Working with LabelMe Bounding Box Annotations in Torchvision

pytorch
image-annotation
object-detection
tutorial
Learn how to work with LabelMe bounding box annotations in torchvision for object detection tasks.
Author

Christian Mills

Published

January 21, 2024

This post is part of the following series:

Introduction

Welcome to this hands-on guide for working with bounding box annotations created with the LabelMe annotation tool in torchvision. Bounding box annotations specify rectangular frames around objects in images to identify and locate them for training object detection models.

The tutorial walks through setting up a Python environment, loading the raw annotations into a Pandas DataFrame, annotating and augmenting images using torchvision’s Transforms V2 API, and creating a custom Dataset class to feed samples to a model.

This guide is suitable for beginners and experienced practitioners, providing the code, explanations, and resources needed to understand and implement each step. By the end, you will have a solid foundation for working with bounding box annotations made with LabelMe for object detection tasks.

Getting Started with the Code

The tutorial code is available as a Jupyter Notebook, which you can run locally or in a cloud-based environment like Google Colab. I have dedicated tutorials for those new to these platforms or who need guidance setting up:

Tutorial Code
Jupyter Notebook: GitHub Repository Open In Colab

Setting Up Your Python Environment

Before diving into the code, we’ll cover the steps to create a local Python environment and install the necessary dependencies.

Creating a Python Environment

First, we’ll create a Python environment using Conda/Mamba. Open a terminal with Conda/Mamba installed and run the following commands:

# Create a new Python 3.10 environment
conda create --name pytorch-env python=3.10 -y
# Activate the environment
conda activate pytorch-env
# Create a new Python 3.10 environment
mamba create --name pytorch-env python=3.10 -y
# Activate the environment
mamba activate pytorch-env

Installing PyTorch

Next, we’ll install PyTorch. Run the appropriate command for your hardware and operating system.

# Install PyTorch with CUDA
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
# MPS (Metal Performance Shaders) acceleration is available on MacOS 12.3+
pip install torch torchvision torchaudio
# Install PyTorch for CPU only
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
# Install PyTorch for CPU only
pip install torch torchvision torchaudio

Installing Additional Libraries

We also need to install some additional libraries for our project.

Package Description
jupyter An open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. (link)
matplotlib This package provides a comprehensive collection of visualization tools to create high-quality plots, charts, and graphs for data exploration and presentation. (link)
pandas This package provides fast, powerful, and flexible data analysis and manipulation tools. (link)
pillow The Python Imaging Library adds image processing capabilities. (link)
tqdm A Python library that provides fast, extensible progress bars for loops and other iterable objects in Python. (link)
distinctipy A lightweight python package providing functions to generate colours that are visually distinct from one another. (link)

Run the following commands to install these additional libraries:

# Install additional dependencies
pip install distinctipy jupyter matplotlib pandas pillow tqdm

Installing Utility Packages

We will also install some utility packages I made, which provide shortcuts for routine tasks.

Package Description
cjm_pil_utils Some PIL utility functions I frequently use. (link)
cjm_psl_utils Some utility functions using the Python Standard Library. (link)
cjm_pytorch_utils Some utility functions for working with PyTorch. (link)
cjm_torchvision_tfms Some custom Torchvision tranforms. (link)

Run the following commands to install the utility packages:

# Install additional utility packages
pip install cjm_pil_utils cjm_psl_utils cjm_pytorch_utils cjm_torchvision_tfms

With our environment set up, we can open our Jupyter Notebook and dive into the code.

Importing the Required Dependencies

First, we will import the necessary Python packages into our Jupyter Notebook.

# Import Python Standard Library dependencies
from functools import partial
from pathlib import Path

# Import utility functions
from cjm_pil_utils.core import get_img_files
from cjm_psl_utils.core import download_file, file_extract
from cjm_pytorch_utils.core import tensor_to_pil
from cjm_torchvision_tfms.core import ResizeMax, PadSquare, CustomRandomIoUCrop

# Import the distinctipy module
from distinctipy import distinctipy

# Import matplotlib for creating plots
import matplotlib.pyplot as plt

# Import numpy
import numpy as np

# Import the pandas package
import pandas as pd

# Do not truncate the contents of cells and display all rows and columns
pd.set_option('max_colwidth', None, 'display.max_rows', None, 'display.max_columns', None)

