changed print statement compatible to run with python3

PCV/classifiers/bayes.py

@@ -31,7 +31,7 @@ def classify(self,points):
         # compute probabilities for each class
         est_prob = array([gauss(m,v,points) for m,v in zip(self.mean,self.var)])
 
-        print 'est prob',est_prob.shape,self.labels
+        print('est prob',est_prob.shape,self.labels)
         # get index of highest probability, this gives class label
         ndx = est_prob.argmax(axis=0)
 

PCV/geometry/warp.py

@@ -110,7 +110,7 @@ def transf(p):
         return (p2[0]/p2[2],p2[1]/p2[2])
 
     if H[1,2]<0: # fromim is to the right
-        print 'warp - right'
+        print('warp - right')
         # transform fromim
         if is_color:
             # pad the destination image with zeros to the right
@@ -125,7 +125,7 @@ def transf(p):
             fromim_t = ndimage.geometric_transform(fromim,transf,
                                     (toim.shape[0],toim.shape[1]+padding)) 
     else:
-        print 'warp - left'
+        print('warp - left')
         # add translation to compensate for padding to the left
         H_delta = array([[1,0,0],[0,1,-delta],[0,0,1]])
         H = dot(H,H_delta)

PCV/imagesearch/imagesearch.py

@@ -42,7 +42,7 @@ def add_to_index(self,imname,descr):
             project on vocabulary and add to database. """
 
         if self.is_indexed(imname): return
-        print 'indexing', imname
+        print('indexing', imname)
 
         # get the imid
         imid = self.get_id(imname)

PCV/localdescriptors/dsift.py

@@ -35,5 +35,5 @@ def process_image_dsift(imagename,resultname,size=20,steps=10,force_orientation=
         cmmd = str("sift "+imagename+" --output="+resultname+
                     " --read-frames=tmp.frame")
     os.system(cmmd)
-    print 'processed', imagename, 'to', resultname
+    print('processed', imagename, 'to', resultname)
 

PCV/localdescriptors/sift.py

@@ -16,7 +16,7 @@ def process_image(imagename,resultname,params="--edge-thresh 10 --peak-thresh 5"
     cmmd = str("sift "+imagename+" --output="+resultname+
                 " "+params)
     os.system(cmmd)
-    print 'processed', imagename, 'to', resultname
+    print('processed', imagename, 'to', resultname)
 
 
 def read_features_from_file(filename):

PCV/tools/imtools.py

@@ -24,7 +24,7 @@ def compute_average(imlist):
         try: 
             averageim += array(Image.open(imname))
         except:
-            print imname + "...skipped"  
+            print(imname + "...skipped")
             skipped += 1
 
     averageim /= (len(imlist) - skipped)

PCV/tools/ncut.py

@@ -9,7 +9,7 @@ def cluster(S,k,ndim):
 
