Add python 3 support

Author: Tim Sheerman-Chase

README.rst

@@ -12,7 +12,7 @@ The code is available under the Simplified BSD License.
 Dependencies
 ============
 
-Python >= 2.6, PIL, Numpy, SciPy >= 0.8, cython
+Python >= 2.6 or >=3.4, PIL, Numpy, SciPy >= 0.8, cython
 
 Development
 ===========
@@ -37,3 +37,4 @@ Usage
     python setup.py build_ext --inplace
 
     python example1.py
+

convolve.py

@@ -1,4 +1,5 @@
 
+from __future__ import print_function
 import math, numpy as np
 from PIL import Image
 
@@ -8,7 +9,7 @@
 	import scipy.ndimage
 	useScipyConvolution = True
 except:
-	print "Warning: Failed to import scipy.ndimage"
+	print("Warning: Failed to import scipy.ndimage")
 
 class ConvolutionKernel:
 	def __init__(self, maxKernelWidth = 71):
@@ -35,7 +36,7 @@ def _computeKernels(sigma):
 
 	# Compute kernels, and automatically determine widths */
 
-	hw = maxKernelWidth / 2
+	hw = int(maxKernelWidth / 2)
 	max_gauss = 1.0
 	max_gaussderiv = float(sigma*math.exp(-0.5))
 
@@ -62,12 +63,12 @@ def _computeKernels(sigma):
 
 	# Shift if width less than maxKernelWidth 
 	for i in range(gauss.width):
-		gauss.data[i] = gauss.data[i+(maxKernelWidth-gauss.width)/2]
+		gauss.data[i] = gauss.data[int(i+(maxKernelWidth-gauss.width)/2)]
 	for i in range(gaussderiv.width):
-		gaussderiv.data[i] = gaussderiv.data[i+(maxKernelWidth-gaussderiv.width)/2]
+		gaussderiv.data[i] = gaussderiv.data[int(i+(maxKernelWidth-gaussderiv.width)/2)]
 
 	# Normalize gauss and deriv 
-	hw = gaussderiv.width / 2
+	hw = int(gaussderiv.width / 2)
 	den = 0.0;
 	for i in range(gauss.width): 
 		den += gauss.data[i]
@@ -237,7 +238,7 @@ def KLTComputeGradients(img, sigma):
 	else:
 		gauss_kernel, gaussderiv_kernel = cachegauss, cachegaussderiv
 
-	#print gauss_kernel
+	#print(gauss_kernel)
 	#plt.plot(gauss_kernel)
 	#plt.show()
 

error.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 
 #*********************************************************************
 #* KLTError
@@ -9,7 +10,7 @@
 #*
 
 def KLTError(err):
-	print err
+	print(err)
 	exit(1)
 
 
@@ -24,5 +25,5 @@ def KLTError(err):
 
 def KLTWarning(err):
 
-	print err
+	print(err)
 

example1.py

@@ -6,6 +6,7 @@
 #**********************************************************************
 
 #include "pnmio.h"
+from __future__ import print_function
 from klt import *
 from PIL import Image
 from selectGoodFeatures import *
@@ -27,9 +28,9 @@ def main():
 
 	fl = KLTSelectGoodFeatures(tc, img1, nFeatures)
 
-	print "\nIn first image:"
+	print("\nIn first image:")
 	for i, feat in enumerate(fl):
-		print "Feature #{0}:  ({1},{2}) with value of {3}".format(i, feat.x, feat.y, feat.val)
+		print("Feature #{0}:  ({1},{2}) with value of {3}".format(i, feat.x, feat.y, feat.val))
 
 	KLTWriteFeatureListToPPM(fl, img1, "feat1.ppm")
 	#KLTWriteFeatureList(fl, "feat1.txt", "%3d")
@@ -53,11 +54,11 @@ def main():
 		KLTTrackFeatures(tc, img1, img2, fl)
 		KLTTrackFeatures(tc, img2, img1, fl)
 		count += 2
-	print (time.clock() - ti) / count
+	print((time.clock() - ti) / count)
 
