opencv：双目视觉 ——实现详细教程（matlabR2015b+opencv-python）

女爷i 2022-04-23 04:08 747阅读 0赞

原本这个文章是打算转载别人的，在经历过各个教程都失败后（1.要不然是前后不对应，估计是随便复制一下，2.要么是讲解很不清楚，没有讲解怎么使用参数），决定自己写一下。记录一下，尽量会非常详细

### 0.前期准备 ###

*  **准备棋盘格**  
    类似于这种：下载地址在这里：[https://pan.baidu.com/s/1qYu0IkgbffuoCgANiQ5-Dw][https_pan.baidu.com_s_1qYu0IkgbffuoCgANiQ5-Dw]  
    必须实际打印出来哦，你用别人的标定图片怎么可能反映你自己的摄像头特征呢。![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70]
 *  **准备标定图片**  
    利用opencv读取你自己的摄像头，按下s键进行截图，分别保存在left文件夹和right文件夹

<table> 
 <tbody> 
  <tr> 
   <td>有可能需要修改的地方： 1.folder 改为自己的路径 2.我这里的双目摄像头图像是在一个框里显示的，所以只需要capture一次，如果你的摄像头是分开的，则要两次capture 3.分辨率设置的时候，要根据自己的摄像机分辨率参数来设置，这很重要！！！</td> 
  </tr> 
 </tbody> 
</table>

import cv2
    import time
    
    AUTO = True  # 自动拍照，或手动按s键拍照
    INTERVAL = 2 # 自动拍照间隔
    camera = cv2.VideoCapture(0)#也许你可能要capture两次
    camera.set(cv2.CAP_PROP_FRAME_WIDTH, 2560)#设置分辨率
    camera.set(cv2.CAP_PROP_FRAME_HEIGHT, 960)#
    utc = time.time()
    folder = "E:/pythonlearning/opencv/screenshot/" # 拍照文件目录
    
    def shot( frame):
        global counter
        leftpath = folder +"left/"+"left_" + str(counter) + ".jpg"
        rightpath=folder + "right/"+ "right_" + str(counter) + ".jpg"
        leftframe=frame[0:960,0:1280]#这里是为了将合在一个窗口显示的图像分为左右摄像头
        rightframe=frame[0:960,1280:2560]
        cv2.imwrite(leftpath, leftframe)
        cv2.imwrite(rightpath, rightframe)
        print("snapshot saved into: " + leftpath)
        print("snapshot saved into: " + rightpath)
    
    while True:
        ret, frame = camera.read()
    
        cv2.imshow("original", frame)
    
        now = time.time()
        key = cv2.waitKey(1)
        if key == ord("q"):
            break
        elif key == ord("s"):
            shot( frame)
            counter += 1
    
    camera.release()
    cv2.destroyWindow("original")

这是我获得的标定图片  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 1]

### 1.Matlab标定 ###

**matlab版本：R2015b**  
获取图片成功后，则要使用这些图片了  
1.打开app->找到stereo calibration camera  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 2]  
2.选定三个复选框后，add images  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 3]  
切记此处的 25是系统默认的，实际的是根据自己打印出来的方格宽度决定的。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 4]  
添加图片  
![在这里插入图片描述][20190123215501975.]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 5]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 6]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 7]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 8]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 9]  
下面将会使用这些参数

### 2.opencv配置参数 ###

*  点进去stereoParams.CameraParameters1.IntrinsicMatrix![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 10]

left_camera_matrix = np.array([[837.046533944197,	0.,	0.],
                                   [-1.38537933662732,	838.772077702795,	0.],
                                   [690.459242460418,	535.874773256865,	1.]])

*  点进去radialdistortion和tangentialdistoration

![在这里插入图片描述][20190123181302537.]![在这里插入图片描述][20190123181501800.]