     # check for symmetry
     if sum(abs(S-S.T)) > 1e-10:
-        print 'not symmetric'
+        print('not symmetric')
 
     # create Laplacian matrix
     rowsum = sum(abs(S),axis=0)

PCV/tools/ransac.py

@@ -80,28 +80,27 @@ def ransac(data,model,n,k,t,d,debug=False,return_all=False):
     best_inlier_idxs = None
     while iterations < k:
         maybe_idxs, test_idxs = random_partition(n,data.shape[0])
-        maybeinliers = data[maybe_idxs,:]
+        maybeinliers = data[maybe_idxs, :]
         test_points = data[test_idxs]
         maybemodel = model.fit(maybeinliers)
-        test_err = model.get_error( test_points, maybemodel)
-        also_idxs = test_idxs[test_err < t] # select indices of rows with accepted points
-        alsoinliers = data[also_idxs,:]
+        test_err = model.get_error(test_points, maybemodel)
+        also_idxs = test_idxs[test_err < t]  # select indices of rows with accepted points
+        alsoinliers = data[also_idxs, :]
         if debug:
-            print 'test_err.min()',test_err.min()
-            print 'test_err.max()',test_err.max()
-            print 'numpy.mean(test_err)',numpy.mean(test_err)
-            print 'iteration %d:len(alsoinliers) = %d'%(
-                iterations,len(alsoinliers))
+            print('test_err.min()', test_err.min())
+            print('test_err.max()', test_err.max())
+            print('numpy.mean(test_err)', numpy.mean(test_err))
+            print('iteration %d:len(alsoinliers) = %d' % (iterations, len(alsoinliers)))
         if len(alsoinliers) > d:
-            betterdata = numpy.concatenate( (maybeinliers, alsoinliers) )
+            betterdata = numpy.concatenate((maybeinliers, alsoinliers))
             bettermodel = model.fit(betterdata)
-            better_errs = model.get_error( betterdata, bettermodel)
-            thiserr = numpy.mean( better_errs )
+            better_errs = model.get_error(betterdata, bettermodel)
+            thiserr = numpy.mean(better_errs)
             if thiserr < besterr:
                 bestfit = bettermodel
                 besterr = thiserr
-                best_inlier_idxs = numpy.concatenate( (maybe_idxs, also_idxs) )
-        iterations+=1
+                best_inlier_idxs = numpy.concatenate((maybe_idxs, also_idxs))
+        iterations += 1
     if bestfit is None:
         raise ValueError("did not meet fit acceptance criteria")
     if return_all:

examples/ch1_morphology.py

@@ -12,10 +12,10 @@
 im = (im<128)
 
 labels, nbr_objects = measurements.label(im)
-print "Number of objects:", nbr_objects
+print("Number of objects:", nbr_objects)
 
 # morphology - opening to separate objects better
 im_open = morphology.binary_opening(im,ones((9,5)),iterations=2) 
 
 labels_open, nbr_objects_open = measurements.label(im_open)
-print "Number of objects:", nbr_objects_open
+print("Number of objects:", nbr_objects_open)

examples/ch2_download_panoramio.py

@@ -20,4 +20,4 @@
 for url in imurls:
     image = urllib.URLopener()
     image.retrieve(url, os.path.basename(urlparse.urlparse(url).path)) 
-    print 'downloading:', url
+    print('downloading:', url)

examples/ch2_harris_matching.py

@@ -25,7 +25,7 @@
 filtered_coords2 = harris.get_harris_points(harrisim,wid+1) 
 d2 = harris.get_descriptors(im2,filtered_coords2,wid)
 
-print 'starting matching'
+print('starting matching')
 matches = harris.match_twosided(d1,d2)
 
 figure()

examples/ch2_matching_graph.py

@@ -28,12 +28,12 @@
 
 for i in range(nbr_images):
     for j in range(i,nbr_images): # only compute upper triangle
-        print 'comparing ', imlist[i], imlist[j]
+        print('comparing ', imlist[i], imlist[j])
         l1,d1 = sift.read_features_from_file(featlist[i]) 
         l2,d2 = sift.read_features_from_file(featlist[j])
         matches = sift.match_twosided(d1,d2)
         nbr_matches = sum(matches > 0)
-        print 'number of matches = ', nbr_matches 
+        print('number of matches = ', nbr_matches)
         matchscores[i,j] = nbr_matches
 
 # copy values

examples/ch8_bayes_points.py

@@ -31,7 +31,7 @@
     labels = pickle.load(f)
 
 # test on the 10 first points
-print bc.classify(class_1[:10])[0]
+print(bc.classify(class_1[:10])[0])
 
 # define function for plotting
 def classify(x, y, bc=bc):

examples/ch8_gesture_recognition.py

@@ -54,20 +54,20 @@ def print_confusion(res,labels,classnames):
     for i in range(len(test_labels)):
         confuse[class_ind[res[i]],class_ind[test_labels[i]]] += 1
 
-    print 'Confusion matrix for'
-    print classnames
-    print confuse
+    print('Confusion matrix for')
+    print(classnames)
+    print(confuse)
 
 
 # read training data
 ####################
 features,labels = read_gesture_features_labels('../data/hand_gesture/train/')
-print 'training data is:', features.shape, len(labels)
+print('training data is:', features.shape, len(labels))
 
