Directly 2to3

SimpleCV/Color.py

@@ -213,7 +213,7 @@ def getHueFromBGR(self,color_tuple):
 
         """
         a = color_tuple
-        print a
+        print(a)
         h_float = colorsys.rgb_to_hsv(*tuple(reversed(color_tuple)))[0]
         return h_float*180
 

SimpleCV/ColorModel.py

@@ -69,7 +69,7 @@ def _makeCanonical(self, data):
         #create a unique set of colors.  I had to look this one up
 
         #create a dict of encoded strings
-        return dict.fromkeys(map(np.ndarray.tostring, uniques), 1)
+        return dict.fromkeys(list(map(np.ndarray.tostring, uniques)), 1)
 
     def reset(self):
         """
@@ -169,7 +169,7 @@ def threshold(self, img):
             b = 0
 
         rs = np.right_shift(img.getNumpy(), self.mBits).reshape(-1, 3) #bitshift down and reshape to Nx3
-        mapped = np.array(map(self.mData.has_key, map(np.ndarray.tostring, rs))) #map to True/False based on the model
+        mapped = np.array(list(map(self.mData.has_key, list(map(np.ndarray.tostring, rs))))) #map to True/False based on the model
         thresh = np.where(mapped, a, b) #replace True and False with fg and bg
         return Image(thresh.reshape(img.width, img.height))
 
@@ -198,7 +198,7 @@ def contains(self, c):
 
        """
         #reverse the color, cast to uint8, right shift, convert to string, check dict
-        return self.mData.has_key(np.right_shift(np.cast['uint8'](c[::-1]), self.mBits).tostring())
+        return np.right_shift(np.cast['uint8'](c[::-1]), self.mBits).tostring() in self.mData
 
     def setIsForeground(self):
         """

SimpleCV/Display.py

@@ -1,7 +1,7 @@
 from SimpleCV.base import *
 import SimpleCV.ImageClass
-import Queue
-from base import *
+import queue
+from .base import *
 
 
 PYGAME_INITIALIZED = False

SimpleCV/DrawingLayer.py

@@ -39,8 +39,9 @@ class DrawingLayer:
     width = 0
     height = 0
 
-    def __init__(self, (width, height)):
+    def __init__(self, xxx_todo_changeme):
         #pg.init()
+        (width, height) = xxx_todo_changeme
         if( not pg.font.get_init() ):
             pg.font.init()
 

SimpleCV/EXIF.py

@@ -1179,7 +1179,7 @@ def s2n_motorola(str):
 # extract multibyte integer in Intel format (big endian)
 def s2n_intel(str):
     x = 0
-    y = 0L
+    y = 0
     for c in str:
         x = x | (ord(c) << y)
         y = y + 8
@@ -1260,7 +1260,7 @@ def s2n(self, offset, length, signed=0):
             val=s2n_motorola(slice)
         # Sign extension ?
         if signed:
-            msb=1L << (8*length-1)
+            msb=1 << (8*length-1)
             if val & msb:
                 val=val-(msb << 1)
         return val
@@ -1409,8 +1409,8 @@ def dump_IFD(self, ifd, ifd_name, dict=EXIF_TAGS, relative=0, stop_tag='UNDEF'):
                                                           values, field_offset,
                                                           count * typelen)
                 if self.debug:
-                    print ' debug:   %s: %s' % (tag_name,
-                                                repr(self.tags[ifd_name + ' ' + tag_name]))
+                    print(' debug:   %s: %s' % (tag_name,
+                                                repr(self.tags[ifd_name + ' ' + tag_name])))
 
