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

Question

我正在学习有关喀拉拉邦的图像分类。我已经下载了甜甜圈和华夫饼的示例数据集，但是它们的大小不同。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？我已经这样尝试过：

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) 

但随后出现此错误：

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

我建议您创建一个文件夹，其中有n个代表类的文件夹，例如“ dog”，“ cat”，并先执行预处理步骤，然后按如下方式保存生成的图像：

from PIL import Image
import glob
from keras.preprocessing import image


W=500
H=825

for folder in glob.glob("*"):     #goes through every folder 
ims = glob.glob(folder+ "\\*.png")   #reads image names from folder assuming images are png
for im in ims:  
    img = Image.open(im)
    print(im)
    if (img.size != (W, H)):
        imgr = process(img, W, H) # where "process" is reszing in your case
        imgr.save(im)

然后将您的数据溢出到训练和验证文件夹中，然后执行：

traingen = image.ImageDataGenerator(rescale=1./255)
validationgen = image.ImageDataGenerator(rescale=1./255)

train = traingen.flow_from_directory("train",target_size=(H,W), batch_size=s,shuffle=True)
val = validationgen.flow_from_directory("validation",target_size=(500, 825), batch_size=32, shuffle=False)

Answer 2

很难说什么[[不起作用而没有错误消息，但是我认为问题是您将列表传递给了ImageDataGenerators。您可以通过将列表转换为numpy-arrays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：一种更好的方法，堆叠而不是附加到列表并进行转换
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)))