使用OpenCV - python进行双目测距

同时从两个摄像头获取图片

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在进行完立体标定后，我们将得到如下的数据：

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应用标定数据

我们使用如下的代码来将其配置到python中，上面的参数都是手动填写至下面的内容中的

# filename: camera_configs.py
import cv2
import numpy as np

left_camera_matrix = np.array([[824.93564, 0., 251.64723],
                               [0., 825.93598, 286.58058],
                               [0., 0., 1.]])
left_distortion = np.array([[0.23233, -0.99375, 0.00160, 0.00145, 0.00000]])



right_camera_matrix = np.array([[853.66485, 0., 217.00856],
                                [0., 852.95574, 269.37140],
                                [0., 0., 1.]])
right_distortion = np.array([[0.30829, -1.61541, 0.01495, -0.00758, 0.00000]])

om = np.array([0.01911, 0.03125, -0.00960]) # 旋转关系向量
R = cv2.Rodrigues(om)[0]  # 使用Rodrigues变换将om变换为R
T = np.array([-70.59612, -2.60704, 18.87635]) # 平移关系向量

size = (640, 480) # 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)

这样，我们得到了左右摄像头的两个map，并得到了立体的Q，这些参数都将应用于下面的转换成深度图中

转换成深度图

import numpy as np
import cv2
import camera_configs

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)
camera1 = cv2.VideoCapture(0)
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:
    ret1, frame1 = camera1.read()
    ret2, frame2 = camera2.read()

    if not ret1 or not ret2:
        break

    # 根据更正map对图片进行重构
    img1_rectified = cv2.remap(frame1, camera_configs.left_map1, camera_configs.left_map2, cv2.INTER_LINEAR)
    img2_rectified = cv2.remap(frame2, camera_configs.right_map1, camera_configs.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., camera_configs.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("./snapshot/BM_left.jpg", imgL)
        cv2.imwrite("./snapshot/BM_right.jpg", imgR)
        cv2.imwrite("./snapshot/BM_depth.jpg", disp)

camera1.release()
camera2.release()
cv2.destroyAllWindows()

下面则是一附成像图，最右侧的为生成的disparity图，按照上面的代码，在图上点击则可以读取到该点的距离

From：使用OpenCV/python进行双目测距

利用BM匹配算法实现双目测距
双目测距原理就不说了，给一篇参考博客：https://blog.csdn.net/u013832707/article/details/53781810

BM匹配算法可参考博客：https://blog.csdn.net/lg1259156776/article/details/53103527