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Open Source Computer Vision Library
https://opencv.org/
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76 lines
2.2 KiB
76 lines
2.2 KiB
#/usr/bin/env python |
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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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cv2.destroyAllWindows()
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