updated the usage of yunet

demo.py

@@ -9,6 +9,7 @@ import argparse
 
 import numpy as np
 import cv2 as cv
+from huggingface_hub import hf_hub_download
 
 # Check OpenCV version
 opencv_python_version = lambda str_version: tuple(map(int, (str_version.split("."))))
@@ -16,10 +17,10 @@ assert opencv_python_version(cv.__version__) >= opencv_python_version("4.10.0"),
        "Please install latest opencv-python for benchmark: python3 -m pip install --upgrade opencv-python"
 
 from sface import SFace
-
-sys.path.append('../face_detection_yunet')
 from yunet import YuNet
 
+yunet_model_path = hf_hub_download(repo_id="opencv/face_detection_yunet", filename="face_detection_yunet_2023mar.onnx")
+
 # Valid combinations of backends and targets
 backend_target_pairs = [
     [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
@@ -114,7 +115,7 @@ if __name__ == '__main__':
                        backendId=backend_id,
                        targetId=target_id)
     # Instantiate YuNet for face detection
-    detector = YuNet(modelPath='../face_detection_yunet/face_detection_yunet_2023mar.onnx',
+    detector = YuNet(modelPath=yunet_model_path,
                      inputSize=[320, 320],
                      confThreshold=0.9,
                      nmsThreshold=0.3,

yunet.py

@@ -0,0 +1,55 @@
+# This file is part of OpenCV Zoo project.
+# It is subject to the license terms in the LICENSE file found in the same directory.
+#
+# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
+# Third party copyrights are property of their respective owners.
+
+from itertools import product
+
+import numpy as np
+import cv2 as cv
+
+class YuNet:
+    def __init__(self, modelPath, inputSize=[320, 320], confThreshold=0.6, nmsThreshold=0.3, topK=5000, backendId=0, targetId=0):
+        self._modelPath = modelPath
+        self._inputSize = tuple(inputSize) # [w, h]
+        self._confThreshold = confThreshold
+        self._nmsThreshold = nmsThreshold
+        self._topK = topK
+        self._backendId = backendId
+        self._targetId = targetId
+
+        self._model = cv.FaceDetectorYN.create(
+            model=self._modelPath,
+            config="",
+            input_size=self._inputSize,
+            score_threshold=self._confThreshold,
+            nms_threshold=self._nmsThreshold,
+            top_k=self._topK,
+            backend_id=self._backendId,
+            target_id=self._targetId)
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def setBackendAndTarget(self, backendId, targetId):
+        self._backendId = backendId
+        self._targetId = targetId
+        self._model = cv.FaceDetectorYN.create(
+            model=self._modelPath,
+            config="",
+            input_size=self._inputSize,
+            score_threshold=self._confThreshold,
+            nms_threshold=self._nmsThreshold,
+            top_k=self._topK,
+            backend_id=self._backendId,
+            target_id=self._targetId)
+
+    def setInputSize(self, input_size):
+        self._model.setInputSize(tuple(input_size))
+
+    def infer(self, image):
+        # Forward
+        faces = self._model.detect(image)
+        return np.empty(shape=(0, 5)) if faces[1] is None else faces[1]