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examples/YOLOv8-SAHI-Inference-Video/yolov8_sahi.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import argparse
+from pathlib import Path
+
+import cv2
+from sahi import AutoDetectionModel
+from sahi.predict import get_sliced_prediction
+from sahi.utils.yolov8 import download_yolov8s_model
+
+from ultralytics.utils.files import increment_path
+
+
+def run(weights="yolov8n.pt", source="test.mp4", view_img=False, save_img=False, exist_ok=False):
+    """
+    Run object detection on a video using YOLOv8 and SAHI.
+
+    Args:
+        weights (str): Model weights path.
+        source (str): Video file path.
+        view_img (bool): Show results.
+        save_img (bool): Save results.
+        exist_ok (bool): Overwrite existing files.
+    """
+
+    # Check source path
+    if not Path(source).exists():
+        raise FileNotFoundError(f"Source path '{source}' does not exist.")
+
+    yolov8_model_path = f"models/{weights}"
+    download_yolov8s_model(yolov8_model_path)
+    detection_model = AutoDetectionModel.from_pretrained(
+        model_type="yolov8", model_path=yolov8_model_path, confidence_threshold=0.3, device="cpu"
+    )
+
+    # Video setup
+    videocapture = cv2.VideoCapture(source)
+    frame_width, frame_height = int(videocapture.get(3)), int(videocapture.get(4))
+    fps, fourcc = int(videocapture.get(5)), cv2.VideoWriter_fourcc(*"mp4v")
+
+    # Output setup
+    save_dir = increment_path(Path("ultralytics_results_with_sahi") / "exp", exist_ok)
+    save_dir.mkdir(parents=True, exist_ok=True)
+    video_writer = cv2.VideoWriter(str(save_dir / f"{Path(source).stem}.mp4"), fourcc, fps, (frame_width, frame_height))
+
+    while videocapture.isOpened():
+        success, frame = videocapture.read()
+        if not success:
+            break
+
+        results = get_sliced_prediction(
+            frame, detection_model, slice_height=512, slice_width=512, overlap_height_ratio=0.2, overlap_width_ratio=0.2
+        )
+        object_prediction_list = results.object_prediction_list
+
+        boxes_list = []
+        clss_list = []
+        for ind, _ in enumerate(object_prediction_list):
+            boxes = (
+                object_prediction_list[ind].bbox.minx,
+                object_prediction_list[ind].bbox.miny,
+                object_prediction_list[ind].bbox.maxx,
+                object_prediction_list[ind].bbox.maxy,
+            )
+            clss = object_prediction_list[ind].category.name
+            boxes_list.append(boxes)
+            clss_list.append(clss)
+
+        for box, cls in zip(boxes_list, clss_list):
+            x1, y1, x2, y2 = box
+            cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (56, 56, 255), 2)
+            label = str(cls)
+            t_size = cv2.getTextSize(label, 0, fontScale=0.6, thickness=1)[0]
+            cv2.rectangle(
+                frame, (int(x1), int(y1) - t_size[1] - 3), (int(x1) + t_size[0], int(y1) + 3), (56, 56, 255), -1
+            )
+            cv2.putText(
+                frame, label, (int(x1), int(y1) - 2), 0, 0.6, [255, 255, 255], thickness=1, lineType=cv2.LINE_AA
+            )
+
+        if view_img:
+            cv2.imshow(Path(source).stem, frame)
+        if save_img:
+            video_writer.write(frame)
+
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+    video_writer.release()
+    videocapture.release()
+    cv2.destroyAllWindows()
+
+
+def parse_opt():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--weights", type=str, default="yolov8n.pt", help="initial weights path")
+    parser.add_argument("--source", type=str, required=True, help="video file path")
+    parser.add_argument("--view-img", action="store_true", help="show results")
+    parser.add_argument("--save-img", action="store_true", help="save results")
+    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+    return parser.parse_args()
+
+
+def main(opt):
+    """Main function."""
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)