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背景
已知相机参数（传感器宽度和高度、图像宽度和高度、焦距、相对航高、像主点坐标 ），在给定像素坐标的前提下，求世界坐标，大部分通过AI来实现，不知道哪个步骤有问题，望大家指正
脚本
import numpy as np
import cv2# 畸变校正
def undistort_pixel(pixel_x, pixel_y, sym_dist, dec_dist):k0,k1,k2,k3=sym_dist# k1, k2, p1, p2, k3 = sym_distp1,p2,p3=dec_distfx = focal_length_mmfy = focal_length_mmcx = image_width_px / 2cy = image_height_px / 2distCoeffs = np.array([k1, k2, p1, p2,k3])cameraMatrix = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])distorted_points = np.array([[pixel_x, pixel_y]], dtype=np.float32)undistorted_points = cv2.undistortPoints(distorted_points, cameraMatrix, distCoeffs)#################################################### 4\对图像去畸变img = cv2.imread('./images/100_0004_0001.JPG')img_undistored = cv2.undistort(img, cameraMatrix, distCoeffs)cv2.imwrite('./images/100_0004_00011.JPG', img_undistored)return undistorted_points[0][0][0], undistorted_points[0][0][1]# 相机坐标转世界坐标
def camera_to_world_coordinates(cam_coords, pos):# 获取相机到世界的转换参数pos_x, pos_y, pos_z, roll, pitch, yaw = pos# 将角度转换为弧度roll = np.radians(roll)pitch = np.radians(pitch)yaw = np.radians(yaw)# 计算旋转矩阵R_roll = np.array([[1, 0, 0],[0, np.cos(roll), -np.sin(roll)],[0, np.sin(roll), np.cos(roll)]])R_pitch = np.array([[np.cos(pitch), 0, np.sin(pitch)],[0, 1, 0],[-np.sin(pitch), 0, np.cos(pitch)]])R_yaw = np.array([[np.cos(yaw), -np.sin(yaw), 0],[np.sin(yaw), np.cos(yaw), 0],[0, 0, 1]])R = R_yaw @ R_pitch @ R_roll# 相机坐标转换到世界坐标cam_coords_homogeneous = np.array([cam_coords[0], cam_coords[1], -H, 1])world_coords = R @ cam_coords_homogeneous[:3] + np.array([pos_x, pos_y, pos_z])return world_coords
####################################################基本参数
# 传感器宽度和高度（毫米）
sensor_width_mm = 12.83331744000000007588
sensor_height_mm = 8.55554496000000064271# 图像宽度和高度（像素）
image_width_px = 5472
image_height_px = 3648# 焦距（毫米）
focal_length_mm = 8.69244671863242679422# 焦距（米）
focal_length_m = 8.69244671863242679422/1000# 相对航高
H=86.93#################################################### 1\计算空间分辨率
# 传感器尺寸转换为米
sensor_width_m = sensor_width_mm / 1000
sensor_height_m = sensor_height_mm / 1000# 计算水平和垂直的 GSD
GSD_x = sensor_width_m * H / (focal_length_m * image_width_px)
GSD_y = sensor_height_m * H / (focal_length_m * image_height_px)# 水平和垂直方向的 GSD
print("水平方向的 GSD:", GSD_x, "米/像素")
print("垂直方向的 GSD:", GSD_y, "米/像素")#################################################### 2\给定像素坐标,计算相机坐标
#像主点偏移
xpoff_px=20.88973563438230485190
ypoff_px=50.51977022866981315019# 像素坐标
pixel_x = image_width_px
pixel_y = image_height_px
# pixel_x = image_width_px/2
# pixel_y = image_height_px/2
# pixel_x = 0
# pixel_y = 0pixel_x=pixel_x+xpoff_px
pixel_y=pixel_y+ypoff_px# 计算相机坐标（假设无畸变）
camera_x = pixel_x * GSD_x
camera_y = pixel_y * GSD_yprint("像素坐标 (", pixel_x, ",", pixel_y, ") 对应的相机坐标 (x, y): (", camera_x, "米, ", camera_y, "米)")#################################################### 3\计算畸变后坐标
# 对称畸变系数
sym_dist = [0, -0.00043396118129128110, 0.00000262222711982075, -0.00000001047488706013]
# 径向畸变
dec_dist = [0.00000205885592671873, -0.00000321714140091248, 0]# 进行畸变校正
undistorted_camera_x, undistorted_camera_y = undistort_pixel(pixel_x, pixel_y, sym_dist, dec_dist)print("畸变校正后像素坐标 (", pixel_x, ",", pixel_y, ") 对应的相机坐标 (x, y): (", undistorted_camera_x, "米, ", undistorted_camera_y, "米)")#################################################### 4\计算世界坐标
# POS数据
pos = [433452.054688, 2881728.519704, 183.789696, 0.648220, -0.226028, 14.490357]# 计算世界坐标
world_coords = camera_to_world_coordinates((undistorted_camera_x, undistorted_camera_y), pos)print("旋转平移变换后像素坐标 (", pixel_x, ",", pixel_y, ") 对应的世界坐标 (x, y): (", world_coords[0], "米, ", world_coords[1], "米)")这篇关于python实现无人机航拍图片像素坐标转世界坐标的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
