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Release v2.3.1
Release Notes New Features Support for Additional Video and Image Extensions: Expanded support to include more video and image file formats, enhancing the versatility and compatibility of our software. Updates Test Updates: Updated tests.png to reflect the latest changes and improvements in the testing suite. Integrations Codecov Integration: Integrated Codecov for code coverage analysis to ensure higher code quality and maintainability.
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‎.github/README.pl.md‎

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<div id="badges">
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<p align="center">
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<img alt="Github Created At" src="https://img.shields.io/github/created-at/BKDDFS/PerfectFrameAI">
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<img alt="GitHub Downloads (all assets, all releases)" src="https://img.shields.io/github/downloads/BKDDFS/PerfectFrameAI/total?style=flat&color=blue">
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<img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/BKDDFS/PerfectFrameAI">
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<a href="https://codecov.io/github/BKDDFS/PerfectFrameAI" >
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<img src="https://codecov.io/github/BKDDFS/PerfectFrameAI/graph/badge.svg?token=GT9TGKBGYI"/>
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</a>
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<img alt="GitHub License" src="https://img.shields.io/github/license/BKDDFS/PerfectFrameAI">
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<img alt="GitHub Release" src="https://img.shields.io/github/v/release/BKDDFS/PerfectFrameAI">
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<img alt="GitHub Tag" src="https://img.shields.io/github/v/tag/BKDDFS/PerfectFrameAI">
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<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/BKDDFS/PerfectFrameAI">
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</p>
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</div>
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</summary>
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<img src="../static/start_frames.png" width="350">
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<ol>
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<p>Input: Folder z plikami video <code>.mp4</code>.</p>
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<p>Input: Folder z plikami video.</p>
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<li>Bierze pierwsze video ze wskazanej lokalizacji.</li>
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<li>
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Dzieli wideo na klatki.
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</summary>
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<img src="../static/start_images.png" width="350">
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<ol>
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<p>Input: Folder z obrazami <code>.jpg</code>.</p>
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<p>Input: Folder z obrazami.</p>
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<li>Wczytuje obrazy. Obrazy są przetwarzane batchach(seriach).</li>
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<li>Ocenia wszystkie obrazy w batchu za pomocą modelu AI i nadaje im ocenę liczbową.</li>
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<li>
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<p>Modyfikuje <code>best_frames_extractor</code> poprzez pominięcie części z AI/ocenianiem klatek.</p>
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<code>python start.py best_frames_extractor --all_frames</code>
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<ol>
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<p>Input: Folder z plikami video <code>.mp4</code>.</p>
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<p>Input: Folder z plikami video.</p>
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<li>Bierze pierwsze video ze wskazanej lokalizacji.</li>
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<li>
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Dzieli wideo na klatki. Klatki są brane co 1 sekundę wideo.
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</div>
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<div id="tests">
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<h2>🧪 Testy</h2>
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<img src="../static/tests_passed.png">
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<img src="../static/tests.png">
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<p>
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Testy możesz uruchomić instalując zależności z <code>pyproject.toml</code>
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i wpisując w terminal w lokalizacj projektu - <code>pytest</code>.
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Podsumowując, dodanie DALI powinno być kolejnym poważnym krokiem naprzód,
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jeśli chodzi o poprawę wydajności.
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</li>
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<li>Przetestowanie działania na starszych wersjach Pythona.</li>
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<li>
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Naprawienie spillingu danych podczas oceniania klatek.
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Obecnie ocenianie ma delikatne spowolnienie w postaci problemu ze spillingiem.

‎.github/workflows/run_tests.yml‎

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# This workflow will install Poetry, install Python dependencies, run tests a single version of Python
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name: CI
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on:
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permissions:
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contents: read
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jobs:
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build:
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runs-on: ubuntu-latest
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run: |
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poetry install
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- name: Run tests
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- name: Run tests with coverage
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env:
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DOCKER_HOST: tcp://localhost:2375
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run: |
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poetry run pytest
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poetry run pytest --cov --cov-report=xml
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- name: Upload coverage to Codecov
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uses: codecov/codecov-action@v3.1.1
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with:
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token: ${{ secrets.CODECOV_TOKEN }}
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files: ./coverage.xml
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fail_ci_if_error: true

