首页 - 技术栈

做网站绑定对应的域名网站管理服务

作者: 五速梦信息网
时间: 2026年04月18日 10:00

当前位置：首页 > news >正文

做网站绑定对应的域名,网站管理服务,wordpress设置文章登录可见,网站主办者是谁一、背景已知相机参数#xff08;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 #xff09;#xff0c;在给定像素坐标的前提下#xff0c;求世界坐标#xff0c;大部分通过AI来实现#xff0c;不知道哪个步骤有问题#xff0c;望大家指正二、代码…一、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二、代码 import numpy as np import cv2 # 畸变校正 def undistort_pixel(pixel_x, pixel_y, sym_dist, dec_dist): k0,k1,k2,k3sym_dist # k1, k2, p1, p2, k3 sym_dist p1,p2,p3dec_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]], dtypenp.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: 取消首页编程手机软件硬件安卓苹果手游教程平面服务器首页脚本专栏 python python无人机航拍图片像素坐标 python实现无人机航拍图片像素坐标转世界坐标的示例代码 2024-06-12 10:42:38 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,k3sym_dist # k1, k2, p1, p2, k3 sym_dist p1,p2,p3dec_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]], dtypenp.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 H86.93 #像主点坐标像素 xpoff_px20.88973563438230485190 ypoff_px50.51977022866981315019 #################################################### 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_xoripixel_x-xpoff_px-image_width_px/2 pixel_yoripixel_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)) 到此这篇关于python实现无人机航拍图片像素坐标转世界坐标的示例代码的文章就介绍到这了。