ICIAR2018_BACH_challenge1：ICIAR2018乳房癌组织学图像分类挑战

# ImageDataAugmentor ImageDataAugmentor is a custom image data generator for Keras supporting the use of modern augmentation modules (e.g. imgaug and albumentations). **NOTICE!** The code is heavily adapted from: https://github.com/keras-team/keras-preprocessing/blob/master/keras_preprocessing/ The usage is analogous to Keras' `ImageDataGenerator` with the exception that the image transformations will be generated with an external augmentations module. To learn more about: * ImageDataGenerator, see: https://keras.io/preprocessing/image/ * albumentations, see: https://github.com/albu/albumentations * imgaug, see: https://github.com/aleju/imgaug For similar projects, see: * https://github.com/davidfreire/Augmentation_project <- a generator that accepts both external and Keras internal augmentations > Example of using `.flow_from_directory(directory)` with `albumentations`: from ImageDataAugmentor.image_data_augmentor import * import albumentations ... AUGMENTATIONS = albumentations.Compose([ albumentations.Transpose(p=0.5), albumentations.Flip(p=0.5), albumentations.OneOf([ albumentations.RandomBrightnessContrast(brightness_limit=0.3, contrast_limit=0.3), albumentations.RandomBrightnessContrast(brightness_limit=0.1, contrast_limit=0.1) ],p=1), albumentations.GaussianBlur(p=0.05), albumentations.HueSaturationValue(p=0.5), albumentations.RGBShift(p=0.5), ]) train_datagen = ImageDataAugmentor( rescale=1./255, augment = AUGMENTATIONS, preprocess_input=None) test_datagen = ImageDataAugmentor(rescale=1./255) train_generator = train_datagen.flow_from_directory( 'data/train', target_size=(224, 224), batch_size=32, class_mode='binary') validation_generator = test_datagen.flow_from_directory( 'data/validation', target_size=(224, 224), batch_size=32, class_mode='binary') model.fit_generator( train_generator, steps_per_epoch=len(train_generator), epochs=50, validation_data=validation_generator, validation_steps=len(validation_generator)) > Example of using `.flow(x, y)` with `imgaug`: from ImageDataAugmentor.image_data_augmentor import * from imgaug import augmenters as iaa import imgaug as ia ... sometimes = lambda aug: iaa.Sometimes(0.5, aug) AUGMENTATIONS = iaa.Sequential([ iaa.Fliplr(0.5), # horizontally flip 50% of all images iaa.Flipud(0.2), # vertically flip 20% of all images sometimes(iaa.Affine( scale={"x": (0.9, 1.1), "y": (0.9, 1.1)}, # scale images to 90-110% of their size, individually per axis translate_percent={"x": (-0.1, 0.1), "y": (-0.1, 0.1)}, # translate by -10 to +10 percent (per axis) rotate=(-45, 45), # rotate by -45 to +45 degrees shear=(-5, 5), # shear by -5 to +5 degrees mode=ia.ALL # use any of scikit-image's warping modes ) )], random_order=True) (x_train, y_train), (x_test, y_test) = cifar10.load_data() y_train = np_utils.to_categorical(y_train, num_classes) y_test = np_utils.to_categorical(y_test, num_classes) datagen = ImageDataAugmentor( featurewise_center=True, featurewise_std_normalization=True, augment = AUGMENTATIONS) # compute quantities required for featurewise normalization datagen.fit(x_train) # fits the model on batches with real-time data augmentation: model.fit_generator(datagen.flow(x_train, y_train, batch_size=32), steps_per_epoch=len(x_train) / 32, epochs=epochs) > Example of using `.flow_from_directory()` with masks for segmentation with `albumentations` (\*): from ImageDataAugmentor.image_data_augmentor import * import albumentations ... AUGMENTATIONS = albumentations.Compose([ albumentations.HorizontalFlip(p=0.5), albumentations.ElasticTransform(), ]) img_data_gen = ImageDataAugmentor(augment=AUGMENTATIONS, augment_seed=123) img_gen = img_data_gen.flow_from_directory('../data/images/', class_mode=None, shuffle=True, seed=123) mask_data_gen = ImageDataAugmentor(augment=AUGMENTATIONS, augment_seed=123, augment_mode='mask') mask_gen = mask_data_gen.flow_from_directory('../data/masks/', class_mode=None, shuffle=True, seed=123) train_gen = zip(img_gen, mask_gen) # Visualize images k = 3 image_batch, mask_batch = next(train_gen) fix, ax = plt.subplots(k,2, figsize=(k*2,10)) for i in range(k): ax[i,0].imshow(image_batch[i,:,:,0]) ax[i,1].imshow(mask_batch[i,:,:,0]) plt.show() (\*) Currently the segmentation masks should be generated using `albumentations` rather than `imgaug`. If you'd still wish to use `imgaug`, make sure that all augmentations are meaningful for both image and mask generation (e.g. no noise augmentations for masks!) and remember to call `.to_deterministic()` to ensure that both the images and the mask are augmented with same transformations. <br /><br /><br /> CITE (BibTex):<br /> @misc{mjkvaak_aug,<br /> author = {Tukiainen, M.},<br /> title = {ImageDataAugmentor},<br /> year = {2019},<br /> publisher = {GitHub},<br /> journal = {GitHub repository},<br /> howpublished = {https://github.com/mjkvaak/ImageDataAugmentor/} <br /> }