update

README.md

@@ -94,6 +94,9 @@ http://127.0.0.1:8080/ocr
 <img width="500" height="300" src="https://github.com/ouyanghuiyu/chineseocr_lite/blob/master/ncnn_project/ocr/res_imgs/res_3.jpg"/>
 <img width="500" height="300" src="https://github.com/ouyanghuiyu/chineseocr_lite/blob/master/ncnn_project/ocr/res_imgs/res_2.jpg"/>
 
+## flask-restful api 内存测试(dbnet)
+200张图片测试稳定在1-1.5G左右的内存
+![](test_imgs/flask-api.png)
 
 
 ## 参考

dbnet/dbnet_infer.py

@@ -21,7 +21,7 @@ def draw_bbox(img_path, result, color=(255, 0, 0), thickness=2):
     return img_path
 
 
-class DBNET(object, metaclass=SingletonType):
+class DBNET(metaclass=SingletonType):
     def __init__(self, MODEL_PATH, short_size=640):
         self.sess = rt.InferenceSession(MODEL_PATH)
         self.short_size = short_size

dbnet/decode.py

@@ -13,7 +13,7 @@ def __init__(self, thresh=0.3, box_thresh=0.5, max_candidates=1000, unclip_ratio
         self.unclip_ratio = unclip_ratio
 
     def __call__(self, pred, height, width):
-        '''
+        """
         batch: (image, polygons, ignore_tags
         batch: a dict produced by dataloaders.
             image: tensor of shape (N, C, H, W).
@@ -25,7 +25,7 @@ def __call__(self, pred, height, width):
             binary: text region segmentation map, with shape (N, H, W)
             thresh: [if exists] thresh hold prediction with shape (N, H, W)
             thresh_binary: [if exists] binarized with threshhold, (N, H, W)
-        '''
+        """
 
         pred = pred[0, :, :]
         segmentation = self.binarize(pred)
@@ -38,10 +38,10 @@ def binarize(self, pred):
         return pred > self.thresh
 
     def boxes_from_bitmap(self, pred, bitmap, dest_width, dest_height):
-        '''
+        """
         _bitmap: single map with shape (H, W),
             whose values are binarized as {0, 1}
-        '''
+        """
 
         assert len(bitmap.shape) == 2
         # bitmap = _bitmap.cpu().numpy()  # The first channel