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Merge branch 'mizaki-phash' into develop

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This commit is contained in:
Timmy Welch 2023-07-01 18:01:26 -07:00
commit 053afaa75e
2 changed files with 61 additions and 61 deletions
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120
comictaggerlib/imagehasher.py
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@ -17,7 +17,9 @@ from __future__ import annotations
import io
import logging
import math
from functools import reduce
from statistics import median
from typing import TypeVar
try:
@ -90,82 +92,80 @@ class ImageHasher:
return result
"""
def dct_average_hash(self) -> None:
def p_hash(self) -> int:
"""
Pure python version of Perceptual Hash computation of https://github.com/JohannesBuchner/imagehash/tree/master
Implementation follows http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
"""
# 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.
def generate_dct2(block, axis=0):
def dct1(block):
"""Perform 1D Discrete Cosine Transform (DCT) on a given block."""
N = len(block)
dct_block = [0.0] * N
2. Reduce color. The image is reduced to a grayscale just to further
simplify the number of computations.
for k in range(N):
sum_val = 0.0
for n in range(N):
cos_val = math.cos(math.pi * k * (2 * n + 1) / (2 * N))
sum_val += block[n] * cos_val
dct_block[k] = sum_val
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.
return dct_block
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.
"""Perform 2D Discrete Cosine Transform (DCT) on a given block along the specified axis."""
rows = len(block)
cols = len(block[0])
dct_block = [[0.0] * cols for _ in range(rows)]
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."
if axis == 0:
# Apply 1D DCT on each row
for i in range(rows):
dct_block[i] = dct1(block[i])
elif axis == 1:
# Apply 1D DCT on each column
for j in range(cols):
column = [block[i][j] for i in range(rows)]
dct_column = dct1(column)
for i in range(rows):
dct_block[i][j] = dct_column[i]
else:
raise ValueError("Invalid axis value. Must be either 0 or 1.")
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.
return dct_block
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.
def convert_image_to_ndarray(image):
width, height = image.size
pixels2 = []
for y in range(height):
row = []
for x in range(width):
pixel = image.getpixel((x, y))
row.append(pixel)
pixels2.append(row)
import numpy
import scipy.fftpack
numpy.set_printoptions(threshold=10000, linewidth=200, precision=2, suppress=True)
return pixels2
# Step 1,2
im = self.image.resize((32, 32), Image.ANTIALIAS).convert("L")
in_data = numpy.asarray(im)
highfreq_factor = 4
img_size = 8 * highfreq_factor
# Step 3
dct = scipy.fftpack.dct(in_data.astype(float))
try:
image = self.image.convert("L").resize((img_size, img_size), Image.Resampling.LANCZOS)
except Exception:
logger.exception("p_hash error converting to greyscale and resizing")
return 0
# 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))
pixels = convert_image_to_ndarray(image)
dct = generate_dct2(generate_dct2(pixels, axis=0), axis=1)
dctlowfreq = [row[:8] for row in dct[:8]]
med = median([item for sublist in dctlowfreq for item in sublist])
# Convert to a bit string
diff = "".join(str(int(item > med)) for row in dctlowfreq for item in row)
result = int(diff, 2)
return result
"""
# accepts 2 hashes (longs or hex strings) and returns the hamming distance

2
comictaggerlib/issueidentifier.py
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@ -134,7 +134,7 @@ class IssueIdentifier:
def calculate_hash(self, image_data: bytes) -> int:
if self.image_hasher == 3:
return -1 # ImageHasher(data=image_data).dct_average_hash()
return ImageHasher(data=image_data).p_hash()
if self.image_hasher == 2:
return -1 # ImageHasher(data=image_data).average_hash2()