add doc、infer_det.py、requirments.txt

configs/det/det_db_mv3.yml

@@ -12,6 +12,7 @@ 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
   
 Architecture:

doc/detection.md

+# 文字检测
+
+本节以icdar15数据集为例，介绍PaddleOCR中检测模型的使用方式。
+
+## 3.1 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表示文本框的位置，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。
+
+
+## 3.2 快速启动训练
+
+首先下载pretrain model，目前支持两种backbone，分别是MobileNetV3、ResNet50，您可以根据需求使用PaddleClas中的模型更换
+backbone。
+```
+# 下载MobileNetV3的预训练模型
+wget -P /PaddleOCR/pretrained_model/ 模型链接
+# 下载ResNet50的预训练模型
+wget -P /PaddleOCR/pretrained_model/ 模型链接
+```
+
+**启动训练**
+```
+cd PaddleOCR/
+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
+```
+
+
+## 3.3 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 checkpoints ./output/best_accuracy
+```
+
+## 3.4 测试检测效果

doc/installation.md

+### 2.1 快速安装
+
+我们提供了PaddleOCR开发环境的docker，您可以pull我们提供的docker运行PaddleOCR的环境。
+
+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
+# 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 paddlepaddle/paddle:1.7.2-gpu-cuda10.0-cudnn7  /bin/bash
+```
+
+2. 克隆PaddleOCR repo代码
+```
+apt-get update
+apt-get install git
+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
+```

requirments.txt

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

tools/infer/predict_system.py

@@ -64,6 +64,9 @@ 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)
@@ -73,6 +76,27 @@ class TextSystem(object):
         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

@@ -106,7 +106,7 @@ def create_predictor(args, mode):
     #             if args.use_fp16 else AnalysisConfig.Precision.Float32,
     #             max_batch_size=args.batch_size)
 
-    config.enable_memory_optim()
+    # config.enable_memory_optim()
     # use zero copy
     config.switch_use_feed_fetch_ops(False)
     predictor = create_paddle_predictor(config)
@@ -136,12 +136,16 @@ if __name__ == '__main__':
     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'))
+    print("det input", predictor.get_input_names())
+    print("det output", predictor.get_output_names())
+    # print(predictor.program(), file=open("det_program.txt", 'w'))
+    outputs = []
+    for output_tensor in output_tensors:
+        output = output_tensor.copy_to_cpu()
+        outputs.append(output)
 
     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())
+    print("rec input", rec_predictor.get_input_names())
+    print("rec output", 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
+
+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(outs, 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()