erstmal bild hochladet dann mapt biometrische data in .yml und detectiert gesichte , muss immer noch debuggt werden -- second commit

bildPy/.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Ignored default folder with query files
+/queries/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/

bildPy/.idea/.name

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+source.py

bildPy/.idea/bildPy.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$/src" isTestSource="false" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.14" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

bildPy/.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

bildPy/.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.14" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.14" project-jdk-type="Python SDK" />
+</project>

bildPy/.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/bildPy.iml" filepath="$PROJECT_DIR$/.idea/bildPy.iml" />
+    </modules>
+  </component>
+</project>

bildPy/src/source.py

@@ -1,5 +1,76 @@
 import cv2
+import os
+import numpy as np
 
-img = cv2.imread("../data_raw/diddy/P_Diddy_2000.webp");
+# da wir in src sind , so können wir zu andrem ordner kommen
+RAW_DATA_PFAD = "../data_raw"
+MODEL_PFAD = "../models"
+MODEL_FILE = os.path.join(MODEL_PFAD, "trained_lbph.yml") # yml für biometrische Data
+NAMES_FILE = os.path.join(MODEL_PFAD, "names.pkl") # für mapping the ids from bio data  to real person
+
+# gesicht detektor erstmal initializieren
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
 
 
+#LBPH Recognizer initializieren
+recognizer = cv2.face.LBPHFaceRecognizer_create()
+
+# dir hersteller
+def create_directory_if_not_exists(directory):
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+# trainiert model
+def train_model():
+    print("\n-training is angefangen")
+    faces = []
+    ids = []
+    names_map = {}
+    current_id = 0
+
+
+# überpruft ob data dir schon exestiert
+    if not os.path.exists(RAW_DATA_PFAD):
+        print(f"Error: Directory '{RAW_DATA_PFAD}' nicht gefunden.")
+        return
+
+
+# geht durch jede ordner in data raw (e.g., diddy, kirk, etc.)
+    for person_name in os.listdir(RAW_DATA_PFAD):
+        person_path = os.path.join(RAW_DATA_PFAD, person_name)
+
+
+
+# verpasst (skip) alles was nicht ordner ist so wie store.ds oder sowas (.txt....)
+        if not os.path.isdir(person_path):
+            continue
+
+        names_map[current_id] = person_name
+        print(f"Processing ID {current_id}: {person_name}")
+
+        # geht durch jedes Bild in der Ordner jeder Person
+        for image_name in os.listdir(person_path):
+
+            if image_name.startswith("."): continue  # Skip unsichbare files die mit . starten
+
+            image_path = os.path.join(person_path, image_name)
+
+            # ladet das bild hoch dann convertiert zum Grayscale
+            img = cv2.imread(image_path)
+            if img is None: continue
+            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+
+             #detectiert gesichte
+            faces_rects = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5)
+            for (x, y, w, h) in faces_rects:
+                # region von interest ist das gesicht selbst
+                roi = gray[y:y + w, x:x + h]
+                faces.append(roi)
+                ids.append(current_id)
+
+        current_id += 1
+
+    if len(faces) == 0:
+        print("No faces found. Please check your 'data_raw' folder.")
+        return