PrepImage

A Unified Framework for Computer Vision Dataset Preparation
Curate, preprocess, annotate, and augment image datasets — all in one desktop app.

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PrepImage is an all-in-one image dataset preparation platform for artificial intelligence and computer vision that integrates Image Curation, Image Preprocessing, Image Annotation, and Image Augmentation into a single, modern PySide6 (Qt) desktop application. It enables users to discard duplicate, blurry, and noisy images; perform image cropping, resizing, and consistent naming; create box and polygon annotations for object detection and image segmentation; and generate augmentations for both images and their annotation files using geometric and non-geometric transformations. By combining these capabilities in one unified platform, PrepImage simplifies the creation of high-quality datasets for image classification, object detection, and segmentation tasks.

Table of Contents

• Why PrepImage
• Modules
  • 1. Image Curation
  • 2. Image Preprocessing
  • 3. Image Annotation
  • 4. Image Augmentation
• Installation
• Usage
• Requirements
• Project Layout
• License
• Contributors
• Citation

Why PrepImage

• One app, four workflows — curation, preprocessing, annotation, and augmentation share one consistent, modern dark-themed interface instead of four different scripts and conventions.
• Built for real datasets — background worker threads keep the UI responsive on folders with thousands of images; every long-running step shows live progress, and can be stopped mid-run without losing what's already been processed.
• Training-format aware — exports YOLO .txt, Pascal VOC .xml, and COCO-style .json annotations, and augments images with their labels so bounding boxes and polygons stay correct after a flip, rotation, or crop.
• Nothing leaves your machine — PrepImage is a local desktop tool; your images and labels never get uploaded anywhere.

Modules

1. Image Curation

Batch-processes large collections of images to automatically identify and organize common image-quality issues that quietly hurt model training if they slip into a dataset:

• Duplicate & near-duplicate images — found via perceptual average-hashing (imagehash), then grouped so you can keep one representative from each group instead of every copy.
• Blurry images — images that may negatively affect model training, detected via a Laplacian-variance sharpness score.
• Noisy images — grainy or poor-visual-quality images, detected via a median-filter residual noise estimate.

Enable any combination of the three Detection Options and run one scan; each category gets its own results tab with its own thumbnail grid, plus a live Statistics panel (images scanned, duplicate/blur/noisy counts, and total unique images). From there you can Forward Unique → Preprocess (send the clean set straight into the next module), Download Unique Images, or Download Duplicates for manual review.

Directories

Field	Purpose
Input Directory	The folder of images to scan. Required.
(no separate output field)	Results stay in memory until you explicitly export them — each export action (Forward, Download Unique, Download Duplicates) prompts for its own destination folder when clicked.

2. Image Preprocessing

Prepares a complete collection of images for consistent, reliable use in downstream processing and model training — the shape most training pipelines expect:

• Crops every image to a square, trimming evenly from the longer side instead of stretching or distorting it, then resizes the square result to a fixed target size in pixels — cropping before resizing keeps proportions intact.
• Renames output files sequentially with a custom prefix.
• Output Format — keep every image in its own original format, or convert the whole batch to one uniform format (JPG or PNG) regardless of what each source file started as.
• Zoomable thumbnail gallery to review and select which images to process before running.

Directories

Field	Purpose
Input Directory	Folder of source images to process. Required.
Output Directory	Folder where cropped/resized/renamed images are written. Required.

3. Image Annotation

A complete image annotation module for creating high-quality datasets for object detection and image segmentation tasks:

• Bounding boxes for object detection and polygons for pixel-level segmentation, with an optional rasterized mask PNG export.
• Per-image class labels, editable at any time (a "⋮" menu on every class and every annotation row covers editing, recoloring, renaming, changing class, and deleting).
• Undo/redo — whole-shape while browsing, per-vertex while a polygon is still being drawn.
• Export to YOLO .txt, Pascal VOC .xml, and/or COCO-style .json — pick any combination — with class names round-tripped through a per-folder classes.txt so relabeling later doesn't lose your class names.
• Zoom/pan canvas, keyboard shortcuts, and an in-app shortcuts reference.

Directories

Field	Purpose
Input Image Folder	Folder of images to annotate. Required.
Custom Save Directory	Where annotation files (and masks, if enabled) are saved. Optional — defaults to the input image folder if left blank.

