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