做网站封面素材图,机械网站建设公司推荐,新公司 做网站 流程,新开河街网站建设公司硬件#xff1a; 首先要要把两个摄像头固定到支架上#xff0c;并且两个摄像头的间距应该在110mm#xff0c;两个摄像头没有落差 相机的内参数包括焦距、主点坐标、像素尺寸等#xff0c;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#xff0c;不会随…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 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-Lstr_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。 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。 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。 步骤三 对极几何 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。
参考 https://www.cnblogs.com/clarenceliang/p/6704970. htmlhttps://cloud.tencent.com/developer/article/2054308 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]average0for u in range (-1,2):for v in range (-1,2):average disp[yu,xv]averageaverage/9Distance -593.97*average**(3) 1506.8*average**(2) - 1373.1*average 522.06Distance np.around(Distance*0.01,decimals2)print(Distance: str(Distance) m)# This section has to be uncommented if you want to take mesurements and store them in the excelws.append([counterdist, average])print(Measure at str(counterdist) cm, the dispasrity is str(average))if (counterdist 85):counterdist 3elif(counterdist 120):counterdist 5else:counterdist 10print(Next distance to measure: str(counterdist)cm)
#将测量结果写入excel
# Mouseclick callback
wbWorkbook()
wswb.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 0t str(i)ChessImaR cv2.imread(/home/nvidia/Desktop/gf-learning/Stereo-Vision-master/save_image/chessboard-Rt.png,0) # Right sideChessImaL cv2.imread(/home/nvidia/Desktop/gf-learning/Stereo-Vision-master/save_image/chessboard-Lt.png,0) # Left sideretR, cornersR cv2.findChessboardCorners(ChessImaR,(9,6),None) # Define the number of chees corners we are looking forretL, cornersL cv2.findChessboardCorners(ChessImaL,(9,6),None) # Left sideif (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
stereoRcv2.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_leftstereo)
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 imagesretR, 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 initialisationRight_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 graygrayR 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)/ 16dispL dispdispR 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(srcfilteredImg, dstfilteredImg, beta0, alpha255, norm_typecv2.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,fx0.7, fy0.7, 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 mapdispc (closing-closing.min())*255dispC 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_Mapfilt_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 clickscv2.setMouseCallback(Filtered Color Depth,coords_mouse_disp,filt_Color)# End the Programmeif cv2.waitKey(1) 0xFF ord( ):break# Save excel
wb.save(data4.xlsx)# Release the Cameras
CamR.release()
CamL.release()
cv2.destroyAllWindows()
