Update app.py

app.py

@@ -8,6 +8,7 @@ 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"
@@ -30,23 +31,17 @@ def detect_person(
 ) -> tuple[np.ndarray, np.ndarray]:
     bboxes, kpss = detector.detect(img)
     bboxes = np.round(bboxes[:, :4]).astype(int)
-    kpss = np.round(kpss).astype(int)
-    kpss[:, :, 0] = np.clip(kpss[:, :, 0], 0, img.shape[1])
-    kpss[:, :, 1] = np.clip(kpss[:, :, 1], 0, img.shape[0])
-    vbboxes = bboxes.copy()
-    vbboxes[:, 0] = kpss[:, 0, 0]
-    vbboxes[:, 1] = kpss[:, 0, 1]
-    vbboxes[:, 2] = kpss[:, 4, 0]
-    vbboxes[:, 3] = kpss[:, 4, 1]
-    return bboxes, vbboxes
-
-
-def extract_persons(image: np.ndarray, bboxes: np.ndarray) -> list[np.ndarray]:
+    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
-        person_images.append(person_image)
+        # 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
 
 
@@ -54,11 +49,11 @@ detector = load_model()
 detector.prepare(-1, nms_thresh=0.5, input_size=(640, 640))
 
 
-def detect(image: np.ndarray) -> list[np.ndarray]:
+def detect(image: np.ndarray) -> list[Image.Image]:
     image = image[:, :, ::-1]  # RGB -> BGR
-    bboxes, vbboxes = detect_person(image, detector)
+    bboxes = detect_person(image, detector)
     person_images = extract_persons(image, bboxes)  # Extract each person as a separate image
-    return [person_img[:, :, ::-1] for person_img in person_images]  # BGR -> RGB
+    return person_images
 
 
 examples = sorted(pathlib.Path("images").glob("*.jpg"))
@@ -66,7 +61,7 @@ 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"),  # Display a gallery of cropped images
+    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,