chore: import upstream snapshot with attribution

examples/tracking/inference_example.py

@@ -0,0 +1,66 @@
+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)