# Import PIL for image manipulation
from PIL import Image

# Import PyTorch dependencies
import torch
from torch.utils.data import Dataset, DataLoader

# Import torchvision dependencies
import torchvision
torchvision.disable_beta_transforms_warning()
from torchvision.tv_tensors import BoundingBoxes
from torchvision.utils import draw_bounding_boxes
import torchvision.transforms.v2  as transforms

# Import tqdm for progress bar
from tqdm.auto import tqdm

Torchvision provides dedicated torch.Tensor subclasses for different annotation types called TVTensors. Torchvision’s V2 transforms use these subclasses to update the annotations based on the applied image augmentations. The TVTensor class for bounding box annotations is called BoundingBoxes. Torchvision also includes a draw_bounding_boxes function to annotate images.

Loading and Exploring the Dataset

After importing the dependencies, we can start working with our data. I annotated a toy dataset with bounding boxes for this tutorial using images from the free stock photo site Pexels. The dataset is available on HuggingFace Hub at the link below:

Setting the Directory Paths

We first need to specify a place to store our dataset and a location to download the zip file containing it. The following code creates the folders in the current directory (./). Update the path if that is not suitable for you.

# Define path to store datasets
dataset_dir = Path("./Datasets/")
# Create the dataset directory if it does not exist
dataset_dir.mkdir(parents=True, exist_ok=True)

# Define path to store archive files
archive_dir = dataset_dir/'../Archive'
# Create the archive directory if it does not exist
archive_dir.mkdir(parents=True, exist_ok=True)

# Creating a Series with the paths and converting it to a DataFrame for display
pd.Series({
    "Dataset Directory:": dataset_dir, 
    "Archive Directory:": archive_dir
}).to_frame().style.hide(axis='columns')
Dataset Directory: Datasets
Archive Directory: Datasets/../Archive

Setting the Dataset Path

Next, we construct the name for the Hugging Face Hub dataset and set where to download and extract the dataset.

# Set the name of the dataset
dataset_name = 'labelme-bounding-box-toy-dataset'

# Construct the HuggingFace Hub dataset name by combining the username and dataset name
hf_dataset = f'cj-mills/{dataset_name}'

# Create the path to the zip file that contains the dataset
archive_path = Path(f'{archive_dir}/{dataset_name}.zip')

# Create the path to the directory where the dataset will be extracted
dataset_path = Path(f'{dataset_dir}/{dataset_name}')

# Creating a Series with the dataset name and paths and converting it to a DataFrame for display
pd.Series({
    "HuggingFace Dataset:": hf_dataset, 
    "Archive Path:": archive_path, 
    "Dataset Path:": dataset_path
}).to_frame().style.hide(axis='columns')
HuggingFace Dataset: cj-mills/labelme-bounding-box-toy-dataset
Archive Path: Datasets/../Archive/labelme-bounding-box-toy-dataset.zip
Dataset Path: Datasets/labelme-bounding-box-toy-dataset

Downloading the Dataset

We can now download the archive file and extract the dataset using the download_file and file_extract functions from the cjm_psl_utils package. We can delete the archive afterward to save space.

# Construct the HuggingFace Hub dataset URL
dataset_url = f"https://huggingface.co/datasets/{hf_dataset}/resolve/main/{dataset_name}.zip"
print(f"HuggingFace Dataset URL: {dataset_url}")

# Set whether to delete the archive file after extracting the dataset
delete_archive = True

# Download the dataset if not present
if dataset_path.is_dir():
    print("Dataset folder already exists")
else:
    print("Downloading dataset...")
    download_file(dataset_url, archive_dir)    
    
    print("Extracting dataset...")
    file_extract(fname=archive_path, dest=dataset_dir)
    
    # Delete the archive if specified
    if delete_archive: archive_path.unlink()

Getting the Image and Annotation Files

The dataset folder contains sample images and annotation files. Each sample image has its own JSON annotation file.

