鱼眼图像转换为任意视角针孔相机图像Python代码

# Author: 两只程序猿 # Date: 2023/06/24 # Description: 将鱼眼图像转换为指定视角角度的针孔透视图像 import cv2 import glob import yaml import numpy as np def _mat3d_rotate(mat:np.ndarray, rot:np.ndarray): """将每个像素对应的3D点做空间旋转 Args: mat (np.ndarray): float64 array with [h x w x 3] rot (np.ndarray): float64 array with [3 x 3] """ # faster when using @ operation return np.transpose(rot @ np.transpose(mat.reshape(-1, 3), axes=(1, 0)), axes=(1, 0)).reshape(*mat.shape) # 载入鱼眼相机的内参和畸变参数（这里的畸变参数是cv2.fisheye校准出的畸变参数） conf = yaml.load(open('480P.yaml'), Loader=yaml.FullLoader) cam = conf['cam0'] K = np.array([[cam['intrinsic']['fx'], 0, cam['intrinsic']['cx']], [0, cam['intrinsic']['fy'], cam['intrinsic']['cy']], [0, 0, 1]]) D = np.array([[cam['distortion']['k1']], [cam['distortion']['k2']], [cam['distortion']['k3']], [cam['distortion']['k4']]]) print(K) print(D) # 配置透视图的参数（虚拟针孔相机） angle_phi = 30 # 俯仰角 angle_theta = 0 # 偏航角 perspective_img_h = 480 # 透视图的高度 perspective_img_w = 640 # 透视图的宽度 perspective_fx = 415.692 # 透视图的横轴焦距（内参） perspective_fy = 415.692 # 透视图的纵轴焦距（内参） perspective_FOV_x = 2 * np.arctan(perspective_img_w / (2 * perspective_fx)) * 180 / np.pi # 透视图的横轴视场角 perspective_FOV_y = 2 * np.arctan(perspective_img_h / (2 * perspective_fy)) * 180 / np.pi # 透视图的纵轴视场角 phi = np.radians(angle_phi) theta = np.radians(angle_theta) ################################ ## 计算透视图-鱼眼图像素映射关系 ################################ # 每个像素对应的归一化的空间3D坐标 X = (((np.tile(np.arange(0, perspective_img_w), (perspective_img_h, 1))) / perspective_img_w)*2 - 1) * np.tan(np.radians(perspective_FOV_x) * 0.5) Y = (((np.tile(np.arange(0, perspective_img_h), (perspective_img_w, 1)).T) / perspective_img_h)*2 - 1) * np.tan(np.radians(perspective_FOV_y) * 0.5) Z = np.ones((perspective_img_h, perspective_img_w), np.float64) XYZ = cv2.merge([X, Y, Z]) # 旋转矩阵 Rx = np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]], dtype=np.float64) Ry = np.array([[np.cos(-theta), 0, np.sin(-theta)], [0, 1, 0], [-np.sin(-theta), 0, np.cos(-theta)]], dtype=np.float64) R_combine = Ry @ Rx # 旋转到鱼眼相机视角下的3D坐标 XYZ_rotated = _mat3d_rotate(XYZ, R_combine) XYZ_rotated = XYZ_rotated/XYZ_rotated[...,2:3] # 计算鱼眼图像的畸变 r = np.sqrt(XYZ_rotated[..., 0]**2 + XYZ_rotated[..., 1]**2) theta = np.arctan(r) theta2 = theta**2 theta4 = theta2**2 theta6 = theta4*theta2 theta8 = theta4*theta4 theta_d = theta * (1 + D[0]*theta2 + D[1]*theta4 + D[2]*theta6 + D[3]*theta8) scale = theta_d / r # 计算透视图到鱼眼图像的映射关系 map1 = (XYZ_rotated[..., 0] * scale * K[0, 0] + K[0, 2]).astype(np.float32) map2 = (XYZ_rotated[..., 1] * scale * K[1, 1] + K[1, 2]).astype(np.float32) ################################ ## 读取鱼眼图像并转换为透视图 ################################ images = glob.glob('calib_imgs_480p_9x6/*.jpg') for image_path in images: img = cv2.imread(image_path) h, w = img.shape[:2] persp_img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) cv2.imshow('original', img) cv2.imshow('persp', persp_img) key = cv2.waitKey(0) if key == ord('q'): break