import multiprocessing import os, time, random import numpy as np import cv2 import os import sys from time import ctime import tensorflow as tfimage_dir = r"D:/sxl/處理圖片/漢字分類/train10/" #圖像文件夾路徑 data_type = 'test' save_path = r'E:/sxl_Programs/Python/CNN/npy/' #存儲(chǔ)路徑 data_name = 'Img10' #npy文件名char_set = np.array(os.listdir(image_dir)) #文件夾名稱列表 np.save(save_path+'ImgShuZi10.npy',char_set) #文件夾名稱列表 char_set_n = len(char_set) #文件夾列表長(zhǎng)度read_process_n = 1 #進(jìn)程數(shù) repate_n = 4 #隨機(jī)移動(dòng)次數(shù) data_size = 1000000 #1個(gè)npy大小shuffled = True #是否打亂#可以讀取帶中文路徑的圖 def cv_imread(file_path,type=0):cv_img=cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),-1)# print(file_path)# print(cv_img.shape)# print(len(cv_img.shape))if(type==0):if(len(cv_img.shape)==3):cv_img = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)return cv_img#多個(gè)數(shù)組按同一規(guī)則打亂數(shù)據(jù) def ShuffledData(features,labels):'''@description:隨機(jī)打亂數(shù)據(jù)與標(biāo)簽，但保持?jǐn)?shù)據(jù)與標(biāo)簽一一對(duì)應(yīng)'''permutation = np.random.permutation(features.shape[0])shuffled_features = features[permutation,:] #多維shuffled_labels = labels[permutation] #1維return shuffled_features,shuffled_labels#函數(shù)功能：簡(jiǎn)單網(wǎng)格 #函數(shù)要求：1.無(wú)關(guān)圖像大小；2.輸入圖像默認(rèn)為灰度圖;3.參數(shù)只有輸入圖像 #返回?cái)?shù)據(jù)：1x64*64維特征 def GetFeature(image):#圖像大小歸一化image = cv2.resize(image,(64,64))img_h = image.shape[0]img_w = image.shape[1]#定義特征向量feature = np.zeros(img_h*img_w,dtype=np.int16)for h in range(img_h):for w in range(img_w):feature[h*img_h+w] = image[h,w]return feature# 寫數(shù)據(jù)進(jìn)程執(zhí)行的代碼: def read_image_to_queue(queue):print('Process to write: %s' % os.getpid())for j,dirname in enumerate(char_set): # dirname 是文件夾名稱label = np.where(char_set==dirname)[0][0] #文件夾名稱對(duì)應(yīng)的下標(biāo)序號(hào)print('序號(hào)：'+str(j),'讀 '+dirname+' 文件夾...時(shí)間：',ctime() )for parent,_,filenames in os.walk(os.path.join(image_dir,dirname)):for filename in filenames:if(filename[-4:]!='.jpg'):continueimage = cv_imread(os.path.join(parent,filename),0)# cv2.imshow(dirname,image)# cv2.waitKey(0)queue.put((image,label))for i in range(read_process_n):queue.put((None,-1))print('讀圖結(jié)束!')return True# 讀數(shù)據(jù)進(jìn)程執(zhí)行的代碼: def extract_feature(queue,lock,count):'''@description:從隊(duì)列中取出圖片進(jìn)行特征提取@queue:先進(jìn)先出隊(duì)列l(wèi)ock：鎖，在計(jì)數(shù)時(shí)上鎖，防止沖突count:計(jì)數(shù)'''print('Process %s start reading...' % os.getpid())global data_nfeatures = [] #存放提取到的特征labels = [] #存放標(biāo)簽flag = True #標(biāo)志著進(jìn)程是否結(jié)束while flag:image,label = queue.get() #從隊(duì)列中獲取圖像和標(biāo)簽if len(features) >= data_size or label == -1: #特征數(shù)組的長(zhǎng)度大于指定長(zhǎng)度，則開(kāi)始存儲(chǔ)array_features = np.array(features) #轉(zhuǎn)換成數(shù)組array_labels = np.array(labels)array_features,array_labels = ShuffledData(array_features,array_labels) #打亂數(shù)據(jù)lock.acquire() # 鎖開(kāi)始# 拆分?jǐn)?shù)據(jù)為訓(xùn)練集，測(cè)試集split_x = int(array_features.shape[0] * 0.8)train_data, test_data = np.split(array_features, [split_x], axis=0) # 拆分特征數(shù)據(jù)集train_labels, test_labels = np.split(array_labels, [split_x], axis=0) # 拆分標(biāo)簽數(shù)據(jù)集count.value += 1 #下標(biāo)計(jì)數(shù)加1str_features_name_train = data_name+'_features_train_'+str(count.value)+'.npy'str_labels_name_train = data_name+'_labels_train_'+str(count.value)+'.npy'str_features_name_test = data_name+'_features_test_'+str(count.value)+'.npy'str_labels_name_test = data_name+'_labels_test_'+str(count.value)+'.npy'lock.release() # 鎖釋放np.save(save_path+str_features_name_train,train_data)np.save(save_path+str_labels_name_train,train_labels)np.save(save_path+str_features_name_test,test_data)np.save(save_path+str_labels_name_test,test_labels)print(os.getpid(),'save:',str_features_name_train)print(os.getpid(),'save:',str_labels_name_train)print(os.getpid(),'save:',str_features_name_test)print(os.getpid(),'save:',str_labels_name_test)features.clear()labels.clear()if label == -1:break# 獲取特征向量，傳入灰度圖feature = GetFeature(image)features.append(feature)labels.append(label)# # 隨機(jī)移動(dòng)4次# for itime in range(repate_n):# rMovedImage = randomMoveImage(image)# feature = SimpleGridFeature(rMovedImage) # 簡(jiǎn)單網(wǎng)格# features.append(feature)# labels.append(label)print('Process %s is done!' % os.getpid())if __name__=='__main__':time_start = time.time() # 開(kāi)始計(jì)時(shí)# 父進(jìn)程創(chuàng)建Queue，并傳給各個(gè)子進(jìn)程：image_queue = multiprocessing.Queue(maxsize=1000) #隊(duì)列l(wèi)ock = multiprocessing.Lock() #鎖count = multiprocessing.Value('i',0) #計(jì)數(shù)#將圖寫入隊(duì)列進(jìn)程write_sub_process = multiprocessing.Process(target=read_image_to_queue, args=(image_queue,))read_sub_processes = [] #讀圖子線程for i in range(read_process_n):read_sub_processes.append(multiprocessing.Process(target=extract_feature, args=(image_queue,lock,count)))# 啟動(dòng)子進(jìn)程pw，寫入:write_sub_process.start()# 啟動(dòng)子進(jìn)程pr，讀取:for p in read_sub_processes:p.start()# 等待進(jìn)程結(jié)束:write_sub_process.join()for p in read_sub_processes:p.join()time_end=time.time()time_h=(time_end-time_start)/3600print('用時(shí)：%.6f 小時(shí)'% time_h)print ("讀圖提取特征存npy,運(yùn)行結(jié)束！")

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