from tensorflow.keras.preprocessingimportimageimportnumpyasnpimportargparse # 用于保存命令行参数FLAGS=None # 初始化vgg19模型,weights参数指的是使用ImageNet图片集训练的模型 # 每种模型第一次使用的时候都会自网络下载保存的h5文件 # vgg19的数据文件约为584M model=vgg19.VGG19(weights='imagenet')defmain(...
import numpy as npfrom tensorflow.keras.preprocessing import imageimg_path = 'path/to/image.jpg'img = image.load_img(img_path, target_size=(224, 224))img_array = image.img_to_array(img)img_array = np.expand_dims(img_array, axis=0)img_array /= 255.prediction = model.predict(img_...
from tensorflow.python.keras.preprocessing.image import load_img,img_to_array def main(): #tagert_size 修改图像大小 image = load_img("./bus/300.jpg",target_size=(50,50)) print(image) image.show() print(img_to_array(image)) if __name__ == '__main__': main() 多思考也是一种努...
import numpy as np from tensorflow.keras.preprocessing import image img_path = 'path/to/image.jpg' img = image.load_img(img_path, target_size=(224, 224)) img_array = image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) img_array /= 255. prediction = model.pred...
下面要对待使用的图像进行处理,使用keras自带的生成器ImageDataGenerator,返回值可以直接用model.fit方法进行拟合。 #图像数据的处理 from keras.preprocessing.image import ImageDataGenerator #将所有图像除255进行缩放 train_datagen = ImageDataGenerator(rescale=1/255) ...
from keras.preprocessing.image import ImageDataGenerator,array_to_img,img_to_array,load_img 1. 首先引入我们需要的函数库,其中引入的这些库中,都是为了后续代码对图片处理进行准备。 3.对图片集的补充 代码如下(示例): datagen = ImageDataGenerator( ...
import matplotlib.pyplot as plt from tensorflow.keras.preprocessing.image import ImageDataGenerator # 定义一个从文件中读取图片的generator image_generator = ImageDataGenerator(rescale=1.0 / 255).flow_from_directory( '../DemoData/cifar2/test', target_size=(32, 32), batch_size=20, class_mode='...
"""# 设置输入层,作为图像数据输入inputs = tf.keras.layers.Input(shape=(IMG_HEIGHT, IMG_WIDTH,3))# 导入预训练模型,include_top=False代表自己重新写输出层vgg16 = VGG16(input_shape=(IMG_HEIGHT, IMG_WIDTH,3), include_top=False)# 将预训练模型的每一层都设置为不可训练,此处我们暂时只训练全连...
import numpy as npfrom google.colab import filesfrom keras.preprocessing import imageuploaded = files.upload()for fn in uploaded.keys(): # predicting images path = '/content/' + fn img = image.load_img(path, target_size=(300, 300)) plt.imshow(img) plt.show() x = image.img...
importkerasimportos, shutilfrom kerasimportlayersfrom kerasimportmodelsfrom kerasimportoptimizersfrom keras.preprocessing.imageimportImageDataGeneratorimportmatplotlib.pyplot as plttrain_dir='./smile/train'train_smiles_dir='./smile/train/...