update detection doc and infer code

doc/detection.md

@@ -32,8 +32,8 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
 
 ## 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。
+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。
 ```
 # 下载MobileNetV3的预训练模型
 wget -P /PaddleOCR/pretrained_model/ 模型链接
@@ -63,7 +63,17 @@ PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall
 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
+python3 tools/eval.py -c configs/det/det_db_mv3.yml  -o checkpoints="./output/best_accuracy"
 ```
 
 ## 3.4 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/"
+```

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

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/infer/utility.py

@@ -99,15 +99,7 @@ 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)

tools/infer_det.py

@@ -44,6 +44,7 @@ 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()
@@ -67,6 +68,50 @@ def draw_det_res(dt_boxes, config, img_name, ino):
         logger.info("The detected Image saved in {}".format(save_path))
 
 
+def simple_reader(img_file, config):
+    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))
+
+    batch_size = config['Global']['test_batch_size_per_card']
+    global_params = config['Global']
+    params = deepcopy(config['TestReader'])
+    params.update(global_params)
+    reader_function = params['process_function']
+    process_function = create_module(reader_function)(params)
+
+    def batch_iter_reader():
+        batch_outs = []
+        for img_path in imgs_lists:
+            img = cv2.imread(img_path)
+            if img.shape[-1] == 1 or len(list(img.shape)) == 2:
+                img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+            if img is None:
+                logger.info("load image error:" + img_path)
+                continue
+            outs = process_function(img)
+            outs.append(os.path.basename(img_path))
+            print(outs[0].shape, outs[2])
+            batch_outs.append(outs)
+            if len(batch_outs) == batch_size:
+                yield batch_outs
+                batch_outs = []
+        if len(batch_outs) != 0:
+            yield batch_outs
+
+    return batch_iter_reader
+
+
 def main():
     config = program.load_config(FLAGS.config)
     program.merge_config(FLAGS.opt)
@@ -103,7 +148,9 @@ def main():
 
     save_res_path = config['Global']['save_res_path']
     with open(save_res_path, "wb") as fout:
-        test_reader = reader_main(config=config, mode='test')
+        # test_reader = reader_main(config=config, mode='test')
+        single_img_path = config['TestReader']['single_img_path']
+        test_reader = simple_reader(img_file=single_img_path, config=config)
         tackling_num = 0
         for data in test_reader():
             img_num = len(data)
@@ -116,6 +163,7 @@ def main():
                 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},\
@@ -126,7 +174,7 @@ def main():
             postprocess_params.update(global_params)
             postprocess = create_module(postprocess_params['function'])\
                 (params=postprocess_params)
-            dt_boxes_list = postprocess(outs, ratio_list)
+            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]

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():