am使用以下代码，我需要使用两个摄像头和立体视觉来测量距离，但我始终会在标题中遇到此错误，尽管我确保两个摄像头都拍摄了正确的照片以保存，但是我从中获取了此代码另一个似乎使代码易于使用的网站，但我总是遇到此错误，因此请帮助</ p>
# Package importation
import numpy as np
import cv2enter code here`enter code here`
from openpyxl import Workbook`enter code here`
from sklearn.preprocessing import normalize
# Filtering
kernel = np.ones((3, 3), np.uint8)
def coords_mouse_disp(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDBLCLK:
# 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')
# Mouseclick callback
wb = Workbook()
ws = wb.active
# *************************************************
# ***** Parameters for Distortion Calibration *****
# *************************************************
# Termination criteria
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
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
for i in range(0,
67): # 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('chessboard/chessboard-R' + t + '.png', 0) # Right side
ChessImaL = cv2.imread('chessboard/chessboard-L' + t + '.png', 0) # Left side
print('chessboard/chessboard-R' + t + '.png')
retR, cornersR = cv2.findChessboardCorners(ChessImaR,
(6, 3), None) # Define the number of chees corners we are looking for
retL, cornersL = cv2.findChessboardCorners(ChessImaL,
(6,3 ), None) # Left side
if (True == retR) & (True == retL):
objpoints.append(objp)
cv2.cornerSubPix(ChessImaR, cornersR, (6 , 6), (-1, -1), criteria)
cv2.cornerSubPix(ChessImaL, cornersL, (6 , 6), (-1, -1), criteria)
imgpointsR.append(cornersR)
imgpointsL.append(cornersL)
# Determine the new values for different parameters
# Right Side
retR, mtxR, distR, rvecsR, tvecsR = cv2.calibrateCamera(objpoints,
imgpointsR,
ChessImaR.shape[::-1], None, None)
hR, wR = ChessImaR.shape[:2]
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
# flags |= cv2.CALIB_FIX_PRINCIPAL_POINT
# flags |= cv2.CALIB_USE_INTRINSIC_GUESS
# flags |= cv2.CALIB_FIX_FOCAL_LENGTH
# flags |= cv2.CALIB_FIX_ASPECT_RATIO
# flags |= cv2.CALIB_ZERO_TANGENT_DIST
# flags |= cv2.CALIB_RATIONAL_MODEL
# flags |= cv2.CALIB_SAME_FOCAL_LENGTH
# flags |= cv2.CALIB_FIX_K3
# flags |= cv2.CALIB_FIX_K4
# flags |= cv2.CALIB_FIX_K5
retS, MLS, dLS, MRS, dRS, R, T, E, F = cv2.stereoCalibrate(objpoints,
imgpointsL,
imgpointsR,
mtxL,
distL,
mtxR,
distR,
ChessImaR.shape[::-1],
criteria_stereo,
flags)
# StereoRectify function
rectify_scale = 0 # if 0 image croped, if 1 image nor croped
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
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,
numDisparities=num_disp,
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
stereoR = cv2.ximgproc.createRightMatcher(stereo) # Create another stereo for right this time
# WLS FILTER Parameters
lmbda = 80000
sigma = 1.8
visual_multiplier = 1.0
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(0) # Wenn 0 then Right Cam and wenn 2 Left Cam
CamL = cv2.VideoCapture(2)
while True:
# Start Reading Camera images
retR, frameR = CamR.read()
retL, frameL = CamL.read()
# Rectify the images on rotation and alignement
Left_nice = cv2.remap(frameL, Left_Stereo_Map[0], Left_Stereo_Map[1], cv2.INTER_LANCZOS4, cv2.BORDER_CONSTANT,
0) # Rectify the image using the kalibration parameters founds during the initialisation
Right_nice = cv2.remap(frameR, Right_Stereo_Map[0], Right_Stereo_Map[1], cv2.INTER_LANCZOS4, cv2.BORDER_CONSTANT, 0)
## # Draw Red lines
## for line in range(0, int(Right_nice.shape[0]/20)): # Draw the Lines on the images Then numer of line is defines by the image Size/20
## Left_nice[line*20,:]= (0,0,255)
## Right_nice[line*20,:]= (0,0,255)
##
## for line in range(0, int(frameR.shape[0]/20)): # Draw the Lines on the images Then numer of line is defines by the image Size/20
## frameL[line*20,:]= (0,255,0)
## frameR[line*20,:]= (0,255,0)
# Show the Undistorted images
# cv2.imshow('Both Images', np.hstack([Left_nice, Right_nice]))
# cv2.imshow('Normal', np.hstack([frameL, frameR]))
# 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
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
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)
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
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 click
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()