Tensorflow分类器项目自定义数据读入的方法介绍（代码示例）

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

IMG_SIZE_X = 30IMG_SIZE_Y = 30

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

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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 = 2000000000IMG_SIZE_X = 30IMG_SIZE_Y = 30np.random.seed(9277)image_path = r'D:ProjectsImageClassifierdataset'path = ".data"data_name = "data.npz"data_path = os.path.join(path, data_name)model_name = "model.h5"model_path = os.path.join(path, model_name)label_name = "labels.txt"label_path = os.path.join(path, label_name)class_names = np.loadtxt(label_path, type(""))print('Load class names')if not os.path.exists(data_path) or re_load:    labels = []    images = []    print('Handle images')    for index, name in enumerate(class_names):        classpath = os.path.join(image_path, name)        if not os.path.isdir(classpath):            continue        limit = 0        for image_name in os.listdir(classpath):            if limit >= max_size:                break            imagepath = os.path.join(classpath, image_name)            count = count + 1            limit = limit + 1            img = Image.open(imagepath)            img = img.resize((30, 30))            img = img.convert("L")            img = np.array(img)            img = np.reshape(img, (1, 30, 30))            # img = skimage.io.imread(imagepath, as_grey=True)            # if img.shape[2] != 3:            #     print("{} shape is {}".format(image_name, img.shape))            #     continue            # data = transform.resize(img, (IMG_SIZE_X, IMG_SIZE_Y))            labels.append([index])            images.append(img)            print("{} class: {} {} limit: {} {}"                  .format(count, index + 1, class_names[index], limit, imagepath))    npy_data = np.array(images)    npy_labels = np.array(labels)    np.savez(data_path, x=npy_data, y=npy_labels)    print("Save images by npz")else:    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_labelsprint("_________________________________________________________________")print("%-28s %-s" % ("Name", "Shape"))print("=================================================================")print("%-28s %-s" % ("Image Data", image_data.shape))print("%-28s %-s" % ("Labels Data", labels_data.shape))print("=================================================================")train_images, test_images, train_labels, test_labels =     train_test_split(image_data, labels_data, test_size=0.2, random_state=6)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("=================================================================")# 归一化# 我们将这些值缩小到 0 到 1 之间，然后将其馈送到神经网络模型。为此，将图像组件的数据类型从整数转换为浮点数，然后除以 255。以下是预处理图像的函数：# 务必要以相同的方式对训练集和测试集进行预处理：print("Normalize images")train_images = train_images / 255.0test_images = test_images / 255.0