# Get a list of image files in the dataset
img_file_paths = get_img_files(dataset_path)

# Get a list of JSON files in the dataset
annotation_file_paths = list(dataset_path.glob('*.json'))

# Display the names of the folders using a Pandas DataFrame
pd.DataFrame({"Image File": [file.name for file in img_file_paths], 
              "Annotation File":[file.name for file in annotation_file_paths]}).head()
Image File Annotation File
0 258421.jpg 258421.json
1 3075367.jpg 3075367.json
2 3076319.jpg 3076319.json
3 3145551.jpg 3145551.json
4 3176048.jpg 3176048.json

Get Image File Paths

Each image file has a unique name that we can use to locate the corresponding annotation data. We can make a dictionary that maps image names to file paths. The dictionary will allow us to retrieve the file path for a given image more efficiently.

# Create a dictionary that maps file names to file paths
img_dict = {file.stem : file for file in img_file_paths}

# Print the number of image files
print(f"Number of Images: {len(img_dict)}")

# Display the first five entries from the dictionary using a Pandas DataFrame
pd.DataFrame.from_dict(img_dict, orient='index').head()
Number of Images: 29
0
258421 Datasets/labelme-bounding-box-toy-dataset/258421.jpg
3075367 Datasets/labelme-bounding-box-toy-dataset/3075367.jpg
3076319 Datasets/labelme-bounding-box-toy-dataset/3076319.jpg
3145551 Datasets/labelme-bounding-box-toy-dataset/3145551.jpg
3176048 Datasets/labelme-bounding-box-toy-dataset/3176048.jpg

Get Image Annotations

Next, we read the content of each JSON annotation file into a single Pandas DataFrame so we can easily query the annotations.

# Create a generator that yields Pandas DataFrames containing the data from each JSON file
cls_dataframes = (pd.read_json(f, orient='index').transpose() for f in tqdm(annotation_file_paths))

# Concatenate the DataFrames into a single DataFrame
annotation_df = pd.concat(cls_dataframes, ignore_index=False)

# Assign the image file name as the index for each row
annotation_df['index'] = annotation_df.apply(lambda row: row['imagePath'].split('.')[0], axis=1)
annotation_df = annotation_df.set_index('index')

# Keep only the rows that correspond to the filenames in the 'img_dict' dictionary
annotation_df = annotation_df.loc[list(img_dict.keys())]

# Print the first 5 rows of the DataFrame
annotation_df.head()
version flags shapes imagePath imageData imageHeight imageWidth
index
258421 5.3.1 {} [{‘label’: ‘person’, ‘points’: [[340.2519836425781, 466.943359375], [418.9939880371094, 777.34423828125]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}, {‘label’: ‘person’, ‘points’: [[386.076124567474, 443.94463667820065], [460.81660899653974, 777.1626297577855]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}] 258421.jpg None 1152 768
3075367 5.3.1 {} [{‘label’: ‘person’, ‘points’: [[413.31866455078125, 41.2171630859375], [919.8128051757812, 763.16552734375]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}] 3075367.jpg None 768 1344
3076319 5.3.1 {} [{‘label’: ‘person’, ‘points’: [[335.30731201171875, 151.749755859375], [711.2194213867188, 1117.489013671875]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}, {‘label’: ‘person’, ‘points’: [[8.10714285714289, 131.87500000000003], [404.2032880329769, 1119.0]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}] 3076319.jpg None 1120 768
3145551 5.3.1 {} [{‘label’: ‘person’, ‘points’: [[658.6324462890625, 281.2455139160156], [687.085693359375, 398.6059265136719]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}, {‘label’: ‘person’, ‘points’: [[642.0, 289.8510638297872], [669.6595744680851, 398.8936170212766]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}] 3145551.jpg None 768 1184
3176048 5.3.1 {} [{‘label’: ‘person’, ‘points’: [[518.2313232421875, 338.9653015136719], [594.632080078125, 466.0799865722656]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}, {‘label’: ‘person’, ‘points’: [[683.419689119171, 356.47668393782385], [638.860103626943, 437.8238341968912]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}] 3176048.jpg None 768 1152

Let’s examine the source JSON content corresponding to the first row in the DataFrame:

{
  "version": "5.3.1",
  "flags": {},
  "shapes": [
    {
      "label": "person",
      "points": [
        [
          340.2519836425781,
          466.943359375
        ],
        [
          418.9939880371094,
          777.34423828125
        ]
      ],
      "group_id": null,
      "description": "",
      "shape_type": "rectangle",
      "flags": {}
    },
    {
      "label": "person",
      "points": [
        [
          386.076124567474,
          443.94463667820065
        ],
        [
          460.81660899653974,
          777.1626297577855
        ]
      ],
      "group_id": null,
      "description": "",
      "shape_type": "rectangle",
      "flags": {}
    }
  ],
  "imagePath": "258421.jpg",
  "imageData": null,
  "imageHeight": 1152,
  "imageWidth": 768
}

The bounding box annotations are in [[Top-Left X, Top-Left Y],[Bottom-Right X, Bottom-Right Y]] format.

Fill empty annotations

Next, we will fill empty entries for images without bounding box annotations with a default value.

# Define a default value for empty annotations
EMPTY_BBOX_FILL = [{'label': 'none', 'points': [[-1000, -1000], [-500, -500]], 'group_id': None, 'description': '', 'shape_type': 'rectangle', 'flags': {}}]

# Fill empty annotations
annotation_df['shapes'] = annotation_df['shapes'].apply(lambda x: EMPTY_BBOX_FILL if not x else x)

print(f"Annotations Filled: {annotation_df['shapes'].apply(lambda x: x == EMPTY_BBOX_FILL).sum()}")
    Annotations Filled: 1

With the annotations loaded, we can start inspecting our dataset.

Inspecting the Class Distribution

First, we get the names of all the classes in our dataset and inspect the distribution of samples among these classes. This step won’t yield any insights for the toy dataset but is worth doing for real-world projects. A balanced dataset (where each class has approximately the same number of instances) is ideal for training a machine-learning model.

Get image classes

# Explode the 'shapes' column in the annotation_df dataframe
# Convert the resulting series to a dataframe
# Apply the pandas Series function to the 'shapes' column of the dataframe
shapes_df = annotation_df['shapes'].explode().to_frame().shapes.apply(pd.Series)

# Get a list of unique labels in the 'annotation_df' DataFrame
class_names = shapes_df['label'].unique().tolist()

# Display labels using a Pandas DataFrame
pd.DataFrame(class_names)
0
0 person
1 none

Visualize the class distribution

# Get the number of samples for each object class
class_counts = shapes_df['label'].value_counts()

# Plot the distribution
class_counts.plot(kind='bar', figsize=(12, 5))
plt.title('Class distribution')
plt.ylabel('Count')
plt.xlabel('Classes')
plt.xticks(range(len(class_counts.index)), class_counts.index, rotation=75)  # Set the x-axis tick labels
plt.show()

Visualizing Image Annotations

In this section, we will annotate a single image with its bounding boxes using torchvision’s BoundingBoxes class and draw_bounding_boxes function.

Generate a color map

While not required, assigning a unique color to bounding boxes for each object class enhances visual distinction, allowing for easier identification of different objects in the scene. We can use the distinctipy package to generate a visually distinct colormap.

# Generate a list of colors with a length equal to the number of labels
colors = distinctipy.get_colors(len(class_names))

# Make a copy of the color map in integer format
int_colors = [tuple(int(c*255) for c in color) for color in colors]

# Generate a color swatch to visualize the color map
distinctipy.color_swatch(colors)

Download a font file

The draw_bounding_boxes function included with torchvision uses a pretty small font size. We can increase the font size if we use a custom font. Font files are available on sites like Google Fonts, or we can use one included with the operating system.

# Set the name of the font file
font_file = 'KFOlCnqEu92Fr1MmEU9vAw.ttf'

# Download the font file
download_file(f"https://fonts.gstatic.com/s/roboto/v30/{font_file}", "./")

Define the bounding box annotation function

We can make a partial function using draw_bounding_boxes since we’ll use the same box thickness and font each time we visualize bounding boxes.

draw_bboxes = partial(draw_bounding_boxes, fill=False, width=2, font=font_file, font_size=25)

Selecting a Sample Image

We can use the unique ID for an image in the image dictionary to get the image file path and the associated annotations from the annotation DataFrame.

