Update app.py

app.py

@@ -59,12 +59,13 @@ def visualize(image: np.ndarray, bboxes: np.ndarray, vbboxes: np.ndarray) -> np.
     return res
 
 
-def extract_persons(image: np.ndarray, bboxes: np.ndarray) -> list[np.ndarray]:
+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)
+        person_pil_image = Image.fromarray(person_image)
+        person_images.append(person_pil_image)
     return person_images
 
 
@@ -72,12 +73,12 @@ detector = load_model()
 detector.prepare(-1, nms_thresh=0.5, input_size=(640, 640))
 
 
-def detect(image: np.ndarray) -> tuple[np.ndarray, list[np.ndarray]]:
+def detect(image: np.ndarray) -> tuple[Image.Image, list[Image.Image]]:
     image = image[:, :, ::-1]  # RGB -> BGR
     bboxes, vbboxes = detect_person(image, detector)
     res = visualize(image, bboxes, vbboxes)
-    person_images = extract_persons(res, bboxes)  # Extract each person as a separate image
-    return res[:, :, ::-1], person_images  # BGR -> RGB
+    person_images = extract_persons(res, bboxes)  # Convert to PIL images
+    return Image.fromarray(res[:, :, ::-1]), person_images  # BGR -> RGB, convert to PIL
 
 
 examples = sorted(pathlib.Path("images").glob("*.jpg"))