Update app.py

app.py

@@ -8,7 +8,6 @@ import huggingface_hub
 import insightface
 import numpy as np
 import onnxruntime as ort
-from PIL import Image  # Importer PIL pour manipuler les images
 
 TITLE = "insightface Person Detection"
 DESCRIPTION = "https://github.com/deepinsight/insightface/tree/master/examples/person_detection"
@@ -28,45 +27,10 @@ def load_model():
 
 def detect_person(
     img: np.ndarray, detector: insightface.model_zoo.retinaface.RetinaFace
-) -> tuple[np.ndarray, np.ndarray]:
+) -> np.ndarray:
     bboxes, kpss = detector.detect(img)
     bboxes = np.round(bboxes[:, :4]).astype(int)
     return bboxes
 
 
-def extract_persons(image: np.ndarray, bboxes: np.ndarray) -> list[Image.Image]:
-    person_images = []
-    for bbox in bboxes:
-        x1, y1, x2, y2 = bbox
-        person_image = image[y1:y2, x1:x2]  # Crop the detected person
-        # Convertir en image PIL pour garantir la compatibilité
-        pil_image = Image.fromarray(cv2.cvtColor(person_image, cv2.COLOR_BGR2RGB))
-        person_images.append(pil_image)
-    return person_images
-
-
-detector = load_model()
-detector.prepare(-1, nms_thresh=0.5, input_size=(640, 640))
-
-
-def detect(image: np.ndarray) -> list[Image.Image]:
-    image = image[:, :, ::-1]  # RGB -> BGR
-    bboxes = detect_person(image, detector)
-    person_images = extract_persons(image, bboxes)  # Extract each person as a separate image
-    return person_images
-
-
-examples = sorted(pathlib.Path("images").glob("*.jpg"))
-
-demo = gr.Interface(
-    fn=detect,
-    inputs=gr.Image(label="Input", type="numpy"),
-    outputs=gr.Gallery(label="Detected Persons").style(grid=2, height="auto"),  # Display a gallery of cropped images
-    examples=examples,
-    examples_per_page=30,
-    title=TITLE,
-    description=DESCRIPTION,
-)
-
-if __name__ == "__main__":
-    demo.queue(max_size=10).launch()
+def extract