             if tag_name == stop_tag:
                 break
@@ -1510,21 +1510,21 @@ def decode_maker_note(self):
         if 'NIKON' in make:
             if note.values[0:7] == [78, 105, 107, 111, 110, 0, 1]:
                 if self.debug:
-                    print "Looks like a type 1 Nikon MakerNote."
+                    print("Looks like a type 1 Nikon MakerNote.")
                 self.dump_IFD(note.field_offset+8, 'MakerNote',
                               dict=MAKERNOTE_NIKON_OLDER_TAGS)
             elif note.values[0:7] == [78, 105, 107, 111, 110, 0, 2]:
                 if self.debug:
-                    print "Looks like a labeled type 2 Nikon MakerNote"
-                if note.values[12:14] != [0, 42] and note.values[12:14] != [42L, 0L]:
+                    print("Looks like a labeled type 2 Nikon MakerNote")
+                if note.values[12:14] != [0, 42] and note.values[12:14] != [42, 0]:
                     raise ValueError("Missing marker tag '42' in MakerNote.")
                 # skip the Makernote label and the TIFF header
                 self.dump_IFD(note.field_offset+10+8, 'MakerNote',
                               dict=MAKERNOTE_NIKON_NEWER_TAGS, relative=1)
             else:
                 # E99x or D1
                 if self.debug:
-                    print "Looks like an unlabeled type 2 Nikon MakerNote"
+                    print("Looks like an unlabeled type 2 Nikon MakerNote")
                 self.dump_IFD(note.field_offset, 'MakerNote',
                               dict=MAKERNOTE_NIKON_NEWER_TAGS)
             return
@@ -1581,7 +1581,7 @@ def canon_decode_tag(self, value, dict):
         for i in range(1, len(value)):
             x=dict.get(i, ('Unknown', ))
             if self.debug:
-                print i, x
+                print(i, x)
             name=x[0]
             if len(x) > 1:
                 val=x[1].get(value[i], 'Unknown')
@@ -1632,7 +1632,7 @@ def process_file(f, stop_tag='UNDEF', details=True, strict=False, debug=False):
 
     # deal with the EXIF info we found
     if debug:
-        print {'I': 'Intel', 'M': 'Motorola'}[endian], 'format'
+        print({'I': 'Intel', 'M': 'Motorola'}[endian], 'format')
     hdr = EXIF_header(f, endian, offset, fake_exif, strict, debug)
     ifd_list = hdr.list_IFDs()
     ctr = 0
@@ -1645,27 +1645,27 @@ def process_file(f, stop_tag='UNDEF', details=True, strict=False, debug=False):
         else:
             IFD_name = 'IFD %d' % ctr
         if debug:
-            print ' IFD %d (%s) at offset %d:' % (ctr, IFD_name, i)
+            print(' IFD %d (%s) at offset %d:' % (ctr, IFD_name, i))
         hdr.dump_IFD(i, IFD_name, stop_tag=stop_tag)
         # EXIF IFD
         exif_off = hdr.tags.get(IFD_name+' ExifOffset')
         if exif_off:
             if debug:
-                print ' EXIF SubIFD at offset %d:' % exif_off.values[0]
+                print(' EXIF SubIFD at offset %d:' % exif_off.values[0])
             hdr.dump_IFD(exif_off.values[0], 'EXIF', stop_tag=stop_tag)
             # Interoperability IFD contained in EXIF IFD
             intr_off = hdr.tags.get('EXIF SubIFD InteroperabilityOffset')
             if intr_off:
                 if debug:
-                    print ' EXIF Interoperability SubSubIFD at offset %d:' \
-                          % intr_off.values[0]
+                    print(' EXIF Interoperability SubSubIFD at offset %d:' \
+                          % intr_off.values[0])
                 hdr.dump_IFD(intr_off.values[0], 'EXIF Interoperability',
                              dict=INTR_TAGS, stop_tag=stop_tag)
         # GPS IFD
         gps_off = hdr.tags.get(IFD_name+' GPSInfo')
         if gps_off:
             if debug:
-                print ' GPS SubIFD at offset %d:' % gps_off.values[0]
+                print(' GPS SubIFD at offset %d:' % gps_off.values[0])
             hdr.dump_IFD(gps_off.values[0], 'GPS', dict=GPS_TAGS, stop_tag=stop_tag)
         ctr += 1
 
