BM算法实现双目视觉测距--python实现
在完成对双目摄像头的标定之后，获得标定的矩阵包括左右相机的内参数矩阵、畸变矩阵、旋转矩阵和平移矩阵。将其放入代码中，如下所示：
import cv2
import numpy as np
# 左相机内参
left_camera_matrix = np.array([(426.61499943, 0, 337.77666426),
(0, 426.50296492, 254.57967858),
(0, 0, 1)])
# 左相机畸变系数:[k1, k2, p1, p2, k3]
left_distortion = np.array([[1.64160258e-02 ,1.90313751e-02 ,3.85843636e-05 ,-6.81027605e-04, -7.77682876e-02]])
# 右相机内参
right_camera_matrix = np.array([( 428.37039364, 0, 335.14944239),
(0, 428.26149388, 253.70369704),
(0, 0, 1)])
# 右相机畸变系数:[k1, k2, p1, p2, k3]
right_distortion = np.array([[1.18624707e-02 , 2.92395356e-02 , 7.84349618e-04 ,-4.59924352e-05, -8.34482148e-02]])
# om = np.array([-0.00009, 0.02300, -0.00372])
# R = cv2.Rodrigues(om)[0]
# 旋转矩阵
R = np.array([(9.99999401e-01 ,-1.08818183e-03 , 1.21838861e-04),
(1.08825693e-03 , 9.99999217e-01, -6.18081935e-04),
(-1.21166180e-04 , 6.18214157e-04 , 9.99999802e-01)])
# 平移向量
T = np.array([[-59.98598553], [-0.81926245], [0.12784464]])
doffs = 0.0
size = (640, 480)
R1, R2, P1, P2, Q, validPixROI1, validPixROI2 = cv2.stereoRectify(left_camera_matrix, left_distortion,
right_camera_matrix, right_distortion, size, R,
T)
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)在进行立体标定和平行校正。代码如下：
img1_rectified = cv2.remap(frame1, left_map1, left_map2, cv2.INTER_LINEAR,
cv2.BORDER_CONSTANT)
img2_rectified = cv2.remap(frame2, right_map1, right_map2, cv2.INTER_LINEAR,
cv2.BORDER_CONSTANT)也可以不用，如果你的相机的平行相机的话，这一步做不做都可以。
完成之后在看一下BM算法：
# BM
numberOfDisparities = ((640 // 8) + 15) & -16 # 640对应是分辨率的宽
stereo = cv2.StereoBM_create(numDisparities=16*10, blockSize=7) # 立体匹配
stereo.setROI1(camera_configs.validPixROI1)
stereo.setROI2(camera_configs.validPixROI2)
stereo.setPreFilterCap(50)
stereo.setBlockSize(9)
stereo.setMinDisparity(0)
stereo.setNumDisparities(numberOfDisparities)
stereo.setTextureThreshold(10)
stereo.setUniquenessRatio(15)
stereo.setSpeckleWindowSize(200)
stereo.setSpeckleRange(32)
stereo.setDisp12MaxDiff(8)
disparity = stereo.compute(img1_rectified, img2_rectified) # 计算视差
disp = cv2.normalize(disparity, disparity, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U) # 归一化函数算法
threeD = cv2.reprojectImageTo3D(disparity, camera_configs.Q, handleMissingValues=True) # 计算三维坐标数据值
threeD = threeD * 16这块就是计算三维坐标了，其中stereo里的都是算法的参数，可以进行调节，找到一种效果好的参数组合即可，当然建议设置一个滑块调节，这样调节起来比较方便，如何设置滑块进行参数调节，我将在另一篇博客中介绍。
cv2.setMouseCallback(WIN_NAME, onmouse_pick_points, threeD)
cv2.imshow("left", frame1)
cv2.imshow(WIN_NAME, disp) # 显示深度图的双目画面
这样就可以进行展示了，是建立在左相机的坐标系下进行计算和展示。
通过鼠标点击输入像素坐标信息，通过BM算法进行对应点的匹配，再根据平行视图的计算公式进行计算，即可得三维坐标了，这样需要乘以16进行换算才是真实坐标。
鼠标点击输入函数如下，其中xy也可以自己设置输入，进行对应坐标的计算：
def onmouse_pick_points(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN:
threeD = param
print('\n像素坐标 x = %d, y = %d' % (x, y))
