非常详细的相机标定（六）（2维坐标点转为3维坐标点）_python/opencv利用相机位姿估计实现2d图像像素坐标到3d世界坐标的转换

根据提取的相机的参数，2维坐标点转为3维坐标点，代码如下：

import argparse
from argparse import RawTextHelpFormatter
import numpy as np
import cv2
 
# 寻找焦点
def cam_calib_find_corners(img, rlt_dir, col, row):
    # 灰度化图片，减少参数的运算
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
    # 寻找角点
    ret, corners = cv2.findChessboardCorners(gray, (col, row), None)
 
    # 为了得到稍微精确一点的角点坐标，进一步对角点进行亚像素寻找
    corners2 = cv2.cornerSubPix(
        gray, corners, (7, 7), (-1, -1), (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 10, 0.001))
    if ret == True:
        # 保存角点图像
        sav_path = rlt_dir + "/1_corner.jpg"
        # 绘制角点
        cv2.drawChessboardCorners(img, (col, row), corners2, ret)
        cv2.imwrite(sav_path, img)
 
    return (ret, corners2)
 
# 相机标定
def cam_calib_calibrate(img_dir, rlt_dir, col, row, img_num):
    w = 0
    h = 0
    all_corners = []
    patterns = []
 
    x, y = np.meshgrid(range(col), range(row))
    prod = row * col
    pattern_points = np.hstack(
        (x.reshape(prod, 1), y.reshape(prod, 1), np.zeros((prod, 1)))).astype(np.float32)
 
    img_path = r"C:\Users\17930\Desktop\1.jpg"
    img = cv2.imread(img_path)
    if img is None:
        print(f"Error reading image at {img_path}")
        pass
    else:
        (h, w) = img.shape[:2]
        ret, corners = cam_calib_find_corners(img, rlt_dir, col, row)
        all_corners.append(corners)
        patterns.append(pattern_points)
 
    rms, cameraMatrix, distCoeffs, rvecs, tvecs = cv2.calibrateCamera(
        patterns, all_corners, (w, h), None, None)
    print('相机内参', cameraMatrix)
    print('相机外参旋转向量', rvecs)
    print('相机外参平移向量', tvecs)
 
 
    return cameraMatrix, distCoeffs, rvecs, tvecs
 
 
if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="读取标定的图片并保存结果", formatter_class=RawTextHelpFormatter)
    parser.add_argument("--img_dir", help="标定图片路径", type=str,
                        metavar='', default=r'D:\螺丝数据集与相机标定代码\camera\calib_img')
    parser.add_argument("--rlt_dir", help="保存路径", type=str,
                        metavar='', default="rlt_dir")
    parser.add_argument("--crct_img_dir", help="待矫正图像路径",
                        type=str, metavar='', default="crct_img")
    parser.add_argument("--row_num", help="每一行有多少个角点，边缘处的不算",
                        type=int, metavar='', default="7")
    parser.add_argument("--col_num", help="每一列有多少个角点，边缘处的不算",
                        type=int, metavar='', default="6")
    parser.add_argument("--img_num", help="多少幅图像",
                        type=int, metavar='', default="4")
 
    args = parser.parse_args()
    cameraMatrix, distCoeffs, rvecs, tvecs = cam_calib_calibrate(
        args.img_dir, args.rlt_dir, args.row_num, args.col_num, args.img_num)
 
    image_points = np.array([[55, 66], [77, 88]], dtype=np.float32)
    world_z = 0
    world_points = []
 
    for i in range(len(rvecs)):
        rotation_matrix, _ = cv2.Rodrigues(rvecs[i])
        projection_matrix = np.dot(cameraMatrix, np.hstack(
            (rotation_matrix, tvecs[i])))
 
        inv_projection_matrix = np.linalg.pinv(projection_matrix)
 
        for pt in image_points:
            img_pt = np.array([pt[0], pt[1], 1])
            ray_dir = np.dot(inv_projection_matrix, img_pt)
            scale = (world_z - tvecs[i][2]) / ray_dir[2]
            world_pt = tvecs[i].reshape(3) + scale * ray_dir[:3]
            world_points.append(world_pt)
 
    world_points = np.array(world_points)
    print(world_points)