Megadetector Raspi5+AI HAT+26TOPSで動かす

cd /mnt/c/Users/name/MyDocker1/HAILO8
mkdir -p ~/mdv6_calib_1024

python - <<'PY'
from pathlib import Path
import random
import shutil

src = next(
    (Path(name) for name in ("OICTURE", "PICTURE") if Path(name).is_dir()),
    None,
)
if src is None:
    raise SystemExit("OICTURE または PICTURE フォルダーが見つかりません")

files = sorted(p for p in src.iterdir() if p.is_file() and p.suffix.lower() == ".jpg")
if len(files) < 1024:
    raise SystemExit(f"JPGが1024枚未満です: {len(files)}枚")

random.seed(42)
dst = Path.home() / "mdv6_calib_1024"
for p in random.sample(files, 1024):
    shutil.copy2(p, dst / p.name)

print(f"校正画像: {len(list(dst.glob('*.jpg')))}枚")
print(f"フォルダー: {dst}")
PY

cd /mnt/c/Users/name/MyDocker1/HAILO8
mkdir -p ~/mdv6_calib_1024

python - <<'PY'
from pathlib import Path
import random
import shutil

src = next(
    (Path(name) for name in ("OICTURE", "PICTURE") if Path(name).is_dir()),
    None,
)
if src is None:
    raise SystemExit("OICTURE または PICTURE フォルダーが見つかりません")

files = sorted(p for p in src.iterdir() if p.is_file() and p.suffix.lower() == ".jpg")
if len(files) < 1024:
    raise SystemExit(f"JPGが1024枚未満です: {len(files)}枚")

random.seed(42)
dst = Path.home() / "mdv6_calib_1024"
for p in random.sample(files, 1024):
    shutil.copy2(p, dst / p.name)

print(f"校正画像: {len(list(dst.glob('*.jpg')))}枚")
print(f"フォルダー: {dst}")
PY

hailomz optimize yolov10n \
cd ~/mdv6_yolov10c_h8

hailomz optimize yolov10n \
  --har ~/mdv6_yolov10c_h8/yolov10n.har \
  --calib-path /home/name/mdv6_calib_1024 \
  --classes 3 \
  --hw-arch hailo8

hailomz optimize yolov10n \
cd ~/mdv6_yolov10c_h8

hailomz optimize yolov10n \
  --har ~/mdv6_yolov10c_h8/yolov10n.har \
  --calib-path /home/name/mdv6_calib_1024 \
  --classes 3 \
  --hw-arch hailo8

hailomz compile yolov10n \
  --hw-arch hailo8 \
  --har "yolov10n.har" 

hailomz compile yolov10n \
  --hw-arch hailo8 \
  --har "yolov10n.har" 

source ~/HAT/bin/activate
python - <<'PY'
import sys, numpy, cv2
import paho.mqtt.client as mqtt
from hailo_platform import HEF, VDevice
print('Python:', sys.executable)
print('NumPy:', numpy.__version__)
print('OpenCV:', cv2.__version__)
print('HailoRT Python: OK')
print('Paho MQTT: OK')
PY

source ~/HAT/bin/activate
python - <<'PY'
import sys, numpy, cv2
import paho.mqtt.client as mqtt
from hailo_platform import HEF, VDevice
print('Python:', sys.executable)
print('NumPy:', numpy.__version__)
print('OpenCV:', cv2.__version__)
print('HailoRT Python: OK')
print('Paho MQTT: OK')
PY

hailortcli run /home/pi/HAT/yolov10n.hef --batch-size 1

hailortcli run /home/pi/HAT/yolov10n.hef --batch-size 1

import cv2
import json
import os
import time
import numpy as np
import paho.mqtt.client as mqtt

from contextlib import ExitStack
from hailo_platform import (
    HEF,
    VDevice,
    HailoStreamInterface,
    ConfigureParams,
    InferVStreams,
    InputVStreamParams,
    OutputVStreamParams,
    FormatType,
)


# ============================================================
# 設定
# ============================================================
HEF_PATH = "/home/pi/HAT/yolov10n.hef"

CAMERAS = [
    {
        "id": "tapo310a_1",
        # USER:PASSWORDを、現在使用している認証情報に置き換えてください
        "rtsp_url": "rtsp://your_ID:Your_Passwd@192.168.1.101:554/stream1",
    },
]

