python实现无人机航拍图片像素坐标转世界坐标的示例代码
作者:GIS从业者
已知相机参数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,本文给大家分享实现脚本,感兴趣的朋友跟随小编一起看看吧
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_dist p1,p2,p3=dec_dist fx = focal_length_mm fy = focal_length_mm cx = xpoff_px cy = ypoff_px distCoeffs = 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 if __name__ == "__main__": ####################################################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(米) H=86.93 #像主点坐标 (像素) xpoff_px=20.88973563438230485190 ypoff_px=50.51977022866981315019 #################################################### 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/image_width_px) * (H / focal_length_m ) GSD_y = (sensor_height_m /image_height_px) * (H / focal_length_m) # 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 # 像素坐标 oripixel_x = image_width_px oripixel_y = image_height_px # oripixel_x = image_width_px/2 # oripixel_y = image_height_px/2 # oripixel_x = 0 # oripixel_y = 0 pixel_x=oripixel_x-xpoff_px-image_width_px/2 pixel_y=oripixel_y-ypoff_px-image_height_px/2 # 计算相机坐标(假设无畸变) camera_x = pixel_x * GSD_x camera_y = pixel_y * GSD_y print("像素坐标 (", oripixel_x, ",", oripixel_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 (", oripixel_x, ",", oripixel_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 (", oripixel_x, ",", oripixel_y, ") 对应的世界坐标 (x, y): (", world_coords[0], "米, ", world_coords[1], "米)")
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