如何通过 keras.load_img 加载多张图片并为​​ CNN 模型数据扩充每张图片

how to load multiple images through keras.load_img and data augment each images for CNN model

我想创建一个 CNN 模型来对 10 种不同的汽车进行分类。首先,我下载了一些图片,现在我想通过数据扩充来增加图片的数量。由于一次处理一张图像很忙,我为此编写了一个 for 循环,但它显示错误。

TypeError                                 Traceback (most recent call last)
<ipython-input-14-9ced4a120c2d> in <module>
     10 
     11 for i in images:
---> 12     x = img_to_array(images[i])
     13     x = x.reshape((1,) + x.shape)
     14     j=0

~\anaconda3\envs\DSEnv\lib\site-packages\keras_preprocessing\image\iterator.py in __getitem__(self, idx)
     51 
     52     def __getitem__(self, idx):
---> 53         if idx >= len(self):
     54             raise ValueError('Asked to retrieve element {idx}, '
     55                              'but the Sequence '

TypeError: '>=' not supported between instances of 'tuple' and 'int'

代码:

images = ImageDataGenerator().flow_from_directory(r'\Users\Mohda\OneDrive\Desktop\ferrari sf90 stradale')
datagen = ImageDataGenerator(
    rotation_range=30, 
    width_shift_range=0.3,
    height_shift_range=0.3, 
    shear_range=0.2, 
    zoom_range=0.2,
    horizontal_flip=True, 
    vertical_flip=True,
    fill_mode='nearest')

for i in images:
    x = img_to_array(images[i])
    x = x.reshape((1,) + x.shape)
    j=0
    for batch in datagen.flow(x,batch_size=1,save_to_dir='preview',save_prefix='ferrari sf90 stradale',save_format='jpeg'):
        i+=1
        if i>20:
            break
    

您无需遍历图像并应用 ImageDataGenerator,而只需在图像路径上使用创建的 ImageDataGenerator,它会即时为您完成。为了获取图像,您可以在生成器上调用 next()

PATH_TO_IMAGES = r'\Users\Mohda\OneDrive\Desktop\ferrari sf90 stradale'

# Specify whatever augmentation methods you want to use here
train_datagen = ImageDataGenerator(
        rotation_range=30, 
        width_shift_range=0.3,
        height_shift_range=0.3, 
        shear_range=0.2, 
        zoom_range=0.2,
        horizontal_flip=True, 
        vertical_flip=True,
        fill_mode='nearest')

train_generator = train_datagen.flow_from_directory(
        PATH_TO_IMAGES,
        target_size=(150, 150),
        batch_size=32,
        save_to_dir=/tmp/img-data-gen-outputs
        class_mode='binary')

# Use the generator by calling .next()

train_generator.next()