RadialDistortion：径向畸变，摄像头由于光学透镜的特性使得成像存在着径向畸变，可由K1，K2，K3确定。

TangentialDistortion：切向畸变，由于装配方面的误差，传感器与光学镜头之间并非完全平行，因此成像存在切向畸变，可由两个参数P1，P2确定。

使用时，需要注意参数的排放顺序，即**K1，K2，P1，P2，K3**。切记不可弄错，否则后续的立体匹配会出现很大的偏差。

left_distortion = np.array([[0.0037, 0.00060366, 0.0101, 0.0013, -0.0626]])

*  同理写出右摄像头的矩阵,stereoParams.CameraParameters2中

right_camera_matrix = np.array([[829.904768800558,	0.,	0.],
                                    [0.449308928342319,	831.157653465328,	0.],
                                    [728.582976346643,	550.936706268738,	1.]])
    right_distortion = np.array([[0.00607433417962732,	-0.0112160302647384, 0.0104597000277839	,0.00265721874066941,-0.0262959511445746]])

*  外部数据：（相机二相对于相机一的旋转变量与平移变量）

![在这里插入图片描述][20190123183015227.]  
![在这里插入图片描述][2019012318313256.]

<table> 
 <tbody> 
  <tr> 
   <td>此处必须提醒，有些教程会有OM，然后再转成R，可能以前matlab这样，但是现在matlab直接提供R了，则不需要你自己再去转换了，直接用就好</td> 
  </tr> 
 </tbody> 
</table>

R = np.array([[1.,	     0.0039,  	-0.0038],
                  [-0.0039,	   1.,	        0.],
                  [0.0038,     0.,	        1.]])#旋转向量
    T = np.array([-59.5777,0.032,-2.9472]) # 平移关系向量TranslationOfCamera2

此段完成代码为

### 3.opencv生成深度图 ###

<table> 
 <tbody> 
  <tr> 
   <td>请务必按照你实际相机的像素参数进行设置，否则一定会报错！！！</td> 
  </tr> 
 </tbody> 
</table>

import numpy as np
    import cv2
    
    
    left_camera_matrix = np.array([[837.046533944197,	0.,	0.],
                                   [-1.38537933662732,	838.772077702795,	0.],
                                   [690.459242460418,	535.874773256865,	1.]])  #CameraParameters1.IntrinsicMatrix
    left_distortion = np.array([[0.00372605723623423,	0.000603658659694726, 0.0100736540032466,	0.00134603874266809, -0.0625795789344168]])
    
    
    
    right_camera_matrix = np.array([[829.904768800558,	0.,	0.],
                                    [0.449308928342319,	831.157653465328,	0.],
                                    [728.582976346643,	550.936706268738,	1.]])
    right_distortion = np.array([[0.00607433417962732,	-0.0112160302647384, 0.0104597000277839	,0.00265721874066941,-0.0262959511445746]])
    
    
    R = np.array([[1.,	     0.0039,  	-0.0038],
                  [-0.0039,	   1.,	        0.],
                  [0.0038,     0.,	        1.]])#旋转向量
    T = np.array([-59.5777,0.032,-2.9472]) # 平移关系向量TranslationOfCamera2
    
    size = (1280, 960) # 图像尺寸
    
    # 进行立体更正
    R1, R2, P1, P2, Q, validPixROI1, validPixROI2 = cv2.stereoRectify(left_camera_matrix, left_distortion,
                                                                      right_camera_matrix, right_distortion, size, R,
                                                                      T)
    # 计算更正map
    left_map1, left_map2 = cv2.initUndistortRectifyMap(left_camera_matrix, left_distortion, R1, P1, size, cv2.CV_16SC2)
    right_map1, right_map2 = cv2.initUndistortRectifyMap(right_camera_matrix, right_distortion, R2, P2, size, cv2.CV_16SC2)
    # ToDO 问题可能出在此处，读取的摄像头并不大，导致
    cv2.namedWindow("left")
    cv2.namedWindow("right")
    cv2.namedWindow("depth")
    cv2.moveWindow("left", 0, 0)
    cv2.moveWindow("right", 600, 0)
    cv2.createTrackbar("num", "depth", 0, 10, lambda x: None)
    cv2.createTrackbar("blockSize", "depth", 5, 255, lambda x: None)
    camera = cv2.VideoCapture(0)
    camera.set(cv2.CAP_PROP_FRAME_WIDTH, 2560)#1920,1280
    camera.set(cv2.CAP_PROP_FRAME_HEIGHT, 960)#1080,720
    # camera2 = cv2.VideoCapture(1)
    