@@ -1706,7 +1706,7 @@ def usage(exit_status):
     msg += '-t TAG --stop-tag TAG   Stop processing when this tag is retrieved.\n'
     msg += '-s --strict   Run in strict mode (stop on errors).\n'
     msg += '-d --debug   Run in debug mode (display extra info).\n'
-    print msg
+    print(msg)
     sys.exit(exit_status)
 
 # library test/debug function (dump given files)
@@ -1742,25 +1742,25 @@ def usage(exit_status):
         try:
             file=open(filename, 'rb')
         except:
-            print "'%s' is unreadable\n"%filename
+            print("'%s' is unreadable\n"%filename)
             continue
-        print filename + ':'
+        print(filename + ':')
         # get the tags
         data = process_file(file, stop_tag=stop_tag, details=detailed, strict=strict, debug=debug)
         if not data:
-            print 'No EXIF information found'
+            print('No EXIF information found')
             continue
 
-        x=data.keys()
+        x=list(data.keys())
         x.sort()
         for i in x:
             if i in ('JPEGThumbnail', 'TIFFThumbnail'):
                 continue
             try:
-                print '   %s (%s): %s' % \
-                      (i, FIELD_TYPES[data[i].field_type][2], data[i].printable)
+                print('   %s (%s): %s' % \
+                      (i, FIELD_TYPES[data[i].field_type][2], data[i].printable))
             except:
-                print 'error', i, '"', data[i], '"'
+                print('error', i, '"', data[i], '"')
         if 'JPEGThumbnail' in data:
-            print 'File has JPEG thumbnail'
-        print
+            print('File has JPEG thumbnail')
+        print()

SimpleCV/Features/BOFFeatureExtractor.py

@@ -70,19 +70,19 @@ def generate(self,imgdirs,numcodes=128,sz=(11,11),imgs_per_dir=50,img_layout=(8,
             for i in range(nimgs):
                 infile = files[i]
                 if verbose:
-                    print(path+" "+str(i)+" of "+str(imgs_per_dir))
-                    print "Opening file: " + infile
+                    print((path+" "+str(i)+" of "+str(imgs_per_dir)))
+                    print("Opening file: " + infile)
                 img = Image(infile)
                 newFeat = self._getPatches(img,sz)
                 if verbose:
-                    print "     Got " + str(len(newFeat)) + " features."
+                    print("     Got " + str(len(newFeat)) + " features.")
                 rawFeatures = np.vstack((rawFeatures,newFeat))
                 del img
         rawFeatures = rawFeatures[1:,:] # pop the fake value we put on the top
         if verbose:
-            print "=================================="
-            print "Got " + str(len(rawFeatures)) + " features "
-            print "Doing K-Means .... this will take a long time"
+            print("==================================")
+            print("Got " + str(len(rawFeatures)) + " features ")
+            print("Doing K-Means .... this will take a long time")
         self.mCodebook = self._makeCodebook(rawFeatures,self.mNumCodes)
         self.mCodebookImg = self._codebook2Img(self.mCodebook,self.mPatchSize,self.mNumCodes,self.mLayout,self.mPadding)
         self.mCodebookImg.save('codebook.png')

SimpleCV/Features/Blob.py

@@ -97,7 +97,7 @@ def __init__(self):
     def __getstate__(self):
         skip = self.pickle_skip_properties
         newdict = {}
-        for k,v in self.__dict__.items():
+        for k,v in list(self.__dict__.items()):
             if k in skip:
                 continue
             else:
@@ -510,21 +510,15 @@ def rotate(self,angle):
         self.mMask = self.mMask.rotate(angle,mode,point)
         self.mHullMask = self.mHullMask.rotate(angle,mode,point)
 
