- added description to use py-feat

- added script:
-- to split a video into 2min clips
-- to extract AUs from a video

dataset_creation/AU_creation/AU_creation_service.py

@@ -0,0 +1,58 @@
+from feat import Detector
+from feat.utils.io import get_test_data_path
+from moviepy.video.io.VideoFileClip import VideoFileClip
+import os
+
+def extract_aus(path, model):
+  detector = Detector(au_model=model)
+
+  video_prediction = detector.detect(
+      path, data_type="video", skip_frames=24*5, face_detection_threshold=0.95 # alle 5 Sekunden einbeziehen - 24 Frames pro Sekunde
+  )
+
+  return video_prediction.aus.sum()
+
+def split_video(path, chunk_length=120): 
+    video = VideoFileClip(path)
+    duration = int(video.duration)
+
+    subclips_dir = os.path.join(os.dirname(path), "subclips")
+    os.makedirs(subclips_dir, exist_ok=True)
+    paths = []
+
+    for start in range(0, duration, chunk_length):
+        end = min(start + chunk_length, duration)
+
+        subclip = (
+            video
+            .subclip(start, end)
+            .without_audio()      
+            .set_fps(video.fps)          
+        )
+
+        output_path = f"{subclips_dir}_part_{start//chunk_length + 1}.mp4"
+        subclip.write_videofile(
+           output_path, 
+           )
+        paths.append(output_path)
+
+    return output_path
+
+def start(path):
+    results = []
+    clips = split_video(path)
+
+    for clip in clips:
+      results.append(extract_aus(clip, 'svm'))
+    return results
+
+if __name__ == "__main__":
+  results = []
+  clips = []
+  test_video_path = "AU_creation/YTDown.com_YouTube_Was-ist-los-bei-7-vs-Wild_Media_Gtj9zu_WikU_001_1080p.mp4"
+  clips = split_video(test_video_path)
+
+  for clippath in clips:
+    results.append(extract_aus(clippath, 'svm'))
+
+  print(results)

dataset_creation/AU_creation/pyfeat_docu.ipynb

@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3b0c6c82",
+   "metadata": {},
+   "source": [
+    "Hier entsteht die Dokumentation, wie die Action Units erzeugt wurden.\n",
+    "Daraus wird dann letztendlich ein Skript erstellt, welches automatisch AUs aus Videodateien erstellen soll.\n",
+    "\n",
+    "Py-Feat besitzt Dependencies, die ab Python 3.12 nicht mehr verfügbar sind.\n",
+    "Dazu muss ein Kernel mit Python 3.11 erstellt werden.\n",
+    "Folgendes Vorgehen:\n",
+    "1. Seite des Jupyter Labs öffnen\n",
+    "2. Terminal öffnen und folgende Befehle eingeben:\n",
+    "  conda create -n py311 python=3.11\n",
+    "  source ~/.bashrc\n",
+    "  conda activate py311\n",
+    "  conda install jupyter\n",
+    "  python -m ipykernel install --user --name=py311 --display-name \"Python 3.11\"\n",
+    "  pip install py-feat\n",
+    "  pip install \"moviepy<2.0\" (falls benötigt)\n",
+    "3. den Kernel neustarten\n",
+    "4. in VSC den Kernel neu hinzufügen und dann den Kernel mit dem Namen \"Python 3.11\" auswählen.\n",
+    "\n",
+    "Der Code unten zeigt eine beispielhafte Integration der py-feat Bibliothek.\n",
+    "Die Klassifizierung zu 0,1 kommt durch die Wahl des AU-Modells zustande. Dabei wird SVM gewählt. (ADABase Paper)\n",
+    "Gibt die Klassifizierung einen Gleitkommawert zwischen 0 & 1 aus, dann kommt XGB zum Einsatz. (REVELIO Paper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c262899f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install git+https://github.com/cosanlab/py-feat.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25d8d708",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from feat import Detector\n",
+    "\n",
+    "# detector = Detector(face_model='RetinaFace', facepose_model='Img2Pose', landmark_model='MobileFaceNet', au_model='svm')\n",
+    "# detector = Detector(face_model='img2pose', landmark_model='mobilefacenet', au_model='xgb', emotion_model='resmasknet', facepose_model='img2pose', identity_model='facenet')\n",
+    "detector = Detector(au_model='svm')\n",
+    "# Detector(face_model='RetinaFace', facepose_model='Img2Pose', landmark_model='MobileFaceNet', au_model='svm')\n",
+    "detector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c361665f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from feat.utils.io import get_test_data_path\n",
+    "from feat.plotting import imshow\n",
+    "import os\n",
+    "\n",
+    "# Helper to point to the test data folder\n",
+    "test_data_dir = get_test_data_path()\n",
+    "print(get_test_data_path())\n",
+    "\n",
+    "# Get the full path\n",
+    "folder = r\"AU_creation\"\n",
+    "paths = [os.path.join(folder, f) for f in os.listdir(folder)]\n",
+    "\n",
+    "# Plot it\n",
+    "imshow(paths[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1e0780c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "single_face_prediction = detector.detect(paths, data_type=\"image\")\n",
+    "\n",
+    "type(single_face_prediction)  # instace of a Fex class\n",
+    "\n",
+    "# Show results\n",
+    "single_face_prediction.aus\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d844b17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "single_face_prediction.emotions.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76771cb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_data_dir = get_test_data_path()\n",
+    "test_video_path = os.path.join(test_data_dir, \"WolfgangLanger_Pexels.mp4\")\n",
+    "\n",
+    "video_prediction = detector.detect(\n",
+    "    test_video_path, data_type=\"video\", skip_frames=24*5, face_detection_threshold=0.95 # alle 5 Sekunden einbeziehen - 24 Frames pro Sekunde\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8a06268",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "au_counts = video_prediction.aus.sum()\n",
+    "print(au_counts)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11",
+   "language": "python",
+   "name": "py311"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}