Random crop image

image_generator_preprocessing.py

from keras.preprocessing.image import ImageDataGenerator,standardize,random_transform
# input generator with standardization on
datagenX = ImageDataGenerator(
    featurewise_center=True,
    featurewise_std_normalization=True,
    featurewise_standardize_axis=(0, 2, 3),
    rotation_range=180,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True,
    fill_mode='reflect',
    seed=0,
    verbose=1)

# output generator with standardization off
datagenY = ImageDataGenerator(
    featurewise_center=False,
    featurewise_std_normalization=False,
    rotation_range=180,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True,
    fill_mode='reflect',
    seed=0)

def center_crop(x, center_crop_size, **kwargs):
    centerw, centerh = x.shape[1]//2, x.shape[2]//2
    halfw, halfh = center_crop_size[0]//2, center_crop_size[1]//2
    return x[:, centerw-halfw:centerw+halfw,centerh-halfh:centerh+halfh]

def random_crop(x, random_crop_size, sync_seed=None, **kwargs):
    np.random.seed(sync_seed)
    w, h = x.shape[1], x.shape[2]
    rangew = (w - random_crop_size[0]) // 2
    rangeh = (h - random_crop_size[1]) // 2
    offsetw = 0 if rangew == 0 else np.random.randint(rangew)
    offseth = 0 if rangeh == 0 else np.random.randint(rangeh)
    return x[:, offsetw:offsetw+random_crop_size[0], offseth:offseth+random_crop_size[1]]

datagenX.config['random_crop_size'] = (800, 800)
datagenY.config['random_crop_size'] = (800, 800)
datagenX.config['center_crop_size'] = (512, 512)
datagenY.config['center_crop_size'] = (360, 360)

# customize the pipeline
datagenX.set_pipeline([random_crop, random_transform, standardize, center_crop])
datagenY.set_pipeline([random_crop, random_transform, center_crop])

# flow from directory is extended to support more format and also you can even use your own reader function
# here is an example of reading image data saved in csv file
# datagenX.flow_from_directory(csvFolder, image_reader=csvReaderGenerator, read_formats={'csv'}, reader_config={'target_size':(572,572),'resolution':20, 'crange':(0,100)}, class_mode=None, batch_size=1)

dgdx= datagenX.flow_from_directory(inputDir, class_mode=None, read_formats={'png'}, batch_size=2)
dgdy= datagenY.flow_from_directory(outputDir,  class_mode=None, read_formats={'png'}, batch_size=2)

# you can now fit a generator as well
datagenX.fit_generator(dgdx, nb_iter=100)

# here we sychronize two generator and combine it into one
train_generator = dgdx+dgdy

model.fit_generator(
        train_generator,
        samples_per_epoch=2000,
        nb_epoch=50,
        validation_data=validation_generator,
        nb_val_samples=800)