python - Keras - 如何将图像数组传递给 ImageDataGenerator.flow

Question

我正在学习 keras 中的图像分类。我已经下载了 donut 和华夫饼的示例数据集，但它们的大小不同。为了标准化它们的大小，我从它们的目录中加载图像，调整它们的大小并将它们存储在 numpy 数组中:

test_data_dir = 'v_data/train/donuts_and_waffles/'
validation_data_dir = 'v_data/test/donuts_and_waffles/'

loaded_test_donuts = list()
for filename in listdir(test_data_dir + 'donuts/'):
    image1 = Image.open(test_data_dir + 'donuts/' + filename)
    img_resized = image1.resize((224,224))
    img_data = asarray(img_resized)
    loaded_test_donuts.append(img_data)

loaded_test_waffles = list()
for filename in listdir(test_data_dir + 'waffles/'):
    image1 = Image.open(test_data_dir + 'waffles/' + filename)
    img_resized = image1.resize((224,224))
    img_data = asarray(img_resized)
    loaded_test_waffles.append(img_data)

loaded_validation_donuts = list()
for filename in listdir(validation_data_dir + 'donuts/'):
    image1 = Image.open(validation_data_dir + 'donuts/' + filename)
    img_resized = image1.resize((224,224))
    img_data = asarray(img_resized)
    loaded_validation_donuts.append(img_data)

loaded_validation_waffles = list()
for filename in listdir(validation_data_dir + 'waffles/'):
    image1 = Image.open(validation_data_dir + 'waffles/' + filename)
    img_resized = image1.resize((224,224))
    img_data = asarray(img_resized)
    loaded_validation_waffles.append(img_data)

test_data = list()
validation_data = list()

test_data.append(np.array(loaded_test_donuts))
test_data.append(np.array(loaded_test_waffles))
validation_data.append(np.array(loaded_validation_donuts))
validation_data.append(np.array(loaded_validation_waffles))

test_data = np.array(test_data)
validation_data = np.array(validation_data)

然后我想为我的数据创建一个 ImageDataGenerator:

train_datagen = ImageDataGenerator( 
    rescale=1. / 255, 
    shear_range=0.2, 
    zoom_range=0.2, 
    horizontal_flip=True) 

test_datagen = ImageDataGenerator(rescale=1. / 255) 

train_generator = train_datagen.flow( 
    #how can I pass here test_data to make it work (along with which parameters)
) 

validation_generator = test_datagen.flow(
    #how can I pass here validation_data to make it work (along with which    parameters)
) 

如何实现这一目标？我试过这样:

train_generator = train_datagen.flow( 
    test_data,                                  #does not work
    batch_size=batch_size) 

validation_generator = test_datagen.flow( 
    validation_data,                            #does not work
    batch_size=batch_size) 

但随后我收到此错误:

Traceback (most recent call last):
...

ValueError: ('Input data in `NumpyArrayIterator` should have rank 4. You passed an array with shape', (2, 770, 224, 224, 3))

Answer 1

如果没有错误消息，很难说什么不起作用，但我认为问题在于您将列表传递给 ImageDataGenerators。您可以通过将列表转换为 numpy 数组来轻松解决此问题:

test_data = list()
validation_data = list()

test_data.append(np.array(loaded_test_donuts))
test_data.append(np.array(loaded_test_waffles))
validation_data.append(np.array(loaded_validation_donuts))
validation_data.append(np.array(loaded_validation_waffles))

test_data = np.array(test_data)
validation_data = np.array(validation_data)

编辑:更好的方法，堆叠而不是附加到列表并转换

test_data = np.vstack((np.array(loaded_test_donuts),np.array(loaded_test_waffles)))

validation_data = np.vstack((np.array(loaded_validation_donuts),np.array(loaded_validation_waffles)))