【OpenCV】Python广角摄像头的畸变矫正

￡神魔★判官ぃ 2023-01-16 14:25 125阅读 0赞

# 效果图 #

三幅图像分别为矫正前、校正后和裁减后的图片。

矫正后的图像有些奇怪，需要把无用的部分裁剪掉。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NlZV9TdGFy_size_16_color_FFFFFF_t_70]

# 代码 #

## 步骤一：拍摄棋盘图片 ##

运行该程序，按**空格键**拍摄棋盘信息（注意拍摄的棋盘要完整），拍摄20张左右即可，按**q**键退出。

import cv2
    
    # 图片保存路径
    IMG_SAVE_PATH = "img/"
    
    
    if __name__ == '__main__':
        num = 1
        camera = cv2.VideoCapture(0)
        while True:
            state, src = camera.read()
            cv2.imshow('src', src)
    
            if cv2.waitKey(10) & 0xff == ord(' '):
                cv2.imwrite(IMG_SAVE_PATH + str(num) + '.jpg', src)
                print("Saved img_" + str(num) + "!")
                num += 1
            if cv2.waitKey(10) & 0xff == ord('q'):
                break
        cv2.destroyAllWindows()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NlZV9TdGFy_size_16_color_FFFFFF_t_70 1]

## 步骤二：计算畸变矩阵 ##

计算畸变矩阵，并保存畸变数据到**parameter**文件。

import cv2
    import glob
    import numpy as np
    import pickle
    import os
    
    # 棋盘规格
    BOARD_RAW = 19
    BOARD_COL = 13
    
    # 图片保存路径
    IMG_SAVE_PATH = "img/"
    IMG_RESULT_PATH = "result/"
    
    if __name__ == '__main__':
        obj_p = np.zeros((BOARD_RAW*BOARD_COL, 3), np.float32)
    
        # np.mgrid[0:raw, 0:col]的shape为(2, 19, 13)转置后为(13, 19, 2)，reshape后为(13*19, 2)
        # obj_p[:, :2]===>obj_p[:, 0] and obj_p[:, 1]
        obj_p[:, :2] = np.mgrid[0:BOARD_RAW, 0:BOARD_COL].T.reshape(-1, 2)
        # print('obj_p:', obj_p)
    
        obj_points = []
        img_points = []
    
        images = glob.glob(IMG_SAVE_PATH + '*.jpg')
        for name in images:
            print('name:', name)
            img = cv2.imread(name)
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
            # 寻找角点
            ret, corners = cv2.findChessboardCorners(gray, (BOARD_RAW, BOARD_COL), None)
            criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
            sub_corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
    
            obj_points.append(obj_p)
            img_points.append(sub_corners)
    
            img = cv2.drawChessboardCorners(gray, (BOARD_RAW, BOARD_COL), sub_corners, ret)
            cv2.imshow('img', img)
            cv2.imwrite(IMG_RESULT_PATH + 'img'+name.split(os.sep)[-1], img)
            cv2.waitKey(500)
    
        # 标定结果：相机的内参数矩阵，畸变系数，旋转矩阵和平移向量
        ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, gray.shape[::-1], None, None)
        print('ret:', ret)
        print('mtx:', mtx)
        print('dist:', dist)
        print('rvecs:', rvecs)
        print('tvecs:', tvecs)
    
        # 保存参数
        cal_parameter = { 'ret': ret, 'mtx': mtx, 'dist': dist, 'rvecs': rvecs, 'tvecs': tvecs}
        pickle.dump(cal_parameter, open("parameter", "wb"), 0)
        print("Save successfully!")

## 步骤三：读取广角摄像头图像 ##

打开广角摄像头，进行矫正，分别获得原图像、矫正图像和裁剪图像。

import cv2
    import pickle
    
    if __name__ == '__main__':
        # 获取矫正参数
        f = pickle.load(open('parameter', 'rb'))  # 读取矫正参数
        ret, mtx, dist, rvecs, tvecs = f['ret'], f['mtx'], f['dist'], f['rvecs'], f['tvecs']
    
        # 获取图像尺寸
        img = cv2.imread('img\\1.jpg')
        h, w = img.shape[:2]
        new_camera_mtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
        x, y, w, h = roi      # roi 提取的不准确，可能需要手动调整
        vid = cv2.VideoCapture(0)
        while True:
            state, src = vid.read()
            cv2.imshow('src', src)
            dst = cv2.undistort(src, mtx, dist, None, new_camera_mtx)
            cv2.imshow('img1', dst)
            dst = dst[y:y + h, x:x + w]
            cv2.imshow('img2', dst)
            cv2.waitKey(1)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NlZV9TdGFy_size_16_color_FFFFFF_t_70 2]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NlZV9TdGFy_size_16_color_FFFFFF_t_70]: /images/20221022/7d591c2dbcc54dac80b95127c8109a78.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NlZV9TdGFy_size_16_color_FFFFFF_t_70 1]: /images/20221022/719cc9483a114d52959049194eb72641.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NlZV9TdGFy_size_16_color_FFFFFF_t_70 2]: /images/20221022/688b304d61ee419695d115c1a6661c3a.png