Load the sample image

# Get the file ID of the first image file
file_id = list(img_dict.keys())[0]

# Open the associated image file as a RGB image
sample_img = Image.open(img_dict[file_id]).convert('RGB')

# Print the dimensions of the image
print(f"Image Dims: {sample_img.size}")

# Show the image
sample_img
Image Dims: (768, 1152)

Inspect the corresponding annotation data

# Get the row from the 'annotation_df' DataFrame corresponding to the 'file_id'
annotation_df.loc[file_id].to_frame()
258421
version 5.3.1
flags {}
shapes [{‘label’: ‘person’, ‘points’: [[340.2519836425781, 466.943359375], [418.9939880371094, 777.34423828125]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}, {‘label’: ‘person’, ‘points’: [[386.076124567474, 443.94463667820065], [460.81660899653974, 777.1626297577855]], ‘group_id’: None, ‘description’: ’‘, ’shape_type’: ‘rectangle’, ‘flags’: {}}]
imagePath 258421.jpg
imageData None
imageHeight 1152
imageWidth 768

Define function that ensures bounding boxes are in a consistent format

LabelMe does not enforce a consistent order for storing the (x,y) coordinates for bounding box annotations. The order depends on how you initiate the bounding box annotation. Therefore, we will create a function that ensures the order is in [top-left x, top-left y, bottom-right x, bottom-right y] format.

def correct_bounding_boxes(bboxes):
    # Ensure input is a NumPy array
    bboxes = np.asarray(bboxes)
    
    # Correct x coordinates
    # Swap the x coordinates if the top-left x is greater than the bottom-right x
    x_min = np.minimum(bboxes[:, 0], bboxes[:, 2])
    x_max = np.maximum(bboxes[:, 0], bboxes[:, 2])
    
    # Correct y coordinates
    # Swap the y coordinates if the top-left y is greater than the bottom-right y
    y_min = np.minimum(bboxes[:, 1], bboxes[:, 3])
    y_max = np.maximum(bboxes[:, 1], bboxes[:, 3])
    
    # Construct the corrected bounding boxes array
    corrected_bboxes = np.stack([x_min, y_min, x_max, y_max], axis=1)
    
    return corrected_bboxes

Annotate sample image

The draw_bounding_boxes function expects bounding box annotations in [top-left X, top-left Y, bottom-right X, bottom-right Y] format, so we don’t need to convert the annotation values.

# Extract the labels and bounding box annotations for the sample image
labels = [shape['label'] for shape in annotation_df.loc[file_id]['shapes']]
bboxes = np.array([shape['points'] for shape in annotation_df.loc[file_id]['shapes']]).reshape(len(labels),4)
bboxes = correct_bounding_boxes(bboxes)

# Annotate the sample image with labels and bounding boxes
annotated_tensor = draw_bboxes(
    image=transforms.PILToTensor()(sample_img), 
    boxes=BoundingBoxes(torch.Tensor(bboxes), format='xyxy', canvas_size=sample_img.size[::-1]),
    labels=labels, 
    colors=[int_colors[i] for i in [class_names.index(label) for label in labels]]
)

tensor_to_pil(annotated_tensor)

We have loaded the dataset, inspected its class distribution, and visualized the annotations for a sample image. In the final section, we will cover how to augment images using torchvision’s Transforms V2 API and create a custom Dataset class for training.

Preparing the Data

In this section, we will first walk through a single example of how to apply augmentations to a single annotated image using torchvision’s Transforms V2 API before putting everything together in a custom Dataset class.

Data Augmentation

Here, we will define some data augmentations to apply to images during training. I created a few custom image transforms to help streamline the code.

The first extends the RandomIoUCrop transform included with torchvision to give the user more control over how much it crops into bounding box areas. The second resizes images based on their largest dimension rather than their smallest. The third applies square padding and allows the padding to be applied equally on both sides or randomly split between the two sides.

All three are available through the cjm-torchvision-tfms package.

Set training image size

Next, we will specify the image size to use during training.

# Set training image size
train_sz = 384

Initialize custom transforms

Now, we can initialize the transform objects.