4. Image Augmentation

Enhances image datasets by generating diverse, realistic variations of each image using both geometric and non-geometric augmentation techniques, in two modes:

• Image Augmentation — augments a folder of images with no annotations to carry along.
• Annotated Image Augmentation — augments images and their existing YOLO .txt annotations together. Pixel/non-geometric augmentations (brightness, contrast, sharpen, Gaussian blur, saturation, Gaussian noise, grayscale) leave box coordinates untouched; geometric ones (horizontal/vertical flip, 90°/180°/270° rotation) transform the coordinates to match, so every saved label still lines up with its image.

Target Images controls how many augmented images to generate in total; Max Aug / Img caps how many augmented variants can come from any single source image. Augmented files follow the naming convention originalname_SHORTCODE1_SHORTCODE2_0001.ext, so it's obvious at a glance which combination of augmentations produced a given output.

Directories

Field	Purpose
Input Image Folder	Folder of source images (in Annotated mode, this folder should also contain the matching .txt label for each image). Required.
Output Image Folder	Folder where augmented images (and, in Annotated mode, their updated .txt labels) are written. Required.

Installation

From PyPI

pip install prepimage

From source (editable / dev mode)

git clone https://github.com/harsh-iasri/PrepImage.git
cd prepimage/prepimage_pkg
pip install -e .

Usage

prepimage

or, without installing the console script:

python -m prepimage

Requirements

• Python >= 3.9
• PySide6 — Qt GUI framework
• Pillow — image I/O and processing
• ImageHash — perceptual hashing for duplicate detection
• NumPy — array operations for preprocessing, blur/noise detection, and augmentation

All of the above are installed automatically as dependencies.

Project Layout

prepimage_pkg/
├── pyproject.toml
├── README.md
├── LICENSE
└── src/
    └── prepimage/
        ├── __init__.py
        ├── __main__.py            # entry point (`prepimage` / `python -m prepimage`)
        ├── app.py                 # QMainWindow + navigation (QStackedWidget)
        ├── theme.py               # colors, fonts, global stylesheet
        ├── widgets.py             # ToolCard (painted card, hover zoom)
        ├── assets.py              # shared logo loading helper
        ├── duplicates.py          # duplicate/blur/noise detection core logic
        ├── preprocess.py          # crop/resize/rename core logic
        ├── augment.py             # augmentation engine + box/polygon transforms
        └── screens/
            ├── __init__.py
            ├── home.py             # landing page (hero + 4 tool cards + About dialog)
            ├── duplicates.py       # Image Curation screen (duplicate/blur/noise)
            ├── preprocess.py       # Image Preprocessing screen
            ├── annotate.py         # Image Annotation screen (boxes + polygons)
            ├── augment.py          # Image Augmentation screen
            ├── working.py          # shared "under construction" placeholder
            └── images/             # screenshots + logo bundled with the package

License

Released under the MIT License.

Contributors

Name	Affiliation
Harsh Sachan	ICAR-Indian Agricultural Statistics Research Institute (IASRI), New Delhi
Shalini Kumari	ICAR-Indian Agricultural Statistics Research Institute (IASRI), New Delhi
Dr. Md Ashraful Haque	ICAR-Indian Agricultural Statistics Research Institute (IASRI), New Delhi
Dr. Sudeep Marwaha	ICAR-Central Institute of Agricultural Engineering (CIAE), Bhopal
Dr. Chandan Kumar Deb	ICAR-Indian Agricultural Statistics Research Institute (IASRI), New Delhi
Dr. Alka Arora	ICAR-Indian Agricultural Statistics Research Institute (IASRI), New Delhi
Dr. Anshu Bharadwaj	ICAR-Indian Agricultural Statistics Research Institute (IASRI), New Delhi

Contributions are welcome — feel free to open an issue or pull request on GitHub.

Citation

If PrepImage is useful in your research or project, please cite it as:

Sachan, H., Kumari, S., Haque, M. A., Marwaha, S., Deb, C. K., Arora, A., & Bharadwaj, A. (2026). PrepImage: A Unified Framework for Computer Vision Dataset Preparation (Version 1.1.0) [Computer software]. ICAR-Indian Agricultural Statistics Research Institute (IASRI), New Delhi. https://github.com/harsh-iasri/PrepImage

@software{prepimage2026,
  author  = {Sachan, Harsh and Kumari, Shalini and Haque, Md Ashraful and
             Marwaha, Sudeep and Deb, Chandan Kumar and Arora, Alka and
             Bharadwaj, Anshu},
  title   = {{PrepImage}: A Unified Framework for Computer Vision Dataset Preparation},
  year    = {2026},
  version = {1.1.0},
  url     = {https://github.com/harsh-iasri/PrepImage},
  note    = {ICAR-Indian Agricultural Statistics Research Institute (IASRI), New Delhi}
}