pmd-python/src/calibration/get_screen_params.py

import cv2
import numpy as np
import glob
from scipy.io import savemat
import matplotlib.pyplot as plt

# 读取文件夹中的所有棋盘格图像
# images = glob.glob('calibration/screen_calibration/*.bmp')

def calibrate_screen(screen_img_path, cam_mtx, cam_dist, chessboard_size, square_size, debug=False):
    # Prepare object points for chessboard corners in world coordinates
    objp = np.zeros((chessboard_size[0] * chessboard_size[1], 3), np.float32)
    objp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2)
    objp *= square_size
    # import pdb; pdb.set_trace()

    objpoints = []  # 三维点
    imgpoints = []  # 二维点
    

    for img_path in screen_img_path:
        
        image_data = np.fromfile(img_path, dtype=np.uint8)
        img = cv2.imdecode(image_data, cv2.IMREAD_COLOR)
       
        # import pdb; pdb.set_trace()
        dst = cv2.undistort(img, cam_mtx, cam_dist, None, None)
        gray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
        # alpha = 3  # 对比度保持不变
        # beta = 0    # 增加亮度
        # bright_image = cv2.convertScaleAbs(gray, alpha=alpha, beta=beta)
        # cv2.namedWindow('bright_image', 0)
        # cv2.imshow('bright_image', bright_image)
        # cv2.namedWindow('gray', 0)
        # cv2.imshow('gray', gray)
        # cv2.waitKey(0)
        # cv2.destroyAllWindows()
        ret, corners = cv2.findChessboardCorners(gray, chessboard_size, flags=cv2.CALIB_CB_FAST_CHECK)
        if ret:
            print('screen img_path = ',img_path)
            objpoints.append(objp)
            criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
            corners_refined = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
            imgpoints.append(corners_refined)

            if debug:
                img = cv2.drawChessboardCorners(img, chessboard_size, corners, ret)
                # 在角点图像上标记坐标原点（第一个角点）
                origin = tuple(corners[0].ravel().astype(int))
                cv2.putText(img, '(0,0)', origin, cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)

                # 添加 x 轴箭头 (右方向)
                end_x = tuple(corners[1].ravel().astype(int))  # 选择横向的下一个角点
                cv2.arrowedLine(img, origin, end_x, (255, 255, 255), 2, tipLength=0.2)
                cv2.putText(img, 'X', (end_x[0] + 10, end_x[1]), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)

                # 添加 y 轴箭头 (下方向)
                end_y = tuple(corners[chessboard_size[0]].ravel().astype(int))  # 选择纵向的下一个角点
                cv2.arrowedLine(img, origin, end_y, (255, 255, 255), 2, tipLength=0.2)
                cv2.putText(img, 'Y', (end_y[0], end_y[1] + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
                cv2.namedWindow('screen', 0)
                cv2.imshow('screen', img)
                cv2.waitKey(0)
                cv2.destroyAllWindows()
    # import pdb; pdb.set_trace()

    objpoints = np.array(objpoints)
    imgpoints = np.array(imgpoints)
    # import pdb; pdb.set_trace()
    # if debug:
    #     # 使用两幅子图绘制 objp 和 corners 的点
    #     fig, ax = plt.subplots(1, 2, figsize=(12, 6))

    #     # 第一个子图：objp 点
    #     ax[0].scatter(objpoints[-1, :, 0], objpoints[-1, :, 1], color='blue', label='objp (Object Points)')
    #     for i in range(len(objpoints[0])):
    #         ax[0].text(objpoints[-1, i, 0], objpoints[-1, i, 1], f'{i}', color='blue', fontsize=12)
    #     ax[0].set_title('Object Points (objp)')
    #     ax[0].set_xlabel('X Axis')
    #     ax[0].set_ylabel('Y Axis')
    #     ax[0].grid(True)
    #     ax[0].axis('equal')

    #     # 第二个子图：corners 点
    #     ax[1].scatter(imgpoints[-1, :, 0, 0], imgpoints[-1, :, 0, 1], color='red', label='corners (Image Points)')
    #     for i in range(len(imgpoints[0])):
    #         ax[1].text(imgpoints[-1, i, 0, 0], imgpoints[-1, i, 0, 1], f'{i}', color='red', fontsize=12)
    #     ax[1].set_title('Screen Image Points (corners)')
    #     ax[1].set_xlabel('X Axis')
    #     ax[1].set_ylabel('Y Axis')
    #     ax[1].grid(True)
    #     ax[1].axis('equal')

    #     # 调整布局并显示图像
    #     plt.tight_layout()
    #     plt.show()
    
    #print("camera Calibration mtx:", cam_mtx)
    #print("Screen Calibration dist:", cam_dist)
    

    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], cam_mtx, cam_dist)
    # 获得最后一张图片的旋转矩阵和平移向量
    # import pdb; pdb.set_trace()
    R = cv2.Rodrigues(rvecs[-1])[0]  # 转换为旋转矩阵
    # R, _ = cv2.Rodrigues(np.array([[0.024044506712974],[0.002032279577832],[-3.138030042895759]]))
    T = tvecs[-1]
    # print("*"*100)
    # print("Screen Calibration mtx:", mtx)
    # print("Screen Calibration dist:", dist)
    # print("camera Calibration mtx1:", cam_mtx)
    # print("Screen Calibration dist1:", cam_dist)
   
    # print("*"*100)
  
    mat_data = {
        'screen_matrix': mtx,
        'screen_distortion': dist,
        'screen_rotation_matrix': R,
        'screen_translation_vector': T,
        'screen_error': ret/len(objpoints)
    }
    return mat_data