Update app.py

app.py

@@ -47,51 +47,4 @@ def visualize(image: np.ndarray, bboxes: np.ndarray, vbboxes: np.ndarray) -> np.
         vbbox = vbboxes[i]
         x1, y1, x2, y2 = bbox
         vx1, vy1, vx2, vy2 = vbbox
-        cv2.rectangle(res, (x1, y1), (x2, y2), (0, 255, 0), 1)
-        alpha = 0.8
-        color = (255, 0, 0)
-        for c in range(3):
-            res[vy1:vy2, vx1:vx2, c] = res[vy1:vy2, vx1:vx2, c] * alpha + color[c] * (1.0 - alpha)
-        cv2.circle(res, (vx1, vy1), 1, color, 2)
-        cv2.circle(res, (vx1, vy2), 1, color, 2)
-        cv2.circle(res, (vx2, vy1), 1, color, 2)
-        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:
-        x1, y1, x2, y2 = bbox
-        person_image = image[y1:y2, x1:x2]  # Crop the detected person
-        person_pil_image = Image.fromarray(person_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)
-    res = visualize(image, bboxes, vbboxes)
-    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"))
-
-demo = gr.Interface(
-    fn=detect,
-    inputs=gr.Image(label="Input", type="numpy"),
-    outputs=[gr.Image(label="Processed Image", type="numpy"), gr.Gallery(label="Detected Persons", type="numpy")],
-    examples=examples,
-    examples_per_page=30,
-    title=TITLE,
-    description=DESCRIPTION,
-)
-
-if __name__ == "__main__":
-    demo.queue(max_size=10).launch()
+        cv2.rectangle(res, (x1, y1), (x2, y2), (0, 255,