具有cv2.reprojectImageTo3D的OpenCV Pointcloud的深度乘以n乘3矩阵

Question

我使用SBGM算法创建了一个视差图像，它使我获得了漂亮的图像

import numpy as np
import cv2

 #load unrectified images
unimgR =cv2.imread("R.jpg")
unimgL =cv2.imread("L.jpg")

#load calibration from calibration file
calibration = np.load(r"C:\Users\XXX\PycharmProjects\rectify\Test3_OpenCV_Rectified.npz", allow_pickle=False)  # load variables from calibration file
imageSize = tuple(calibration["imageSize"])
leftMatrix = calibration["leftMatrix"]
leftDist = calibration["leftDist"]
leftMapX = calibration["leftMapX"]
leftMapY = calibration["leftMapY"]
leftROI = tuple(calibration["leftROI"])
rightMatrix = calibration["rightMatrix"]
rightDist = calibration["rightDist"]
rightMapX = calibration["rightMapX"]
rightMapY = calibration["rightMapY"]
rightROI = tuple(calibration["rightROI"])
disparityToDepthMap = calibration["disparityToDepthMap"]

# Rectify images (including monocular undistortion)
imgL = cv2.remap(unimgL, leftMapX, leftMapY, cv2.INTER_LINEAR)
imgR = cv2.remap(unimgR, rightMapX, rightMapY, cv2.INTER_LINEAR)

# SGBM Parameters 
window_size = 15  # wsize default 3; 5; 7 for SGBM reduced size image; 15 for SGBM full size image (1300px and above); 5 Works nicely
left_matcher = cv2.StereoSGBM_create(
    minDisparity=0,
    numDisparities=160,  # max_disp has to be dividable by 16 f. E. HH 192, 256
    blockSize=5,
    P1=8 * 3 * window_size ** 2,
    # wsize default 3; 5; 7 for SGBM reduced size image; 15 for SGBM full size image (1300px and above); 5 Works nicely
    P2=32 * 3 * window_size ** 2,
    disp12MaxDiff=1,
    uniquenessRatio=15,
    speckleWindowSize=0,
    speckleRange=2,
    preFilterCap=63,
    mode=cv2.STEREO_SGBM_MODE_SGBM_3WAY
)
right_matcher = cv2.ximgproc.createRightMatcher(left_matcher)

# FILTER Parameters
lmbda = 80000
sigma = 1.2
visual_multiplier = 1.0

# Weighted least squares filter to fill sparse (unpopulated) areas of the disparity map
    # by aligning the images edges and propagating disparity values from high- to low-confidence regions
wls_filter = cv2.ximgproc.createDisparityWLSFilter(matcher_left=left_matcher)
wls_filter.setLambda(lmbda)
wls_filter.setSigmaColor(sigma)

# Get depth information/disparity map using SGBM
displ = left_matcher.compute(imgL, imgR)  # .astype(np.float32)/16
dispr = right_matcher.compute(imgR, imgL)  # .astype(np.float32)/16
displ = np.int16(displ)
dispr = np.int16(dispr)

filteredImg = wls_filter.filter(displ, imgL, None, dispr)  # important to put "imgL" here!!!
filteredImg = cv2.normalize(src=filteredImg, dst=filteredImg, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX);
filteredImg = np.uint8(filteredImg)

# Calculate 3D point cloud
pointCloud = cv2.reprojectImageTo3D(filteredImg,disparityToDepthMap) / 420  # needs to be divided by 420 to obtain metric values (80 without normalization)
print('...shape of the pointcloud:', pointCloud.shape)


print(pointCloud[1000][550])

cv2.imshow('Disparity Map', filteredImg)
cv2.waitKey()
cv2.destroyAllWindows()

现在我要根据此ReprojectImageTo3D corresponding pixel in image (Stereo Vision)

用pointCloud = cv2.reprojectImageTo3D计算z坐标

X，Y = 1000,550点的距离为10m，但它给了我[-0.09156016 0.09407288 0.32270285]（3维而不是1维）

我不知道该怎么做：（

我认为这是3D点，对吗？但是如何从XY坐标中获得单个单元中Z分量的距离？

Answer 1

如果您根据X，Y坐标计算出欧几里得距离以获得Z值（这是您想要的距离），将会很有帮助。

print("Distance pointcloud in m :",np.sqrt(np.sum(np.power(pointCloud[1000][550],2))))