Nevar pievienot vairāk kā 25 tēmas
Tēmai ir jāsākas ar burtu vai ciparu, tā var saturēt domu zīmes ('-') un var būt līdz 35 simboliem gara.
44 rindas
1.4 KiB
44 rindas
1.4 KiB
import matplotlib.pyplot as plt
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# 正则化图像
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def regularizeImage(img, size = (256, 256)):
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return img.resize(size).convert('RGB')
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# 画出直方图图像
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def drawHistogram(hg1, hg2):
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plt.plot(range(len(hg1)), hg1, color='blue', linewidth=1.5, label='img1')
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plt.plot(range(len(hg2)), hg2, color='red', linewidth=1.5, label='img2')
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plt.legend(loc='upper left')
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plt.title('Histogram Similarity')
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plt.show()
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# 分块图像4x4
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def splitImage(img, part_size = (64, 64)):
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w, h = img.size
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pw, ph = part_size
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data = []
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for i in range(0, w, pw):
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for j in range(0, h, ph):
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data.append(img.crop((i, j, i + pw, j + ph)).copy())
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return data
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# 利用单块图片的直方图距离计算相似度
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def calSingleHistogramSimilarity(hg1, hg2):
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if len(hg1) != len(hg2):
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raise Exception('样本点个数不一样')
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sum = 0
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for x1, x2 in zip(hg1, hg2):
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if x1 != x2:
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sum += 1 - float(abs(x1 - x2) / max(x1, x2))
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else:
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sum += 1
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return sum / len(hg1)
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# 利用分块图片的直方图距离计算相似度
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def calMultipleHistogramSimilarity(img1, img2):
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answer = 0
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for sub_img1, sub_img2 in zip(splitImage(img1), splitImage(img2)):
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answer += calSingleHistogramSimilarity(sub_img1.histogram(), sub_img2.histogram())
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return float(answer / 16.0)
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__all__ = ['regularizeImage', 'drawHistogram', 'calMultipleHistogramSimilarity']
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