import osimport numpy as npimport skimageimport matplotlib.pyplot as pltfrom skimage import io 				#讀取圖片from skimage import exposure		#調用調對比度的方法	rescale_intensity、equalize_histfrom skimage.filters import gaussian	#高斯from skimage import img_as_float  #圖片unit8類型到floatfrom scipy.cluster.vq import kmeans,vq,whiten  #聚類算法import shutil	#文件夾內容刪除 class Path(object):	def __init__(self):		self.path = r"D:/PYscrapy/get_lixiaoran/picture"		self.pathlist = []	#原始圖片列表		self.page = 0 	def append(self):					#將每張圖片的路徑加載到列表中		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 = []	#改變對比度之后的圖片列表		self.path2 = r"D:/PYscrapy/get_lixiaoran/picture2"		self.page2 = 0 	def balance(self):			#將每張圖片進行對比度的處理，兩種方式 1：均衡化 2：從某個值開始取極值		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)	#方法一這里使用個人人為更好的均衡化處理對比度的方法		# 	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取兩端極值			self.contrastlist.append(high_contrast) 		for img in self.contrastlist:			repath = os.path.join(self.path2,str(self.page2)+'.jpg')		#保存修改后的圖片			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)	#讀取文件內容		if os.path.exists(self.path32) == False:			os.mkdir(self.path32)		for lis in range(len(img)):			#循環做每張圖片的高斯過濾			path = os.path.join(self.path31,str(lis)+r'.jpg')			img = skimage.io.imread(path)			gas = gaussian(img,sigma=3)		#multichannel=False	去掉顏色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)	#獲取文件夾內容		os.chdir(self.path41)		#切換路徑		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))		#將圖像ndarry nint8->float			hist,bin_centers = exposure.histogram(img_float,nbins=10)	#取圖像的	每個區間的像素值	分隔區間			self.diff[cnt][0] = number			self.diff[cnt][1] = img_float			self.diff[cnt][2] = bin_centers	#把數據添加到diff中			self.diff[cnt][3] = hist 		for i,j in enumerate(self.diff):		#使用hist和bin_centers相乘來降維,向量化			self.calculate.append([y*self.diff[i][3][x] for x,y in enumerate(self.diff[i][2])])	#這里可能需要理解一下，就是涉及的參數有點多		for i in range(len(self.diff)):			self.diff[i].append(self.calculate[i])	#將特征向量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)			#這里使用scipy的k-means方法來對圖片進行分類		center,_ = kmeans(spot,self.K)		#如果對scipy的k-means不熟悉，前面有專門的講解		cluster,_ = vq(spot,center)		return diff,cluster 	#獲得預測值		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):			#創建K個文件夾			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])	#根據圖片的分類來將它們保存至對應的文件夾 if __name__=="__main__":	np.random.seed(10)	#文件路徑添加	start = Path()	pathlist = start.append() 	#對比度類	second = Contrast(pathlist)	second.balance()	#get改變對比度后的圖片個數 	#高斯過濾	filte = Filter(pathlist)	filter_path = filte.filte_r() 	#數據提取及向量化	vectoring = Vectoring(filter_path) 	#K值的自定義	K = 3 	#建模	modeling = Modeling(filter_path,K)	predicted_diff,predicted_cluster = modeling.model() 	#預測	predicted = Predicting(predicted_diff,predicted_cluster,K)	predicted.predicted()