python多进程读图提取特征存npy

import multiprocessingimport os, time, randomimport numpy as npimport cv2import osimport sysfrom time import ctimeimport tensorflow as tf image_dir = r"D:/sxl/处理图片/汉字分类/train10/"    #图像文件夹路径data_type = 'test'save_path = r'E:/sxl_Programs/Python/CNN/npy/'    #存储路径data_name = 'Img10'                #npy文件名 char_set = np.array(os.listdir(image_dir))      #文件夹名称列表np.save(save_path+'ImgShuZi10.npy',char_set)     #文件夹名称列表char_set_n = len(char_set)              #文件夹列表长度 read_process_n = 1  #进程数repate_n = 4     #随机移动次数data_size = 1000000  #1个npy大小 shuffled = True   #是否打乱 #可以读取带中文路径的图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 #多个数组按同一规则打乱数据def ShuffledData(features,labels):  '''  @description:随机打乱数据与标签，但保持数据与标签一一对应  '''  permutation = np.random.permutation(features.shape[0])  shuffled_features = features[permutation,:] #多维  shuffled_labels = labels[permutation]    #1维  return shuffled_features,shuffled_labels #函数功能：简单网格#函数要求：1.无关图像大小；2.输入图像默认为灰度图;3.参数只有输入图像#返回数据：1x64*64维特征def GetFeature(image):   #图像大小归一化  image = cv2.resize(image,(64,64))  img_h = image.shape[0]  img_w = image.shape[1]   #定义特征向量  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 # 写数据进程执行的代码:def read_image_to_queue(queue):  print('Process to write: %s' % os.getpid())  for j,dirname in enumerate(char_set): # dirname 是文件夹名称    label = np.where(char_set==dirname)[0][0]   #文件夹名称对应的下标序号    print('序号：'+str(j),'读 '+dirname+' 文件夹...时间：',ctime() )    for parent,_,filenames in os.walk(os.path.join(image_dir,dirname)):      for filename in filenames:        if(filename[-4:]!='.jpg'):          continue        image = 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('读图结束!')  return True    # 读数据进程执行的代码:def extract_feature(queue,lock,count):  '''  @description:从队列中取出图片进行特征提取  @queue:先进先出队列   lock：锁，在计数时上锁，防止冲突   count:计数  '''   print('Process %s start reading...' % os.getpid())   global data_n  features = [] #存放提取到的特征  labels = [] #存放标签  flag = True #标志着进程是否结束  while flag:    image,label = queue.get() #从队列中获取图像和标签     if len(features) >= data_size or label == -1:  #特征数组的长度大于指定长度，则开始存储       array_features = np.array(features) #转换成数组      array_labels = np.array(labels)       array_features,array_labels = ShuffledData(array_features,array_labels) #打乱数据            lock.acquire()  # 锁开始       # 拆分数据为训练集，测试集      split_x = int(array_features.shape[0] * 0.8)      train_data, test_data = np.split(array_features, [split_x], axis=0)   # 拆分特征数据集      train_labels, test_labels = np.split(array_labels, [split_x], axis=0) # 拆分标签数据集       count.value += 1  #下标计数加1      str_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()  # 锁释放       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     # 获取特征向量，传入灰度图    feature = GetFeature(image)    features.append(feature)    labels.append(label)     # # 随机移动4次    # for itime in range(repate_n):    #   rMovedImage = randomMoveImage(image)    #   feature = SimpleGridFeature(rMovedImage) # 简单网格    #   features.append(feature)    #   labels.append(label)    print('Process %s is done!' % os.getpid()) if __name__=='__main__':  time_start = time.time() # 开始计时   # 父进程创建Queue，并传给各个子进程：  image_queue = multiprocessing.Queue(maxsize=1000) #队列  lock = multiprocessing.Lock()           #锁  count = multiprocessing.Value('i',0)        #计数   #将图写入队列进程  write_sub_process = multiprocessing.Process(target=read_image_to_queue, args=(image_queue,))   read_sub_processes = []              #读图子线程  for i in range(read_process_n):    read_sub_processes.append(      multiprocessing.Process(target=extract_feature, args=(image_queue,lock,count))    )   # 启动子进程pw，写入:  write_sub_process.start()   # 启动子进程pr，读取:  for p in read_sub_processes:    p.start()   # 等待进程结束:  write_sub_process.join()  for p in read_sub_processes:    p.join()   time_end=time.time()  time_h=(time_end-time_start)/3600  print('用时：%.6f 小时'% time_h)  print ("读图提取特征存npy,运行结束！")