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- 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']
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