python - Keras - 如何将图像数组传递给 ImageDataGenerator.flow
问题描述
我正在学习 keras 中的图像分类。我已经下载了甜甜圈和华夫饼的样本数据集,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)
)
如何做到这一点?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)
但后来我得到这个错误:
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))
解决方案
It's hard to say what does not work without error message, but I assume the problem is that you pass lists to your ImageDataGenerators. You can fix this easily by converting your lists to numpy-arrays:
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)
Edit: A better way, stacking instead of appending to lists and converting
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)))
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