MQTT_BROKER = "192.168.1.200"#Your MQTT Server Address
MQTT_PORT = 1883
MQTT_TOPIC = "megadt"

SAVE_DIR = "/home/pi/HAT/detected_images"
SAVE_INTERVAL_SEC = 5
CLASS_COOLDOWN_SEC = 10
DEBUG_INTERVAL_SEC = 5

INPUT_SIZE = 640

CLASS_NAMES = {
    0: "animal",
    1: "person",
    2: "vehicle",
}

# HEFのScore threshold
CLASS_THRESHOLDS = {
    0: 0.80,
    1: 0.80,
    2: 0.80,
}

os.makedirs(SAVE_DIR, exist_ok=True)


# ============================================================
# 前処理
# ============================================================
def letterbox_rgb(frame_bgr, size=INPUT_SIZE):
    """縦横比を維持して640x640へリサイズし、RGBに変換する。"""
    height, width = frame_bgr.shape[:2]
    scale = min(size / width, size / height)

    resized_w = int(round(width * scale))
    resized_h = int(round(height * scale))

    resized = cv2.resize(
        frame_bgr,
        (resized_w, resized_h),
        interpolation=cv2.INTER_LINEAR,
    )

    pad_w = size - resized_w
    pad_h = size - resized_h
    left = pad_w // 2
    right = pad_w - left
    top = pad_h // 2
    bottom = pad_h - top

    padded = cv2.copyMakeBorder(
        resized,
        top,
        bottom,
        left,
        right,
        cv2.BORDER_CONSTANT,
        value=(114, 114, 114),
    )

    rgb = cv2.cvtColor(padded, cv2.COLOR_BGR2RGB)
    return np.ascontiguousarray(rgb), scale, left, top


# ============================================================
# NMS出力をクラス別の配列へ整形
# InferVStreamsの戻り値は、空のときと検出ありのときで
# listの構造が異なる場合があるため、両方を処理する。
# ============================================================
def nms_output_by_class(nms_output):
    """HailoRTのNMS出力をクラス別の (N, 5) 配列3つにそろえる。"""

    # バッチなどの1要素ラッパーを外す
    value = nms_output
    while isinstance(value, (list, tuple)) and len(value) == 1:
        value = value[0]

    def as_rows(item, class_id):
        # クラスごとの出力にも1要素ラッパーがある場合に備える
        while isinstance(item, (list, tuple)) and len(item) == 1:
            item = item[0]

        try:
            arr = np.asarray(item, dtype=np.float32)
        except (TypeError, ValueError) as exc:
            raise RuntimeError(
                f"NMSのクラス{class_id}出力を配列化できません。"
                f"type={type(item)}, value={item!r}"
            ) from exc

        if arr.size == 0:
            return np.empty((0, 5), dtype=np.float32)

        if arr.ndim >= 2 and arr.shape[-1] == 5:
            return arr.reshape(-1, 5)

        if arr.ndim == 1 and arr.size % 5 == 0:
            return arr.reshape(-1, 5)

        raise RuntimeError(
            f"NMSのクラス{class_id}出力形状が想定外です: "
            f"shape={arr.shape}, dtype={arr.dtype}"
        )

    # クラスごとの可変長配列を持つリスト／タプル
    if isinstance(value, (list, tuple)) and len(value) == 3:
        return [as_rows(value[i], i) for i in range(3)]

    # (3, N, 5) 形式。N=0の未検出出力もここで扱う
    try:
        arr = np.asarray(value, dtype=np.float32)
    except (TypeError, ValueError) as exc:
        details = []
        if isinstance(value, (list, tuple)):
            for i, item in enumerate(value):
                details.append(
                    f"[{i}] type={type(item).__name__}, "
                    f"shape={getattr(item, 'shape', None)}"
                )
        raise RuntimeError(
            "NMS出力を解釈できません。"
            f"外側の型={type(value).__name__}; " + "; ".join(details)
        ) from exc

    if arr.ndim == 4 and arr.shape[0] == 1:
        arr = arr[0]

    if arr.ndim == 3 and arr.shape[0] == 3 and arr.shape[-1] == 5:
        return [arr[i].reshape(-1, 5) for i in range(3)]

    raise RuntimeError(
        f"NMS出力形状が想定外です: shape={arr.shape}, dtype={arr.dtype}"
    )