-	print "\nIn second image:"
+	print("\nIn second image:")
 	for i, feat in enumerate(fl):
-		print "Feature #{0}:  ({1},{2}) with value of {3}".format(i, feat.x, feat.y, feat.val)
+		print("Feature #{0}:  ({1},{2}) with value of {3}".format(i, feat.x, feat.y, feat.val))
 
 	KLTWriteFeatureListToPPM(fl, img2, "feat2.ppm")
 	#KLTWriteFeatureList(fl, "feat2.fl", NULL)      # binary file 

klt.py

@@ -4,6 +4,8 @@
 #* Kanade-Lucas-Tomasi tracker
 #*********************************************************************/
 
+from __future__ import print_function
+
 #typedef float KLT_locType;
 #typedef unsigned char KLT_PixelType;
 
@@ -123,7 +125,7 @@ def KLTChangeTCPyramid(self,search_range):
 			#   Then, the value is rounded up to the nearest integer. 
 			val = float (math.log(7.0*subsampling+1.0)/math.log(8.0))
 			self.nPyramidLevels = int(val + 0.99)
-		  	self.subsampling = 8
+			self.subsampling = 8
 
 
 
@@ -282,33 +284,33 @@ class KLT_FeatureTable:
 
 def KLTPrintTrackingContext(tc):
 
-	print tc
-	print "\n\nTracking context:\n"
-	print "\tmindist = {0}".format(tc.mindist)
-	print "\twindow_width = {0}".format(tc.window_width)
-	print "\twindow_height = {0}".format(tc.window_height)
-	print "\tsequentialMode = {0}".format(tc.sequentialMode)
-	print "\tsmoothBeforeSelecting = {0}".format(tc.smoothBeforeSelecting)
-	print "\twriteInternalImages = {0}".format(tc.writeInternalImages)
-
-	print "\tmin_eigenvalue = {0}".format(tc.min_eigenvalue)
-	print "\tmin_determinant = {0}".format(tc.min_determinant)
-	print "\tmin_displacement = {0}".format(tc.min_displacement)
-	print "\tmax_iterations = {0}".format(tc.max_iterations)
-	print "\tmax_residue = {0}".format(tc.max_residue)
-	print "\tgrad_sigma = {0}".format(tc.grad_sigma)
-	print "\tsmooth_sigma_fact = {0}".format(tc.smooth_sigma_fact)
-	print "\tpyramid_sigma_fact = {0}".format(tc.pyramid_sigma_fact)
-	print "\tnSkippedPixels = {0}".format(tc.nSkippedPixels)
-	print "\tborderx = {0}".format(tc.borderx)
-	print "\tbordery = {0}".format(tc.bordery)
-	print "\tnPyramidLevels = {0}".format(tc.nPyramidLevels)
-	print "\tsubsampling = {0}".format(tc.subsampling)
-
-	print "\n\tpyramid_last = {0}".format(tc.pyramid_last)
-	print "\tpyramid_last_gradx = {0}".format(tc.pyramid_last_gradx)
-	print "\tpyramid_last_grady = {0}".format(tc.pyramid_last_grady)
-	print "\n"
+	print(tc)
+	print("\n\nTracking context:\n")
+	print("\tmindist = {0}".format(tc.mindist))
+	print("\twindow_width = {0}".format(tc.window_width))
+	print("\twindow_height = {0}".format(tc.window_height))
+	print("\tsequentialMode = {0}".format(tc.sequentialMode))
+	print("\tsmoothBeforeSelecting = {0}".format(tc.smoothBeforeSelecting))
+	print("\twriteInternalImages = {0}".format(tc.writeInternalImages))
+
+	print("\tmin_eigenvalue = {0}".format(tc.min_eigenvalue))
+	print("\tmin_determinant = {0}".format(tc.min_determinant))
+	print("\tmin_displacement = {0}".format(tc.min_displacement))
+	print("\tmax_iterations = {0}".format(tc.max_iterations))
+	print("\tmax_residue = {0}".format(tc.max_residue))
+	print("\tgrad_sigma = {0}".format(tc.grad_sigma))
+	print("\tsmooth_sigma_fact = {0}".format(tc.smooth_sigma_fact))
+	print("\tpyramid_sigma_fact = {0}".format(tc.pyramid_sigma_fact))
+	print("\tnSkippedPixels = {0}".format(tc.nSkippedPixels))
+	print("\tborderx = {0}".format(tc.borderx))
+	print("\tbordery = {0}".format(tc.bordery))
+	print("\tnPyramidLevels = {0}".format(tc.nPyramidLevels))
+	print("\tsubsampling = {0}".format(tc.subsampling))
+
+	print("\n\tpyramid_last = {0}".format(tc.pyramid_last))
+	print("\tpyramid_last_gradx = {0}".format(tc.pyramid_last_gradx))
+	print("\tpyramid_last_grady = {0}".format(tc.pyramid_last_grady))
+	print("\n")
 
