Added pose estimation example. Changed import statements for PCV modu…

…les.

PCV/geometry/homography.py

@@ -51,7 +51,7 @@ def H_from_ransac(fp,tp,model,maxiter=1000,match_theshold=10):
         
         input: fp,tp (3*n arrays) points in hom. coordinates. """
 
-    import ransac
+    from PCV.tools import ransac
 
     # group corresponding points
     data = vstack((fp,tp))

PCV/geometry/warp.py

@@ -3,7 +3,7 @@
 from pylab import *
 from numpy import *
 
-import homography
+from PCV.geometry import homography
 
 
 def image_in_image(im1,im2,tp):

PCV/imagesearch/vocabulary.py

@@ -1,6 +1,7 @@
 from numpy import *
 from scipy.cluster.vq import *
-import sift
+
+from PCV.localdescriptors import sift
 
 
 class Vocabulary(object):

PCV/localdescriptors/dsift.py

@@ -1,7 +1,8 @@
 from PIL import Image
-import os
 from numpy import *
-import sift
+import os
+
+from PCV.localdescriptors import sift
 
 
 def process_image_dsift(imagename,resultname,size=20,steps=10,force_orientation=False,resize=None):

PCV/localdescriptors/sift.py

@@ -1,7 +1,7 @@
 from PIL import Image
-import os
 from numpy import *
 from pylab import *
+import os
 
 
 def process_image(imagename,resultname,params="--edge-thresh 10 --peak-thresh 5"):

PCV/tools/graphcut.py

@@ -4,7 +4,7 @@
 from pygraph.classes.digraph import digraph
 from pygraph.algorithms.minmax import maximum_flow
 
-import bayes
+from PCV.tools import bayes
 
 """ 
 Graph Cut image segmentation using max-flow/min-cut. 

examples/ar_cube.py

@@ -0,0 +1,124 @@
+from pylab import *
+from numpy import *
+from PIL import Image
+
+# If you have PCV installed, these imports should work
+from PCV.geometry import homography, camera
+from PCV.localdescriptors import sift
+
+"""
+This is the augmented reality and pose estimation cube example from section 4.3.
+"""
+
+def cube_points(c,wid):
+    """ Creates a list of points for plotting
+        a cube with plot. (the first 5 points are
+        the bottom square, some sides repeated). """
+    p = []
+    # bottom
+    p.append([c[0]-wid,c[1]-wid,c[2]-wid])
+    p.append([c[0]-wid,c[1]+wid,c[2]-wid])
+    p.append([c[0]+wid,c[1]+wid,c[2]-wid])
+    p.append([c[0]+wid,c[1]-wid,c[2]-wid])
+    p.append([c[0]-wid,c[1]-wid,c[2]-wid]) #same as first to close plot
+
+    # top
+    p.append([c[0]-wid,c[1]-wid,c[2]+wid])
+    p.append([c[0]-wid,c[1]+wid,c[2]+wid])
+    p.append([c[0]+wid,c[1]+wid,c[2]+wid])
+    p.append([c[0]+wid,c[1]-wid,c[2]+wid])
+    p.append([c[0]-wid,c[1]-wid,c[2]+wid]) #same as first to close plot
+
+    # vertical sides
+    p.append([c[0]-wid,c[1]-wid,c[2]+wid])
+    p.append([c[0]-wid,c[1]+wid,c[2]+wid])
+    p.append([c[0]-wid,c[1]+wid,c[2]-wid])
+    p.append([c[0]+wid,c[1]+wid,c[2]-wid])
+    p.append([c[0]+wid,c[1]+wid,c[2]+wid])
+    p.append([c[0]+wid,c[1]-wid,c[2]+wid])
+    p.append([c[0]+wid,c[1]-wid,c[2]-wid])
+
+    return array(p).T
+
+
+def my_calibration(sz):
+    """
+    Calibration function for the camera (iPhone4) used in this example.
+    """
+    row,col = sz
+    fx = 2555*col/2592
+    fy = 2586*row/1936
+    K = diag([fx,fy,1])
+    K[0,2] = 0.5*col
+    K[1,2] = 0.5*row
+    return K
+
+
+
+# compute features
+sift.process_image('book_frontal.JPG','im0.sift')
+l0,d0 = sift.read_features_from_file('im0.sift')
+
+sift.process_image('book_perspective.JPG','im1.sift')
+l1,d1 = sift.read_features_from_file('im1.sift')
+
+
+# match features and estimate homography
+matches = sift.match_twosided(d0,d1)
+ndx = matches.nonzero()[0]
+fp = homography.make_homog(l0[ndx,:2].T) 
+ndx2 = [int(matches[i]) for i in ndx]
+tp = homography.make_homog(l1[ndx2,:2].T)
+
+model = homography.RansacModel()
+H, inliers = homography.H_from_ransac(fp,tp,model) 
+
+# camera calibration
+K = my_calibration((747,1000))
+
+# 3D points at plane z=0 with sides of length 0.2
+box = cube_points([0,0,0.1],0.1)
+
+# project bottom square in first image
+cam1 = camera.Camera( hstack((K,dot(K,array([[0],[0],[-1]])) )) )
+# first points are the bottom square
+box_cam1 = cam1.project(homography.make_homog(box[:,:5])) 
+
+
+# use H to transfer points to the second image
+box_trans = homography.normalize(dot(H,box_cam1))
+
+# compute second camera matrix from cam1 and H
+cam2 = camera.Camera(dot(H,cam1.P))
+A = dot(linalg.inv(K),cam2.P[:,:3])
+A = array([A[:,0],A[:,1],cross(A[:,0],A[:,1])]).T 
+cam2.P[:,:3] = dot(K,A)
+
+# project with the second camera
+box_cam2 = cam2.project(homography.make_homog(box))
+
+
+
+# plotting
+im0 = array(Image.open('book_frontal.JPG'))
+im1 = array(Image.open('book_perspective.JPG'))
+
+figure()
+imshow(im0)
+plot(box_cam1[0,:],box_cam1[1,:],linewidth=3)
+title('2D projection of bottom square')
+axis('off')
+
+figure()
+imshow(im1)
+plot(box_trans[0,:],box_trans[1,:],linewidth=3)
+title('2D projection transfered with H')
+axis('off')
+
+figure()
+imshow(im1)
+plot(box_cam2[0,:],box_cam2[1,:],linewidth=3)
+title('3D points projected in second image')
+axis('off')
+
+show()