# ============================================================
# 後処理
# 検出1件は y_min, x_min, y_max, x_max, score
# ============================================================
def decode_hailo_nms(
    nms_output,
    scale,
    pad_left,
    pad_top,
    orig_w,
    orig_h,
):
    class_arrays = nms_output_by_class(nms_output)
    detections = []

    for class_id, boxes in enumerate(class_arrays):
        for row in boxes:
            if not np.all(np.isfinite(row)):
                continue

            y1, x1, y2, x2, score = map(float, row)

            if score < CLASS_THRESHOLDS[class_id]:
                continue

            # 640x640の正規化座標を元画像座標へ戻す
            x1 = (x1 * INPUT_SIZE - pad_left) / scale
            x2 = (x2 * INPUT_SIZE - pad_left) / scale
            y1 = (y1 * INPUT_SIZE - pad_top) / scale
            y2 = (y2 * INPUT_SIZE - pad_top) / scale

            x1 = float(np.clip(x1, 0, orig_w - 1))
            x2 = float(np.clip(x2, 0, orig_w - 1))
            y1 = float(np.clip(y1, 0, orig_h - 1))
            y2 = float(np.clip(y2, 0, orig_h - 1))

            if x2 <= x1 or y2 <= y1:
                continue

            detections.append({
                "class": CLASS_NAMES[class_id],
                "confidence": round(score, 3),
                "box": [
                    int(round(x1)),
                    int(round(y1)),
                    int(round(x2)),
                    int(round(y2)),
                ],
            })

    return detections


def print_nms_summary(nms_output):
    class_arrays = nms_output_by_class(nms_output)
    summaries = []

    for class_id, class_name in CLASS_NAMES.items():
        boxes = class_arrays[class_id]

        if boxes.size > 0:
            scores = boxes[:, 4]
            scores = scores[np.isfinite(scores) & (scores > 0)]
        else:
            scores = np.empty((0,), dtype=np.float32)

        count = len(scores)
        max_score = float(np.max(scores)) if count else 0.0

        summaries.append(
            f"{class_name}: count={count}, max_score={max_score:.3f}"
        )

    print("[NMS] " + " | ".join(summaries))


# ============================================================
# HEF・MQTT・カメラの初期化
# ============================================================
print("Hailo-8用HEFを読み込み中...")

hef = HEF(HEF_PATH)
input_info = hef.get_input_vstream_infos()[0]
output_infos = hef.get_output_vstream_infos()

if len(output_infos) != 1:
    raise RuntimeError(
        f"HEF出力数が想定外です。期待=1、実際={len(output_infos)}"
    )

output_info = output_infos[0]
OUTPUT_NMS_NAME = output_info.name

print(f"入力: {input_info.name}, shape={input_info.shape}")
print(f"出力: {output_info.name}, shape={output_info.shape}")

mqtt_client = mqtt.Client(client_id="mdv6-pi5")
mqtt_connected = False

try:
    mqtt_client.connect(MQTT_BROKER, MQTT_PORT, 60)
    mqtt_client.loop_start()
    mqtt_connected = True
    print(f"MQTT接続に成功しました: {MQTT_BROKER}:{MQTT_PORT}")
except Exception as exc:
    print(f"MQTT接続に失敗しました。検出処理は続行します: {exc}")

print("カメラストリームを開いています...")

for camera in CAMERAS:
    camera["cap"] = cv2.VideoCapture(
        camera["rtsp_url"],
        cv2.CAP_FFMPEG,
    )
    camera["cap"].set(cv2.CAP_PROP_BUFFERSIZE, 1)
    camera["last_saved_time"] = 0.0
    camera["last_detected_by_class"] = {}
    camera["last_debug_time"] = 0.0

    if camera["cap"].isOpened():
        print(f"{camera['id']}: カメラ接続完了")
    else:
        print(f"{camera['id']}: カメラ接続失敗。RTSP URLを確認してください。")


