{"id":30842822,"url":"https://github.com/tks18/open-memory-lane","last_synced_at":"2026-05-09T05:34:33.468Z","repository":{"id":310871359,"uuid":"1041536546","full_name":"tks18/open-memory-lane","owner":"tks18","description":"Open Source, Memory Efficient Version of Windows Recall made with Python","archived":false,"fork":false,"pushed_at":"2025-09-02T04:05:35.000Z","size":486,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-09-06T21:25:09.128Z","etag":null,"topics":["ffmpeg","opencv","python","recall","windows-recall"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tks18.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-08-20T16:23:24.000Z","updated_at":"2025-09-02T04:05:39.000Z","dependencies_parsed_at":"2025-08-20T19:36:15.843Z","dependency_job_id":"6b77687a-3f5b-4528-a4a3-ee4320c50103","html_url":"https://github.com/tks18/open-memory-lane","commit_stats":null,"previous_names":["tks18/open-memory-lane"],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/tks18/open-memory-lane","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tks18%2Fopen-memory-lane","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tks18%2Fopen-memory-lane/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tks18%2Fopen-memory-lane/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tks18%2Fopen-memory-lane/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tks18","download_url":"https://codeload.github.com/tks18/open-memory-lane/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tks18%2Fopen-memory-lane/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32808538,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-08T08:22:46.396Z","status":"online","status_checked_at":"2026-05-09T02:00:06.633Z","response_time":123,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ffmpeg","opencv","python","recall","windows-recall"],"created_at":"2025-09-06T21:08:26.948Z","updated_at":"2026-05-09T05:34:33.456Z","avatar_url":"https://github.com/tks18.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Commitizen friendly](https://img.shields.io/badge/commitizen-friendly-brightgreen.svg)](http://commitizen.github.io/cz-cli/) [![semantic-release](https://img.shields.io/badge/%20%20%F0%9F%93%A6%F0%9F%9A%80-semantic--release-e10079.svg)](https://github.com/semantic-release/semantic-release)\n\n# 🧠 Open Memory Lane\n\n**Open-source, Memory-efficient \u0026 usable version of Windows Recall** — lightweight background capture + timelapse summaries, built for daily use. 🚀\n\n---\n\n## ✨ What it does\n\n- 📸 Capture desktop screenshots as **WebP** (small, fast).\n- 🔎 Detect meaningful changes using **dhash_bits (OpenCV)** — fast perceptual hash; ignores tiny cursor flickers.\n- 🗄️ Store images + metadata (timestamp, session, wintitle, app name) in an **SQLite** index.\n- 🎞️ Build 15-min detailed clips and a short **daily timelapse summary** (timelapse speed derived from summary FPS vs detailed FPS).\n- 🔁 Robust backlog processing: missed videos/summaries are queued and created on next run.\n- 🖱️ System tray app to control / exit the recorder easily.\n\n---\n\n## 🖥️ Client-side features\n\n- 📜 **Scrollable Timeline View**\n  - Navigate through your day visually with a smooth, zoomable timeline.\n  - Jump to exact sessions (15-min detailed clips or daily summaries).\n  - Filter by **app name**, **window title**, or **time range**.\n\n- 🔎 **Query the Database**\n  - Built-in query console to search the SQLite metadata:\n    - Example: _“Show all Chrome windows between 2–4 PM”_\n    - Example: _“List apps I used more than 30 mins today”_\n  - Fast results with indexed lookups.\n\n- 📤 **Export Data as CSV**\n  - Export metadata (timestamp, app name, window title, etc.) into a clean CSV.\n  - Perfect for analytics in Excel / Power BI / Python notebooks.\n  - Optional: include file paths to screenshots for external use.\n\n---\n\n## 🧩 End-to-end workflow\n\n1. **Capture**: lightweight background screenshots + metadata storage.\n2. **Process**: timelapse clips auto-generated daily.\n3. **Explore**: open the timeline view, scroll through your day.\n4. **Query**: filter/search by apps, titles, or times.\n5. **Export**: save your activity log to CSV for analysis or sharing.\n\n---\n\n## ⚙️ Quick start\n\n1. 🐣 Create venv \u0026 install:\n\n   ```bash\n   uv init\n   .venv\\Scripts\\activate\n   uv sync\n   ```\n\n2. 🛠️ Install **ffmpeg** and make sure it’s on your PATH.\n3. ⚙️ Edit `.config.yml` (paths, fps, intervals) if needed.\n4. ▶️ Run the tray app:\n\n   ```bash\n   uv run start_app.py\n   ```\n\n   or run the `start.bat`\n\n   or build a single exe with PyInstaller and add a shortcut to `shell:startup` for autostart. ✨\n\n---\n\n## 📦 Minimal dependencies\n\n- `mss`, `Pillow`, `numpy`, `opencv-python`, `pystray`\n  (see `pyproject.toml`) ✅\n\n---\n\n## 🎯 Key design choices\n\n- **WebP** for compact storage. 🗜️\n- **dhash_bits via OpenCV** for quick, low-false-positive change detection (mouse wiggles ignored). 🧠\n- **SQLite** DB for metadata + reliable backlog processing (never lose pending processing). 💾\n- **FFmpeg concat + per-image durations / fps-based timelapse** to create short, watchable daily summaries. 🎬\n\n---\n\n## 🧰 Use cases\n\n- ⏱️ **Productivity tracking** — glance at a short visual timeline.\n- 🎞️ **Video/Recap generation** — daily highlights in minutes.\n- 💚 **Digital wellbeing** — understand app usage visually.\n\n---\n\n## 📝 Notes\n\n- Runs CPU-only (no GPU required). ⚙️\n- All data stays local — privacy-first. 🔒\n- Want the summary always X minutes? tweak `summary_fps` in `.config.yml`. ⚙️\n- Want to change the capture interval? tweak `capture_interval` in `.config.yml`. ⚙️\n- Want to change the WebP quality? tweak `webp_quality` in `.config.yml`. ⚙️\n\n---\n\nSudharshan TK © 2025 — Built for simplicity \u0026 privacy. ❤️\n\n[![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge\u0026logo=github\u0026logoColor=white)](https://github.com/sudharshan-tk/open-memory-lane)\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftks18%2Fopen-memory-lane","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftks18%2Fopen-memory-lane","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftks18%2Fopen-memory-lane/lists"}