.

codes/sim/aHash.py

@@ -0,0 +1,40 @@
+# 正则化图像
+def regularizeImage(img, size = (8, 8)):
+    return img.resize(size).convert('L')
+
+# 计算hash值
+def getHashCode(img, size = (8, 8)):
+
+    pixel = []
+    for i in range(size[0]):
+        for j in range(size[1]):
+            pixel.append(img.getpixel((i, j)))
+
+    mean = sum(pixel) / len(pixel)
+
+    result = []
+    for i in pixel:
+        if i > mean:
+            result.append(1)
+        else:
+            result.append(0)
+    
+    return result
+
+# 比较hash值
+def compHashCode(hc1, hc2):
+    cnt = 0
+    for i, j in zip(hc1, hc2):
+        if i == j:
+            cnt += 1
+    return cnt
+
+# 计算平均哈希算法相似度
+def calaHashSimilarity(img1, img2):
+    img1 = regularizeImage(img1)
+    img2 = regularizeImage(img2)
+    hc1 = getHashCode(img1)
+    hc2 = getHashCode(img2)
+    return compHashCode(hc1, hc2)
+
+__all__ = ['calaHashSimilarity']

codes/sim/compute_similarity.py

@@ -0,0 +1,89 @@
+from PIL import Image
+from multiprocessing import Process
+import sim.histogram as htg
+import sim.aHash as ah
+import sim.pHash as ph
+import sim.dHash as dh
+import os
+import feature
+from flask import redirect,url_for
+
+
+# if __name__ == '__main__':
+
+class simi:
+    def similarity(self, path):
+        print("begin")
+        # print(m)
+        # print(path)
+
+        folder_path = "upload/" + path
+        dirs = os.listdir(folder_path)
+
+        # 文件夹下有tmp张图片
+        tmp = 0
+        for item in dirs:
+            if os.path.isfile(os.path.join(folder_path, item)):
+                tmp += 1
+        # print(tmp)
+
+        list = [[] for j in range(int(tmp*(tmp-1)*0.5))] # [[], [], [], [], [], []]
+
+        index = 0
+        for i in range(1,tmp+1):
+            for j in range(i+1,tmp+1):
+                str1 = folder_path + '/img' + str(i) + '.png'
+                str2 = folder_path + '/img' + str(j) + '.png'
+                # print(str1)
+                # print(index)
+                # print('------------------')
+                list[index].append(str1)
+                list[index].append(str2)
+
+                img1 = Image.open(str1)
+                img2 = Image.open(str2)
+                img1_htg = htg.regularizeImage(img1)
+                img2_htg = htg.regularizeImage(img2)
+                hg1 = img1_htg.histogram()
+                hg2 = img2_htg.histogram()
+
+                # draw the histogram in a no-blocking way
+                sub_thread = Process(target=htg.drawHistogram, args=(hg1, hg2,))
+                sub_thread.start()
+
+                # print the histogram similarity
+                htg_result = htg.calMultipleHistogramSimilarity(img1_htg, img2_htg)
+                list[index].append(htg_result)
+                print('依据图片直方图距离计算相似度：{}'.format(htg_result))
+
+                # aHash Calculation
+                ah_result = ah.calaHashSimilarity(img1, img2)
+                list[index].append(ah_result)
+                print('依据平均哈希算法计算相似度：{}/{}'.format(ah_result, 64))
+
+                # pHash Calculation
+                ph_result = ph.calpHashSimilarity(img1, img2)
+                list[index].append(ph_result)
+                print('依据感知哈希算法计算相似度：{}/{}'.format(ph_result, 64))
+
+                # dHash Calculation
+                dh_result = dh.caldHashSimilarity(img1, img2)
+                list[index].append(dh_result)
+                print('依据差异哈希算法计算相似度：{}/{}'.format(dh_result, 64))
+
+                index = index + 1
+        # print(list)
+        # print(index)
+        # print(tmp)
+
+        the_feature = feature.feat()
+        message = the_feature.call_feature_extraction_1(folder_path,list,index,tmp)
+
+        # return redirect(url_for('call_feature',alist = list))
+        return message
+
+
+
+
+
+

codes/sim/dHash.py

@@ -0,0 +1,36 @@
+# 正则化图像
+def regularizeImage(img, size = (9, 8)):
+    return img.resize(size).convert('L')
+
+# 计算hash值
+def getHashCode(img, size = (9, 8)):
+
+    result = []
+    for i in range(size[0] - 1):
+        for j in range(size[1]):
+            current_val = img.getpixel((i, j))
+            next_val = img.getpixel((i + 1, j))
+            if current_val > next_val:
+                result.append(1)
+            else:
+                result.append(0)
+    
+    return result
+
+# 比较hash值
+def compHashCode(hc1, hc2):
+    cnt = 0
+    for i, j in zip(hc1, hc2):
+        if i == j:
+            cnt += 1
+    return cnt
+
+# 计算差异哈希算法相似度
+def caldHashSimilarity(img1, img2):
+    img1 = regularizeImage(img1)
+    img2 = regularizeImage(img2)
+    hc1 = getHashCode(img1)
+    hc2 = getHashCode(img2)
+    return compHashCode(hc1, hc2)
+
+__all__ = ['caldHashSimilarity']

codes/sim/histogram.py

@@ -0,0 +1,44 @@
+import matplotlib.pyplot as plt
+
+# 正则化图像
+def regularizeImage(img, size = (256, 256)):
+    return img.resize(size).convert('RGB')
+
+# 画出直方图图像
+def drawHistogram(hg1, hg2):
+    plt.plot(range(len(hg1)), hg1, color='blue', linewidth=1.5, label='img1')
+    plt.plot(range(len(hg2)), hg2, color='red', linewidth=1.5, label='img2')
+    plt.legend(loc='upper left')
+    plt.title('Histogram Similarity')
+    plt.show()
+
+# 分块图像4x4
+def splitImage(img, part_size = (64, 64)):
+    w, h = img.size
+    pw, ph = part_size
+    data = []
+    for i in range(0, w, pw):
+        for j in range(0, h, ph):
+            data.append(img.crop((i, j, i + pw, j + ph)).copy())
+    return data
+
+# 利用单块图片的直方图距离计算相似度
+def calSingleHistogramSimilarity(hg1, hg2):
+    if len(hg1) != len(hg2):
+        raise Exception('样本点个数不一样')
+    sum = 0
+    for x1, x2 in zip(hg1, hg2):
+        if x1 != x2:
+            sum += 1 - float(abs(x1 - x2) / max(x1, x2))
+        else:
+            sum += 1
+    return sum / len(hg1)
+
+# 利用分块图片的直方图距离计算相似度
+def calMultipleHistogramSimilarity(img1, img2):
+    answer = 0
+    for sub_img1, sub_img2 in zip(splitImage(img1), splitImage(img2)):
+        answer += calSingleHistogramSimilarity(sub_img1.histogram(), sub_img2.histogram())
+    return float(answer / 16.0)
+
+__all__ = ['regularizeImage', 'drawHistogram', 'calMultipleHistogramSimilarity']

codes/sim/pHash.py

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