Update app.py

app.py

@@ -11,7 +11,6 @@ from PIL import Image
 TITLE = "insightface Person Detection"
 DESCRIPTION = "https://github.com/deepinsight/insightface/tree/master/examples/person_detection"
 
-
 def load_model():
     path = huggingface_hub.hf_hub_download("public-data/insightface", "models/scrfd_person_2.5g.onnx")
     options = ort.SessionOptions()
@@ -23,7 +22,6 @@ def load_model():
     model = insightface.model_zoo.retinaface.RetinaFace(model_file=path, session=session)
     return model
 
-
 def detect_person(
     img: np.ndarray, detector: insightface.model_zoo.retinaface.RetinaFace
 ) -> tuple[np.ndarray, np.ndarray]:
@@ -39,7 +37,6 @@ def detect_person(
     vbboxes[:, 3] = kpss[:, 4, 1]
     return bboxes, vbboxes
 
-
 def visualize(image: np.ndarray, bboxes: np.ndarray, vbboxes: np.ndarray) -> np.ndarray:
     res = image.copy()
     for i in range(bboxes.shape[0]):
@@ -58,7 +55,6 @@ def visualize(image: np.ndarray, bboxes: np.ndarray, vbboxes: np.ndarray) -> np.
         cv2.circle(res, (vx2, vy2), 1, color, 2)
     return res
 
-
 def extract_persons(image: np.ndarray, bboxes: np.ndarray) -> list[Image.Image]:
     person_images = []
     for bbox in bboxes:
@@ -68,11 +64,9 @@ def extract_persons(image: np.ndarray, bboxes: np.ndarray) -> list[Image.Image]:
         person_images.append(person_pil_image)
     return person_images
 
-
 detector = load_model()
 detector.prepare(-1, nms_thresh=0.5, input_size=(640, 640))
 
-
 def detect(image: np.ndarray) -> tuple[Image.Image, list[Image.Image]]:
     image = image[:, :, ::-1]  # RGB -> BGR
     bboxes, vbboxes = detect_person(image, detector)
@@ -80,7 +74,6 @@ def detect(image: np.ndarray) -> tuple[Image.Image, list[Image.Image]]:
     person_images = extract_persons(res, bboxes)
     return Image.fromarray(res[:, :, ::-1], 'RGB'), person_images  # BGR -> RGB
 
-
 examples = sorted(pathlib.Path("images").glob("*.jpg"))
 
 demo = gr.Interface(