python多进程读图提取特征存npy

本文实例为大家分享了python/' target='_blank'>python多进程读图提取特征存npy的具体代码，供大家参考，具体内容如下

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！")

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