Global']['use_gpu']
+    program.check_gpu(use_gpu)
+
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    det_model = create_module(config['Architecture']['function'])(params=config)
+
+    startup_prog = fluid.Program()
+    eval_prog = fluid.Program()
+    with fluid.program_guard(eval_prog, startup_prog):
+        with fluid.unique_name.guard():
+            _, eval_outputs = det_model(mode="test")
+            fetch_name_list = list(eval_outputs.keys())
+            eval_fetch_list = [eval_outputs[v].name for v in fetch_name_list]
+
+    eval_prog = eval_prog.clone(for_test=True)
+    exe.run(startup_prog)
+
+    # load checkpoints
+    checkpoints = config['Global'].get('checkpoints')
+    if checkpoints:
+        path = checkpoints
+        fluid.load(eval_prog, path, exe)
+        logger.info("Finish initing model from {}".format(path))
+    else:
+        raise Exception("{} not exists!".format(checkpoints))
+
+    save_res_path = config['Global']['save_res_path']
+    with open(save_res_path, "wb") as fout:
+        test_reader = reader_main(config=config, mode='test')
+        tackling_num = 0
+        for data in test_reader():
+            img_num = len(data)
+            tackling_num = tackling_num + img_num
+            logger.info("tackling_num:%d", tackling_num)
+            img_list = []
+            ratio_list = []
+            img_name_list = []
+            for ino in range(img_num):
+                img_list.append(data[ino][0])
+                ratio_list.append(data[ino][1])
+                img_name_list.append(data[ino][2])
+
+            img_list = np.concatenate(img_list, axis=0)
+            outs = exe.run(eval_prog,\
+                feed={'image': img_list},\
+                fetch_list=eval_fetch_list)
+
+            global_params = config['Global']
+            postprocess_params = deepcopy(config["PostProcess"])
+            postprocess_params.update(global_params)
+            postprocess = create_module(postprocess_params['function'])\
+                (params=postprocess_params)
+            dt_boxes_list = postprocess({"maps": outs[0]}, ratio_list)
+            for ino in range(img_num):
+                dt_boxes = dt_boxes_list[ino]
+                img_name = img_name_list[ino]
+                dt_boxes_json = []
+                for box in dt_boxes:
+                    tmp_json = {"transcription": ""}
+                    tmp_json['points'] = box.tolist()
+                    dt_boxes_json.append(tmp_json)
+                otstr = img_name + "\t" + json.dumps(dt_boxes_json) + "\n"
+                fout.write(otstr.encode())
+                draw_det_res(dt_boxes, config, img_name, ino)
+
+    logger.info("success!")
+
+
+if __name__ == '__main__':
+    parser = program.ArgsParser()
+    FLAGS = parser.parse_args()
+    main()

tools/program.py

@@ -185,22 +185,6 @@ def build(config, main_prog, startup_prog, mode):
 
 def build_export(config, main_prog, startup_prog):
     """
-    Build a program using a model and an optimizer
-        1. create feeds
-        2. create a dataloader
-        3. create a model
-        4. create fetchs
-        5. create an optimizer
-
-    Args:
-        config(dict): config
-        main_prog(): main program
-        startup_prog(): startup program
-        is_train(bool): train or valid
-
-    Returns:
-        dataloader(): a bridge between the model and the data
-        fetchs(dict): dict of model outputs(included loss and measures)
     """
     with fluid.program_guard(main_prog, startup_prog):
         with fluid.unique_name.guard():