Python:实现感知哈希算法(附完整源码)
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本文介绍了一种基于感知哈希算法的Python实现方法。文章主要分为以下内容：首先导入必要的库（如numpy、PIL和matplotlib），并定义了一个名为Extra_Featrue的函数用于从图像中提取特征；接着定义了一个名为MRIFindCT的函数用于利用MRI图像的特征找到候选CT图像；最后展示了如何设置数据源路径，并通过调用上述函数完成整个流程。该方法通过将图像进行二进制化处理，并计算其与目标图像的差异性来实现特征匹配。
Python:实现感知哈希算法
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
# extract feature
# lines: src_img path
def Extra_Featrue(lines, new_rows=64, new_cols=64):
for name in lines:
ori_img = Image.open(name.strip())
feature_img = ori_img.resize((new_rows, new_cols))
feature_img = feature_img.convert('L')
mean_value = np.mean(np.mean(feature_img))
feature = feature_img >= mean_value
feature = np.matrix(feature, np.int8)
if 'features' in locals():
temp = np.reshape(feature, (1, new_cols * new_rows))
features = np.vstack([features, temp])
else:
features = np.matrix(np.reshape(feature, (1, new_cols * new_rows)))
return features
# use MRI image features to find candidate CT images
def MRIFindCT(mri_feature, mri_lines, ct_feature, ct_lines):
for i in np.arange(0, np.shape(mri_feature)[0]):
dist = []
mri = mri_feature[i, :]
for j in np.arange(0, np.shape(ct_feature)[0]):
ct = ct_feature[j, :]
temp = mri != ct
sum = np.sum(temp)
dist.append(sum)
# find minimum while ignoring the zeros on the diagonal
index = np.argsort(dist)
img1_path = mri_lines[i]
img2_path = ct_lines[index[1]]
img3_path = ct_lines[index[2]]
img4_path = ct_lines[index[3]]
img1 = Image.open(img1_path)
img2 = Image.open(img2_path)
img3 = Image.open(img3_path)
img4 = Image.open(img4_path)
plt.subplot(2, 2, 1)
plt.imshow(img1)
plt.title('MRI Image(%s)' % img1_path[img1_path.__len__() - 10:])
plt.axis('off')
plt.xlabel(img1_path[img1_path.__len__() - 10:])
plt.subplot(2, 2, 2)
plt.imshow(img2)
plt.title('Candidate CT Image1(%s)' % img2_path[img2_path.__len__() - 10:])
plt.axis('off')
plt.xlabel(img2_path[img2_path.__len__() - 10:])
plt.subplot(2, 2, 3)
plt.imshow(img3)
plt.title('Candidate CT Image2(%s)' % img3_path[img3_path.__len__() - 10:])
plt.axis('off')
plt.xlabel(img3_path[img3_path.__len__() - 10:])
plt.subplot(2, 2, 4)
plt.imshow(img4)
plt.title('Candidate CT Image3(%s)' % img4_path[img4_path.__len__() - 10:])
plt.axis('off')
plt.xlabel(img4_path[img4_path.__len__() - 10:])
plt.show()
# set data source
src_path = './1794-MR/MR.txt' # *.txt file contain MRI images
dst_path = './1794-CT/CT.txt' # *.txt file contain CT images
mri_lines = [line.strip() for line in open(src_path)]
ct_lines = [line.strip() for line in open(dst_path)]
# extract feature
set_size = 8
mri_feature = Extra_Featrue(mri_lines, set_size, set_size)
ct_feature = Extra_Featrue(ct_lines, set_size, set_size)
# use feature to find candidate img
MRIFindCT(mri_feature, mri_lines, ct_feature, ct_lines)
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