python数据处理 根据颜色对图片进行分类的方法

import osimport numpy as npimport skimageimport matplotlib.pyplot as pltfrom skimage import io 				#读取图片from skimage import exposure		#调用调对比度的方法	rescale_intensity、equalize_histfrom skimage.filters import gaussian	#高斯from skimage import img_as_float  #图片unit8类型到floatfrom scipy.cluster.vq import kmeans,vq,whiten  #聚类算法import shutil	#文件夹内容删除 class Path(object):	def __init__(self):		self.path = r"D:/PYscrapy/get_lixiaoran/picture"		self.pathlist = []	#原始图片列表		self.page = 0 	def append(self):					#将每张图片的路径加载到列表中		much = os.listdir(self.path)		for i in range(len(much)):			repath = os.path.join(self.path,str(self.page)+'.jpg')			self.page +=1			self.pathlist.append(repath)		return self.pathlist class Contrast(object):	def __init__(self,pathlist):		self.pathlist = pathlist		self.contrastlist = []	#改变对比度之后的图片列表		self.path2 = r"D:/PYscrapy/get_lixiaoran/picture2"		self.page2 = 0 	def balance(self):			#将每张图片进行对比度的处理，两种方式 1：均衡化 2：从某个值开始取极值		if os.path.exists(self.path2) == False:			os.mkdir(self.path2) 		# for lis in self.pathlist:		# 	data = skimage.io.imread(lis)		# 	equalized = exposure.equalize_hist(data)	#方法一这里使用个人人为更好的均衡化处理对比度的方法		# 	self.contrastlist.append(equalized) 		for lis in self.pathlist:			data = skimage.io.imread(lis)			high_contrast = exposure.rescale_intensity(data,in_range=(20,220))	#方法二 以20和220取两端极值			self.contrastlist.append(high_contrast) 		for img in self.contrastlist:			repath = os.path.join(self.path2,str(self.page2)+'.jpg')		#保存修改后的图片			skimage.io.imsave(repath,img)			self.page2 +=1 class Filter(Contrast):	def __init__(self,pathlist):		super().__init__(pathlist)		self.path31 = self.path2		self.path32 = r"D:/PYscrapy/get_lixiaoran/picture3"		self.page3 = 0		self.filterlist = [] 	def filte_r(self):		img = os.listdir(self.path31)	#读取文件内容		if os.path.exists(self.path32) == False:			os.mkdir(self.path32)		for lis in range(len(img)):			#循环做每张图片的高斯过滤			path = os.path.join(self.path31,str(lis)+r'.jpg')			img = skimage.io.imread(path)			gas = gaussian(img,sigma=3)		#multichannel=False	去掉颜色2D			self.filterlist.append(gas)			path_gas = os.path.join(self.path32,str(self.page3)+r'.jpg')			skimage.io.imsave(path_gas,gas)			self.page3 +=1		return self.path32 class Vectoring(object):	def __init__(self,filter_path):		self.path41 = filter_path		self.diff = []		self.calculate = [] 	def vector(self):		numbers = os.listdir(self.path41)	#获取文件夹内容		os.chdir(self.path41)		#切换路径		for i in range(len(numbers)):			self.diff.append([])			for j in range(4):				self.diff[i].append([])		#diff[[number],[img_float],[bin_centers],[hist]] 		for cnt,number in enumerate(numbers):			img_float = img_as_float(skimage.io.imread(number))		#将图像ndarry nint8->float			hist,bin_centers = exposure.histogram(img_float,nbins=10)	#取图像的	每个区间的像素值	分隔区间			self.diff[cnt][0] = number			self.diff[cnt][1] = img_float			self.diff[cnt][2] = bin_centers	#把数据添加到diff中			self.diff[cnt][3] = hist 		for i,j in enumerate(self.diff):		#使用hist和bin_centers相乘来降维,向量化			self.calculate.append([y*self.diff[i][3][x] for x,y in enumerate(self.diff[i][2])])	#这里可能需要理解一下，就是涉及的参数有点多		for i in range(len(self.diff)):			self.diff[i].append(self.calculate[i])	#将特征向量calculate也加入到diff中 		return self.diff 			#diff[[number],[img_float],[bin_centers],[hist],[calculate]] class Modeling(Vectoring):	def __init__(self,filter_path,K):		super().__init__(filter_path)		self.K = K 	def model(self):		diff = self.vector()		calculate = []		for i in range(len(diff)):			calculate.append(diff[i][4])		spot = whiten(calculate)			#这里使用scipy的k-means方法来对图片进行分类		center,_ = kmeans(spot,self.K)		#如果对scipy的k-means不熟悉，前面有专门的讲解		cluster,_ = vq(spot,center)		return diff,cluster 	#获得预测值		class Predicting(object):	def __init__(self,predicted_diff,predicted_cluster,K):		self.diff = predicted_diff		self.cluster = predicted_cluster		self.path42 = r'D:/PYscrapy/get_lixiaoran/picture4'		self.K = K 	def predicted(self):		if os.path.exists(self.path42) == True:			much = shutil.rmtree(self.path42)			os.mkdir(self.path42)		else:			os.mkdir(self.path42)		os.chdir(self.path42)		for i in range(self.K):			#创建K个文件夹			os.mkdir('classify{}'.format(i))		for i,j in enumerate(self.cluster):			skimage.io.imsave('classify{}//{}'.format(j,self.diff[i][0]),self.diff[i][1])	#根据图片的分类来将它们保存至对应的文件夹 if __name__=="__main__":	np.random.seed(10)	#文件路径添加	start = Path()	pathlist = start.append() 	#对比度类	second = Contrast(pathlist)	second.balance()	#get改变对比度后的图片个数 	#高斯过滤	filte = Filter(pathlist)	filter_path = filte.filte_r() 	#数据提取及向量化	vectoring = Vectoring(filter_path) 	#K值的自定义	K = 3 	#建模	modeling = Modeling(filter_path,K)	predicted_diff,predicted_cluster = modeling.model() 	#预测	predicted = Predicting(predicted_diff,predicted_cluster,K)	predicted.predicted()