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