1from __future__ import annotations
2
3import os
4
5import daft
6from daft import col
7
8from daft.aihc.common.udf import aihc_udf
9from daft.aihc.functions.video.video_motion_score import VideoMotionScore
10
11if __name__ == "__main__":
12    if os.getenv("DAFT_RUNNER", "native") == "ray":
13        import ray
14        ray.init(dashboard_host="0.0.0.0", ignore_reinit_error=True)
15        daft.set_runner_ray()
16    daft.set_execution_config(actor_udf_ready_timeout=6000, min_cpu_per_task=0)
17
18    # TODO: 根据实际场景准备样本数据
19    samples = {
20        "video_paths": ["bos://bucket/test/aihc/test_data/test_video.mp4"],
21    }
22    ds = daft.from_pydict(samples)
23    constructor_kwargs = {
24        "optical_flow_algorithm": 'farneback',
25        "sample_ratio": 0.125,
26        "mag_threshold": 0.01,
27        "flow_threshold": 6,
28        "smooth_window": 5,
29    }
30    ds = ds.with_column(
31        "result",
32        aihc_udf(
33            VideoMotionScore,
34            construct_args=constructor_kwargs,
35            num_cpus=1,
36            concurrency=4,
37            batch_size=8,
38        )(col("video_paths"), col("video_binaries"), col("video_formats")),
39    )
40    ds.show()