PoseLandmarker

from aitoolkit_base import PoseLandmarker

PoseLandmarker(
    input_source=None,               # 输入源，决定运行模式
    min_detection_confidence=0.5,    # 最小检测置信度
    min_tracking_confidence=0.5,     # 最小跟踪置信度
    enable_gpu=False,                # 是否启用GPU加速
    result_callback=None,            # 可选的回调函数
    num_poses=1,                     # 最大检测人数
    output_segmentation_masks=False  # 是否输出分割蒙版
)

poses = landmarker.run(frame=None)

result_image = landmarker.draw(image, poses=None, connection_drawing_spec=None)

fps = landmarker.get_fps()

landmarker.close()

from aitoolkit_base import PoseLandmarker
import cv2

# 方法1：使用图片路径
with PoseLandmarker(input_source="person.jpg") as landmarker:
    poses = landmarker.run()
    image = cv2.imread("person.jpg")
    result = landmarker.draw(image, poses)
    cv2.imwrite("pose_result.jpg", result)

# 方法2：使用图片数据
image = cv2.imread("person.jpg")
with PoseLandmarker(input_source=image) as landmarker:
    poses = landmarker.run()
    result = landmarker.draw(image, poses)
    cv2.imwrite("pose_result.jpg", result)

from aitoolkit_base import PoseLandmarker, Camera
import cv2

# 实时检测
with Camera(0) as camera, PoseLandmarker() as landmarker:
    for frame in camera:
        # 运行检测
        poses = landmarker.run(frame)

        # 绘制结果
        result = landmarker.draw(frame, poses)

        # 显示FPS和检测到的人数
        fps = landmarker.get_fps()
        cv2.putText(result, f"FPS: {fps:.1f}", (10, 30), 
                   cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
        cv2.putText(result, f"人数: {len(poses) if poses else 0}", (10, 70), 
                   cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

        # 显示结果
        cv2.imshow("人体姿态", result)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

from aitoolkit_base import PoseLandmarker, Camera
import cv2
import numpy as np

# 启用分割蒙版输出
with Camera(0) as camera, PoseLandmarker(output_segmentation_masks=True) as landmarker:
    for frame in camera:
        # 运行检测
        poses = landmarker.run(frame)

        if poses and 'segmentation_mask' in poses[0]:
            # 获取分割蒙版
            mask = poses[0]['segmentation_mask']

            # 应用蒙版到原始图像
            background = np.zeros_like(frame)
            background[:] = (0, 0, 128)  # 蓝色背景

            # 扩展蒙版到3通道
            mask_3d = np.stack((mask,) * 3, axis=-1)

            # 合成前景和背景
            result = frame * mask_3d + background * (1 - mask_3d)
            result = result.astype(np.uint8)

            # 在合成结果上绘制关键点
            result = landmarker.draw(result, poses)
        else:
            # 如果没有检测到人体或没有蒙版，直接绘制关键点
            result = frame.copy()
            if poses:
                result = landmarker.draw(result, poses)

        # 显示结果
        cv2.imshow("人体分割", result)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

[
    {
        "pose_landmarks": [
            {"x": 0.4, "y": 0.3, "z": 0.01, "visibility": 0.98},  # 第1个关键点(NOSE)
            {"x": 0.41, "y": 0.305, "z": 0.011, "visibility": 0.97},  # 第2个关键点(LEFT_EYE_INNER)
            # ... 共33个关键点
        ],
        "pose_world_landmarks": [  # 如果可用
            {"x": 0.23, "y": -0.15, "z": -0.5, "visibility": 0.98},  # 第1个关键点(NOSE)，单位为米
            # ... 共33个关键点
        ],
        "segmentation_mask": array(...)  # 如果启用了output_segmentation_masks，值范围[0,1]
    },
    # 可能有多个人...
]