fly6516
39d6065521
- 新增 camera_calibration.py 文件，实现相机标定功能 - 新增 motion_estimation.py 文件，实现运动估计功能- 编写实验报告，总结相机标定与运动估计的原理和实验结果
56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
import cv2
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import numpy as np
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import glob
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# 棋盘格尺寸：内角点数量（宽度，高度）
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chessboard_size = (9, 6)
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# 创建棋盘格世界坐标系下的三维点阵
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# 生成形如(0,0,0), (1,0,0)...(8,5,0)的三维坐标
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objp = np.zeros((chessboard_size[0] * chessboard_size[1], 3), np.float32)
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objp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2)
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# 存储对象点和图像点的列表
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objpoints = [] # 世界坐标系中的3D点
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imgpoints = [] # 图像坐标系中的2D点
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# 加载所有棋盘格图像路径
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images = glob.glob('images/*.jpg')
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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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# 寻找棋盘格角点
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ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None)
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if ret:
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objpoints.append(objp)
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imgpoints.append(corners)
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# 绘制并显示检测到的角点
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cv2.drawChessboardCorners(img, chessboard_size, corners, ret)
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cv2.imshow('Corners', img)
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cv2.waitKey(0) # 修改此处：改为等待用户按键关闭窗口
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# 销毁所有OpenCV创建的窗口
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cv2.destroyAllWindows()
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if len(objpoints) > 0:
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# 执行相机标定，获取相机矩阵、畸变系数等参数
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
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# 输出相机矩阵和畸变系数
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print("相机矩阵:\n", mtx)
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print("畸变系数:\n", dist)
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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("平均重投影误差: ", mean_error / len(objpoints))
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else:
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print("未找到有效的棋盘格图像。") |