4/7/2023 0 Comments Image data generator kerasidx: index of data point in the dataframe fed to generator.ImageDataGenerator saves the generated images with file names defined by a pattern: prefix_idx_randn If you would like to save the images to a directory, provide a path to save_to_dir. df_sample is the dataframe I have shown above. The above code feeds the images to the data generator. Source: Image by author # Feed images to the data generator aug_gen = datagen.flow_from_dataframe(dataframe=df_sample, directory=img_path, save_to_dir=aug_img_path, save_prefix='aug', save_format='jpeg', x_col="fname", y_col="class", batch_size=DATA_AUG_BATCH_SIZE, seed=SEED, shuffle=False, class_mode="categorical", target_size=img_size) Output: Found 10 validated image filenames belonging to 10 classes. In the Google Colab notebook, you can look at the code to see how I create this dataframe.ĭataframe with image file names and corresponding labels. Below you can see how the dataframe is structured. We will use flow_from_dataframe to feed the images to the data generator. Each subdirectory corresponds to a label - an example of a file path structure, imgs/dogs/img01.png, imgs/cats/img01.png. flow_from_directory: You pass the directory path that contains the images categorized by subdirectories.The dataframe has two columns, one column contains the file path relative to the directory path, and the second column contains the labels. flow_from_dataframe: You pass a dataframe and a path to the directory that contains the images.flow: You pass image data and label data as arrays.There are a few ways to pass the images to the data generator. If you would like to learn more about the transformations and other options, visit TensorFlow Core v2.4.1 - ImageDataGenerator. Empty spaces occur when images are shifted along their width or height. fill_mode fills the empty space in an image using various techniques selected by the user.A random number is picked in the range provided. brightness_range alters the brightness of the image.horizontal_flip randomly flips images, horizontally.height_shift shifts the image by the fraction of the total image height, if float provided.width_shift shifts the image by the fraction of the total image width, if float provided.rotation_range rotates the image randomly with maximum rotation angle, 40.rescale multiplies each pixel value with the rescale factor.Transformations to be applied datagen = ImageDataGenerator(rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, zoom_range=0.2, horizontal_flip=True, brightness_range=, fill_mode='nearest') Iterating the generator n_steps_data_aug times generates the required number of images. In this tutorial, we generate a new image corresponding to every sampled image.
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