    # 添加点击事件，打印当前点的距离
    def callbackFunc(e, x, y, f, p):
        if e == cv2.EVENT_LBUTTONDOWN:
            print (threeD[y][x])
    
    cv2.setMouseCallback("depth", callbackFunc, None)
    
    while True:
        ret, frame= camera.read()
        # ret2, frame2 = camera2.read()
    
        if not ret :
            print('can\'t open camera or camera has been opened' )
            break
        frame1=frame[0:960,0:1280]
        frame2=frame[0:960,1280:2560]
        # 根据更正map对图片进行重构
        img1_rectified = cv2.remap(frame1, left_map1, left_map2, cv2.INTER_LINEAR)
        img2_rectified = cv2.remap(frame2, right_map1, right_map2, cv2.INTER_LINEAR)
    
        # 将图片置为灰度图，为StereoBM作准备
        imgL = cv2.cvtColor(img1_rectified, cv2.COLOR_BGR2GRAY)
        imgR = cv2.cvtColor(img2_rectified, cv2.COLOR_BGR2GRAY)
    
        # 两个trackbar用来调节不同的参数查看效果
        num = cv2.getTrackbarPos("num", "depth")
        blockSize = cv2.getTrackbarPos("blockSize", "depth")
        if blockSize % 2 == 0:
            blockSize += 1
        if blockSize < 5:
            blockSize = 5
    
        # 根据Block Maching方法生成差异图（opencv里也提供了SGBM/Semi-Global Block Matching算法，有兴趣可以试试）
        stereo = cv2.StereoBM_create(numDisparities=16*num, blockSize=blockSize)
        disparity = stereo.compute(imgL, imgR)
    
        disp = cv2.normalize(disparity, disparity, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
        # 将图片扩展至3d空间中，其z方向的值则为当前的距离
        threeD = cv2.reprojectImageTo3D(disparity.astype(np.float32)/16., Q)
    
        cv2.imshow("left", img1_rectified)
        cv2.imshow("right", img2_rectified)
        cv2.imshow("depth", disp)
    
        key = cv2.waitKey(1)
        if key == ord("q"):
            break
        elif key == ord("s"):
            cv2.imwrite("E:/pythonlearning/opencv/screenshot/BM_left.jpg", imgL)
            cv2.imwrite("E:/pythonlearning/opencv/screenshot/BM_right.jpg", imgR)
            cv2.imwrite("E:/pythonlearning/opencv/screenshot/BM_depth.jpg", disp)
    
    camera.release()
    # camera2.release()
    cv2.destroyAllWindows()

[https_pan.baidu.com_s_1qYu0IkgbffuoCgANiQ5-Dw]: https://pan.baidu.com/s/1qYu0IkgbffuoCgANiQ5-Dw
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70]: /images/20220323/c0d09461c3e14ac280b8712d558f9cb4.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 1]: /images/20220323/9780cc4f81be41d899c323e96ab32820.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 2]: /images/20220323/da040e5eac4f4eed93e08b0b454243c5.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 3]: /images/20220323/528d75282f554e9981e21a2a451b87b4.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 4]: /images/20220323/0035ac200df64f4a95fb5f1cec566d37.png
[20190123215501975.]: /images/20220323/414cfcbcaeec4378b45be63158f16e11.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 5]: /images/20220323/554d39e137a94dd3a599d821f05ea9bf.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 6]: /images/20220323/27c4495baccb4e9b8034107c9a78b0f6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 7]: /images/20220323/1bab2b0776aa4cd59a6d4cac22c376b7.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 8]: /images/20220323/2d9122363df844f6be3418fec67033a6.png
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80Mjc1NTM4NA_size_16_color_FFFFFF_t_70 10]: /images/20220323/74f14f2303f346ce8ec0d1182c995c9d.png
[20190123181302537.]: https://img-blog.csdnimg.cn/20190123181302537.
[20190123181501800.]: /images/20220323/577e7266a1954d0f96a1a840dbf99d36.png
[20190123183015227.]: /images/20220323/fce5f46275074379a73df12c8a483928.png
[2019012318313256.]: /images/20220323/229cbdcb945a4ef98310bcac5ac7bbc5.png