73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
import cv2
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import numpy as np
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import glob
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# 图片储存的路径
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img_path = "/home/sunrise/yiliao_ws/imagebag_outputs"
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# 棋盘格参数
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CHECKERBOARD = (8, 5) # 内角点数量（列×行）
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square_size = 27 # 棋盘格方块实际尺寸（单位：毫米）
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# 存储3D和2D点
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objpoints = [] # 世界坐标系中的点
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imgpoints = [] # 图像坐标系中的点
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# 生成世界坐标系中的角点坐标（z=0）
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objp = np.zeros((CHECKERBOARD[0]*CHECKERBOARD[1], 3), np.float32)
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objp[:, :2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2) * square_size
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# 读取所有棋盘格图片
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images = glob.glob(f'{img_path}/*.png')
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for fname in images:
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img = cv2.imread(fname)
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 检测角点
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ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD, None)
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if ret:
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objpoints.append(objp)
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# 亚像素角点优化
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
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corners2 = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)
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imgpoints.append(corners2)
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# 绘制角点（可选）
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# cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret)
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# cv2.imshow('Detected Corners', img)
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# cv2.waitKey(500)
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# cv2.destroyAllWindows()
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# 相机标定
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(
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objpoints, imgpoints, gray.shape[::-1], None, None
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)
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# 输出标定结果
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print("相机内参矩阵:\n", mtx)
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print("\n畸变系数:", dist.ravel())
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# 计算重投影误差
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mean_error = 0
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for i in range(len(objpoints)):
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imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
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error = cv2.norm(imgpoints[i], imgpoints2, cv2.NORM_L2) / len(imgpoints2)
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mean_error += error
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print("\n平均重投影误差:", mean_error / len(objpoints))
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# 保存参数
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np.savez('cali.npz', mtx=mtx, dist=dist)
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# 测试校正效果
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img = cv2.imread(fname) # 替换为实际的图片路径
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# ret, img = cap.read()
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h, w = img.shape[:2]
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newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
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