cocodataset/cocoapi
Cocoapi
This project is a toolkit and API designed for parsing, manipulating, and visualizing image annotations for computer vision tasks. It provides a programming interface to load and organize Common Objects in Context annotations, specifically for object detection, image segmentation, and keypoint estimation.
The library includes tools for converting formatted JSON files into data structures that support the analysis of pixel-level masks and skeletal markers. It enables the visual verification of ground truth accuracy by rendering bounding boxes, segmentation masks, and keypoint markers directly onto images.
The API covers broader dataset management capabilities, including coordinate mapping, annotation loading, and the use of wrappers to provide unified access to image metadata across different dataset versions.
Features
- Image Annotation Tools - Renders bounding boxes, segmentation masks, and keypoint markers onto images for visual verification.
- Pixel Coordinate Mappings - Translates normalized dataset annotations into exact pixel coordinates for rendering bounding boxes and masks.
- Bounding Box Visualizers - Overlays detection coordinates and labels onto images for ground truth verification.
- Computer Vision Annotation - Loads and organizes large-scale image annotations for object detection and segmentation using the COCO format.
- Computer Vision Toolkits - Offers a comprehensive set of tools for parsing labels and visualizing vision dataset annotations.
- Detection Visualization - Renders labels and keypoints onto images to visually verify ground truth accuracy.
- Dataset Wrappers - Encapsulates metadata in a class structure for unified access to various dataset versions.
- COCO Dataset Parsers - Provides a dedicated API for parsing and manipulating Common Objects in Context (COCO) annotations.
- Dataset Loading - Provides utilities for loading image dataset labels into memory for analysis.
- JSON Parsing - Provides utilities to parse structured JSON annotation files into native Python objects.
- Run-Length Encoding Converters - Implements run-length encoding to compress segmentation masks and reduce memory usage.
- Image Segmentation - Processes and visualizes pixel-level masks to evaluate the accuracy of object shape identification.
- Evaluation Utilities - Manages and displays skeletal markers to analyze human pose estimation accuracy.
