Add images from disk to a Tensorflow dataset

I am using Tensorflow Datasets’ tfds.load function to load my data:

import tensorflow_datasets as tfds
import tensorflow as tf

(raw_train, raw_validation, raw_test), metadata = tfds.load(
    'cats_vs_dogs',
    split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'],
    with_info=True,
    as_supervised=True,
)

Now, I have some additional images of cats and dogs on my local pc (for example Cat1.jpg). I would like to add them to these data. How can I do this?

Note that I have not just one file, but a lot and furthermore this is just a binary classification example; same question holds for multi-class classification, so it would be good to also have a solution for that.

Update: I tried different ways, like trying to read in images with tf-nightly with tf.keras.preprocessing.image_dataset_from_directory, however, it is not that easy, unforunately. There are a lot of problems, like the resulting dataset is in different dtype and cannot be merged with the original one. I have no solution for this problem. I put a bounty on it, because I really need detailed code, a working solution and not just some general thoughts how in theory this could be achieved. I don’t need a solution with image_dataset_from_directory, if anyone has any solution, detailed code which works, I am fine with that.

I did not want to post any code, as I think there are better ways to solve this. However, please find the way I tried it here (in colab):

!pip install tf-nightly
#!pip uninstall tf-nightly

import tensorflow as tf
print(tf.__version__)

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    '/tmp/Test/',
    image_size = (224,224),
    batch_size = 32,
    # label_mode = 'int'
)

There is a Test folder in tmp. One subfolder cat and the other dog. Includes some random pictures from search for cat and dog.

Resulting train_ds is a <BatchDataset shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int32)>

import os
import shutil

os.listdir("/tmp/Test") #First find where the ".ipynb_checkpoints" is located.

shutil.rmtree("/tmp/Test/.ipynb_checkpoints")

import tensorflow_datasets as tfds
(raw_train, raw_validation, raw_test), metadata = tfds.load(
    'cats_vs_dogs',
    split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'],
    with_info=True,
    as_supervised=True,
)

raw_train for example is a <DatasetV1Adapter shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>.

  def _normalize_img(img, label):
  img = tf.cast(img, tf.float32) / 255.
  img = tf.image.resize(img, (224,224))
  label = tf.cast(label, tf.int64)
  img = tf.cast(img, tf.uint8)
  return (img, label)
  # ds = tfds.load('mnist', split='train', as_supervised=True)
  ds = raw_train.map(_normalize_img)

ds is now a <DatasetV1Adapter shapes: ((224, 224, 3), ()), types: (tf.uint8, tf.int64)>

test=ds.concatenate(raw_train)

Does not solve it, as data is not properly matched/concatenated. Furthermore in multi-class case I have no control to check the match of the labels.

So I do not need any general thoughts about how this could be achieved in theory. I need a detailed working solution, detailed code. And not just for binary as here in this example, but I also need it for multi-class problems, as I also have this problem there. So how to match the "read-in labels" with the labels resulting from tfds.load in multi-class case. That there are no miss-matching, like mixing the classes or so. E.g. cats becomes horse (in case of cats vs dogs vs horses).

Second way:
I also tried to point a ImageDataGenerator directly to the raw_train dataset. If that worked I could have proceeded with using ImageDataGenerator in general, alhough I actually did not want this. So I just want to add images to the raw_train dataset. I tried this:

from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_image_generator = ImageDataGenerator(
    rescale=1./255,
)

train_datagen = train_image_generator.flow_from_directory(
  directory=raw_train,
  target_size=(224, 224),
  shuffle=True,
  batch_size=128,
  class_mode='binary'
)

And then match/concatenate the results of these datagenerators. But it is not possible to just point this on raw_train, it gives an error.