CannyDetektor

CannyDetektor is a software toolkit for edge detection in digital images, implementing the classic Canny edge detection algorithm with additional features for real-time processing and integration into image analysis workflows. It operates on grayscale input but can process color images by converting to grayscale and optionally performing color edge enhancement.

It was developed as an open-source project in the early 2020s by an international community of contributors and has since evolved through multiple releases. The project emphasizes portability, easy integration with existing pipelines, and extensibility via a modular plugin system.

Technical approach: The core pipeline follows the standard Canny steps: Gaussian smoothing to reduce noise, computation of image gradients (typically using Sobel operators), non-maximum suppression to thin candidate edges, and hysteresis thresholding to connect edge segments. CannyDetektor offers adjustable parameters such as Gaussian kernel size, high and low threshold, and optional multi-scale analysis. Optional GPU acceleration via CUDA or OpenCL is provided in some builds.

Features and usage: It provides a command-line tool, a Python API, and a C++ library, with bindings for OpenCV-compatible workflows. It supports batch processing, real-time video streams, and integration into robotic vision or quality-control systems. The tool includes visualization utilities for edge maps and supports saving results in common formats.

Applications and limitations: Widely used in computer vision tasks like object detection, contour extraction, and shape analysis; in industrial inspection, medical imaging, and satellite imagery. Limitations include sensitivity to threshold settings and noise in complex scenes; users often combine it with other detectors or pre-processing steps.

Related topics include the Canny edge detector and edge detection in image processing.