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inference_example.py
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66 lines (52 loc) · 2.22 KB
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import os
from inference.models.utils import get_roboflow_model
from tqdm import tqdm
import supervision as sv
def main(
source_video_path: str,
target_video_path: str,
roboflow_api_key: str,
model_id: str = "yolov8x-1280",
confidence_threshold: float = 0.3,
iou_threshold: float = 0.7,
) -> None:
"""
Video Processing with Inference and ByteTrack.
Args:
source_video_path: Path to the source video file
target_video_path: Path to the target video file (output)
roboflow_api_key: Roboflow API key
model_id: Roboflow model ID
confidence_threshold: Confidence threshold for the model
iou_threshold: IOU threshold for the model
"""
api_key = os.environ.get("ROBOFLOW_API_KEY", roboflow_api_key)
if api_key is None:
raise ValueError(
"Roboflow API key is missing. Please provide it as an argument or set the "
"ROBOFLOW_API_KEY environment variable."
)
model = get_roboflow_model(model_id=model_id, api_key=api_key)
tracker = sv.ByteTrack()
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
with sv.VideoSink(target_path=target_video_path, video_info=video_info) as sink:
for frame in tqdm(frame_generator, total=video_info.total_frames):
results = model.infer(
frame, confidence=confidence_threshold, iou_threshold=iou_threshold
)[0]
detections = sv.Detections.from_inference(results)
detections = tracker.update_with_detections(detections)
annotated_frame = box_annotator.annotate(
scene=frame.copy(), detections=detections
)
annotated_labeled_frame = label_annotator.annotate(
scene=annotated_frame, detections=detections
)
sink.write_frame(frame=annotated_labeled_frame)
if __name__ == "__main__":
from jsonargparse import auto_cli, set_parsing_settings
set_parsing_settings(parse_optionals_as_positionals=True)
auto_cli(main, as_positional=False)