"""A class to manage creating image content hashes, and calculate hamming distances""" # Copyright 2013 Anthony Beville # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import StringIO import sys from functools import reduce try: from PIL import Image from PIL import WebPImagePlugin pil_available = True except ImportError: pil_available = False class ImageHasher(object): def __init__(self, path=None, data=None, width=8, height=8): #self.hash_size = size self.width = width self.height = height if path is None and data is None: raise IOError else: try: if path is not None: self.image = Image.open(path) else: self.image = Image.open(StringIO.StringIO(data)) except: print("Image data seems corrupted!") # just generate a bogus image self.image = Image.new("L", (1, 1)) def average_hash(self): try: image = self.image.resize( (self.width, self.height), Image.ANTIALIAS).convert("L") except Exception as e: sys.exc_clear() print "average_hash error:", e return long(0) pixels = list(image.getdata()) avg = sum(pixels) / len(pixels) def compare_value_to_avg(i): return (1 if i > avg else 0) bitlist = map(compare_value_to_avg, pixels) # build up an int value from the bit list, one bit at a time def set_bit(x, idx_val): (idx, val) = idx_val return (x | (val << idx)) result = reduce(set_bit, enumerate(bitlist), 0) # print("{0:016x}".format(result)) return result def average_hash2(self): pass """ # Got this one from somewhere on the net. Not a clue how the 'convolve2d' # works! from numpy import array from scipy.signal import convolve2d im = self.image.resize((self.width, self.height), Image.ANTIALIAS).convert('L') in_data = array((im.getdata())).reshape(self.width, self.height) filt = array([[0,1,0],[1,-4,1],[0,1,0]]) filt_data = convolve2d(in_data,filt,mode='same',boundary='symm').flatten() result = reduce(lambda x, (y, z): x | (z << y), enumerate(map(lambda i: 0 if i < 0 else 1, filt_data)), 0) #print("{0:016x}".format(result)) return result """ def dct_average_hash(self): pass """ # Algorithm source: http://syntaxcandy.blogspot.com/2012/08/perceptual-hash.html 1. Reduce size. Like Average Hash, pHash starts with a small image. However, the image is larger than 8x8; 32x32 is a good size. This is really done to simplify the DCT computation and not because it is needed to reduce the high frequencies. 2. Reduce color. The image is reduced to a grayscale just to further simplify the number of computations. 3. Compute the DCT. The DCT separates the image into a collection of frequencies and scalars. While JPEG uses an 8x8 DCT, this algorithm uses a 32x32 DCT. 4. Reduce the DCT. This is the magic step. While the DCT is 32x32, just keep the top-left 8x8. Those represent the lowest frequencies in the picture. 5. Compute the average value. Like the Average Hash, compute the mean DCT value (using only the 8x8 DCT low-frequency values and excluding the first term since the DC coefficient can be significantly different from the other values and will throw off the average). Thanks to David Starkweather for the added information about pHash. He wrote: "the dct hash is based on the low 2D DCT coefficients starting at the second from lowest, leaving out the first DC term. This excludes completely flat image information (i.e. solid colors) from being included in the hash description." 6. Further reduce the DCT. This is the magic step. Set the 64 hash bits to 0 or 1 depending on whether each of the 64 DCT values is above or below the average value. The result doesn't tell us the actual low frequencies; it just tells us the very-rough relative scale of the frequencies to the mean. The result will not vary as long as the overall structure of the image remains the same; this can survive gamma and color histogram adjustments without a problem. 7. Construct the hash. Set the 64 bits into a 64-bit integer. The order does not matter, just as long as you are consistent. """ """ import numpy import scipy.fftpack numpy.set_printoptions(threshold=10000, linewidth=200, precision=2, suppress=True) # Step 1,2 im = self.image.resize((32, 32), Image.ANTIALIAS).convert("L") in_data = numpy.asarray(im) # Step 3 dct = scipy.fftpack.dct(in_data.astype(float)) # Step 4 # Just skip the top and left rows when slicing, as suggested somewhere else... lofreq_dct = dct[1:9, 1:9].flatten() # Step 5 avg = (lofreq_dct.sum()) / (lofreq_dct.size) median = numpy.median(lofreq_dct) thresh = avg # Step 6 def compare_value_to_thresh(i): return (1 if i > thresh else 0) bitlist = map(compare_value_to_thresh, lofreq_dct) #Step 7 def set_bit(x, (idx, val)): return (x | (val << idx)) result = reduce(set_bit, enumerate(bitlist), long(0)) #print("{0:016x}".format(result)) return result """ # accepts 2 hashes (longs or hex strings) and returns the hamming distance @staticmethod def hamming_distance(h1, h2): if isinstance(h1, long) or isinstance(h1, int): n1 = h1 n2 = h2 else: # convert hex strings to ints n1 = long(h1, 16) n2 = long(h2, 16) # xor the two numbers n = n1 ^ n2 # count up the 1's in the binary string return sum(b == '1' for b in bin(n)[2:])