attention-gate-residual-unet/data_loader.py at main · Pathfinder1996/attention-gate-residual-unet · GitHub

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import os

from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import skimage.io as io
import skimage.transform as trans

def adjust_data(img, mask):
    if np.max(img) > 1:
        img = img / 255
        mask = mask / 255
        mask[mask > 0.5] = 1
        mask[mask <= 0.5] = 0
    return img, mask

def train_generator(batch_size, train_path, image_folder, mask_folder, aug_dict,
                   image_color_mode="grayscale", mask_color_mode="grayscale",
                   image_save_prefix="image", mask_save_prefix="mask",
                   save_to_dir=None, target_size=(256, 256), seed=1):
    image_datagen = ImageDataGenerator(**aug_dict)
    mask_datagen = ImageDataGenerator(**aug_dict)

    image_generator = image_datagen.flow_from_directory(
        train_path,
        classes=[image_folder],
        class_mode=None,
        color_mode=image_color_mode,
        target_size=target_size,
        batch_size=batch_size,
        save_to_dir=save_to_dir,
        save_prefix=image_save_prefix,
        seed=seed)

    mask_generator = mask_datagen.flow_from_directory(
        train_path,
        classes=[mask_folder],
        class_mode=None,
        color_mode=mask_color_mode,
        target_size=target_size,
        batch_size=batch_size,
        save_to_dir=save_to_dir,
        save_prefix=mask_save_prefix,
        seed=seed)

    train_generator = zip(image_generator, mask_generator)
    for img, mask in train_generator:
        img, mask = adjust_data(img, mask)
        yield img, mask

def test_generator(test_path, num_image=30, target_size=(256, 256), as_gray=True):
    for i in range(num_image):
        img = io.imread(os.path.join(test_path, f"{i}.png"), as_gray=as_gray)
        img = img / 255
        img = trans.resize(img, target_size)
        img = np.reshape(img, img.shape + (1,))
        img = np.reshape(img, (1,) + img.shape)
        yield img

def save_result(save_path, npyfile):
    for i, item in enumerate(npyfile):
        img = item[:, :, 0]
        img = (img * 255).astype(np.uint8)
        io.imsave(os.path.join(save_path, f"{i}_predict.png"), img)