97 lines
4.3 KiB
Python
97 lines
4.3 KiB
Python
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# -*- coding: UTF-8 -*-
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"""
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@Project ：onestar
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@File ：GLDM.py
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@Contact：
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scikit-image feature计算图像特征：https://blog.csdn.net/lyxleft/article/details/102904909
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python如何在二维图像上进行卷积：https://www.xz577.com/j/281686.html
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利用python的skimage计算灰度共生矩阵：https://zhuanlan.zhihu.com/p/147066037
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@function ：计算图像灰度共生矩阵
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@Author ：SHJ
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@Date ：2021/11/10 14:42
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@Version ：1.0.0
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"""
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import numpy as np
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import os
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from skimage.feature import greycomatrix, greycoprops
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import datetime
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from tool.algorithm.image.ImageHandle import ImageHandler
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class GLDM:
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def __init__(self,win_size = 15, step=2,levels=16,angles=[0,45,90,135],
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prop=['contrast', 'dissimilarity', 'homogeneity', 'energy', 'correlation', 'ASM']):
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self._win_size = win_size # 计算灰度共生矩阵窗口尺寸，为奇数
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self._step = step # 步长
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self._levels = levels # 灰度等级：例如16，256
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self._angles = list(np.deg2rad(np.array(angles))) #角度，使用弧度制
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"""
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'contrast':对比度：反映了图像的清晰度和纹理沟纹深浅的程度
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'dissimilarity':差异性
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'homogeneity':同质性/逆差距：度量图像纹理局部变化的多少。其值大则说明图像纹理的不同区域间缺少变化，局部非常均匀。
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'energy':能量：是灰度共生矩阵元素值的平方和，所以也称能量，反映了图像灰度分布均匀程度和纹理粗细度
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'correlation':相关性：它度量空间灰度共生矩阵元素在行或列方向上的相似程度
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'ASM':二阶距
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"""
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self._prop = prop #纹理特征名称
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def get_glcm_value(self,input):
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values_temp = []
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# 统计得到glcm
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# 得到共生矩阵，参数：图像矩阵，距离，方向，灰度级别，是否对称，是否标准化
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# para2: [0, np.pi / 4, np.pi / 2, np.pi * 3 / 4] 一共计算了四个方向，你也可以选择一个方向
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glcm = greycomatrix(input, [self._step], self._angles, self._levels, symmetric=False, normed=True)
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# print(glcm.shape)
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# 循环计算表征纹理的参数
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for prop in self._prop:
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temp = greycoprops(glcm, prop)
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# print(temp)
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values_temp.append(np.mean(temp))
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return values_temp
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def get_glcm_array(self,inputs: np.ndarray, win_size):
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h, w = inputs.shape
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pad = (win_size - 1) // 2
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inputs = np.pad(inputs, pad_width=[(pad, pad), (pad, pad)], mode="constant", constant_values=0)
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glcm_array ={}
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for name in self._prop:
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glcm_array.update({name:np.zeros(shape=(h, w),dtype=np.float32)})
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for i in range(h): # 行号
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for j in range(w): # 列号
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window = inputs[i: i + win_size, j: j + win_size]
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value = self.get_glcm_value(window)
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print('i:%s,j:%s',i,j)
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# print(value)
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for n,array in zip(range(len(glcm_array)),glcm_array.values()):
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array[i,j] = value[n]
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return glcm_array
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@staticmethod
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def standardization(data, num=1):
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# 矩阵标准化到[0,1]
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data[np.isnan(data)] = np.min(data) # 异常值填充为0
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_range = np.max(data) - np.min(data)
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return (data - np.min(data)) / _range * num
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def api_get_glcm_array(self,out_dir,in_tif_path,name=''):
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ih = ImageHandler()
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proj, geotrans, array = ih.read_img(in_tif_path)
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array[np.where(array > 500000)]=500000 #去除过大的值，避免标准化时，大部分的值都接近0
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array = self.standardization(array,self._levels-1) #标准化到0~（self._levels-1）
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array = np.uint8(array)
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glcm_array = self.get_glcm_array(array, self._win_size)
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for key,value in glcm_array.items():
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out_path = os.path.join(out_dir,name+'_'+key+'.tif')
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ih.write_img(out_path, proj, geotrans,value)
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if __name__ == '__main__':
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start = datetime.datetime.now()
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gldm = GLDM(win_size=9,levels=16,step=3,angles=[0,45,90,135])
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gldm.api_get_glcm_array('D:\glcm','D:\glcm\src_img.tif',)
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end = datetime.datetime.now()
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msg = 'running use time: %s ' % (end - start)
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print(msg)
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# 666*720尺寸影像消耗的running use time: 0:04:23.155424
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