 # read test data
 ####################
 test_features,test_labels = read_gesture_features_labels('../data/hand_gesture/test/')
-print 'test data is:', test_features.shape, len(test_labels)
+print('test data is:', test_features.shape, len(test_labels))
 
 classnames = unique(labels)
 nbr_classes = len(classnames)
@@ -129,6 +129,6 @@ def print_confusion(res,labels,classnames):
 
 # accuracy
 acc = sum(1.0*(res==test_labels)) / len(test_labels)
-print 'Accuracy:', acc
+print('Accuracy:', acc)
 
 print_confusion(res,test_labels,classnames)

examples/ch8_knn_points.py

@@ -28,12 +28,14 @@
     labels = pickle.load(f)
 
 # test on the first point
-print model.classify(class_1[0])
+print(model.classify(class_1[0]))
+
 
 # define function for plotting
 def classify(x, y, model=model):
     return array([model.classify([xx,yy]) for (xx,yy) in zip(x,y)])
 
+
 # plot the classification boundary
 imtools.plot_2D_boundary([-6,6,-6,6], [class_1,class_2], classify, [1,-1])
 show()

examples/ch8_process_handgestures.py

@@ -29,7 +29,7 @@
 # show an image with features
 l,d = sift.read_features_from_file(featfile)
 im = array(Image.open(filename).resize((50,50)))
-print im.shape
+print(im.shape)
 
 figure()
 sift.plot_features(im, l, True)

pcv_book/bayes.py

@@ -31,7 +31,7 @@ def classify(self,points):
         # compute probabilities for each class
         est_prob = array([gauss(m,v,points) for m,v in zip(self.mean,self.var)])
 
-        print 'est prob',est_prob.shape,self.labels
+        print('est prob',est_prob.shape,self.labels)
         # get index of highest probability, this gives class label
         ndx = est_prob.argmax(axis=0)
 

pcv_book/dsift.py

@@ -34,5 +34,5 @@ def process_image_dsift(imagename,resultname,size=20,steps=10,force_orientation=
         cmmd = str("sift "+imagename+" --output="+resultname+
                     " --read-frames=tmp.frame")
     os.system(cmmd)
-    print 'processed', imagename, 'to', resultname
+    print('processed', imagename, 'to', resultname)
 

pcv_book/imagesearch.py

@@ -42,7 +42,7 @@ def add_to_index(self,imname,descr):
             project on vocabulary and add to database. """
 
         if self.is_indexed(imname): return
-        print 'indexing', imname
+        print('indexing', imname)
 
         # get the imid
         imid = self.get_id(imname)

pcv_book/imtools.py

@@ -24,7 +24,7 @@ def compute_average(imlist):
         try: 
             averageim += array(Image.open(imname))
         except:
-            print imname + "...skipped"  
+            print(imname + "...skipped")
             skipped += 1
 
     averageim /= (len(imlist) - skipped)

pcv_book/ncut.py

@@ -9,7 +9,7 @@ def cluster(S,k,ndim):
 
     # check for symmetry
     if sum(abs(S-S.T)) > 1e-10:
-        print 'not symmetric'
+        print('not symmetric')
 
     # create Laplacian matrix
     rowsum = sum(abs(S),axis=0)

pcv_book/ransac.py

@@ -1,6 +1,6 @@
 import numpy
-import scipy # use numpy if scipy unavailable
-import scipy.linalg # use numpy if scipy unavailable
+import scipy  # use numpy if scipy unavailable
+import scipy.linalg  # use numpy if scipy unavailable
 
