Some tweaks and clean-ups
git-svn-id: http://comictagger.googlecode.com/svn/trunk@21 6c5673fe-1810-88d6-992b-cd32ca31540c
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beville@gmail.com
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d3c275a5c6
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4706f9479f
@ -2,11 +2,8 @@
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import Image
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import StringIO
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import numpy
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import scipy.signal
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#from bitarray import bitarray
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class ImageHasher(object):
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def __init__(self, path=None, data=None, width=8, height=8):
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@ -21,46 +18,46 @@ class ImageHasher(object):
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else:
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self.image = Image.open(StringIO.StringIO(data))
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def average_hash(self):
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#image = self.image.resize((self.hash_size, self.hash_size), Image.ANTIALIAS).convert("L")
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image = self.image.resize((self.width, self.height), Image.ANTIALIAS).convert("L")
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pixels = list(image.getdata())
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avg = sum(pixels) / len(pixels)
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diff = []
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for pixel in pixels:
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value = 1 if pixel > avg else 0
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diff.append(str(value))
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#ba = bitarray("".join(diff), endian='little')
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#h = ba.tobytes().encode('hex')
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# This isn't super pretty, but we avoid the bitarray inclusion.
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# (Build up a hex string from the binary list of bits)
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hash = ""
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binary_string = "".join(diff)
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for i in range(0, self.width*self.height, 8):
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# 8 bits at time, reverse, for little-endian
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s = binary_string[i:i+8][::-1]
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hash = hash + "{0:02x}".format( int(s,2))
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def compare_value_to_avg(i):
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return ( 1 if i > avg else 0 )
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bitlist = map(compare_value_to_avg, pixels)
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# build up an int value from the bit list, one bit at a time
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def set_bit( x, (idx, val) ):
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return (x | (val << idx))
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return hash
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result = reduce(set_bit, enumerate(bitlist), 0)
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#print "{0:016x}".format(result)
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return result
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def average_hash2( self ):
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# Got this one from somewhere on the net. Not a clue how the 'convolve2d'
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# works!
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from numpy import array
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from scipy.signal import convolve2d
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im = self.image.resize((self.width, self.height), Image.ANTIALIAS).convert('L')
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in_data = numpy.array((im.getdata())).reshape(self.width, self.height)
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filt = numpy.array([[0,1,0],[1,-4,1],[0,1,0]])
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filt_data = scipy.signal.convolve2d(in_data,filt,mode='same',boundary='symm').flatten()
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in_data = array((im.getdata())).reshape(self.width, self.height)
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filt = array([[0,1,0],[1,-4,1],[0,1,0]])
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filt_data = convolve2d(in_data,filt,mode='same',boundary='symm').flatten()
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result = reduce(lambda x, (y, z): x | (z << y),
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enumerate(map(lambda i: 0 if i < 0 else 1, filt_data)),
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0)
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#print "{0:016x}".format(result)
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return result
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def perceptual_hash(self):
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def dct_average_hash(self):
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"""
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# Algorithm source: http://syntaxcandy.blogspot.com/2012/08/perceptual-hash.html
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@ -99,10 +96,11 @@ class ImageHasher(object):
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7. Construct the hash. Set the 64 bits into a 64-bit integer. The order does not
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matter, just as long as you are consistent.
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"""
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import numpy
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import scipy.fftpack
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# Step 1,2
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im = self.image.resize((32, 32), Image.ANTIALIAS).convert("L")
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in_data = numpy.array(im.getdata(), dtype=numpy.dtype('float')).reshape(self.width, self.height)
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in_data = numpy.array(im.getdata(), dtype=numpy.dtype('float')).reshape(32, 32)
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#print len(im.getdata())
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#print in_data
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@ -112,9 +110,11 @@ class ImageHasher(object):
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# Step 4
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# NO! -- lofreq_dct = dct[:8,:8].flatten()
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# NO? -- lofreq_dct = dct[24:32, 24:32].flatten()
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lofreq_dct = dct[:8, 24:32].flatten()
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#print dct[:8, 24:32]
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#lofreq_dct = dct[:8, 24:32].flatten()
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#print dct[24:32, :8]
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# NO! -- lofreq_dct = dct[24:32, :8 ].flatten()
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#lofreq_dct = dct[1:9, 1:9].flatten()
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lofreq_dct = dct[:8, 24:32].flatten()
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#omit = 0
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#omit = 7
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@ -130,35 +130,23 @@ class ImageHasher(object):
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# Step 6
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def compare_value_to_avg(i):
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if i > avg:
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return (1)
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else:
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return (0)
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return ( 1 if i > avg else 0 )
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bitlist = map(compare_value_to_avg, lofreq_dct)
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#Step 7
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def accumulate( accumulator, (idx, val) ):
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return (accumulator | (val << idx))
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def set_bit( x, (idx, val) ):
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return (x | (val << idx))
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result = reduce(accumulate, enumerate(bitlist), long(0))
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result = reduce(set_bit, enumerate(bitlist), long(0))
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print "{0:016x}".format(result)
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#print "{0:016x}".format(result)
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return result
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@staticmethod
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def count_bits(number):
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bit = 1
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count = 0
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while number >= bit:
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if number & bit:
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count += 1
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bit <<= 1
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return count
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#accepts 2 hashes (long or hex strings) and returns the hamming distance
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#accepts 2 hashes (longs or hex strings) and returns the hamming distance
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@staticmethod
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def hamming_distance(h1, h2):
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@ -167,14 +155,16 @@ class ImageHasher(object):
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n1 = h1
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n2 = h2
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else:
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# conver hex strings to ints
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# convert hex strings to ints
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n1 = long( h1, 16)
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n2 = long( h2, 16)
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# xor the two numbers
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n = n1 ^ n2
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# now count the ones
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return ImageHasher.count_bits( n )
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#count up the 1's in the binary string
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return sum( b == '1' for b in bin(n)[2:] )
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