 #*********************************************************************
 #* KLTCountRemainingFeatures

pyramid.py

@@ -62,14 +62,14 @@ def Compute(self, img, sigma_fact):
 
 			# Subsample
 			oldncols = ncols
-			ncols /= subsampling
-			nrows /= subsampling
+			ncols = int(ncols / subsampling)
+			nrows = int(nrows / subsampling)
 			#subsampImg = Image.new("F",(ncols,nrows))
 			subsampImg = np.empty((nrows,ncols), np.float32)
 			#subsampImgL = subsampImg.load()
 			for y in range(nrows):
 				for x in range(ncols):
-					subsampImg[y,x] = tmpimg[subsampling*y+subhalf, subsampling*x+subhalf]
+					subsampImg[y,x] = tmpimg[int(subsampling*y+subhalf), int(subsampling*x+subhalf)]
 
 			self.img[i] = subsampImg
 

selectGoodFeatures.py

@@ -1,4 +1,5 @@
 
+from __future__ import print_function
 import math, numpy as np
 from PIL import Image
 from klt import *
@@ -89,7 +90,7 @@ def _enforceMinimumDistance(pointlist, featurelist, ncols, nrows, mindist, min_e
 					featurelist[indx].aff_Ayx = 0.0
 					featurelist[indx].aff_Axy = 0.0
 					featurelist[indx].aff_Ayy = 1.0
-			  	indx = indx + 1
+				indx = indx + 1
 			break
 
 		pointdata = pointlist[pointlistIndx]
@@ -230,11 +231,11 @@ def _KLTSelectGoodFeatures(tc,img,nFeatures,mode):
 		grady, borderx, bordery, window_hw, window_hh, tc.nSkippedPixels)
 
 	# Sort the features 
-	pointlist = zip(pointlistval, pointlistx, pointlisty)
+	pointlist = list(zip(pointlistval, pointlistx, pointlisty))
 	pointlist.sort()
 	pointlist.reverse()
 
-	#print pointlist
+	#print(pointlist)
 
 	# Check tc.mindist 
 	if tc.mindist < 0:
@@ -281,14 +282,14 @@ def KLTSelectGoodFeatures(tc, img, nFeatures):
 
 	#int ncols, int nrows,
 	if KLT_verbose >= 1:
-		print "(KLT) Selecting the {0} best features from a {1} by {2} image...  ".format(nFeatures, ncols, nrows)
+		print("(KLT) Selecting the {0} best features from a {1} by {2} image...  ".format(nFeatures, ncols, nrows))
 
 	fl = _KLTSelectGoodFeatures(tc, img, nFeatures, selectionMode.SELECTING_ALL)
 
 	if KLT_verbose >= 1:
-		print "\n\t{0} features found.\n".format(KLTCountRemainingFeatures(fl))
-    	if tc.writeInternalImages:
-		print "\tWrote images to 'kltimg_sgfrlf*.pgm'.\n"
+		print("\n\t{0} features found.\n".format(KLTCountRemainingFeatures(fl)))
+		if tc.writeInternalImages:
+			print("\tWrote images to 'kltimg_sgfrlf*.pgm'.\n")
 
 	return fl
 

trackFeatures.py

@@ -3,6 +3,7 @@
 #*
 #*********************************************************************/
 