 ## Copyright (c) 2004-2007, Andrew D. Straw. All rights reserved.
 
@@ -79,130 +79,135 @@ def ransac(data,model,n,k,t,d,debug=False,return_all=False):
     besterr = numpy.inf
     best_inlier_idxs = None
     while iterations < k:
-        maybe_idxs, test_idxs = random_partition(n,data.shape[0])
-        maybeinliers = data[maybe_idxs,:]
+        maybe_idxs, test_idxs = random_partition(n, data.shape[0])
+        maybeinliers = data[maybe_idxs, :]
         test_points = data[test_idxs]
         maybemodel = model.fit(maybeinliers)
-        test_err = model.get_error( test_points, maybemodel)
-        also_idxs = test_idxs[test_err < t] # select indices of rows with accepted points
-        alsoinliers = data[also_idxs,:]
+        test_err = model.get_error(test_points, maybemodel)
+        also_idxs = test_idxs[test_err < t]  # select indices of rows with accepted points
+        alsoinliers = data[also_idxs, :]
         if debug:
-            print 'test_err.min()',test_err.min()
-            print 'test_err.max()',test_err.max()
-            print 'numpy.mean(test_err)',numpy.mean(test_err)
-            print 'iteration %d:len(alsoinliers) = %d'%(
-                iterations,len(alsoinliers))
+            print('test_err.min()', test_err.min())
+            print('test_err.max()', test_err.max())
+            print('numpy.mean(test_err)', numpy.mean(test_err))
+            print('iteration {}:len(alsoinliers) = {}'.format(iterations, len(alsoinliers)))
         if len(alsoinliers) > d:
-            betterdata = numpy.concatenate( (maybeinliers, alsoinliers) )
+            betterdata = numpy.concatenate((maybeinliers, alsoinliers))
             bettermodel = model.fit(betterdata)
-            better_errs = model.get_error( betterdata, bettermodel)
-            thiserr = numpy.mean( better_errs )
+            better_errs = model.get_error(betterdata, bettermodel)
+            thiserr = numpy.mean(better_errs)
             if thiserr < besterr:
                 bestfit = bettermodel
                 besterr = thiserr
-                best_inlier_idxs = numpy.concatenate( (maybe_idxs, also_idxs) )
-        iterations+=1
+                best_inlier_idxs = numpy.concatenate((maybe_idxs, also_idxs))
+        iterations += 1
     if bestfit is None:
         raise ValueError("did not meet fit acceptance criteria")
     if return_all:
-        return bestfit, {'inliers':best_inlier_idxs}
+        return bestfit, {'inliers': best_inlier_idxs}
     else:
         return bestfit
 
-def random_partition(n,n_data):
+
+def random_partition(n, n_data):
     """return n random rows of data (and also the other len(data)-n rows)"""
-    all_idxs = numpy.arange( n_data )
+    all_idxs = numpy.arange(n_data)
     numpy.random.shuffle(all_idxs)
     idxs1 = all_idxs[:n]
     idxs2 = all_idxs[n:]
     return idxs1, idxs2
 
+
 class LinearLeastSquaresModel:
     """linear system solved using linear least squares
 
     This class serves as an example that fulfills the model interface
     needed by the ransac() function.
     
     """
-    def __init__(self,input_columns,output_columns,debug=False):
+
+    def __init__(self, input_columns, output_columns, debug=False):
         self.input_columns = input_columns
         self.output_columns = output_columns
         self.debug = debug
+
     def fit(self, data):
-        A = numpy.vstack([data[:,i] for i in self.input_columns]).T
-        B = numpy.vstack([data[:,i] for i in self.output_columns]).T
-        x,resids,rank,s = numpy.linalg.lstsq(A,B)
+        A = numpy.vstack([data[:, i] for i in self.input_columns]).T
+        B = numpy.vstack([data[:, i] for i in self.output_columns]).T
+        x, resids, rank, s = numpy.linalg.lstsq(A, B)
         return x
-    def get_error( self, data, model):
-        A = numpy.vstack([data[:,i] for i in self.input_columns]).T
-        B = numpy.vstack([data[:,i] for i in self.output_columns]).T
-        B_fit = scipy.dot(A,model)
-        err_per_point = numpy.sum((B-B_fit)**2,axis=1) # sum squared error per row
+
+    def get_error(self, data, model):
+        A = numpy.vstack([data[:, i] for i in self.input_columns]).T
+        B = numpy.vstack([data[:, i] for i in self.output_columns]).T
+        B_fit = scipy.dot(A, model)
+        err_per_point = numpy.sum((B - B_fit) ** 2, axis=1)  # sum squared error per row
         return err_per_point
-
+
+
 def test():
     # generate perfect input data
 
