图片总共40个人，每人10张图片，每张图片高57，宽47。共400张图片。
读取图片的py文件import numpy import pandas from PIL import Image from keras import backend as K from keras.utils import np_utils """ 加载图像数据的函数,dataset_path即图像olivettifaces的路径 加载olivettifaces后，划分为train_data,valid_data,test_data三个数据集 函数返回train_data,valid_data,test_data以及对应的label """ # 400个样本，40个人，每人10张样本图。每张样本图高57*宽47，需要2679个像素点。每个像素点做了归一化处理 def load_data(dataset_path): img = Image.open(dataset_path) img_ndarray = numpy.asarray(img, dtype=\'float64\') / 256 print(img_ndarray.shape) faces = numpy.empty((400,57,47)) for row in range(20): for column in range(20): faces[row * 20 + column] = img_ndarray[row * 57:(row + 1) * 57, column * 47:(column + 1) * 47] # 设置400个样本图的标签 label = numpy.empty(400) for i in range(40): label[i * 10:i * 10 + 10] = i label = label.astype(numpy.int) label = np_utils.to_categorical(label, 40) # 将40分类类标号转化为one-hot编码 # 分成训练集、验证集、测试集，大小如下 train_data = numpy.empty((320, 57,47)) # 320个训练样本 train_label = numpy.empty((320,40)) # 320个训练样本，每个样本40个输出概率 valid_data = numpy.empty((40, 57,47)) # 40个验证样本 valid_label = numpy.empty((40,40)) # 40个验证样本，每个样本40个输出概率 test_data = numpy.empty((40, 57,47)) # 40个测试样本 test_label = numpy.empty((40,40)) # 40个测试样本，每个样本40个输出概率 for i in range(40): train_data[i * 8:i * 8 + 8] = faces[i * 10:i * 10 + 8] train_label[i * 8:i * 8 + 8] = label[i * 10:i * 10 + 8] valid_data[i] = faces[i * 10 + 8] valid_label[i] = label[i * 10 + 8] test_data[i] = faces[i * 10 + 9] test_label[i] = label[i * 10 + 9] return [(train_data, train_label), (valid_data, valid_label),(test_data, test_label)] if __name__ == \'__main__\': [(train_data, train_label), (valid_data, valid_label), (test_data, test_label)] = load_data(\'olivettifaces.gif\') oneimg = train_data[0]*256 print(oneimg) im = Image.fromarray(oneimg) im.show()