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import cv2
import khandy
import numpy as np
def _convert_bool_matrix_to_int(bool_mat):
hash_val = int(0)
for item in bool_mat.flatten():
hash_val <<= 1
hash_val |= int(item)
return hash_val
def calc_image_ahash(image):
"""Average Hashing
References:
http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
"""
assert khandy.is_numpy_image(image)
if image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
resized = cv2.resize(image, (8, 8))
mean_val = np.mean(resized)
hash_mat = resized >= mean_val
hash_val = _convert_bool_matrix_to_int(hash_mat)
return f'{hash_val:016x}'
def calc_image_dhash(image):
"""Difference Hashing
References:
http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
"""
assert khandy.is_numpy_image(image)
if image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
resized = cv2.resize(image, (9, 8))
hash_mat = resized[:,:-1] >= resized[:,1:]
hash_val = _convert_bool_matrix_to_int(hash_mat)
return f'{hash_val:016x}'
def calc_image_phash(image):
"""Perceptual Hashing
References:
http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
"""
assert khandy.is_numpy_image(image)
if image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
resized = cv2.resize(image, (32, 32))
dct_coeff = cv2.dct(resized.astype(np.float32))
reduced_dct_coeff = dct_coeff[:8, :8]
# # mean of coefficients excluding the DC term (0th term)
# mean_val = np.mean(reduced_dct_coeff.flatten()[1:])
# median of coefficients
median_val = np.median(reduced_dct_coeff)
hash_mat = reduced_dct_coeff >= median_val
hash_val = _convert_bool_matrix_to_int(hash_mat)
return f'{hash_val:016x}'
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