Facial Recognition

facial-rec/main.py

@@ -0,0 +1,237 @@
+import cv2
+import os
+import numpy as np
+
+# -------------------------
+# Global configurations
+# -------------------------
+dataset_dir = "dataset"                 # All captured faces are stored here; each subfolder is a user
+model_path = "trainer.yml"              # File to store the trained model
+face_cascade_path = "haarcascade_frontalface_default.xml"
+unknown_threshold = 70                  # Adjust threshold (lower value: stricter recognition)
+
+# -------------------------
+# Helper: Enrollment Function
+# -------------------------
+def enroll_user(name, sample_count=100):
+    """
+    Captures face samples from the webcam for a given user.
+    If the user already exists (folder exists in the dataset), images are appended.
+    Press 'q' to exit enrollment mode early.
+    """
+    user_dir = os.path.join(dataset_dir, name)
+    if not os.path.exists(user_dir):
+        os.makedirs(user_dir)
+        print(f"Created new folder for user '{name}'.")
+    else:
+        print(f"Folder for user '{name}' exists. New images will be added.")
+
+    face_cascade = cv2.CascadeClassifier(face_cascade_path)
+    cap = cv2.VideoCapture(0)
+    count = 0
+
+    print("Enrolling user. Press 'q' to quit enrollment early.")
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            print("Failed to grab frame.")
+            break
+
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+        
+        for (x, y, w, h) in faces:
+            count += 1
+            face_img = gray[y:y+h, x:x+w]
+            file_name = os.path.join(user_dir, f"{name}_{count}.jpg")
+            cv2.imwrite(file_name, face_img)
+            # Draw a rectangle for feedback
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+        
+        cv2.imshow("Enrollment - Press 'q' to Quit", frame)
+        if cv2.waitKey(1) & 0xFF == ord('q') or count >= sample_count:
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+    print(f"Enrollment complete. Collected {count} samples for '{name}'.")
+
+# -------------------------
+# Helper: Training Function
+# -------------------------
+def train_model():
+    """
+    Scans the dataset folder and trains an LBPH face recognizer with images from each user.
+    The folder name is used as the user's label.
+    The trained model is saved to model_path.
+    Returns the mapping dictionary (id_to_name) for recognition.
+    """
+    face_samples = []
+    labels = []
+    label_map = {}  # Mapping from user name to a numerical ID
+    current_id = 0
+
+    # Walk through the dataset folder (each subfolder represents a user)
+    for user in os.listdir(dataset_dir):
+        user_path = os.path.join(dataset_dir, user)
+        if not os.path.isdir(user_path):
+            continue
+
+        if user not in label_map:
+            label_map[user] = current_id
+            current_id += 1
+
+        for filename in os.listdir(user_path):
+            if filename.endswith(".jpg") or filename.endswith(".png"):
+                image_path = os.path.join(user_path, filename)
+                img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+                if img is None:
+                    continue
+                face_samples.append(img)
+                labels.append(label_map[user])
+    
+    if len(face_samples) == 0:
+        print("No face images found in dataset. Please enroll a user first.")
+        return None
+
+    recognizer = cv2.face.LBPHFaceRecognizer_create()
+    recognizer.train(face_samples, np.array(labels))
+    recognizer.save(model_path)
+    print(f"Model trained successfully and saved as '{model_path}'.")
+
+    # Create an id-to-name mapping (reverse of label_map)
+    id_to_name = {v: k for k, v in label_map.items()}
+    return id_to_name
+
+# -------------------------
+# Helper: Real-Time Recognition
+# -------------------------
+def recognize_faces():
+    """
+    Runs real-time face recognition. If a face is not confidently recognized,
+    it is labeled as 'Unknown'. While running, press:
+        - 'q' to quit recognition.
+        - 'n' to enroll a new user (this stops recognition, enrolls the new user,
+          re-trains the model, and resumes recognition).
+    """
+    if not os.path.exists(model_path):
+        print("No trained model found. Please run training first.")
+        return