# ============================================================
# 推論ループ
# ============================================================
mqtt_started = False

print("MegaDetector監視を開始しました。終了するには Ctrl+C を押してください。")

try:
    with VDevice() as target:
        configure_params = ConfigureParams.create_from_hef(
            hef=hef,
            interface=HailoStreamInterface.PCIe,
        )

        network_group = target.configure(hef, configure_params)[0]
        network_group_params = network_group.create_params()

        input_vstreams_params = InputVStreamParams.make(
            network_group,
            format_type=FormatType.UINT8,
        )
        output_vstreams_params = OutputVStreamParams.make(
            network_group,
            format_type=FormatType.FLOAT32,
        )

        # 推論パイプラインとネットワークを開いたまま連続推論する
        with ExitStack() as stack:
            infer_pipeline = stack.enter_context(
                InferVStreams(
                    network_group,
                    input_vstreams_params,
                    output_vstreams_params,
                )
            )
            stack.enter_context(
                network_group.activate(network_group_params)
            )

            while True:
                active_camera_count = 0

                for camera in CAMERAS:
                    cap = camera["cap"]

                    if not cap.isOpened():
                        continue

                    active_camera_count += 1
                    ret, frame = cap.read()

                    if not ret:
                        print(f"{camera['id']}: フレームを取得できません")
                        time.sleep(0.1)
                        continue

                    frame_h, frame_w = frame.shape[:2]
                    input_rgb, scale, pad_left, pad_top = letterbox_rgb(frame)

                    input_buffer = np.expand_dims(input_rgb, axis=0)
                    input_buffer = input_buffer.astype(
                        np.uint8,
                        copy=False,
                    )

                    outputs = infer_pipeline.infer({
                        input_info.name: input_buffer
                    })
                    nms_output = outputs[OUTPUT_NMS_NAME]

                    if (
                        time.monotonic() - camera["last_debug_time"]
                        >= DEBUG_INTERVAL_SEC
                    ):
                        camera["last_debug_time"] = time.monotonic()
                        print_nms_summary(nms_output)

                    detections = decode_hailo_nms(
                        nms_output,
                        scale,
                        pad_left,
                        pad_top,
                        frame_w,
                        frame_h,
                    )

                    now = time.time()
                    last_by_class = camera["last_detected_by_class"]

                    active_detections = [
                        detection
                        for detection in detections
                        if now - last_by_class.get(
                            detection["class"], 0.0
                        ) >= CLASS_COOLDOWN_SEC
                    ]

                    if not active_detections:
                        continue

                    active_classes = sorted({
                        detection["class"]
                        for detection in active_detections
                    })

                    for class_name in active_classes:
                        last_by_class[class_name] = now
                        print(f"{camera['id']}: 検出 -> {class_name}")

                    if mqtt_connected:
                        payload = {
                            "timestamp": now,
                            "camera_id": camera["id"],
                            "detected": True,
                            "count": len(active_detections),
                            "objects": active_detections,
                        }
                        mqtt_client.publish(
                            MQTT_TOPIC,
                            json.dumps(payload),
                        )

                    if now - camera["last_saved_time"] >= SAVE_INTERVAL_SEC:
                        timestamp = time.strftime("%Y%m%d_%H%M%S")
                        save_path = os.path.join(
                            SAVE_DIR,
                            f"{camera['id']}_detected_{timestamp}.jpg",
                        )
                        cv2.imwrite(save_path, frame)
                        camera["last_saved_time"] = now
                        print(f"画像保存: {save_path}")

                if active_camera_count == 0:
                    print("有効なカメラがありません。RTSP URLを確認してください。")
                    time.sleep(5)
                else:
                    time.sleep(0.01)

except KeyboardInterrupt:
    print("\n停止します。")

finally:
    for camera in CAMERAS:
        cap = camera.get("cap")
        if cap is not None:
            cap.release()

    if mqtt_connected:
        mqtt_client.loop_stop()
        mqtt_client.disconnect()

    print("カメラとMQTTを終了しました。")
    

import cv2
import json
import os
import time
import numpy as np
import paho.mqtt.client as mqtt

from contextlib import ExitStack
from hailo_platform import (
    HEF,
    VDevice,
    HailoStreamInterface,
    ConfigureParams,
    InferVStreams,
    InputVStreamParams,
    OutputVStreamParams,
    FormatType,
)


