Open Source Python Change Detection Tools

Open-CD is an open source change detection toolbox based on a series of open source general vision task tools.

Loved by smart shoppers, data journalists, research engineers, data scientists, security researchers, and more. From simply monitoring website pages that have a change (such as watching prices, and restocking notifications), to deep inspection such as PDF text support, JSON and XML monitoring, and extensive text triggers. Monitor out-of-stock products and get alerts when those products are back in stock, get restock alerts via Discord, Slack, email, and many other platforms. Using the browser steps configuration, add basic steps before performing change detection, such as logging into websites, adding a product to a cart, accepting cookie logins, entering dates, and refining searches. Monitor and track PDF file changes, and know when a PDF file has text changes. Know when your favourite product is on sale, or other special deals are announced before anyone else. Detect and monitor changes in JSON API responses.

AET is a system that detects visual changes on websites and performs basic page health checks (like w3c compliance, accessibility, HTTP status codes, JS Error checks and others). AET is designed as a flexible system that can be adapted and tailored to the regression requirements of a given project. The tool has been developed to aid front-end client-side layout regression testing of websites or portfolios, in essence assessing the impact or change of a website from one snapshot to the next.

Here, we provide the PyTorch implementation of the paper: A Transformer-Based Siamese Network for Change Detection.

Changelog CI is a GitHub Action that enables a project to automatically generate changelogs. Changelog CI can be triggered on pull_request, workflow_dispatch, and any other events that can provide the required inputs. Changelog CI uses python and GitHub API to generate a changelog for a repository. First, it tries to get the latest release from the repository (If available). Then, it checks all the pull requests/commits merged after the last release using the GitHub API. After that, it parses the data and generates the changelog. It is able to use Markdown or reStructuredText to generate a Changelog. Finally, It writes the generated changelog at the beginning of the CHANGELOG.md/CHANGELOG.rst (or user-provided filename) file. In addition to that, if a user provides a configuration file (JSON/YAML), Changelog CI parses the user-provided configuration file and renders the changelog according to user's configuration.

This is the Pytorch implementation of Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models. The generated images contain objects that we commonly see in real remote sensing images, such as buildings, trees, roads, vegetation, water surfaces, etc., demonstrating the powerful ability of the diffusion models to extract key semantics that can be further used in remote sensing change detection. We fine-tune a light-weight change detection head which takes multi-level feature representations from the pre-trained diffusion model as inputs and outputs change prediction map.