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122 lines
2.9 KiB
122 lines
2.9 KiB
import math
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import unittest
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# 正则化图像
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def regularizeImage(img, size = (32, 32)):
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return img.resize(size).convert('L')
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# 获得图像像素矩阵
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def getMatrix(img):
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matrix = []
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size = img.size
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for i in range(size[1]):
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pixel = []
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for j in range(size[0]):
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pixel.append(img.getpixel((j, i)))
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matrix.append(pixel)
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return matrix
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# 计算系数矩阵
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def getCoefficient(length):
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matrix = []
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sqr = 1.0 / math.sqrt(length)
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value = []
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for i in range(length):
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value.append(sqr)
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matrix.append(value)
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for i in range(1, length):
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value = []
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for j in range(0, length):
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value.append(math.sqrt(2.0 / length) * math.cos(i * math.pi * (j + 0.5) / length))
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matrix.append(value)
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return matrix
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# 计算矩阵转秩
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def getTranspose(matrix):
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new_matrix = []
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for i in range(len(matrix)):
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value = []
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for j in range(len(matrix[i])):
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value.append(matrix[j][i])
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new_matrix.append(value)
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return new_matrix
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# 计算矩阵乘法
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def getMultiply(matrix1, matrix2):
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new_matrix = []
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for i in range(len(matrix1)):
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value = []
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for j in range(len(matrix2[i])):
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ans = 0.0
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for h in range(len(matrix1[i])):
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ans += matrix1[i][h] * matrix2[h][j]
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value.append(ans)
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new_matrix.append(value)
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return new_matrix
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# 计算DCT
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def DCT(matrix):
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length = len(matrix)
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A = getCoefficient(length)
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AT = getTranspose(A)
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temp = getMultiply(A, matrix)
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DCT_matrix = getMultiply(matrix, AT)
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return DCT_matrix
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# 计算左上角8*8并转化为list
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def submatrix_list(matrix, size = (8, 8)):
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value = []
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for i in range(size[0]):
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for j in range(size[1]):
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value.append(matrix[i][j])
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return value
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# 计算hash值
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def getHashCode(sub_list):
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length = len(sub_list)
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mean = sum(sub_list) / length
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result = []
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for i in sub_list:
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if i > mean:
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result.append(1)
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else:
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result.append(0)
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return result
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# 比较hash值
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def compHashCode(hc1, hc2):
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cnt = 0
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for i, j in zip(hc1, hc2):
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if i == j:
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cnt += 1
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return cnt
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# 计算感知哈希算法相似度
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def calpHashSimilarity(img1, img2):
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img1 = regularizeImage(img1)
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img2 = regularizeImage(img2)
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matrix1 = getMatrix(img1)
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matrix2 = getMatrix(img2)
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DCT1 = DCT(matrix1)
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DCT2 = DCT(matrix2)
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sub_list1 = submatrix_list(DCT1)
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sub_list2 = submatrix_list(DCT2)
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hc1 = getHashCode(sub_list1)
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hc2 = getHashCode(sub_list2)
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return compHashCode(hc1, hc2)
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# 单元测试
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class TestpHash(unittest.TestCase):
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def test_getHashCode(self):
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self.assertEqual(getHashCode([1, 2, 3]), [0, 0, 1])
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if __name__ == '__main__':
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unittest.main()
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__all__ = ['calpHashSimilarity']
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