     n_samples = 500
     n_inputs = 1
     n_outputs = 1
-    A_exact = 20*numpy.random.random((n_samples,n_inputs) )
-    perfect_fit = 60*numpy.random.normal(size=(n_inputs,n_outputs) ) # the model
-    B_exact = scipy.dot(A_exact,perfect_fit)
-    assert B_exact.shape == (n_samples,n_outputs)
+    A_exact = 20 * numpy.random.random((n_samples, n_inputs))
+    perfect_fit = 60 * numpy.random.normal(size=(n_inputs, n_outputs))  # the model
+    B_exact = scipy.dot(A_exact, perfect_fit)
+    assert B_exact.shape == (n_samples, n_outputs)
 
     # add a little gaussian noise (linear least squares alone should handle this well)
-    A_noisy = A_exact + numpy.random.normal(size=A_exact.shape )
-    B_noisy = B_exact + numpy.random.normal(size=B_exact.shape )
+    A_noisy = A_exact + numpy.random.normal(size=A_exact.shape)
+    B_noisy = B_exact + numpy.random.normal(size=B_exact.shape)
 
     if 1:
         # add some outliers
         n_outliers = 100
-        all_idxs = numpy.arange( A_noisy.shape[0] )
+        all_idxs = numpy.arange(A_noisy.shape[0])
         numpy.random.shuffle(all_idxs)
         outlier_idxs = all_idxs[:n_outliers]
         non_outlier_idxs = all_idxs[n_outliers:]
-        A_noisy[outlier_idxs] =  20*numpy.random.random((n_outliers,n_inputs) )
-        B_noisy[outlier_idxs] = 50*numpy.random.normal(size=(n_outliers,n_outputs) )
+        A_noisy[outlier_idxs] = 20 * numpy.random.random((n_outliers, n_inputs))
+        B_noisy[outlier_idxs] = 50 * numpy.random.normal(size=(n_outliers, n_outputs))
 
     # setup model
 
-    all_data = numpy.hstack( (A_noisy,B_noisy) )
-    input_columns = range(n_inputs) # the first columns of the array
-    output_columns = [n_inputs+i for i in range(n_outputs)] # the last columns of the array
+    input_columns = range(n_inputs)  # the first columns of the array
+    output_columns = [n_inputs+i for i in range(n_outputs)]  # the last columns of the array
     debug = True
-    model = LinearLeastSquaresModel(input_columns,output_columns,debug=debug)
-
-    linear_fit,resids,rank,s = numpy.linalg.lstsq(all_data[:,input_columns],all_data[:,output_columns])
+    model = LinearLeastSquaresModel(input_columns, output_columns, debug=debug)
+
+    linear_fit, resids, rank, s = numpy.linalg.lstsq(numpy.hstack((A_noisy, B_noisy))[:, input_columns],
+                                                     numpy.hstack((A_noisy, B_noisy))[:, output_columns])
 
     # run RANSAC algorithm
-    ransac_fit, ransac_data = ransac(all_data,model,
-                                     5, 5000, 7e4, 50, # misc. parameters
-                                     debug=debug,return_all=True)
+    ransac_fit, ransac_data = ransac(numpy.hstack((A_noisy, B_noisy)), model,
+                                     5, 5000, 7e4, 50,  # misc. parameters
+                                     debug=debug, return_all=True)
     if 1:
         import pylab
 
-        sort_idxs = numpy.argsort(A_exact[:,0])
-        A_col0_sorted = A_exact[sort_idxs] # maintain as rank-2 array
+        sort_idxs = numpy.argsort(A_exact[:, 0])
+        A_col0_sorted = A_exact[sort_idxs]  # maintain as rank-2 array
 
