Tensorflow分类器项目自定义数据读入的实现

import osimport kerasimport matplotlib.pyplot as pltfrom PIL import Imagefrom keras.preprocessing.image import ImageDataGeneratorfrom sklearn.model_selection import train_test_split

image_path = r'D:/Projects/ImageClassifier/data/set'path = "./data"# 你也可以使用相对路径的方式# image_path =os.path.join(path, "set")

label_name = "labels.txt"label_path = os.path.join(path, label_name)class_names = np.loadtxt(label_path, type(""))

re_load = Falsere_build = False# re_load = Truere_build = Truedata_name = "data.npz"data_path = os.path.join(path, data_name)model_name = "model.h5"model_path = os.path.join(path, model_name)count = 0# 这里判断是否存在序列化之后的数据，re_load是一个开关，是否强制重新处理，测试用，可以去除。if not os.path.exists(data_path) or re_load:  labels = []  images = []  print('Handle images')  # 由于label.txt是和图片防止目录的分类目录一一对应的，即每个子目录的目录名就是labels.txt里的一个label，所以这里可以通过读取class_names的每一项去拼接path后读取  for index, name in enumerate(class_names):    # 这里是拼接后的子目录path    classpath = os.path.join(image_path, name)    # 先判断一下是否是目录    if not os.path.isdir(classpath):      continue    # limit是测试时候用的这里可以去除    limit = 0    for image_name in os.listdir(classpath):      if limit >= max_size:        break      # 这里是拼接后的待处理的图片path      imagepath = os.path.join(classpath, image_name)      count = count + 1      limit = limit + 1      # 利用Image打开图片      img = Image.open(imagepath)      # 缩放到你最初确定要处理的图片分辨率大小      img = img.resize((IMG_SIZE_X, IMG_SIZE_Y))      # 转为灰度图片，这里彩色通道会干扰结果，并且会加大计算量      img = img.convert("L")      # 转为numpy数组      img = np.array(img)      # 由（30，30）转为（1，30，30）（即`channels_first`），当然你也可以转换为（30，30，1）（即`channels_last`）但为了之后预览处理后的图片方便这里采用了（1，30，30）的格式存放      img = np.reshape(img, (1, IMG_SIZE_X, IMG_SIZE_Y))      # 这里利用循环生成labels数据，其中存放的实际是class_names中对应元素的索引      labels.append([index])      # 添加到images中，最后统一处理      images.append(img)      # 循环中一些状态的输出，可以去除      print("{} class: {} {} limit: {} {}"         .format(count, index + 1, class_names[index], limit, imagepath))  # 最后一次性将images和labels都转换成numpy数组  npy_data = np.array(images)  npy_labels = np.array(labels)  # 处理数据只需要一次，所以我们选择在这里利用numpy自带的方法将处理之后的数据序列化存储  np.savez(data_path, x=npy_data, y=npy_labels)  print("Save images by npz")else:  # 如果存在序列化号的数据，便直接读取，提高速度  npy_data = np.load(data_path)["x"]  npy_labels = np.load(data_path)["y"]  print("Load images by npz")image_data = npy_datalabels_data = npy_labels

# 最后一步就是将原始数据分成训练数据和测试数据train_images, test_images, train_labels, test_labels = /  train_test_split(image_data, labels_data, test_size=0.2, random_state=6)

print("_________________________________________________________________")print("%-28s %-s" % ("Name", "Shape"))print("=================================================================")print("%-28s %-s" % ("Image Data", image_data.shape))print("%-28s %-s" % ("Labels Data", labels_data.shape))print("=================================================================")print('Split train and test data,p=%')print("_________________________________________________________________")print("%-28s %-s" % ("Name", "Shape"))print("=================================================================")print("%-28s %-s" % ("Train Images", train_images.shape))print("%-28s %-s" % ("Test Images", test_images.shape))print("%-28s %-s" % ("Train Labels", train_labels.shape))print("%-28s %-s" % ("Test Labels", test_labels.shape))print("=================================================================")

print("Normalize images")train_images = train_images / 255.0test_images = test_images / 255.0

import osimport kerasimport matplotlib.pyplot as pltfrom PIL import Imagefrom keras.layers import *from keras.models import *from keras.optimizers import Adamfrom keras.preprocessing.image import ImageDataGeneratorfrom sklearn.model_selection import train_test_splitos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'# 支持中文plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号re_load = Falsere_build = False# re_load = Truere_build = Trueepochs = 50batch_size = 5count = 0max_size = 2000000000