resize image to optimize performance

app.py

@@ -5,7 +5,21 @@ from insightface.app import FaceAnalysis
 from hsemotion_onnx.facial_emotions import HSEmotionRecognizer
 
 
+def resize(image, target_size):
+    # Get the dimensions of the input image
+    height, width = image.shape[0], image.shape[1]
+
+    # Calculate the scaling factor needed to resize the image to the target size
+    scaling_factor = min(target_size[0] / width, target_size[1] / height)
+    # Resize the image using cv2.resize
+    resized_image = cv2.resize(image, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_LINEAR)
+
+    return resized_image
+
+
 def facial_emotion_recognition(img):
+
+    img = resize(img, target_size)
     
     faces = face_detector.get(img)
 
@@ -35,10 +49,12 @@ def facial_emotion_recognition(img):
     return img
 
 face_margin = 0.1
+target_size = (640, 640)
 model_name = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'buffalo_sc')
 face_detector = FaceAnalysis(name=model_name, allowed_modules=['detection'], providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
 face_detector.prepare(ctx_id=0, det_size=(640, 640))
 
+
 hse_emo_model = HSEmotionRecognizer(model_name='enet_b0_8_best_vgaf')
 
 webcam = gr.Image(image_mode='RGB', type='numpy', source='webcam', label='Input Image')