# Create a RandomIoUCrop object
iou_crop = CustomRandomIoUCrop(min_scale=0.3, 
                               max_scale=1.0, 
                               min_aspect_ratio=0.5, 
                               max_aspect_ratio=2.0, 
                               sampler_options=[0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0],
                               trials=400, 
                               jitter_factor=0.25)

# Create a `ResizeMax` object
resize_max = ResizeMax(max_sz=train_sz)

# Create a `PadSquare` object
pad_square = PadSquare(shift=True)

Test the transforms

Torchvision’s V2 image transforms take an image and a targets dictionary. The targets dictionary contains the annotations and labels for the image.

We will pass input through the CustomRandomIoUCrop transform first and then through ResizeMax and PadSquare. We can pass the result through a final resize operation to ensure both sides match the train_sz value.

# Prepare bounding box targets
targets = {
    'boxes': BoundingBoxes(torch.Tensor(bboxes), 
                           format='xyxy', 
                           canvas_size=sample_img.size[::-1]), 
    'labels': torch.Tensor([class_names.index(label) for label in labels])
}

# Crop the image
cropped_img, targets = iou_crop(sample_img, targets)

# Resize the image
resized_img, targets = resize_max(cropped_img, targets)

# Pad the image
padded_img, targets = pad_square(resized_img, targets)

# Ensure the padded image is the target size
resize = transforms.Resize([train_sz] * 2, antialias=True)
resized_padded_img, targets = resize(padded_img, targets)
sanitized_img, targets = transforms.SanitizeBoundingBoxes()(resized_padded_img, targets)

# Annotate the augmented image with updated labels and bounding boxes
annotated_tensor = draw_bboxes(
    image=transforms.PILToTensor()(sanitized_img), 
    boxes=targets['boxes'], 
    labels=[class_names[int(label.item())] for label in targets['labels']], 
    colors=[int_colors[i] for i in [class_names.index(label) for label in labels]]
)

# Display the annotated image
display(tensor_to_pil(annotated_tensor))

pd.Series({
    "Source Image:": sample_img.size,
    "Cropped Image:": cropped_img.size,
    "Resized Image:": resized_img.size,
    "Padded Image:": padded_img.size,
    "Resized Padded Image:": resized_padded_img.size,
}).to_frame().style.hide(axis='columns')

Source Image: (768, 1152)
Cropped Image: (768, 1152)
Resized Image: (256, 384)
Padded Image: (384, 384)
Resized Padded Image: (384, 384)

Now that we know how to apply data augmentations, we can put all the steps we’ve covered into a custom Dataset class.

Training Dataset Class

The following custom Dataset class is responsible for loading a single image, preparing the associated annotations, applying any image transforms, and returning the final image tensor and its target dictionary during training.

class LabelMeBBoxDataset(Dataset):
    """
    A custom dataset class for handling LabelMe bounding box datasets.

    This class is designed to work with datasets where annotations are
    provided in a DataFrame and images are referenced by keys.

    Attributes:
        _img_keys (list): A list of image keys identifying each image.
        _annotation_df (DataFrame): A DataFrame containing image annotations.
        _img_dict (dict): A dictionary mapping image keys to image file paths.
        _class_to_idx (dict): A dictionary mapping class names to class indices.
        _transforms (function): A function or series of functions to apply transformations to the images and targets.
    """

    def __init__(self, img_keys, annotation_df, img_dict, class_to_idx, transforms=None):
        """
        Initializes the dataset with image keys, annotation data, image dictionary, class indices, and transforms.

        Args:
            img_keys (list): A list of image keys.
            annotation_df (DataFrame): A DataFrame containing image annotations.
            img_dict (dict): A dictionary mapping image keys to image file paths.
            class_to_idx (dict): A dictionary mapping class names to class indices.
            transforms (function, optional): A function for transforming images and targets. Defaults to None.
        """
        super(Dataset, self).__init__()
        
        self._img_keys = img_keys
        self._annotation_df = annotation_df
        self._img_dict = img_dict
        self._class_to_idx = class_to_idx
        self._transforms = transforms

    def __len__(self):
        """
        Returns the total number of items in the dataset.

        Returns:
            int: The total number of images in the dataset.
        """
        return len(self._img_keys)
        
    def __getitem__(self, index):
        """
        Retrieves an image and its corresponding target (annotations) at the specified index.

        Args:
            index (int): The index of the item to retrieve.

