双目测距工程Stereo-Vision-master学习笔记

硬件：
首先要要把两个摄像头固定到支架上，并且两个摄像头的间距应该在110mm，两个摄像头没有落差
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相机的内参数包括焦距、主点坐标、像素尺寸等，这些参数决定了相机成像的几何变换关系。内参数是相机固有的属性，不会随着相机在空间中的位置和方向而改变。

相机的外参数包括相机的旋转矩阵和平移向量，用来描述相机在世界坐标系中的位置和方向。相机的外参数可以将相机坐标系中的点转换到世界坐标系中，或者将世界坐标系中的点转换到相机坐标系中。

步骤一：

首先要通过/Take_images_for_calibration_thread.py来保存正畸用的棋盘格图像;代码的逻辑是只要通过findChessboardCorners找到充足且满足预设个数的棋盘格脚点时保存双目的图像。
核心代码

retL, frameL= CamL.read()  
grayR= cv2.cvtColor(frameL,cv2.COLOR_BGR2GRAY)
retL, cornersL = cv2.findChessboardCorners(grayL,(9,6),None) 
cv2.cornerSubPix(grayL,cornersL,(11,11),(-1,-1),criteria)
cv2.drawChessboardCorners(grayR,(9,6),corners2R,retR)

if(retL)& 0xFF == ord('q'):
cv2.imwrite('chessboard-L'+str_id_image+'.png',frameL)

如果存在角点且符合预设个数，会打印绘制完角点图后的图像,并且允许按键保存。
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左摄像头：

右摄像头：

步骤二:消除畸变

常见的相机失真类型有径向失真（radial distortion）和切向失真（tangential distortion）。
径向失真包括：桶型和枕性，实际的相机使用弯曲的镜头来形成图像，并且光线通常在这些镜头的边缘弯曲得太多或太少。这会产生扭曲图像边缘的效果，从而使线条或对象看起来比实际弯曲的程度或大或小。这称为径向变形，这是最常见的变形类型。
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另一类失真是切向失真。当相机镜头未完全平行于相机胶卷或传感器所在的成像平面对齐时，就会发生这种情况。这会使图像看起来倾斜，从而使某些对象看起来比实际位置更远或更近。

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步骤三对极几何

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但是在实际测距中，计算视差被转换成了，计算两个对应像素位置之间的不同（distance），没有解析解，只能算一个数值解。
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参考：
https://www.cnblogs.com/clarenceliang/p/6704970.
htmlhttps://cloud.tencent.com/developer/article/2054308
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代码

首先，程序会对摄像头进行畸变校正和立体标定，以获得相机的内参和外参。
程序会打开两个摄像头，并循环读取左右眼的图像。
对图像进行矫正，消除旋转和对齐带来的畸变。
将彩色图像转为灰度图像。
使用StereoSGBM算法计算出视差图。
使用WLS滤波器对视差图进行滤波，获得更加准确的深度图。
对深度图进行颜色映射，显示出深度信息。
通过鼠标双击深度图中的点，可以在控制台输出该点的实际距离。
按空格键退出程序。

#      ▄▀▄     ▄▀▄
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# ▄▄  █░░░░░░░░░░░█  ▄▄
#█▄▄█ █░░▀░░┬░░▀░░█ █▄▄█

###################################
##### Authors:                #####
##### Stephane Vujasinovic    #####
##### Frederic Uhrweiller     ##### 
#####                         #####
##### Creation: 2017          #####
###################################


#***********************
#**** Main Programm ****
#***********************


# Package importation
import numpy as np
import cv2
from openpyxl import Workbook # Used for writing data into an Excel file
from sklearn.preprocessing import normalize
import time
# Filtering
kernel= np.ones((3,3),np.uint8)

def coords_mouse_disp(event,x,y,flags,param):
    if event == cv2.EVENT_LBUTTONDBLCLK:#左键双击
        #print x,y,disp[y,x],filteredImg[y,x]
        average=0
        for u in range (-1,2):
            for v in range (-1,2):
                average += disp[y+u,x+v]
        average=average/9
        Distance= -593.97*average**(3) + 1506.8*average**(2) - 1373.1*average + 522.06
        Distance= np.around(Distance*0.01,decimals=2)
        print('Distance: '+ str(Distance)+' m')
        
# This section has to be uncommented if you want to take mesurements and store them in the excel
        ws.append([counterdist, average])
        print('Measure at '+str(counterdist)+' cm, the dispasrity is ' + str(average))
        if (counterdist <= 85):
           counterdist += 3
        elif(counterdist <= 120):
           counterdist += 5
        else:
           counterdist += 10
        print('Next distance to measure: '+str(counterdist)+'cm')
#将测量结果写入excel
# Mouseclick callback
wb=Workbook()
ws=wb.active  

#*************************************************
#***** Parameters for Distortion Calibration *****
#*************************************************

# Termination criteria
#设置了相机校准的参数，误差小于0.001或者迭代30次就停止
criteria =(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
criteria_stereo= (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

