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安徽省建设厅网站张天培,汉中专业网站建设价格,苏州到深圳物流公司,阿里域名注册查询理论基础#xff1a;焦距 f 就是胶片到小孔的距离摄像机焦距和fov存在一一对应的关系#xff0c;fov其实就是摄像机焦距的另一种更直观的表达方式摄像机坐标系的原点就是针孔位置#xff1a;摄像机焦距就是原点到像平面的距离通过将#xff1a; #xff08; 点的物理坐标/…理论基础焦距 f 就是胶片到小孔的距离摄像机焦距和fov存在一一对应的关系fov其实就是摄像机焦距的另一种更直观的表达方式摄像机坐标系的原点就是针孔位置摄像机焦距就是原点到像平面的距离通过将 点的物理坐标/点的深度z * f-[相机以像素为单位的焦距] 就可以得到点的物理坐标到像平面坐标的转换计算方式根据图像长宽和fov得到图像的相机内参从而得到3D点到图像平面的映射关系代码实现从图像通过假设fov的方式得到相机内参的代码def generate_camera_params(image_path, fxNone, fyNone, cxNone, cyNone, fov60): Generate camera parameters for 3D visualization. Args: image_path: Path to the image fx, fy: Focal lengths in pixels (if None, will be calculated from fov) cx, cy: Principal point coordinates in pixels (if None, will be set to image center) fov: Field of view in degrees (default: 60°) Returns: dict: Camera parameters with keys fx, fy, cx, cy image Image.open(image_path) w, h image.size # Generate pseudo camera params if not provided if fx is None or fy is None: fx round(w / (2 * np.tan(np.deg2rad(fov) / 2)), 2) fy round(h / (2 * np.tan(np.deg2rad(fov) / 2)), 2) if cx is None or cy is None: cx round(w / 2, 2) cy round(h / 2, 2) cam_params {fx: fx, fy: fy, cx: cx, cy: cy} return cam_params由摄像机坐标系下的物理3D点根据对应摄像机内参来得到对应像平面上2D像素坐标的代码def convert_3dbbox(point, cam_params): Convert 3D bounding box to 2D image coordinates We represent 3D bounding boxes as: [x_center, y_center, z_center, x_size, y_size, z_size, roll, pitch, yaw] - **x_center, y_center, z_center**: Object center in camera coordinates (meters) - **x_size, y_size, z_size**: Object dimensions (meters) - **roll, pitch, yaw**: Rotation angles (radians) x, y, z, x_size, y_size, z_size, pitch, yaw, roll point hx, hy, hz x_size / 2, y_size / 2, z_size / 2 local_corners [ [ hx, hy, hz], [ hx, hy, -hz], [ hx, -hy, hz], [ hx, -hy, -hz], [-hx, hy, hz], [-hx, hy, -hz], [-hx, -hy, hz], [-hx, -hy, -hz] ] def rotate_xyz(_point, _pitch, _yaw, _roll): # 这是一个 XYZ 顺序的欧拉角旋转 x0, y0, z0 _point x1 x0 y1 y0 * math.cos(_pitch) - z0 * math.sin(_pitch) z1 y0 * math.sin(_pitch) z0 * math.cos(_pitch) x2 x1 * math.cos(_yaw) z1 * math.sin(_yaw) y2 y1 z2 -x1 * math.sin(_yaw) z1 * math.cos(_yaw) x3 x2 * math.cos(_roll) - y2 * math.sin(_roll) y3 x2 * math.sin(_roll) y2 * math.cos(_roll) z3 z2 return [x3, y3, z3] img_corners [] for corner in local_corners: # 8个顶点 rotated rotate_xyz(corner, np.deg2rad(pitch), np.deg2rad(yaw), np.deg2rad(roll)) X, Y, Z rotated[0] x, rotated[1] y, rotated[2] z if Z 0: x_2d cam_params[fx] * (X / Z) cam_params[cx] y_2d cam_params[fy] * (Y / Z) cam_params[cy] img_corners.append([x_2d, y_2d]) return img_corners多视图情况多视图中的物体表示方法1所有帧都以第一帧的相机坐标系为世界坐标系直接预测物体在世界坐标系中的3D位置2每一帧都 预测目标物体在当前帧的相机坐标系下的3d坐标这样z_center就是对应的相对于当前帧的相机的深度