+from __future__ import print_function
 from selectGoodFeatures import KLT_verbose
 from klt import *
 from error import *
@@ -117,7 +118,7 @@ def _trackFeature(
 	if status == kltState.KLT_TRACKED and max_residue is not None:
 		if lighting_insensitive:
 			trackFeaturesUtils.computeIntensityDifferenceLightingInsensitive(img1Patch, img2, x1, y1, x2, y2, workingPatch, imgdiff)
-	  	else:
+		else:
 			trackFeaturesUtils.computeIntensityDifference(img1Patch, img2, x2, y2, workingPatch, imgdiff)
 
 		if np.abs(np.array(imgdiff)).sum()/(width*height) > max_residue:
@@ -218,8 +219,8 @@ def KLTTrackFeatures(tc, img1, img2, featurelist):
 	DEBUG_AFFINE_MAPPING = False
 
 	if KLT_verbose >= 1:
-		print "(KLT) Tracking {0} features in a {1} by {2} image...  ".format( \
-			KLTCountRemainingFeatures(featurelist), ncols, nrows)
+		print("(KLT) Tracking {0} features in a {1} by {2} image...  ".format( \
+			KLTCountRemainingFeatures(featurelist), ncols, nrows))
 
 	# Check window size (and correct if necessary) 
 	if tc.window_width % 2 != 1:
@@ -403,9 +404,9 @@ def KLTTrackFeatures(tc, img1, img2, featurelist):
 		tc.pyramid_last_grady = pyramid2_grady
 
 	if KLT_verbose >= 1:
-		print "\n\t{0} features successfully tracked.".format(KLTCountRemainingFeatures(featurelist))
+		print("\n\t{0} features successfully tracked.".format(KLTCountRemainingFeatures(featurelist)))
 		if tc.writeInternalImages:
-			print "\tWrote images to 'kltimg_tf*.pgm'."
+			print("\tWrote images to 'kltimg_tf*.pgm'.")
 
 
 

writeFeatures.py

@@ -1,4 +1,5 @@
 
+from __future__ import print_function
 from selectGoodFeatures import KLT_verbose
 from klt import *
 
@@ -16,7 +17,7 @@ def KLTWriteFeatureListToPPM(featurelist, greyimg, filename):
 	ncols, nrows = greyimg.size
 
 	if KLT_verbose:
-		print "(KLT) Writing {0} features to PPM file: '{1}'".format(KLTCountRemainingFeatures(featurelist), filename)
+		print("(KLT) Writing {0} features to PPM file: '{1}'".format(KLTCountRemainingFeatures(featurelist), filename))
 
 	tmp = greyimg.copy()
 	tmp = tmp.convert("RGB")
@@ -46,7 +47,7 @@ def KLTWriteFeatureListToPPM(featurelist, greyimg, filename):
 #* INPUTS
 #* fname: name of file to write data; if NULL, then print to stderr
 #* fmt:   format for printing (e.g., "%5.1f" or "%3d");
-#*        if NULL, and if fname is not NULL, then write to binary file.
+#*		if NULL, and if fname is not NULL, then write to binary file.
 #*
 
 def KLTWriteFeatureList(fl, fname, fmt):
@@ -58,11 +59,11 @@ def KLTWriteFeatureList(fl, fname, fmt):
 
 	fmtStr = "binary" if fmt is None else "text"
 	if KLT_verbose >= 1 and fname is not None:
-		print "(KLT) Writing feature list to {0} file: '{1}'".format(fmtStr, fname)
+		print("(KLT) Writing feature list to {0} file: '{1}'".format(fmtStr, fname))
 
 	if fmt is not None: # text file or stderr
-		fp = _printSetupTxt(fname, fmt, format, type);
-	  	_printHeader(fp, format, FEATURE_LIST, 0, fl.nFeatures);
+		fp = _printSetupTxt(fname, fmt, format, type)
+		_printHeader(fp, format, FEATURE_LIST, 0, fl.nFeatures)
 
 	  	#for (i = 0 ; i < fl->nFeatures ; i++):
 		#	fprintf(fp, "%7d | ", i)