# Prepare object points棋盘格座标点初始化
objp = np.zeros((9*6,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)

# Arrays to store object points and image points from all images
objpoints= []   # 3d points in real world space
imgpointsR= []   # 2d points in image plane
imgpointsL= []

# Start calibration from the camera
print('Starting calibration for the 2 cameras... ')
# Call all saved images
#重新检测棋盘格角点
print(time.localtime())
for i in range(0,60):   # Put the amount of pictures you have taken for the calibration inbetween range(0,?) wenn starting from the image number 0
    t= str(i)
    ChessImaR= cv2.imread('/home/nvidia/Desktop/gf-learning/Stereo-Vision-master/save_image/chessboard-R'+t+'.png',0)    # Right side
    ChessImaL= cv2.imread('/home/nvidia/Desktop/gf-learning/Stereo-Vision-master/save_image/chessboard-L'+t+'.png',0)    # Left side
    retR, cornersR = cv2.findChessboardCorners(ChessImaR,
                                               (9,6),None)  # Define the number of chees corners we are looking for
    retL, cornersL = cv2.findChessboardCorners(ChessImaL,
                                               (9,6),None)  # Left side
    if (True == retR) & (True == retL):
        objpoints.append(objp)
        cv2.cornerSubPix(ChessImaR,cornersR,(11,11),(-1,-1),criteria)
        cv2.cornerSubPix(ChessImaL,cornersL,(11,11),(-1,-1),criteria)
        imgpointsR.append(cornersR)
        imgpointsL.append(cornersL)#保存角点信息
print(time.localtime())

# Determine the new values for different parameters
#   Right Side
#右相机标定，ret：标定结果的精度，mtx内参矩阵，dist畸变系数；外参数：rvecs旋转向量：描述相机坐标系相对于世界坐标系的旋转关系，tvecs平移向量：描述相机坐标系相对于世界坐标系的平移关系。
retR, mtxR, distR, rvecsR, tvecsR = cv2.calibrateCamera(objpoints,
                                                        imgpointsR,
                                                        ChessImaR.shape[::-1],None,None)
hR,wR= ChessImaR.shape[:2]
#畸变矫正，motx经过优化后相机内参，roi优化后有效区域的ROI坐标
OmtxR, roiR= cv2.getOptimalNewCameraMatrix(mtxR,distR,
                                                   (wR,hR),1,(wR,hR))

#   Left Side
retL, mtxL, distL, rvecsL, tvecsL = cv2.calibrateCamera(objpoints,
                                                        imgpointsL,
                                                        ChessImaL.shape[::-1],None,None)
hL,wL= ChessImaL.shape[:2]
OmtxL, roiL= cv2.getOptimalNewCameraMatrix(mtxL,distL,(wL,hL),1,(wL,hL))

print('Cameras Ready to use')

#********************************************
#***** Calibrate the Cameras for Stereo *****
#********************************************

# StereoCalibrate function
flags = 0
flags |= cv2.CALIB_FIX_INTRINSIC

#相机立体校准，stereoCalibrate计算立体校准所需要的参数，R两个相机间旋转矩阵，T平移矩阵，E本质矩阵，F基本矩阵
retS, MLS, dLS, MRS, dRS, R, T, E, F= cv2.stereoCalibrate(objpoints,#真实世界下坐标
                                                          imgpointsL,#相机下投影角点左边
                                                          imgpointsR,
                                                          mtxL,#内参
                                                          distL,#畸变系数
                                                          mtxR,
                                                          distR,
                                                          ChessImaR.shape[::-1],
                                                          criteria = criteria_stereo,
                                                          flags = cv2.CALIB_FIX_INTRINSIC)

# StereoRectify function
rectify_scale= 0 # if 0 image croped, if 1 image nor croped
#R用于校正图像的旋转矩阵、P用于校正图像的投影矩阵、Q重投影矩阵，可以用于计算深度信息
RL, RR, PL, PR, Q, roiL, roiR= cv2.stereoRectify(MLS, dLS, MRS, dRS,
                                                 ChessImaR.shape[::-1], R, T,
                                                 rectify_scale,(0,0))  # last paramater is alpha, if 0= croped, if 1= not croped
# initUndistortRectifyMap function
#初始化图像校正映射
Left_Stereo_Map= cv2.initUndistortRectifyMap(MLS, dLS, RL, PL,
                                             ChessImaR.shape[::-1], cv2.CV_16SC2)   # cv2.CV_16SC2 this format enables us the programme to work faster
Right_Stereo_Map= cv2.initUndistortRectifyMap(MRS, dRS, RR, PR,
                                              ChessImaR.shape[::-1], cv2.CV_16SC2)
#*******************************************
#***** Parameters for the StereoVision *****
#*******************************************

