├── README.md
├── .gitignore
└── dHash.py


/README.md:
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 1 | # DHash
 2 | Image comparing with dHash algorithm.
 3 | 使用dHash算法实现图片对比、相似图片查重。
 4 | 
 5 | ## dHash简介
 6 | dHash算法属于感知哈希算法，用于图片相似度对比。
 7 | 
 8 | 感知哈希算法目前有：
 9 | 
10 | 1. aHash：平均值哈希，速度快，但准确率较低。
11 | 2. pHash：感知哈希，准确率高，但速度较慢。
12 | 3. `dHash：差异值哈希，速度快，且准确率高。`
13 | 
14 | ## Function
15 | #### 1. 获得图片的dHash值：
16 | ```python
17 | hash = DHash.calculate_hash(image)
18 | ```
19 | 
20 | #### 2. 计算两张图片间的汉明距离：
21 | ```python
22 | hamming_distance = DHash.hamming_distance(image1, image2)
23 | ```
24 | 
25 | #### 3. 计算两个dHash值间的汉明距离：
26 | ```python
27 | hamming_distance = DHash.hamming_distance(dHash1, dHash2)
28 | ```
29 | 
30 | 
31 | #### dHash算法实现详解：
32 | http://www.jianshu.com/p/193f0089b7a2
33 | 


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/.gitignore:
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 2 | __pycache__/
 3 | *.py[cod]
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90 | 


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/dHash.py:
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 1 | class DHash(object):
 2 |     @staticmethod
 3 |     def calculate_hash(image):
 4 |         """
 5 |         计算图片的dHash值
 6 |         :param image: PIL.Image
 7 |         :return: dHash值,string类型
 8 |         """
 9 |         difference = DHash.__difference(image)
10 |         # 转化为16进制(每个差值为一个bit,每8bit转为一个16进制)
11 |         decimal_value = 0
12 |         hash_string = ""
13 |         for index, value in enumerate(difference):
14 |             if value:  # value为0, 不用计算, 程序优化
15 |                 decimal_value += value * (2 ** (index % 8))
16 |             if index % 8 == 7:  # 每8位的结束
17 |                 hash_string += str(hex(decimal_value)[2:].rjust(2, "0"))  # 不足2位以0填充。0xf=>0x0f
18 |                 decimal_value = 0
19 |         return hash_string
20 | 
21 |     @staticmethod
22 |     def hamming_distance(first, second):
23 |         """
24 |         计算两张图片的汉明距离(基于dHash算法)
25 |         :param first: Image或者dHash值(str)
26 |         :param second: Image或者dHash值(str)
27 |         :return: hamming distance. 值越大,说明两张图片差别越大,反之,则说明越相似
28 |         """
29 |         # A. dHash值计算汉明距离
30 |         if isinstance(first, str):
31 |             return DHash.__hamming_distance_with_hash(first, second)
32 | 
33 |         # B. image计算汉明距离
34 |         hamming_distance = 0
35 |         image1_difference = DHash.__difference(first)
36 |         image2_difference = DHash.__difference(second)
37 |         for index, img1_pix in enumerate(image1_difference):
38 |             img2_pix = image2_difference[index]
39 |             if img1_pix != img2_pix:
40 |                 hamming_distance += 1
41 |         return hamming_distance
42 | 
43 |     @staticmethod
44 |     def __difference(image):
45 |         """
46 |         *Private method*
47 |         计算image的像素差值
48 |         :param image: PIL.Image
49 |         :return: 差值数组。0、1组成
50 |         """
51 |         resize_width = 9
52 |         resize_height = 8
53 |         # 1. resize to (9,8)
54 |         smaller_image = image.resize((resize_width, resize_height))
55 |         # 2. 灰度化 Grayscale
56 |         grayscale_image = smaller_image.convert("L")
57 |         # 3. 比较相邻像素
58 |         pixels = list(grayscale_image.getdata())
59 |         difference = []
60 |         for row in range(resize_height):
61 |             row_start_index = row * resize_width
62 |             for col in range(resize_width - 1):
63 |                 left_pixel_index = row_start_index + col
64 |                 difference.append(pixels[left_pixel_index] > pixels[left_pixel_index + 1])
65 |         return difference
66 | 
67 |     @staticmethod
68 |     def __hamming_distance_with_hash(dhash1, dhash2):
69 |         """
70 |         *Private method*
71 |         根据dHash值计算hamming distance
72 |         :param dhash1: str
73 |         :param dhash2: str
74 |         :return: 汉明距离(int)
75 |         """
76 |         difference = (int(dhash1, 16)) ^ (int(dhash2, 16))
77 |         return bin(difference).count("1")
78 | 
79 | 


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