![]() ![]() Val_generator = zip(img_val_generator, mask_val_generator) Train_generator = zip(img_train_generator, mask_train_generator) Mask_val_generator = mask_datagen.flow(imgs_mask_train, subset='validation',batch_size=8,save_to_dir='./mypath',save_prefix='mask_val', seed=seed) Img_val_generator = image_datagen.flow(imgs_train, subset='validation',batch_size=8,save_to_dir='./mypath',save_prefix='img_val', seed=seed) Mask_train_generator = mask_datagen.flow(imgs_mask_train, shuffle=False,subset='training',batch_size=8,save_to_dir='./mypath',save_prefix='mask_train', seed=seed) Img_train_generator = image_datagen.flow(imgs_train,shuffle=False, subset='training', batch_size=8,save_to_dir='./mypath',save_prefix='img_train', seed=seed) Mask_datagen.fit(imgs_mask_train, augment=True, seed=seed) ![]() Image_datagen.fit(imgs_train, augment=True, seed=seed) Imgs_mask_train = imgs_mask_train.astype('float32') Imgs_train = imgs_train.astype('float32') Imgs_train, imgs_mask_train, _ = load_train_data() Split train data into training and validation when using ImageDataGenerator:.using loop and yield to combine two generators:.Note: make sure you don't have other np random seed generators in the code before this function.įor those who don't get a GPU and get stuck with zip(), please read this: Return zip(image_generator, mask_generator)` Mask_generator = mask_datagen.flow(masks, Image_generator = image_datagen.flow(imgs, Image_datagen.fit(imgs, augment=True, seed=seed) ** "Error when checking target: expected activation_layer(final softmax) to have 3 dimensions, but got array with shape (32, Height, Width, 3)"** #TRYING TO FIT_GENERATOR THROWS THIS ERROR(BOTH FOR FCNN AND SEGNET) Train_generator = zip(image_generator, mask_generator) #Combine generators into one which yields image and masks Mask_generator = mask_datagen.flow_from_directory('dataset/train_masks', Image_generator = image_datagen.flow_from_directory('dataset/train_images', # Provide the same seed and keyword arguments to the fit and flow methods Mask_datagen = ImageDataGenerator(**data_gen_args) # We create two instances with the same arguments of images for both generators.įinally,can we use Imagedatagenerator for segmentation problem?ĭef applyImageAugmentationAndRetrieveGenerator():įrom import ImageDataGenerator Ii)if i give path for image generator as data/images instead of data/ and for mask generator data/masks instead of data/,its showing found 0 images in 0 classes for both cases.įor classification problem its fine,for segmentation problem how to give path correctly? if path to be given as in case(i),then why its showing double the no. How it is working(flow_from_directory)? The problem is image segmentation,720 training images and its masks. In this case while executing, I'm getting ,found 1440 images belonging to 2 classes for image generatorĪnd found 1440 images belonging to 2 classes for mask generator.Īnd data/masks contain 720 ground truth images.īut the generators in both cases stating 1440 images belonging to 2 classes. Train_generator = zip(image_generator, path in flow from directory, i gave the directory as path which contains two subdirectories one named images contains images and other named masks contain all masks. Seed=seed) combine generators into one which yields image and masks Mask_generator = mask_datagen.flow_from_directory( Image_generator = image_datagen.flow_from_directory( Mask_datagen.fit(masks, augment=True, seed=seed) Image_datagen.fit(images, augment=True, seed=seed) Mask_datagen = ImageDataGenerator(**data_gen_args) Provide the same seed and keyword arguments to the fit and flow methods Image_datagen = ImageDataGenerator(**data_gen_args) we create two instances with the same argumentsĭata_gen_args = dict(featurewise_center=True, If anyone else gets here from search, the new answer is that you can do this with the imagedatageneratorĮxample of transforming images and masks together.
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