# ============================================================
# 設定
# ============================================================
HEF_PATH = "/home/pi/HAT/yolov10n.hef"

CAMERAS = [
    {
        "id": "tapo310a_1",
        # USER:PASSWORDを、現在使用している認証情報に置き換えてください
        "rtsp_url": "rtsp://your_ID:Your_Passwd@192.168.1.101:554/stream1",
    },
]

MQTT_BROKER = "192.168.1.200"#Your MQTT Server Address
MQTT_PORT = 1883
MQTT_TOPIC = "megadt"

SAVE_DIR = "/home/pi/HAT/detected_images"
SAVE_INTERVAL_SEC = 5
CLASS_COOLDOWN_SEC = 10
DEBUG_INTERVAL_SEC = 5

INPUT_SIZE = 640

CLASS_NAMES = {
    0: "animal",
    1: "person",
    2: "vehicle",
}

# HEFのScore threshold
CLASS_THRESHOLDS = {
    0: 0.80,
    1: 0.80,
    2: 0.80,
}

os.makedirs(SAVE_DIR, exist_ok=True)


# ============================================================
# 前処理
# ============================================================
def letterbox_rgb(frame_bgr, size=INPUT_SIZE):
    """縦横比を維持して640x640へリサイズし、RGBに変換する。"""
    height, width = frame_bgr.shape[:2]
    scale = min(size / width, size / height)

    resized_w = int(round(width * scale))
    resized_h = int(round(height * scale))

    resized = cv2.resize(
        frame_bgr,
        (resized_w, resized_h),
        interpolation=cv2.INTER_LINEAR,
    )

    pad_w = size - resized_w
    pad_h = size - resized_h
    left = pad_w // 2
    right = pad_w - left
    top = pad_h // 2
    bottom = pad_h - top

    padded = cv2.copyMakeBorder(
        resized,
        top,
        bottom,
        left,
        right,
        cv2.BORDER_CONSTANT,
        value=(114, 114, 114),
    )

    rgb = cv2.cvtColor(padded, cv2.COLOR_BGR2RGB)
    return np.ascontiguousarray(rgb), scale, left, top


# ============================================================
# NMS出力をクラス別の配列へ整形
# InferVStreamsの戻り値は、空のときと検出ありのときで
# listの構造が異なる場合があるため、両方を処理する。
# ============================================================
def nms_output_by_class(nms_output):
    """HailoRTのNMS出力をクラス別の (N, 5) 配列3つにそろえる。"""

    # バッチなどの1要素ラッパーを外す
    value = nms_output
    while isinstance(value, (list, tuple)) and len(value) == 1:
        value = value[0]

    def as_rows(item, class_id):
        # クラスごとの出力にも1要素ラッパーがある場合に備える
        while isinstance(item, (list, tuple)) and len(item) == 1:
            item = item[0]

        try:
            arr = np.asarray(item, dtype=np.float32)
        except (TypeError, ValueError) as exc:
            raise RuntimeError(
                f"NMSのクラス{class_id}出力を配列化できません。"
                f"type={type(item)}, value={item!r}"
            ) from exc

        if arr.size == 0:
            return np.empty((0, 5), dtype=np.float32)

        if arr.ndim >= 2 and arr.shape[-1] == 5:
            return arr.reshape(-1, 5)

        if arr.ndim == 1 and arr.size % 5 == 0:
            return arr.reshape(-1, 5)

        raise RuntimeError(
            f"NMSのクラス{class_id}出力形状が想定外です: "
            f"shape={arr.shape}, dtype={arr.dtype}"
        )

    # クラスごとの可変長配列を持つリスト／タプル
    if isinstance(value, (list, tuple)) and len(value) == 3:
        return [as_rows(value[i], i) for i in range(3)]

