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--- |
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license: apache-2.0 |
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task_categories: |
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- image-segmentation |
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language: |
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- en |
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--- |
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# IITKGP_Fence dataset |
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## Overview |
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The **IITKGP_Fence dataset** is designed for tasks related to fence-like occlusion detection, defocus blur, depth mapping, and object segmentation. The captured data vaies in scene composition, background defocus, and object occlusions. |
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The dataset comprises both labeled and unlabeled data, as well as additional video and RGB-D data. The contains ground truth occlusion masks (`GT`) for the corresponding images. We created the ground truth occlusion labels in a semi-automatic way with user interaction. |
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## Dataset Structure |
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<details> |
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<summary>Click to expand directory structure</summary> |
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<pre style="font-size: 12px;"> |
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└───IITKGP_Fence dataset |
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├───Labeled |
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│ ├───GT |
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│ │ (1).png |
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│ │ ... |
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│ │ (175).png |
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│ │ |
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│ ├───Imgs |
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│ │ (1).png |
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│ │ ... |
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│ │ (175).png |
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│ │ |
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│ └───Others |
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│ ├───Bird |
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│ │ c1.png |
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│ │ ... |
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│ │ f1.png |
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│ │ |
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│ ├───Blurred_bg |
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│ │ ├───GT |
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│ │ │ gt1.jpg |
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│ │ │ ... |
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│ │ │ gt88.jpg |
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│ │ │ |
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│ │ └───Imgs |
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│ │ im (1).jpg |
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│ │ ... |
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│ │ im (88).jpg |
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│ │ |
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│ └───... |
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└───Unlabeled |
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├───DefocusBlurred |
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│ └───Blurred_fg |
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│ │ BlMov_01.mov |
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│ │ ... |
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│ │ BlMov_46.mov |
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│ │ |
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│ └───Imgs |
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│ BlJPG_01.jpg |
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│ ... |
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├───RGB |
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│ ├───Imgs |
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│ │ BlJPG_001.jpg |
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│ │ ... |
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│ │ BlJPG_205.jpg |
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│ │ |
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│ └───Vids |
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│ ├───utils |
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│ └───Zoo |
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│ Zoo_001.mp4 |
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│ ... |
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│ Zoo_205.mp4 |
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└───RGBD |
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│ data1.mat |
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│ ... |
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├───Othersamples |
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│ ├───DATA1 |
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│ └───... |
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└───utils |
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</pre> |
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</details> |
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### Dataset Description |
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Here's an overview of its structure and contents: |
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1. **Labeled Data**: |
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- **GT (Ground Truth)**: Contains 175 PNG images representing the ground truth labels for corresponding input images. |
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- **Imgs**: Contains 175 PNG images, which are RGB images that correspond to the ground truth. |
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- **Others**: |
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- **Multiple Scenes**: Contains various scenes (e.g., `Bird/`, `Tennis/`, etc.). Each scene consists of four pairs of RGB images and their corresponding ground truth masks. |
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- **Blurred_bg**: This folder includes images with blurred backgrounds and corresponding ground truth occlusion segmentation labels. |
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- **GT**: Contains 88 ground truth occlusion mask. |
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- **Imgs**: Contains 88 blurred background images in JPG format. |
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2. **Unlabeled Data**: |
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- **DefocusBlurred**: Focused on data related to blurred foreground occlusions. |
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- **Blurred_fg**: Contains 46 video files and `Imgs/'. |
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- **RGB**: Contains regular RGB images and videos. |
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- **Imgs**: 205 JPEG images of various scenes with occlusions. |
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- **Vids**: Includes a total of 214 video files. |
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- **RGBD**: Contains data for scenes with RGB images and depth data. |
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- **MAT files**: These files store all the data values and additional camera information for various samples. |
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- **Othersamples**: Includes additional data samples captured in laboratory. |
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### Key Dataset Features: |
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- **Fence Detection**: Designed for detecting fences or fence-like structures that might occlude objects. |
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- **Defocus Blur**: Contains images and videos with blurred objects, likely to challenge detection and segmentation algorithms. |
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- **RGBD Data**: Offers depth information alongside RGB images, which can be used for tasks like 3D reconstruction or occlusion handling. |
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- **Unlabeled and Labeled Data**: Facilitates both supervised and unsupervised learning tasks. The `Labeled` <a href="https://huggingface.co/datasets/NeuroVizv0yaZ3R/IITKGP_Fence_dataset/tree/main/Labeled" style="color: darkorange;">folder</a> data provides ground truth occlusion masks, while the `Unlabeled` <a href="https://huggingface.co/datasets/NeuroVizv0yaZ3R/IITKGP_Fence_dataset/tree/main/Unlabeled" style="color: teal;">folder</a> data allows for further experimentation or self-supervised methods. |
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### Dataset Repository |
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- **GitHub Repository:** <a href="https://github.com/Moushumi9medhi/Occlusion-Removal" style="color: magenta;">Occlusion-Removal</a> |
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- **Paper:** [Deep Generative Adversarial Network for Occlusion Removal from a Single Image](https://arxiv.org/abs/2409.13242) |
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- **Authors:** [Sankaraganesh Jonna](https://www.linkedin.com/in/ganeshjonna/), Moushumi Medhi, [Rajiv Ranjan Sahay](https://www.iitkgp.ac.in/department/EE/faculty/ee-rajiv) |
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## Usage |
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```bash |
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pip install datasets |
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from datasets import load_dataset |
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dataset = load_dataset('NeuroVizv0yaZ3R/IITKGP_Fence_dataset') |
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``` |
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## Citation |
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**BibTeX:** |
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``` |
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@misc{jonna2024deepgenerativeadversarialnetwork, |
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title={Deep Generative Adversarial Network for Occlusion Removal from a Single Image}, |
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author={Sankaraganesh Jonna and Moushumi Medhi and Rajiv Ranjan Sahay}, |
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year={2024}, |
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eprint={2409.13242}, |
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archivePrefix={arXiv}, |
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url={https://arxiv.org/abs/2409.13242}, |
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} |
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``` |
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## License |
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For license details, refer to the [LICENSE](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) file. |
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## Acknowledgments |
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We would like to express our gratitude to **Dr. Sreeja S R** for her valuable contributions to the [Imgs folder](https://huggingface.co/datasets/NeuroVizv0yaZ3R/IITKGP_Fence_dataset/tree/main/Unlabeled/RGB/Imgs) within the RGB directory of the Unlabeled dataset, which greatly enriched its diversity and quality. |
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## Contact |
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[[email protected]](mailto:[email protected]) |
