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3rdparty/opencv-4.5.4/samples/python/stereo_match.py 2.32 KB
f4334277   Hu Chunming   提交3rdparty
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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()