    # (3, N, 5) 形式。N=0の未検出出力もここで扱う
    try:
        arr = np.asarray(value, dtype=np.float32)
    except (TypeError, ValueError) as exc:
        details = []
        if isinstance(value, (list, tuple)):
            for i, item in enumerate(value):
                details.append(
                    f"[{i}] type={type(item).__name__}, "
                    f"shape={getattr(item, 'shape', None)}"
                )
        raise RuntimeError(
            "NMS出力を解釈できません。"
            f"外側の型={type(value).__name__}; " + "; ".join(details)
        ) from exc

    if arr.ndim == 4 and arr.shape[0] == 1:
        arr = arr[0]

    if arr.ndim == 3 and arr.shape[0] == 3 and arr.shape[-1] == 5:
        return [arr[i].reshape(-1, 5) for i in range(3)]

    raise RuntimeError(
        f"NMS出力形状が想定外です: shape={arr.shape}, dtype={arr.dtype}"
    )


# ============================================================
# 後処理
# 検出1件は y_min, x_min, y_max, x_max, score
# ============================================================
def decode_hailo_nms(
    nms_output,
    scale,
    pad_left,
    pad_top,
    orig_w,
    orig_h,
):
    class_arrays = nms_output_by_class(nms_output)
    detections = []

    for class_id, boxes in enumerate(class_arrays):
        for row in boxes:
            if not np.all(np.isfinite(row)):
                continue

            y1, x1, y2, x2, score = map(float, row)

            if score < CLASS_THRESHOLDS[class_id]:
                continue

            # 640x640の正規化座標を元画像座標へ戻す
            x1 = (x1 * INPUT_SIZE - pad_left) / scale
            x2 = (x2 * INPUT_SIZE - pad_left) / scale
            y1 = (y1 * INPUT_SIZE - pad_top) / scale
            y2 = (y2 * INPUT_SIZE - pad_top) / scale

            x1 = float(np.clip(x1, 0, orig_w - 1))
            x2 = float(np.clip(x2, 0, orig_w - 1))
            y1 = float(np.clip(y1, 0, orig_h - 1))
            y2 = float(np.clip(y2, 0, orig_h - 1))

            if x2 <= x1 or y2 <= y1:
                continue

            detections.append({
                "class": CLASS_NAMES[class_id],
                "confidence": round(score, 3),
                "box": [
                    int(round(x1)),
                    int(round(y1)),
                    int(round(x2)),
                    int(round(y2)),
                ],
            })

    return detections


def print_nms_summary(nms_output):
    class_arrays = nms_output_by_class(nms_output)
    summaries = []

    for class_id, class_name in CLASS_NAMES.items():
        boxes = class_arrays[class_id]

        if boxes.size > 0:
            scores = boxes[:, 4]
            scores = scores[np.isfinite(scores) & (scores > 0)]
        else:
            scores = np.empty((0,), dtype=np.float32)

        count = len(scores)
        max_score = float(np.max(scores)) if count else 0.0

        summaries.append(
            f"{class_name}: count={count}, max_score={max_score:.3f}"
        )

    print("[NMS] " + " | ".join(summaries))


# ============================================================
# HEF・MQTT・カメラの初期化
# ============================================================
print("Hailo-8用HEFを読み込み中...")

hef = HEF(HEF_PATH)
input_info = hef.get_input_vstream_infos()[0]
output_infos = hef.get_output_vstream_infos()

if len(output_infos) != 1:
    raise RuntimeError(
        f"HEF出力数が想定外です。期待=1、実際={len(output_infos)}"
    )

output_info = output_infos[0]
OUTPUT_NMS_NAME = output_info.name

print(f"入力: {input_info.name}, shape={input_info.shape}")
print(f"出力: {output_info.name}, shape={output_info.shape}")

mqtt_client = mqtt.Client(client_id="mdv6-pi5")
mqtt_connected = False

try:
    mqtt_client.connect(MQTT_BROKER, MQTT_PORT, 60)
    mqtt_client.loop_start()
    mqtt_connected = True
    print(f"MQTT接続に成功しました: {MQTT_BROKER}:{MQTT_PORT}")
except Exception as exc:
    print(f"MQTT接続に失敗しました。検出処理は続行します: {exc}")

print("カメラストリームを開いています...")

for camera in CAMERAS:
    camera["cap"] = cv2.VideoCapture(
        camera["rtsp_url"],
        cv2.CAP_FFMPEG,
    )
    camera["cap"].set(cv2.CAP_PROP_BUFFERSIZE, 1)
    camera["last_saved_time"] = 0.0
    camera["last_detected_by_class"] = {}
    camera["last_debug_time"] = 0.0

    if camera["cap"].isOpened():
        print(f"{camera['id']}: カメラ接続完了")
    else:
        print(f"{camera['id']}: カメラ接続失敗。RTSP URLを確認してください。")