print("世界坐标xyz 是：", threeD[y][x][0] / 1000.0, threeD[y][x][1] / 1000.0, threeD[y][x][2] / 1000.0, "m")
distance = math.sqrt(threeD[y][x][0] ** 2 + threeD[y][x][1] ** 2 + threeD[y][x][2] ** 2)
distance = distance / 1000.0 # mm -> m
print("距离是：", distance, "m")
cv2.setMouseCallback(WIN_NAME, onmouse_pick_points, threeD)把这些函数组合一下就可以得到整个函数了，图片输入识别深度和摄像头实时识别都是可以的，计算速度快，实际测量的话，感觉精度还行，当然是短距离。效果的话没有SGBM算法好的，但是胜在计算速度快。
完整的代码如下了，最重要的就是。
# -*- coding: utf-8 -*-
import numpy as np
import cv2
import camera_configs
import random
import math
cap = cv2.VideoCapture(0)
cap.set(3, 1280)
cap.set(4, 480) # 打开并设置摄像头
# 鼠标回调函数
def onmouse_pick_points(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN:
threeD = param
print('\n像素坐标 x = %d, y = %d' % (x, y))
# print("世界坐标是：", threeD[y][x][0], threeD[y][x][1], threeD[y][x][2], "mm")
print("世界坐标xyz 是：", threeD[y][x][0] / 1000.0, threeD[y][x][1] / 1000.0, threeD[y][x][2] / 1000.0, "m")
distance = math.sqrt(threeD[y][x][0] ** 2 + threeD[y][x][1] ** 2 + threeD[y][x][2] ** 2)
distance = distance / 1000.0 # mm -> m
print("距离是：", distance, "m")
WIN_NAME = 'Deep disp'
cv2.namedWindow(WIN_NAME, cv2.WINDOW_AUTOSIZE)
while True:
ret, frame = cap.read()
frame1 = frame[0:480, 0:640]
frame2 = frame[0:480, 640:1280] # 割开双目图像
imgL = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY) # 将BGR格式转换成灰度图片
imgR = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
# cv2.remap 重映射，就是把一幅图像中某位置的像素放置到另一个图片指定位置的过程。
# 依据MATLAB测量数据重建无畸变图片
img1_rectified = cv2.remap(imgL, camera_configs.left_map1, camera_configs.left_map2, cv2.INTER_LINEAR)
img2_rectified = cv2.remap(imgR, camera_configs.right_map1, camera_configs.right_map2, cv2.INTER_LINEAR)
imageL = cv2.cvtColor(img1_rectified, cv2.COLOR_GRAY2BGR)
imageR = cv2.cvtColor(img2_rectified, cv2.COLOR_GRAY2BGR)
# BM
numberOfDisparities = ((640 // 8) + 15) & -16 # 640对应是分辨率的宽
stereo = cv2.StereoBM_create(numDisparities=16*10, blockSize=7) # 立体匹配
stereo.setROI1(camera_configs.validPixROI1)
stereo.setROI2(camera_configs.validPixROI2)
stereo.setPreFilterCap(50)
stereo.setBlockSize(9)
stereo.setMinDisparity(0)
stereo.setNumDisparities(numberOfDisparities)
stereo.setTextureThreshold(10)
stereo.setUniquenessRatio(15)
stereo.setSpeckleWindowSize(200)
stereo.setSpeckleRange(32)
stereo.setDisp12MaxDiff(8)
disparity = stereo.compute(img1_rectified, img2_rectified) # 计算视差
disp = cv2.normalize(disparity, disparity, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U) # 归一化函数算法
threeD = cv2.reprojectImageTo3D(disparity, camera_configs.Q, handleMissingValues=True) # 计算三维坐标数据值
threeD = threeD * 16
# threeD[y][x] x:0~640; y:0~480; !!!!!!!!!!
cv2.setMouseCallback(WIN_NAME, onmouse_pick_points, threeD)
cv2.imshow("left", frame1)
cv2.imshow(WIN_NAME, disp) # 显示深度图的双目画面
key = cv2.waitKey(1)
if key == ord("q"):
break
cap.release()
cv2.destroyAllWindows()