cocodataset/cocoapi

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.

Star history