Skip to content

board_test

birdnet_stm32.cli.board_test

CLI entry point for standalone on-board inference tests.

get_args()

Parse command-line arguments for board test.

Source code in birdnet_stm32/cli/board_test.py 
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
def get_args() -> argparse.Namespace:
    """Parse command-line arguments for board test."""
    parser = argparse.ArgumentParser(
        description=(
            "Run standalone inference on the STM32N6570-DK: firmware reads WAV "
            "from SD card, computes STFT on Cortex-M55, runs NPU inference, "
            "and streams results over UART."
        ),
    )
    parser.add_argument(
        "--model_path",
        type=str,
        default="",
        help="Path to quantized .tflite model (default: from config.json)",
    )
    parser.add_argument(
        "--model_config",
        type=str,
        default="",
        help="Path to _model_config.json (required)",
    )
    parser.add_argument("--labels", type=str, default="", help="Path to _labels.txt")
    parser.add_argument(
        "--serial_port",
        type=str,
        default="/dev/ttyACM0",
        help="Serial port for UART capture (default: /dev/ttyACM0)",
    )
    parser.add_argument("--top_k", type=int, default=5, help="Top-K predictions per file")
    parser.add_argument(
        "--score_threshold",
        type=float,
        default=0.01,
        help="Minimum score to display",
    )
    parser.add_argument("--config", type=str, default="config.json", help="Deploy config JSON")
    parser.add_argument(
        "--timeout",
        type=int,
        default=300,
        help="Max seconds to wait for firmware (default: 300)",
    )
    parser.add_argument(
        "--save_results",
        type=str,
        default="",
        help="Save results summary to a CSV file",
    )
    return parser.parse_args()

main()

Run standalone on-board inference test.

Source code in birdnet_stm32/cli/board_test.py 
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
def main():
    """Run standalone on-board inference test."""
    args = get_args()

    # Resolve deploy config
    cli_overrides = {}
    if args.model_path:
        cli_overrides["model_path"] = args.model_path
    deploy_cfg = resolve_deploy_config(cli_args=cli_overrides, config_path=args.config)

    # Resolve model config path
    model_config = args.model_config
    if not model_config:
        root, _ = os.path.splitext(deploy_cfg.model_path)
        root = root.replace("_quantized", "")
        candidate = root + "_model_config.json"
        if os.path.isfile(candidate):
            model_config = candidate
    if not model_config or not os.path.isfile(model_config):
        print("[ERROR] Model config not found. Supply --model_config path.")
        raise SystemExit(1)

    # Resolve labels path
    labels_path = args.labels
    if not labels_path:
        root, _ = os.path.splitext(deploy_cfg.model_path)
        root = root.replace("_quantized", "")
        candidate = root + "_labels.txt"
        if os.path.isfile(candidate):
            labels_path = candidate

    board_cfg = BoardTestConfig(
        deploy_cfg=deploy_cfg,
        model_config_path=model_config,
        labels_path=labels_path,
        serial_port=args.serial_port,
        top_k=args.top_k,
        score_threshold=args.score_threshold,
        timeout=args.timeout,
    )

    result = run_board_test(board_cfg)

    if args.save_results and result["results"]:
        import csv

        with open(args.save_results, "w", newline="") as f:
            writer = csv.writer(f)
            writer.writerow(["file", "top_label", "top_score"])
            for r in result["results"]:
                top = r["detections"][0] if r["detections"] else {"label": "", "score": 0.0}
                writer.writerow([r["file"], top["label"], f"{top['score']:.4f}"])
        print(f"\nResults saved to {args.save_results}")