added stereo_match.py sample

samples/python2/stereo_match.py

@@ -0,0 +1,73 @@
+'''
+Simple example of stereo image matching and point cloud generation.
+
+Resulting .ply file cam be easily viewed using MeshLab (http://meshlab.sourceforge.net/)
+'''
+
+import numpy as np
+import cv2, 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, 'w') as f:
+        f.write(ply_header % dict(vert_num=len(verts)))
+        np.savetxt(f, verts, '%f %f %f %d %d %d')
+
+
+if __name__ == '__main__':
+    print 'loading images...'
+    imgL = cv2.pyrDown( cv2.imread('../gpu/aloeL.jpg') )  # downscale images for faster processing
+    imgR = cv2.pyrDown( cv2.imread('../gpu/aloeR.jpg') )
+
+    # disparity range is tuned for 'aloe' image pair
+    window_size = 3
+    min_disp = 16
+    num_disp = 112-min_disp
+    stereo = cv2.StereoSGBM(minDisparity = min_disp, 
+        numDisparities = num_disp, 
+        SADWindowSize = window_size,
+        uniquenessRatio = 10,
+        speckleWindowSize = 100,
+        speckleRange = 32,
+        disp12MaxDiff = 1,
+        P1 = 8*3*window_size**2,
+        P2 = 32*3*window_size**2,
+        fullDP = False
+    )
+
+    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 = cv2.reprojectImageTo3D(disp, Q)
+    colors = cv2.cvtColor(imgL, cv.CV_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()