Image Prediction - Properly load any image dataset as ImageDataset

Preparing the dataset for ImagePredictor is not difficult at all, however, we’d like to introduce the recommended ways to initialize the dataset, so you will have smoother experience using autogluon.vision.ImagePredictor.

There are generally three ways to load a dataset for ImagePredictor:

• Load a csv file or construct your own pandas DataFrame with image and label columns

• Load a image folder directly with ImageDataset

• Convert a list of images to dataset directly with ImageDataset

We will go through these four methods one by one. First of all, let’s import autogluon

%matplotlib inline
import autogluon.core as ag
from autogluon.vision import ImageDataset
import pandas as pd

/var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/venv/lib/python3.7/site-packages/gluoncv/__init__.py:40: UserWarning: Both mxnet==1.7.0 and torch==1.9.0+cu102 are installed. You might encounter increased GPU memory footprint if both framework are used at the same time.
  warnings.warn(f'Both mxnet=={mx.__version__} and torch=={torch.__version__} are installed. '

Load a csv file or construct a DataFrame object

We use a csv file from PetFinder competition as an example. You may use any tabular data as long as you can create image(absolute or relative paths to images) and label(category for each image) columns.

csv_file = ag.utils.download('https://autogluon.s3-us-west-2.amazonaws.com/datasets/petfinder_example.csv')
df = pd.read_csv(csv_file)
df.head()

100%|██████████| 820/820 [00:00<00:00, 34558.83KB/s]

	image	PetID	label
0	petfinder_data/train_images/015da9e87-1.jpg	015da9e87	0
1	petfinder_data/train_images/022606901-1.jpg	022606901	0
2	petfinder_data/train_images/02f89bdcb-1.jpg	02f89bdcb	0
3	petfinder_data/train_images/03f217352-1.jpg	03f217352	0
4	petfinder_data/train_images/040a9a6f9-1.jpg	040a9a6f9	0

If the image paths are not relative to current working directory, you may use the helper function to prepend prefix for each image, using absolute paths can reduce the chance of OSError happening to file access:

df = ImageDataset.from_csv(csv_file, root='/home/ubuntu')
df.head()

	image	PetID	label
0	/home/ubuntu/petfinder_data/train_images/015da...	015da9e87	0
1	/home/ubuntu/petfinder_data/train_images/02260...	022606901	0
2	/home/ubuntu/petfinder_data/train_images/02f89...	02f89bdcb	0
3	/home/ubuntu/petfinder_data/train_images/03f21...	03f217352	0
4	/home/ubuntu/petfinder_data/train_images/040a9...	040a9a6f9	0

Or you can perform the correction by yourself:

import os
df['image'] = df['image'].apply(lambda x: os.path.join('/home/ubuntu', x))
df.head()

	image	PetID	label
0	/home/ubuntu/petfinder_data/train_images/015da...	015da9e87	0
1	/home/ubuntu/petfinder_data/train_images/02260...	022606901	0
2	/home/ubuntu/petfinder_data/train_images/02f89...	02f89bdcb	0
3	/home/ubuntu/petfinder_data/train_images/03f21...	03f217352	0
4	/home/ubuntu/petfinder_data/train_images/040a9...	040a9a6f9	0

Otherwise you may use the DataFrame as-is, ImagePredictor will apply auto conversion during fit to ensure other metadata is available for training. You can have multiple columns in the DataFrame, ImagePredictor only cares about image and label columns during training.

Load an image directory

It’s pretty common that sometimes you only have a folder of images, organized by the category names. Recursively loop through images is tedious. You can use ImageDataset.from_folders or ImageDataset.from_folder to avoid implementing recursive search.

The difference between from_folders and from_folder is the targeting folder structure. If you have a folder with splits, e.g., train, test, like:

• root/train/car/0001.jpg

• root/train/car/xxxa.jpg

• root/val/bus/123.png

• root/test/bus/023.jpg

Then you can load the splits with from_folders:

train_data, _, test_data = ImageDataset.from_folders('https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip', train='train', test='test')
print('train #', len(train_data), 'test #', len(test_data))
train_data.head()

data/
├── test/
└── train/
train # 800 test # 80

	image	label
0	/var/lib/jenkins/.gluoncv/datasets/shopee-iet/...	0
1	/var/lib/jenkins/.gluoncv/datasets/shopee-iet/...	0
2	/var/lib/jenkins/.gluoncv/datasets/shopee-iet/...	0
3	/var/lib/jenkins/.gluoncv/datasets/shopee-iet/...	0
4	/var/lib/jenkins/.gluoncv/datasets/shopee-iet/...	0

If you have a folder without train or test root folders, like:

• root/car/0001.jpg

• root/car/xxxa.jpg

• root/bus/123.png

• root/bus/023.jpg

Then you can load the splits with from_folder:

# use the train from shopee-iet as new root
root = os.path.join(os.path.dirname(train_data.iloc[0]['image']), '..')
all_data = ImageDataset.from_folder(root)
all_data.head()

	image	label
0	/var/lib/jenkins/.gluoncv/datasets/shopee-iet/...	0
1	/var/lib/jenkins/.gluoncv/datasets/shopee-iet/...	0
2	/var/lib/jenkins/.gluoncv/datasets/shopee-iet/...	0
3	/var/lib/jenkins/.gluoncv/datasets/shopee-iet/...	0
4	/var/lib/jenkins/.gluoncv/datasets/shopee-iet/...	0

# you can manually split the dataset or use `random_split`
train, val, test = all_data.random_split(val_size=0.1, test_size=0.1)
print('train #:', len(train), 'test #:', len(test))

train #: 650 test #: 81

Convert a list of images to dataset

You can create dataset from a list of images with a function, the function is used to determine the label of each image. We use the Oxford-IIIT Pet Dataset mini pack as an example, where images are scattered in images directory but with unique pattern: filenames of cat starts with capital letter, otherwise dogs. So we can use a function to distinguish and assign label to each image:

pets = ag.utils.download('https://autogluon.s3-us-west-2.amazonaws.com/datasets/oxford-iiit-pet-mini.zip')
pets = ag.utils.unzip(pets)
image_list = [x for x in os.listdir(os.path.join(pets, 'images')) if x.endswith('jpg')]
def label_fn(x):
    return 'cat' if os.path.basename(x)[0].isupper() else 'dog'
new_data = ImageDataset.from_name_func(image_list, label_fn, root=os.path.join(os.getcwd(), pets, 'images'))
new_data

100%|██████████| 35730/35730 [00:00<00:00, 50605.49KB/s]

	image	label
0	/var/lib/jenkins/workspace/workspace/autogluon...	0
1	/var/lib/jenkins/workspace/workspace/autogluon...	0
2	/var/lib/jenkins/workspace/workspace/autogluon...	0
3	/var/lib/jenkins/workspace/workspace/autogluon...	1
4	/var/lib/jenkins/workspace/workspace/autogluon...	0
...	...	...
95	/var/lib/jenkins/workspace/workspace/autogluon...	0
96	/var/lib/jenkins/workspace/workspace/autogluon...	1
97	/var/lib/jenkins/workspace/workspace/autogluon...	1
98	/var/lib/jenkins/workspace/workspace/autogluon...	1
99	/var/lib/jenkins/workspace/workspace/autogluon...	1

100 rows × 2 columns

Visualize images

You can use show_images to visualize the images, as well as the corresponding labels:

new_data.show_images()

For raw DataFrame objects, you can convert them to Dataset first to use show_images.

Congratulations, you can now proceed to Image Prediction - Quick Start to start training the ImagePredictor.