add blazeface

python/paddle_serving_app/models/model_list.py

@@ -24,7 +24,8 @@ class ServingModels(object):
             "SentimentAnalysis"] = ["senta_bilstm", "senta_bow", "senta_cnn"]
         self.model_dict["SemanticRepresentation"] = ["ernie"]
         self.model_dict["ChineseWordSegmentation"] = ["lac"]
-        self.model_dict["ObjectDetection"] = ["faster_rcnn", "yolov4"]
+        self.model_dict[
+            "ObjectDetection"] = ["faster_rcnn", "yolov4", "blazeface"]
         self.model_dict["ImageSegmentation"] = [
             "unet", "deeplabv3", "deeplabv3+cityscapes"
         ]

python/paddle_serving_app/reader/functional.py

@@ -29,6 +29,7 @@ def normalize(img, mean, std, channel_first):
     else:
         img_mean = np.array(mean).reshape((1, 1, 3))
         img_std = np.array(std).reshape((1, 1, 3))
+    img = np.array(img).astype("float32")
     img -= img_mean
     img /= img_std
     return img

python/paddle_serving_app/reader/image_reader.py

@@ -440,8 +440,33 @@ class RCNNPostprocess(object):
             self.label_file, self.output_dir)
 
 
+class BlazeFacePostprocess(RCNNPostprocess):
+    def clip_bbox(self, bbox, im_size=None):
+        h = 1. if im_size is None else im_size[0]
+        w = 1. if im_size is None else im_size[1]
+        xmin = max(min(bbox[0], w), 0.)
+        ymin = max(min(bbox[1], h), 0.)
+        xmax = max(min(bbox[2], w), 0.)
+        ymax = max(min(bbox[3], h), 0.)
+        return xmin, ymin, xmax, ymax
+
+    def _get_bbox_result(self, fetch_map, fetch_name, clsid2catid):
+        result = {}
+        is_bbox_normalized = True  #for blaze face, set true here
+        output = fetch_map[fetch_name]
+        lod = [fetch_map[fetch_name + '.lod']]
+        lengths = self._offset_to_lengths(lod)
+        np_data = np.array(output)
+        result['bbox'] = (np_data, lengths)
+        result['im_id'] = np.array([[0]])
+        result["im_shape"] = np.array(fetch_map["im_shape"]).astype(np.int32)
+        bbox_results = self._bbox2out([result], clsid2catid, is_bbox_normalized)
+        return bbox_results
+
+
 class Sequential(object):
     """
+
     Args:
         sequence (sequence of ``Transform`` objects): list of transforms to chain.