-        self.mContour = map(lambda x:
-                            (x[0]*np.cos(theta)-x[1]*np.sin(theta),
-                             x[0]*np.sin(theta)+x[1]*np.cos(theta)),
-                             self.mContour)
-        self.mConvexHull = map(lambda x:
-                               (x[0]*np.cos(theta)-x[1]*np.sin(theta),
-                                x[0]*np.sin(theta)+x[1]*np.cos(theta)),
-                               self.mConvexHull)
+        self.mContour = [(x[0]*np.cos(theta)-x[1]*np.sin(theta),
+                             x[0]*np.sin(theta)+x[1]*np.cos(theta)) for x in self.mContour]
+        self.mConvexHull = [(x[0]*np.cos(theta)-x[1]*np.sin(theta),
+                                x[0]*np.sin(theta)+x[1]*np.cos(theta)) for x in self.mConvexHull]
 
         if( self.mHoleContour is not None):
             for h in self.mHoleContour:
-                h = map(lambda x:
-                    (x[0]*np.cos(theta)-x[1]*np.sin(theta),
-                     x[0]*np.sin(theta)+x[1]*np.cos(theta)),
-                     h)
+                h = [(x[0]*np.cos(theta)-x[1]*np.sin(theta),
+                     x[0]*np.sin(theta)+x[1]*np.cos(theta)) for x in h]
 
 
     def drawAppx(self, color = Color.HOTPINK,width=-1,alpha=-1,layer=None):

SimpleCV/Features/BlobMaker.py

@@ -97,7 +97,7 @@ def extractFromBinary(self,binaryImg,colorImg, minsize = 5, maxsize = -1,appx_le
             # note to self
             # http://code.activestate.com/recipes/474088-tail-call-optimization-decorator/
             retVal = self._extractFromBinary(seq,False,colorImg,minsize,maxsize,appx_level)
-        except RuntimeError,e:
+        except RuntimeError as e:
             logger.warning("You exceeded the recursion limit. This means you probably have too many blobs in your image. We suggest you do some morphological operations (erode/dilate) to reduce the number of blobs in your image. This function was designed to max out at about 5000 blobs per image.")
         except e:
             logger.warning("SimpleCV Find Blobs Failed - This could be an OpenCV python binding issue")

SimpleCV/Features/Detection.py

@@ -1016,7 +1016,7 @@ def __init__(self, i, at_x, at_y, r):
         points = [(at_x-r,at_y-r),(at_x+r,at_y-r),(at_x+r,at_y+r),(at_x-r,at_y+r)]
         super(Circle, self).__init__(i, at_x, at_y, points)
         segments = 18
-        rng = range(1,segments+1)
+        rng = list(range(1,segments+1))
         self.mContour = []
         for theta in rng:
             rp = 2.0*math.pi*float(theta)/float(segments)
@@ -1256,7 +1256,7 @@ def __init__(self, i, keypoint, descriptor=None, flavor="SURF" ):
         super(KeyPoint, self).__init__(i, x, y, points)
 
         segments = 18
-        rng = range(1,segments+1)
+        rng = list(range(1,segments+1))
         self.points = []
         for theta in rng:
             rp = 2.0*math.pi*float(theta)/float(segments)
@@ -2278,7 +2278,7 @@ def CoordTransformPts(self,pts,intype="ROI",output="SRC"):
 
         x = self._transform(x,self.image.width,self.w,self.xtl,intype,output)
         y = self._transform(y,self.image.height,self.h,self.ytl,intype,output)
-        return zip(x,y) 
+        return list(zip(x,y)) 
 
 
     def _transform(self,x,imgsz,roisz,offset,intype,output):
@@ -2362,7 +2362,7 @@ def splitX(self,x,unitVals=False,srcVals=False):
 
         x.insert(0,self.xtl)
         x.append(self.xtl+self.w)
-        for i in xrange(0,len(x)-1):
+        for i in range(0,len(x)-1):
             xstart = x[i]
             xstop = x[i+1]
             w = xstop-xstart
@@ -2416,7 +2416,7 @@ def splitY(self,y,unitVals=False,srcVals=False):
 
         y.insert(0,self.ytl)
         y.append(self.ytl+self.h)
-        for i in xrange(0,len(y)-1):
+        for i in range(0,len(y)-1):
             ystart = y[i]
             ystop = y[i+1]
             h = ystop-ystart
@@ -2616,7 +2616,7 @@ def _standardize(self,x,y=None,w=None,h=None):
             h = theFeature.height()
 