# Create StereoSGBM and prepare all parameters
window_size = 3
min_disp = 2
num_disp = 130-min_disp
stereo = cv2.StereoSGBM_create(minDisparity = min_disp,#最小视差值
    numDisparities = num_disp,#视差范围
    blockSize = window_size,
    uniquenessRatio = 10,
    speckleWindowSize = 100,
    speckleRange = 32,
    disp12MaxDiff = 5,
    P1 = 8*3*window_size**2,
    P2 = 32*3*window_size**2)

# Used for the filtered image
#计算右视角图像中每个像素与左视角图像中对应像素的差异，视差计算
stereoR=cv2.ximgproc.createRightMatcher(stereo) # Create another stereo for right this time

# WLS FILTER Parameters
lmbda = 80000# 平滑度惩罚系数
sigma = 1.8
visual_multiplier = 1.0
 #提高深度估计的精度
wls_filter = cv2.ximgproc.createDisparityWLSFilter(matcher_left=stereo)
wls_filter.setLambda(lmbda)
wls_filter.setSigmaColor(sigma)

#*************************************
#***** Starting the StereoVision *****
#*************************************

# Call the two cameras
CamR= cv2.VideoCapture(6)   # Wenn 6 then Right Cam and wenn 7 Left Cam
CamL= cv2.VideoCapture(7)

CamR.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
CamR.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
CamR.set(cv2.CAP_PROP_FPS, 30)

CamL.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
CamL.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
CamL.set(cv2.CAP_PROP_FPS, 30)

while True:
    # Start Reading Camera images
    retR, frameR= CamR.read()
    retL, frameL= CamL.read()

    # Rectify the images on rotation and alignement重映射是一种将图像从一个坐标系统映射到另一个坐标系统的过程
    Left_nice= cv2.remap(frameL,Left_Stereo_Map[0],Left_Stereo_Map[1], interpolation = cv2.INTER_LANCZOS4, borderMode = cv2.BORDER_CONSTANT)  # Rectify the image using the kalibration parameters founds during the initialisation
    Right_nice= cv2.remap(frameR,Right_Stereo_Map[0],Right_Stereo_Map[1], interpolation = cv2.INTER_LANCZOS4, borderMode = cv2.BORDER_CONSTANT)



    # Convert from color(BGR) to gray
    grayR= cv2.cvtColor(Right_nice,cv2.COLOR_BGR2GRAY)
    grayL= cv2.cvtColor(Left_nice,cv2.COLOR_BGR2GRAY)

    # Compute the 2 images for the Depth_image计算深度信息
    disp= stereo.compute(grayL,grayR)#.astype(np.float32)/ 16
    dispL= disp
    dispR= stereoR.compute(grayR,grayL)
    dispL= np.int16(dispL)
    dispR= np.int16(dispR)

    # Using the WLS filter加权最小二乘滤波平滑
    filteredImg= wls_filter.filter(dispL,grayL,None,dispR)
    filteredImg = cv2.normalize(src=filteredImg, dst=filteredImg, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX);
    filteredImg = np.uint8(filteredImg)
    #cv2.imshow('Disparity Map', filteredImg)
    #归一化：这个操作的目的是将原始的视差值映射到一定范围内的浮点数。通常，在计算机视觉中，视差图的像素值表示了对应像素点的深度信息，而视差值的范围通常较大。为了方便处理和可视化，我们需要将其缩放到一个合适的范围，以便更好地表达深度差异。
    disp= ((disp.astype(np.float32)/ 16)-min_disp)/num_disp # Calculation allowing us to have 0 for the most distant object able to detect

##    # Resize the image for faster executions
##    dispR= cv2.resize(disp,None,fx=0.7, fy=0.7, interpolation = cv2.INTER_AREA)

    # Filtering the Results with a closing filter
    #用一矩阵去噪
    closing= cv2.morphologyEx(disp,cv2.MORPH_CLOSE, kernel) # Apply an morphological filter for closing little "black" holes in the picture(Remove noise) 

    # Colors map
    dispc= (closing-closing.min())*255
    dispC= dispc.astype(np.uint8)                                   # Convert the type of the matrix from float32 to uint8, this way you can show the results with the function cv2.imshow()
    disp_Color= cv2.applyColorMap(dispC,cv2.COLORMAP_OCEAN)         # Change the Color of the Picture into an Ocean Color_Map
    filt_Color= cv2.applyColorMap(filteredImg,cv2.COLORMAP_OCEAN) 

    # Show the result for the Depth_image
    #cv2.imshow('Disparity', disp)
    #cv2.imshow('Closing',closing)
    #cv2.imshow('Color Depth',disp_Color)
    cv2.imshow('Filtered Color Depth',filt_Color)

    # Mouse clicks
    cv2.setMouseCallback("Filtered Color Depth",coords_mouse_disp,filt_Color)
    
    # End the Programme
    if cv2.waitKey(1) & 0xFF == ord(' '):
        break
    
# Save excel
wb.save("data4.xlsx")

# Release the Cameras
CamR.release()
CamL.release()
cv2.destroyAllWindows()