如何区分视差图像的好坏

Question

将numpy作为np导入 从sklearn.preprocessing导入规范化 进口cv2

print('loading images...')
imgL = cv2.imread("C:/Users/admin/jupyter/car/challenge_pictrue/right/right2.jpg ")
imgR = cv2.imread("C:/Users/admin/jupyter/car/challenge_pictrue/left/left2.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
)

right_matcher = cv2.ximgproc.createRightMatcher(left_matcher)

# 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()

我不知道如何区分这个视差图像的好坏。 有谁能帮我解释一下这个差异图像吗

Answer 1

要获得良好的视差图，请执行以下操作：

1. 您的场景应该具有随机纹理。如果场景是高度纹理化的，您将获得良好的视差贴图

错误的视差贴图：

1. 当校准数据出错时。您应该获得正确的校准数据
2. 当你的场景有重复的图案或者没有纹理时，你的视差贴图会丢失更多的数据。我们不能直接称它为坏映射，但它的预期行为