6e7660c3d9
Add tests for IssueIdentifier Change tags to a set from a string Add copy and replace convenience functions on GenericMetadata Update deprecated resampling code for Pillow Change comicvine test data to be the same as the test comic Cleanup tests
189 lines
6.6 KiB
Python
Executable File
189 lines
6.6 KiB
Python
Executable File
"""A class to manage creating image content hashes, and calculate hamming distances"""
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#
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# Copyright 2013 Anthony Beville
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import io
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import logging
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from functools import reduce
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from typing import TypeVar
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try:
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from PIL import Image
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pil_available = True
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except ImportError:
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pil_available = False
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logger = logging.getLogger(__name__)
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class ImageHasher:
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def __init__(self, path: str | None = None, data: bytes = b"", width: int = 8, height: int = 8) -> None:
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self.width = width
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self.height = height
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if path is None and not data:
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raise OSError
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try:
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if path is not None:
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self.image = Image.open(path)
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else:
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self.image = Image.open(io.BytesIO(data))
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except Exception:
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logger.exception("Image data seems corrupted!")
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# just generate a bogus image
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self.image = Image.new("L", (1, 1))
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def average_hash(self) -> int:
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try:
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image = self.image.resize((self.width, self.height), Image.Resampling.LANCZOS).convert("L")
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except Exception:
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logger.exception("average_hash error")
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return 0
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pixels = list(image.getdata())
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avg = sum(pixels) / len(pixels)
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def compare_value_to_avg(i: int) -> int:
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return 1 if i > avg else 0
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bitlist = list(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: int, idx_val: tuple[int, int]) -> int:
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(idx, val) = idx_val
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return x | (val << idx)
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result = reduce(set_bit, enumerate(bitlist), 0)
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return result
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def average_hash2(self) -> None:
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"""
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# Got this one from somewhere on the net. Not a clue how the 'convolve2d' 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 = 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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return result
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"""
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def dct_average_hash(self) -> None:
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"""
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# Algorithm source: http://syntaxcandy.blogspot.com/2012/08/perceptual-hash.html
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1. Reduce size. Like Average Hash, pHash starts with a small image.
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However, the image is larger than 8x8; 32x32 is a good size. This
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is really done to simplify the DCT computation and not because it
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is needed to reduce the high frequencies.
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2. Reduce color. The image is reduced to a grayscale just to further
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simplify the number of computations.
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3. Compute the DCT. The DCT separates the image into a collection of
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frequencies and scalars. While JPEG uses an 8x8 DCT, this algorithm
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uses a 32x32 DCT.
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4. Reduce the DCT. This is the magic step. While the DCT is 32x32,
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just keep the top-left 8x8. Those represent the lowest frequencies in
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the picture.
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5. Compute the average value. Like the Average Hash, compute the mean DCT
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value (using only the 8x8 DCT low-frequency values and excluding the first
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term since the DC coefficient can be significantly different from the other
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values and will throw off the average). Thanks to David Starkweather for the
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added information about pHash. He wrote: "the dct hash is based on the low 2D
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DCT coefficients starting at the second from lowest, leaving out the first DC
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term. This excludes completely flat image information (i.e. solid colors) from
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being included in the hash description."
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6. Further reduce the DCT. This is the magic step. Set the 64 hash bits to 0 or
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1 depending on whether each of the 64 DCT values is above or below the average
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value. The result doesn't tell us the actual low frequencies; it just tells us
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the very-rough relative scale of the frequencies to the mean. The result will not
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vary as long as the overall structure of the image remains the same; this can
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survive gamma and color histogram adjustments without a problem.
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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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import numpy
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import scipy.fftpack
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numpy.set_printoptions(threshold=10000, linewidth=200, precision=2, suppress=True)
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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.asarray(im)
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# Step 3
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dct = scipy.fftpack.dct(in_data.astype(float))
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# Step 4
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# Just skip the top and left rows when slicing, as suggested somewhere else...
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lofreq_dct = dct[1:9, 1:9].flatten()
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# Step 5
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avg = (lofreq_dct.sum()) / (lofreq_dct.size)
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median = numpy.median(lofreq_dct)
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thresh = avg
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# Step 6
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def compare_value_to_thresh(i):
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return (1 if i > thresh else 0)
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bitlist = map(compare_value_to_thresh, lofreq_dct)
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#Step 7
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def set_bit(x, (idx, val)):
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return (x | (val << idx))
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result = reduce(set_bit, enumerate(bitlist), long(0))
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return result
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"""
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# accepts 2 hashes (longs or hex strings) and returns the hamming distance
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T = TypeVar("T", int, str)
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@staticmethod
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def hamming_distance(h1: T, h2: T) -> int:
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if isinstance(h1, int) or isinstance(h2, int):
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n1 = h1
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n2 = h2
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else:
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# convert hex strings to ints
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n1 = int(h1, 16)
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n2 = int(h2, 16)
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# xor the two numbers
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n = n1 ^ n2
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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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