Create trainImage.py

trainImage.py

@@ -0,0 +1,42 @@
+import cv2
+import os
+import numpy as np
+
+def TrainImages():
+    # Path to the training images
+    trainimage_path = "TrainingImage"
+    
+    # Prepare for face recognition
+    recognizer = cv2.face.LBPHFaceRecognizer_create()
+    detector = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
+    
+    faces = []
+    ids = []
+    
+    # Get all student directories
+    for dir_name in os.listdir(trainimage_path):
+        student_path = os.path.join(trainimage_path, dir_name)
+        
+        # Check if it's a directory
+        if os.path.isdir(student_path):
+            for filename in os.listdir(student_path):
+                img_path = os.path.join(student_path, filename)
+                
+                # Read image
+                img = cv2.imread(img_path)
+                gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+                
+                # Detect faces
+                faces_detected = detector.detectMultiScale(gray, 1.3, 5)
+                
+                for (x, y, w, h) in faces_detected:
+                    # Add the face and associated ID
+                    faces.append(gray[y:y + h, x:x + w])
+                    ids.append(int(dir_name))
+    
+    # Train the recognizer
+    recognizer.train(faces, np.array(ids))
+    
+    # Save the model to file
+    recognizer.write("trainer.yml")
+    print("Model trained successfully!")