OpenCv立体视觉

Question

我想在立体阅读中使用热图。我有两个相机，做深度功能（立体），但如何确定最近的点（应该变成红色）和大小？ 它是一个会计的人使用立体视觉。 就像下面的视频。 https://www.youtube.com/watch?v=2lCQehzmUuc 我使用了这个页面上的代码（http://timosam.com/python_opencv_depthimage），它工作了，但是我想知道如何绘制热量图，如何识别离相机最近的物体以及这个物体的大小。你知道吗

import numpy as np
from sklearn.preprocessing import normalize
import cv2

print('loading images...')
imgL = cv2.imread('imgL.jpg')  # downscale images for faster processing if you like
imgR = cv2.imread('imgR.jpg')

# SGBM Parameters -----------------
window_size = 3                     # 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
)

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

wls_filter = cv2.ximgproc.createDisparityWLSFilter(matcher_left=left_matcher)
wls_filter.setLambda(lmbda)
wls_filter.setSigmaColor(sigma)

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