3 repositorios
Transformations specifically for recalculating landmark positions during training data expansion.
Distinct from Keypoint Detection: Focuses on the augmentation of keypoints rather than the identification algorithms themselves.
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Albumentations is a computer vision image augmentation library designed to increase training data diversity for deep learning models. It provides a toolset for applying geometric and color transformations to images and annotations, including a specialized collection of 3D operations for volumetric data used in medical and scientific imaging. The library functions as an image mask and bounding box transformer, automatically updating masks, bounding boxes, and keypoints when images undergo geometric changes. This ensures that spatial alterations remain synchronized across images and their assoc
Recalculates landmark keypoint positions during geometric rotations and flips of training images.
MMPose is a PyTorch-based pose estimation toolbox and deep learning training pipeline designed for detecting 2D and 3D keypoints on humans, animals, and faces. It serves as a computer vision model zoo and a framework for both 2D pose estimation and 3D pose lifting. The project is distinguished by its modular architecture and extensibility, employing a registry-based system and hierarchical configurations to allow for custom algorithm integration and model pipeline customization. It supports diverse estimation paradigms, including top-down, bottom-up, and two-stage pose lifting workflows. The
Transforms raw image coordinates into normalized values or Gaussian heatmaps required for model training.
Roboflow Sports is a sports video analysis system that combines object detection and tracking with bird's-eye field visualization. Its core pipeline detects and tracks players, referees, and balls across video frames, then maps those tracked positions onto a radar-style overhead view of the playing field. The system goes beyond basic detection by localizing field boundaries and key landmarks such as pitch lines and corners, enabling spatial mapping of player positions relative to the field geometry. It classifies detected players by team affiliation through visual feature extraction and clust
Locates predefined landmarks such as pitch lines or court corners to map field geometry and player positions.