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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)
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