‎README.md‎

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<p align="center">
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<img alt="Github Created At" src="https://img.shields.io/github/created-at/BKDDFS/PerfectFrameAI">
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<img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/BKDDFS/PerfectFrameAI">
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<a href="https://codecov.io/github/BKDDFS/PerfectFrameAI" >
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<img src="https://codecov.io/github/BKDDFS/PerfectFrameAI/graph/badge.svg?token=GT9TGKBGYI"/>
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</a>
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<img alt="GitHub License" src="https://img.shields.io/github/license/BKDDFS/PerfectFrameAI">
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<img alt="GitHub Tag" src="https://img.shields.io/github/v/tag/BKDDFS/PerfectFrameAI">
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<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/BKDDFS/PerfectFrameAI">
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</summary>
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<img src="static/start_frames.png" width="350">
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<ol>
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<p>Input: Folder with <code>.mp4</code> video files.</p>
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<p>Input: Folder with video files.</p>
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<li>Takes the first video from the specified location.</li>
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<li>
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Splits the video into frames.
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</summary>
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<img src="static/start_images.png" width="350">
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<ol>
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<p>Input: Folder with <code>.jpg</code> images.</p>
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<p>Input: Folder with images.</p>
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<li>Loads the images. Images are processed in batches.</li>
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<li>Evaluates all images in the batch using an AI model and assigns them a numerical score.</li>
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<li>
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<p>Modifying <code>best_frames_extractor</code> by skipping AI evaluation part.</p>
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<code>python start.py best_frames_extractor --all_frames</code>
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<ol>
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<p>Input: Folder with <code>.mp4</code> video files.</p>
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<p>Input: Folder with video files.</p>
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<li>Takes the first video from the specified location.</li>
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<li>
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Splits the video into frames.
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</div>
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<div id="tests">
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<h2>🧪 Tests</h2>
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<img src="static/tests_passed.png" width="1000" style="border-radius: 10px;">
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<img src="static/tests.png" width="1000" style="border-radius: 10px;">
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<p>
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You can run the tests by installing the dependencies from <code>pyproject.toml</code>
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and typing in the terminal in the project location - <code>pytest</code>.
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In summary, adding DALI should be another significant step forward
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in terms of performance improvement.
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</li>
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<li>Testing on older versions of Python.</li>
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<li>
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Fixing data spilling during frame evaluation.
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Currently, evaluation has a slight slowdown in the form of a spilling issue.

‎extractor_service/app/schemas.py‎

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"""
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input_directory: DirectoryPath = Path("/app/input_directory")
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output_directory: DirectoryPath = Path("/app/output_directory")
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video_extensions: tuple[str] = (".mp4",)
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images_extensions: tuple[str] = (".jpg",)
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video_extensions: tuple[str] = (".mp4", ".mov", ".webm", ".mkv", ".avi") # add more containers here
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images_extensions: tuple[str] = (".jpg", ".jpeg", ".png", ".webp") # add more containers here
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processed_video_prefix: str = "frames_extracted_"
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batch_size: int = 100
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compering_group_size: int = 5

‎pyproject.toml‎

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[tool.poetry]
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name = "PerfectFrameAI"
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version = "2.3.0"
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version = "2.3.1"
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description = "AI tool for finding the most aesthetic frames in a video. 🎞️➜🖼️"
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authors = ["Bartłomiej Flis <Bartekdawidflis@gmail.com>"]
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license = "GPL-3.0 license"

‎static/tests.png‎

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‎static/tests_passed.png‎

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‎tests/extractor_service/unit/schemas_test.py‎

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config = ExtractorConfig()
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assert config.input_directory == Path("/app/input_directory")
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assert config.output_directory == Path("/app/output_directory")
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assert config.video_extensions == (".mp4",)
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assert config.images_extensions == (".jpg",)
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assert config.video_extensions == (".mp4", ".mov", ".webm", ".mkv", ".avi")
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assert config.images_extensions == (".jpg", ".jpeg", ".png", ".webp")
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assert config.processed_video_prefix == "frames_extracted_"
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assert isinstance(config.compering_group_size, int)
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assert isinstance(config.batch_size, int)

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