        Returns:
            tuple: A tuple containing the image and its corresponding target (annotations).
        """
        img_key = self._img_keys[index]
        annotation = self._annotation_df.loc[img_key]
        image, target = self._load_image_and_target(annotation)
        
        # Apply transformations if any
        if self._transforms:
            image, target = self._transforms(image, target)
        
        return image, target

    def _load_image_and_target(self, annotation):
        """
        Loads an image and its corresponding target (annotations) based on the given annotation.

        Args:
            annotation (Series): A pandas Series containing the annotation data for a single image.

        Returns:
            tuple: A tuple containing the image and its corresponding target (annotations).
        """
        # Load the image from the filepath
        filepath = self._img_dict[annotation.name]
        image = Image.open(filepath).convert('RGB')

        # Process bounding box annotations
        bbox_list = np.array([shape['points'] for shape in annotation['shapes']]).reshape(len(annotation['shapes']), 4)
        bbox_list = correct_bounding_boxes(bbox_list)
        bbox_tensor = torch.Tensor(bbox_list)
        boxes = BoundingBoxes(bbox_tensor, format='xyxy', canvas_size=image.size[::-1])

        # Process label annotations
        annotation_labels = [shape['label'] for shape in annotation['shapes']]
        labels = torch.Tensor([self._class_to_idx[label] for label in annotation_labels])

        return image, {'boxes': boxes, 'labels': labels}

Image Transforms

Here, we will specify and organize all the image transforms to apply during training.

# Compose transforms for data augmentation
data_aug_tfms = transforms.Compose(
    transforms=[
        iou_crop,
        transforms.ColorJitter(
                brightness = (0.875, 1.125),
                contrast = (0.5, 1.5),
                saturation = (0.5, 1.5),
                hue = (-0.05, 0.05),
        ),
        transforms.RandomGrayscale(),
        transforms.RandomEqualize(),
        transforms.RandomPosterize(bits=3, p=0.5),
        transforms.RandomHorizontalFlip(p=0.5),
    ],
)

# Compose transforms to resize and pad input images
resize_pad_tfm = transforms.Compose([
    resize_max, 
    pad_square,
    transforms.Resize([train_sz] * 2, antialias=True)
])

# Compose transforms to sanitize bounding boxes and normalize input data
final_tfms = transforms.Compose([
    transforms.ToImage(), 
    transforms.ToDtype(torch.float32, scale=True),
    transforms.SanitizeBoundingBoxes(),
])

# Define the transformations for training and validation datasets
train_tfms = transforms.Compose([
    data_aug_tfms, 
    resize_pad_tfm, 
    final_tfms
])
Important

Always use the SanitizeBoundingBoxes transform to clean up annotations after using data augmentations that alter bounding boxes (e.g., cropping, warping, etc.).

Initialize Dataset

Now, we can create the dataset object using the image dictionary, the annotation DataFrame, and the image transforms.

# Create a mapping from class names to class indices
class_to_idx = {c: i for i, c in enumerate(class_names)}

# Instantiate the dataset using the defined transformations
train_dataset = LabelMeBBoxDataset(list(img_dict.keys()), annotation_df, img_dict, class_to_idx, train_tfms)

# Print the number of samples in the training dataset
pd.Series({
    'Training dataset size:': len(train_dataset),
}).to_frame().style.hide(axis='columns')
Training dataset size: 29

Inspect Samples

To close out, we should verify the dataset object works as intended by inspecting the first sample.

Inspect training set sample

dataset_sample = train_dataset[0]

annotated_tensor = draw_bboxes(
    image=(dataset_sample[0]*255).to(dtype=torch.uint8), 
    boxes=dataset_sample[1]['boxes'], 
    labels=[class_names[int(i.item())] for i in dataset_sample[1]['labels']], 
    colors=[int_colors[int(i.item())] for i in dataset_sample[1]['labels']]
)

tensor_to_pil(annotated_tensor)

Conclusion

In this tutorial, we covered how to load custom bounding box annotations made with the LabelMe annotation tool and work with them using torchvision’s Transforms V2 API. The skills and knowledge you acquired here provide a solid foundation for future object detection projects.

As a next step, perhaps try annotating a custom object detection dataset with LabelMe and loading it with this tutorial’s code. Once you’re comfortable with that, try adapting the code in the following tutorial to train an object detection model on your custom dataset.