- added notes for XGBoost without accuracy details
- deleted unused files for data_creation and modified the project_report file overview - translated the documentation for the pyfeat implementation
This commit is contained in:
TimoKurz
@@ -1,58 +0,0 @@
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from feat import Detector
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from feat.utils.io import get_test_data_path
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from moviepy.video.io.VideoFileClip import VideoFileClip
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import os
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def extract_aus(path, model, skip_frames):
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detector = Detector(au_model=model)
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video_prediction = detector.detect(
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path, data_type="video", skip_frames=skip_frames, face_detection_threshold=0.95 # alle 5 Sekunden einbeziehen - 24 Frames pro Sekunde
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)
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return video_prediction.aus.sum()
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def split_video(path, chunk_length=120):
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video = VideoFileClip(path)
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duration = int(video.duration)
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subclips_dir = os.path.join(os.dirname(path), "subclips")
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os.makedirs(subclips_dir, exist_ok=True)
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paths = []
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for start in range(0, duration, chunk_length):
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end = min(start + chunk_length, duration)
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subclip = (
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video
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.subclip(start, end)
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.without_audio()
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.set_fps(video.fps)
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)
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output_path = f"{subclips_dir}_part_{start//chunk_length + 1}.mp4"
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subclip.write_videofile(
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output_path,
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)
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paths.append(output_path)
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return output_path
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# def start(path):
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# results = []
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# clips = split_video(path)
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# for clip in clips:
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# results.append(extract_aus(clip, 'svm', 25*5))
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# return results
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if __name__ == "__main__":
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results = []
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clips = []
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test_video_path = "AU_creation/YTDown.com_YouTube_Was-ist-los-bei-7-vs-Wild_Media_Gtj9zu_WikU_001_1080p.mp4"
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clips = split_video(test_video_path)
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for clippath in clips:
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results.append(extract_aus(clippath, 'svm', 25*5))
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print(results)
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@@ -5,27 +5,47 @@
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"id": "3b0c6c82",
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"metadata": {},
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"source": [
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"Hier entsteht die Dokumentation, wie die Action Units erzeugt wurden.\n",
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"Daraus wird dann letztendlich ein Skript erstellt, welches automatisch AUs aus Videodateien erstellen soll.\n",
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"## Action Unit Documentation and Setup\n",
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"\n",
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"Py-Feat besitzt Dependencies, die ab Python 3.12 nicht mehr verfügbar sind.\n",
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"Dazu muss ein Kernel mit Python 3.11 erstellt werden.\n",
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"Folgendes Vorgehen:\n",
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"1. Seite des Jupyter Labs öffnen\n",
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"2. Terminal öffnen und folgende Befehle eingeben:\n",
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" conda create -n py311 python=3.11\n",
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" source ~/.bashrc\n",
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" conda activate py311\n",
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" conda install jupyter\n",
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" python -m ipykernel install --user --name=py311 --display-name \"Python 3.11\"\n",
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" pip install py-feat\n",
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" pip install \"moviepy<2.0\" (falls benötigt)\n",
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"3. den Kernel neustarten\n",
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"4. in VSC den Kernel neu hinzufügen und dann den Kernel mit dem Namen \"Python 3.11\" auswählen.\n",
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"This documentation outlines the process for generating **Action Units (AUs)** and the eventual creation of a script to automate AU extraction from video files.\n",
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"\n",
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"Der Code unten zeigt eine beispielhafte Integration der py-feat Bibliothek.\n",
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"Die Klassifizierung zu 0,1 kommt durch die Wahl des AU-Modells zustande. Dabei wird SVM gewählt. (ADABase Paper)\n",
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"Gibt die Klassifizierung einen Gleitkommawert zwischen 0 & 1 aus, dann kommt XGB zum Einsatz. (REVELIO Paper)"
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"### Python Environment Configuration\n",
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"\n",
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"**Py-Feat** relies on dependencies that are incompatible with Python 3.12 and later. To ensure functionality, you must set up a dedicated **Python 3.11** kernel.\n",
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"\n",
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"#### Setup Instructions:\n",
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"\n",
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"1. Open your **Jupyter Lab** interface.\n",
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"2. Open a **Terminal** and execute the following commands:\n",
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"```bash\n",
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"conda create -n py311 python=3.11\n",
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"source ~/.bashrc\n",
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"conda activate py311\n",
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"conda install jupyter\n",
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"python -m ipykernel install --user --name=py311 --display-name \"Python 3.11\"\n",
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"pip install py-feat\n",
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"pip install \"moviepy<2.0\" # Only if required\n",
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"\n",
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"```\n",
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"\n",
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"\n",
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"3. **Restart** the kernel.\n",
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"4. In **VS Code**, refresh your kernel list and select the one labeled **\"Python 3.11\"**.\n",
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"\n",
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"---\n",
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"\n",
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"### Implementation Details\n",
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"\n",
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"The following code demonstrates a sample integration of the `py-feat` library. The classification output format is determined by the specific AU model selected:\n",
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"\n",
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"| Model | Output Type | Reference Paper |\n",
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"| --- | --- | --- |\n",
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"| **SVM** | Binary (0 or 1) | *ADABase* |\n",
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"| **XGB** | Floating Point (0.0 - 1.0) | *REVELIO* |\n",
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"\n",
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"---\n",
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"\n",
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"Would you like me to provide the Python code block to implement the **SVM** or **XGB** detector using these libraries?"
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]
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},
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{
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