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#!/usr/bin/env python
'''
Simple example of stereo image matching and point cloud generation.
Resulting .ply file cam be easily viewed using MeshLab ( http://meshlab.sourceforge.net/ )
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
ply_header = '''ply
format ascii 1.0
element vertex %(vert_num)d
property float x
property float y
property float z
property uchar red
property uchar green
property uchar blue
end_header
'''
def write_ply(fn, verts, colors):
verts = verts.reshape(-1, 3)
colors = colors.reshape(-1, 3)
verts = np.hstack([verts, colors])
with open(fn, 'wb') as f:
f.write((ply_header % dict(vert_num=len(verts))).encode('utf-8'))
np.savetxt(f, verts, fmt='%f %f %f %d %d %d ')
def main():
print('loading images...')
imgL = cv.pyrDown(cv.imread(cv.samples.findFile('aloeL.jpg'))) # downscale images for faster processing
imgR = cv.pyrDown(cv.imread(cv.samples.findFile('aloeR.jpg')))
# disparity range is tuned for 'aloe' image pair
window_size = 3
min_disp = 16
num_disp = 112-min_disp
stereo = cv.StereoSGBM_create(minDisparity = min_disp,
numDisparities = num_disp,
blockSize = 16,
P1 = 8*3*window_size**2,
P2 = 32*3*window_size**2,
disp12MaxDiff = 1,
uniquenessRatio = 10,
speckleWindowSize = 100,
speckleRange = 32
)
print('computing disparity...')
disp = stereo.compute(imgL, imgR).astype(np.float32) / 16.0
print('generating 3d point cloud...',)
h, w = imgL.shape[:2]
f = 0.8*w # guess for focal length
Q = np.float32([[1, 0, 0, -0.5*w],
[0,-1, 0, 0.5*h], # turn points 180 deg around x-axis,
[0, 0, 0, -f], # so that y-axis looks up
[0, 0, 1, 0]])
points = cv.reprojectImageTo3D(disp, Q)
colors = cv.cvtColor(imgL, cv.COLOR_BGR2RGB)
mask = disp > disp.min()
out_points = points[mask]
out_colors = colors[mask]
out_fn = 'out.ply'
write_ply(out_fn, out_points, out_colors)
print('%s saved' % out_fn)
cv.imshow('left', imgL)
cv.imshow('disparity', (disp-min_disp)/num_disp)
cv.waitKey()
print('Done')
if __name__ == '__main__':
print(__doc__)
main()
cv.destroyAllWindows()
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