+
+    # Load the recognizer model
+    recognizer = cv2.face.LBPHFaceRecognizer_create()
+    recognizer.read(model_path)
+
+    # Build a mapping dictionary from the dataset folder structure
+    label_map = {}
+    current_id = 0
+    for user in os.listdir(dataset_dir):
+        user_path = os.path.join(dataset_dir, user)
+        if not os.path.isdir(user_path):
+            continue
+        if user not in label_map:
+            label_map[user] = current_id
+            current_id += 1
+    id_to_name = {v: k for k, v in label_map.items()}
+
+    face_cascade = cv2.CascadeClassifier(face_cascade_path)
+    cap = cv2.VideoCapture(0)
+
+    print("Starting real-time recognition. Press 'q' to quit, 'n' to enroll a new user.")
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            print("Failed to grab frame.")
+            break
+
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+
+        for (x, y, w, h) in faces:
+            roi_gray = gray[y:y+h, x:x+w]
+
+            # Get prediction (label and confidence)
+            label_id, confidence = recognizer.predict(roi_gray)
+            # If confidence is above the threshold, mark as Unknown
+            if confidence > unknown_threshold:
+                name = "Unknown"
+            else:
+                name = id_to_name.get(label_id, "Unknown")
+
+            # Draw rectangle and label on the frame
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
+            text = f"{name} ({confidence:.2f})"
+            cv2.putText(frame, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+        
+        cv2.imshow("Real-Time Recognition", frame)
+        key = cv2.waitKey(1) & 0xFF
+
+        if key == ord('q'):
+            break
+        elif key == ord('n'):
+            # Enroll a new user while recognition is running.
+            cap.release()
+            cv2.destroyAllWindows()
+            new_name = input("Enter the new person's name for enrollment: ")
+            enroll_user(new_name)
+            # Re-train the model after enrollment
+            mapping = train_model()
+            if mapping is None:
+                print("Error during training. Please enroll a user first.")
+                return
+            print("Model re-trained with new user. Resuming recognition...")
+            # Reopen capture and reinitialize recognizer and mapping
+            recognizer = cv2.face.LBPHFaceRecognizer_create()
+            recognizer.read(model_path)
+            label_map = {}
+            current_id = 0
+            for user in os.listdir(dataset_dir):
+                user_path = os.path.join(dataset_dir, user)
+                if not os.path.isdir(user_path):
+                    continue
+                if user not in label_map:
+                    label_map[user] = current_id
+                    current_id += 1
+            id_to_name = {v: k for k, v in label_map.items()}
+            cap = cv2.VideoCapture(0)
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+# -------------------------
+# Main Menu
+# -------------------------
+def main():
+    # Ensure the dataset directory exists
+    if not os.path.exists(dataset_dir):
+        os.makedirs(dataset_dir)
+
+    while True:
+        print("\n************ MAIN MENU ************")
+        print("1. Enroll (add images for a new/existing user)")
+        print("2. Train model")
+        print("3. Real-time face recognition")
+        print("4. Quit")
+        choice = input("Enter your choice (1-4): ").strip()
+
+        if choice == '1':
+            user_name = input("Enter the user's name (if already exists, new images will be added): ").strip()
+            enroll_user(user_name)
+        elif choice == '2':
+            mapping = train_model()
+            if mapping is not None:
+                print("Current user mapping (ID: Name):")
+                for id_val, name in mapping.items():
+                    print(f"{id_val}: {name}")
+        elif choice == '3':
+            recognize_faces()
+        elif choice == '4':
+            print("Exiting program.")
+            break
+        else:
+            print("Invalid choice. Please select a valid option.")
+
+if __name__ == "__main__":
+    main()

physicaly-based-renderer/imgui.ini

@@ -4,7 +4,7 @@ Size=400,400
 Collapsed=0
 
 [Window][Scene Controls]
-Pos=13,10
-Size=484,570
+Pos=5,2
+Size=647,1175
 Collapsed=0