         if 1:
-            pylab.plot( A_noisy[:,0], B_noisy[:,0], 'k.', label='data' )
-            pylab.plot( A_noisy[ransac_data['inliers'],0], B_noisy[ransac_data['inliers'],0], 'bx', label='RANSAC data' )
+            pylab.plot(A_noisy[:, 0], B_noisy[:, 0], 'k.', label='data')
+            pylab.plot(A_noisy[ransac_data['inliers'], 0], B_noisy[ransac_data['inliers'], 0], 'bx', label='RANSAC data')
         else:
-            pylab.plot( A_noisy[non_outlier_idxs,0], B_noisy[non_outlier_idxs,0], 'k.', label='noisy data' )
-            pylab.plot( A_noisy[outlier_idxs,0], B_noisy[outlier_idxs,0], 'r.', label='outlier data' )
-        pylab.plot( A_col0_sorted[:,0],
-                    numpy.dot(A_col0_sorted,ransac_fit)[:,0],
-                    label='RANSAC fit' )
-        pylab.plot( A_col0_sorted[:,0],
-                    numpy.dot(A_col0_sorted,perfect_fit)[:,0],
-                    label='exact system' )
-        pylab.plot( A_col0_sorted[:,0],
-                    numpy.dot(A_col0_sorted,linear_fit)[:,0],
-                    label='linear fit' )
+            pylab.plot(A_noisy[non_outlier_idxs, 0], B_noisy[non_outlier_idxs, 0], 'k.', label='noisy data')
+            pylab.plot(A_noisy[outlier_idxs, 0], B_noisy[outlier_idxs, 0], 'r.', label='outlier data')
+        pylab.plot(A_col0_sorted[:, 0],
+                   numpy.dot(A_col0_sorted, ransac_fit)[:, 0],
+                   label='RANSAC fit')
+        pylab.plot(A_col0_sorted[:, 0],
+                   numpy.dot(A_col0_sorted, perfect_fit)[:, 0],
+                   label='exact system')
+        pylab.plot(A_col0_sorted[:, 0],
+                   numpy.dot(A_col0_sorted, linear_fit)[:, 0],
+                   label='linear fit')
         pylab.legend()
         pylab.show()
 
-if __name__=='__main__':
+
+if __name__ == '__main__':
     test()
-

pcv_book/sift.py

@@ -4,7 +4,7 @@
 from pylab import *
 
 
-def process_image(imagename,resultname,params="--edge-thresh 10 --peak-thresh 5"):
+def process_image(imagename, resultname, params="--edge-thresh 10 --peak-thresh 5"):
     """ Process an image and save the results in a file. """
 
     if imagename[-3:] != 'pgm':
@@ -13,22 +13,21 @@ def process_image(imagename,resultname,params="--edge-thresh 10 --peak-thresh 5"
         im.save('tmp.pgm')
         imagename = 'tmp.pgm'
 
-    cmmd = str("sift "+imagename+" --output="+resultname+
-                " "+params)
+    cmmd = str("sift " + imagename + " --output=" + resultname + " " + params)
     os.system(cmmd)
-    print 'processed', imagename, 'to', resultname
+    print('processed', imagename, 'to', resultname)
 
 
 def read_features_from_file(filename):
     """ Read feature properties and return in matrix form. """
-    
+
     f = loadtxt(filename)
-    return f[:,:4],f[:,4:] # feature locations, descriptors
+    return f[:, :4], f[:, 4:]  # feature locations, descriptors
 
 
-def write_features_to_file(filename,locs,desc):
+def write_features_to_file(filename, locs, desc):
     """ Save feature location and descriptor to file. """
-    savetxt(filename,hstack((locs,desc)))
+    savetxt(filename, hstack((locs, desc)))
 
 
 def plot_features(im,locs,circle=False):

pcv_book/warp.py

@@ -110,7 +110,7 @@ def transf(p):
         return (p2[0]/p2[2],p2[1]/p2[2])
 
     if H[1,2]<0: # fromim is to the right
-        print 'warp - right'
+        print('warp - right')
         # transform fromim
         if is_color:
             # pad the destination image with zeros to the right
@@ -125,7 +125,7 @@ def transf(p):
             fromim_t = ndimage.geometric_transform(fromim,transf,
                                     (toim.shape[0],toim.shape[1]+padding)) 
     else:
-        print 'warp - left'
+        print('warp - left')
         # add translation to compensate for padding to the left
         H_delta = array([[1,0,0],[0,1,-delta],[0,0,1]])
         H = dot(H,H_delta)