我有立体声图像(来自picryl.com),我想将它们转换为可以上传到Facebook(.glb格式)的3D图像
尝试生成深度图并将其转换为3D模型。
import numpy as np
from sklearn.preprocessing import normalize
import cv2
print('loading images...')
imgL = cv2.imread('l_active.png') # downscale images for faster processing
imgR = cv2.imread('r_active.png')
# 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=32, #160 # max_disp has to be dividable by 16 f. E. HH 192, 256
blockSize=5,
P1=8 * 5 * 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)
//save here
最终的3D模型甚至看起来都不像原始的