已知一帧点云的检测结果（x， y， z， w， h， l， yaw），calib知道文件所在路径格式为kitti数据集的格式，图像的shape。要得到同一帧2D图像的检测框（x1，y1，x2，y2）保证检测框在图像内和alpha朝向角，要求用Python写出一个函数，写出代码并给出实例。

当我们有一帧点云的检测结果(x, y, z, w, h, l, yaw)以及calib文件的路径格式为kitti数据集的格式，并且已知图像的shape，我们可以使用以下Python函数来获取同一帧2D图像的检测框(x1, y1, x2, y2)和alpha朝向角：

import numpy as np

def get_detection_box_2d(point_cloud_detection, calib_path, image_shape):
    # Load calibration parameters from calib file
    with open(calib_path, 'r') as f:
        lines = f.readlines()

    # Extract relevant calibration parameters
    P2 = np.array(lines[2].strip().split(' ')[1:], dtype=np.float32)
    Tr_velo_to_cam = np.array(lines[5].strip().split(' ')[1:], dtype=np.float32)

    # Convert point cloud detection to 3D bounding box coordinates
    x, y, z, w, h, l, yaw = point_cloud_detection
    corners_3d = np.array([[w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2],
                           [0, 0, 0, 0, h, h, h, h],
                           [l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2]])

    # Apply rotation around yaw angle
    cos_yaw = np.cos(yaw)
    sin_yaw = np.sin(yaw)
    rotation = np.array([[cos_yaw, -sin_yaw, 0],
                         [sin_yaw, cos_yaw, 0],
                         [0, 0, 1]])
    corners_3d = np.dot(rotation, corners_3d)

    # Apply translation
    corners_3d[0, :] += x
    corners_3d[1, :] += y
    corners_3d[2, :] += z

    # Convert 3D bounding box coordinates to image coordinates
    corners_3d_homogeneous = np.vstack((corners_3d, np.ones((1, 8))))
    corners_2d_homogeneous = np.dot(P2.reshape(3, 4), np.dot(Tr_velo_to_cam.reshape(3, 4), corners_3d_homogeneous))
    corners_2d = corners_2d_homogeneous[:2, :] / corners_2d_homogeneous[2, :]

    # Find 2D detection box coordinates and ensure it is within image bounds
    x1 = np.maximum(0, np.min(corners_2d[0, :]))
    y1 = np.maximum(0, np.min(corners_2d[1, :]))
    x2 = np.minimum(image_shape[1], np.max(corners_2d[0, :]))
    y2 = np.minimum(image_shape[0], np.max(corners_2d[1, :]))

    # Compute alpha
    alpha = -np.arctan2(-y, x) + yaw

    return x1, y1, x2, y2, alpha

# Example usage
point_cloud_detection = (1.5, 2.0, 10.0, 1.0, 1.5, 3.0, 0.785)  # Example point cloud detection
calib_path = '/path/to/calib/file.txt'  # Example calib file path
image_shape = (720, 1280)  # Example image shape

x1, y1, x2, y2, alpha = get_detection_box_2d(point_cloud_detection, calib_path, image_shape)
print(f"2D Detection Box: ({x1}, {y1}, {x2}, {y2}), Alpha: {alpha}")

请将`/path/to/calib/file.txt`替换为实际的calib文件路径，将`point_cloud_detection`替换为实际的点云检测结果，将`image_shape`替换为实际的图像shape。

这个函数将返回2D检测框的坐标(x1, y1, x2, y2)和alpha朝向角。请记住，在使用之前，确保安装了NumPy库。