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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 time
- import numpy as np
- import cv2
- 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 ')
- if __name__ == '__main__':
- print('loading images...')
- imgL = cv2.pyrDown( cv2.imread('aloeL.jpg') ) # downscale images for faster processing
- imgR = cv2.pyrDown( cv2.imread('aloeR.jpg') )
- # disparity range is tuned for 'aloe' image pair
- window_size = 3
- min_disp = 16
- num_disp = 112-min_disp
- stereo = cv2.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...')
- start_time = time.time()
- disp = stereo.compute(imgL, imgR).astype(np.float32) / 16.0
- elapsed_time = time.time() - start_time
- print("'done!...'={0}".format(elapsed_time))
- #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 = cv2.reprojectImageTo3D(disp, Q)
- #colors = cv2.cvtColor(imgL, cv2.COLOR_BGR2RGB)
- #mask = disp > disp.min()
- #out_points = points[mask]
- #out_colors = colors[mask]
- #out_fn = 'out.ply'
- #write_ply('out.ply', out_points, out_colors)
- #print('%s saved' % 'out.ply')
- cv2.imshow('left', imgL)
- cv2.imshow('disparity', (disp-min_disp)/num_disp)
- cv2.waitKey()
- cv2.destroyAllWindows()
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