# ============================================================
# 推論ループ
# ============================================================
mqtt_started = False

print("MegaDetector監視を開始しました。終了するには Ctrl+C を押してください。")

try:
    with VDevice() as target:
        configure_params = ConfigureParams.create_from_hef(
            hef=hef,
            interface=HailoStreamInterface.PCIe,
        )

        network_group = target.configure(hef, configure_params)[0]
        network_group_params = network_group.create_params()

        input_vstreams_params = InputVStreamParams.make(
            network_group,
            format_type=FormatType.UINT8,
        )
        output_vstreams_params = OutputVStreamParams.make(
            network_group,
            format_type=FormatType.FLOAT32,
        )

        # 推論パイプラインとネットワークを開いたまま連続推論する
        with ExitStack() as stack:
            infer_pipeline = stack.enter_context(
                InferVStreams(
                    network_group,
                    input_vstreams_params,
                    output_vstreams_params,
                )
            )
            stack.enter_context(
                network_group.activate(network_group_params)
            )

            while True:
                active_camera_count = 0

                for camera in CAMERAS:
                    cap = camera["cap"]

                    if not cap.isOpened():
                        continue

                    active_camera_count += 1
                    ret, frame = cap.read()

                    if not ret:
                        print(f"{camera['id']}: フレームを取得できません")
                        time.sleep(0.1)
                        continue

                    frame_h, frame_w = frame.shape[:2]
                    input_rgb, scale, pad_left, pad_top = letterbox_rgb(frame)

                    input_buffer = np.expand_dims(input_rgb, axis=0)
                    input_buffer = input_buffer.astype(
                        np.uint8,
                        copy=False,
                    )

                    outputs = infer_pipeline.infer({
                        input_info.name: input_buffer
                    })
                    nms_output = outputs[OUTPUT_NMS_NAME]

                    if (
                        time.monotonic() - camera["last_debug_time"]
                        >= DEBUG_INTERVAL_SEC
                    ):
                        camera["last_debug_time"] = time.monotonic()
                        print_nms_summary(nms_output)

                    detections = decode_hailo_nms(
                        nms_output,
                        scale,
                        pad_left,
                        pad_top,
                        frame_w,
                        frame_h,
                    )

                    now = time.time()
                    last_by_class = camera["last_detected_by_class"]

                    active_detections = [
                        detection
                        for detection in detections
                        if now - last_by_class.get(
                            detection["class"], 0.0
                        ) >= CLASS_COOLDOWN_SEC
                    ]

                    if not active_detections:
                        continue

                    active_classes = sorted({
                        detection["class"]
                        for detection in active_detections
                    })

                    for class_name in active_classes:
                        last_by_class[class_name] = now
                        print(f"{camera['id']}: 検出 -> {class_name}")

                    if mqtt_connected:
                        payload = {
                            "timestamp": now,
                            "camera_id": camera["id"],
                            "detected": True,
                            "count": len(active_detections),
                            "objects": active_detections,
                        }
                        mqtt_client.publish(
                            MQTT_TOPIC,
                            json.dumps(payload),
                        )

                    if now - camera["last_saved_time"] >= SAVE_INTERVAL_SEC:
                        timestamp = time.strftime("%Y%m%d_%H%M%S")
                        save_path = os.path.join(
                            SAVE_DIR,
                            f"{camera['id']}_detected_{timestamp}.jpg",
                        )
                        cv2.imwrite(save_path, frame)
                        camera["last_saved_time"] = now
                        print(f"画像保存: {save_path}")

                if active_camera_count == 0:
                    print("有効なカメラがありません。RTSP URLを確認してください。")
                    time.sleep(5)
                else:
                    time.sleep(0.01)

except KeyboardInterrupt:
    print("\n停止します。")

finally:
    for camera in CAMERAS:
        cap = camera.get("cap")
        if cap is not None:
            cap.release()

    if mqtt_connected:
        mqtt_client.loop_stop()
        mqtt_client.disconnect()

    print("カメラとMQTTを終了しました。")