         # [x,y,w,h] (x,y,w,h)
-        elif(isinstance(x, (tuple,list)) and len(x) == 4 and isinstance(x[0],(int, long, float))
+        elif(isinstance(x, (tuple,list)) and len(x) == 4 and isinstance(x[0],(int, float))
              and y == None and w == None and h == None):
             x,y,w,h = x
         # x of the form [(x,y),(x1,y1),(x2,y2),(x3,y3)]
@@ -2644,7 +2644,7 @@ def _standardize(self,x,y=None,w=None,h=None):
         elif(isinstance(x, (tuple,list)) and
              isinstance(y, (tuple,list)) and
              len(x) > 4 and len(y) > 4 ):
-            if(isinstance(x[0],(int, long, float)) and isinstance(y[0],(int, long, float))):
+            if(isinstance(x[0],(int, float)) and isinstance(y[0],(int, float))):
                 xmax = np.max(x)
                 ymax = np.max(y)
                 xmin = np.min(x)
@@ -2660,7 +2660,7 @@ def _standardize(self,x,y=None,w=None,h=None):
         # x of the form [(x,y),(x,y),(x,y),(x,y),(x,y),(x,y)]
         elif(isinstance(x, (list,tuple)) and
              len(x) > 4 and len(x[0]) == 2 and y == None and w == None and h == None):
-            if(isinstance(x[0][0],(int, long, float))):
+            if(isinstance(x[0][0],(int, float))):
                 xs = [pt[0] for pt in x]
                 ys = [pt[1] for pt in x]
                 xmax = np.max(xs)

SimpleCV/Features/FaceRecognizer.py

@@ -27,7 +27,7 @@ def __init__(self):
         try:
             import cv2
             self.model = cv2.createFisherFaceRecognizer()
-        except ImportError, AttributeError:
+        except ImportError as AttributeError:
             self.supported = False
             warnings.warn("Fisher Recognizer is supported by OpenCV >= 2.4.4")
 

SimpleCV/Features/FeatureExtractorBase.py

@@ -3,16 +3,14 @@
 from SimpleCV.Color import Color
 from SimpleCV.ImageClass import Image
 
-class FeatureExtractorBase(object):
+class FeatureExtractorBase(object, metaclass=abc.ABCMeta):
     """
     The featureExtractorBase class is a way of abstracting the process of collecting
     descriptive features within an image. A feature is some description of the image
     like the mean color, or the width of a center image, or a histogram of edge
     lengths. This feature vectors can then be composed together and used within
     a machine learning algorithm to descriminate between different classes of objects.
     """
-
-    __metaclass__ = abc.ABCMeta
     def load(cls, fname):
         """
         load segmentation settings to file.

SimpleCV/Features/FeatureUtils.py

@@ -24,7 +24,7 @@ def GetParallelSets(line_fs,parallel_thresh=2):
     result = result.reshape(sz,sz)
     # find the lines that are less than our thresh
     l1,l2=np.where(result<parallel_thresh)
-    idxs = zip(l1,l2)
+    idxs = list(zip(l1,l2))
     retVal = []
     # now construct the line pairs
     for idx in idxs:

SimpleCV/Features/Features.py

@@ -44,7 +44,7 @@ def __getitem__(self,key):
         functions on sub-lists
 
         """
-        if type(key) is types.SliceType: #Or can use 'try:' for speed
+        if type(key) is slice: #Or can use 'try:' for speed
             return FeatureSet(list.__getitem__(self, key))
         else:
             return list.__getitem__(self,key)