im trying to do 3d head model fitting and monocular head tracking  to a image to see visually can you help fix code . this is my code :   
3d head model.py : '''
import numpy as np

class HeadModel3D:
    def __init__(self):
        # Simplified 3D model with 68 landmarks
        self.landmarks = np.zeros((68, 3))
        
    def update_geometry(self, params):
        # In a real implementation, this would update the 3D model
        # Here, we'll just store the parameters
        self.geometry_params = params
        
    def update_expression(self, params):
        self.expression_params = params
        
    def update_pose(self, params):
        self.pose_params = params
        
    def update_reflectance(self, params):
        self.reflectance_params = params
        
    def update_illumination(self, params):
        self.illumination_params = params
        
    def get_2d_landmarks(self):
        # In a real implementation, this would project 3D landmarks to 2D
        # Here, we'll just return the x and y coordinates
        return self.landmarks[:, :2]'''

monocular head tracking.py :

'''
import cv2
import dlib
import numpy as np

class MonocularHeadTracker:
    def __init__(self):
        self.detector = dlib.get_frontal_face_detector()
        self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
        
    def track(self, image, head_model):
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        faces = self.detector(gray)
        
        if len(faces) == 0:
            return None
        
        face = faces[0]
        shape = self.predictor(gray, face)
        landmarks_2d = np.array([(p.x, p.y) for p in shape.parts()])
        
        # Update head model parameters (simplified)
        head_model.update_geometry(self._estimate_geometry(face))
        head_model.update_expression(self._estimate_expression(landmarks_2d))
        head_model.update_pose(self._estimate_pose(landmarks_2d))
        
        return landmarks_2d
    
    def _estimate_geometry(self, face):
        return {
            "width": face.width(),
            "height": face.height()
        }
    
    def _estimate_expression(self, landmarks):
        return {
            "mouth_open": landmarks[66, 1] - landmarks[62, 1],
            "left_eye_open": landmarks[41, 1] - landmarks[37, 1],
            "right_eye_open": landmarks[46, 1] - landmarks[44, 1]
        }
    
    def _estimate_pose(self, landmarks):
        # Simplified pose estimation
        nose_tip = landmarks[30]
        return {
            "x": nose_tip[0],
            "y": nose_tip[1]
        }
'''

main.py:'''
import cv2
from monocular_tracker import MonocularHeadTracker
from head_model_3d import HeadModel3D

def main():
    # Initialize 3D head model and tracker
    head_model = HeadModel3D()
    tracker = MonocularHeadTracker()
    
    # Load image
    image_path = "path/to/your/image.jpg"
    image = cv2.imread(image_path)
    
    if image is None:
        print(f"Failed to load image: {image_path}")
        return
    
    # Track face and update model
    landmarks_2d = tracker.track(image, head_model)
    
    if landmarks_2d is None:
        print("No face detected in the image.")
        return
    
    # Draw landmarks on the image
    for (x, y) in landmarks_2d:
        cv2.circle(image, (int(x), int(y)), 2, (0, 255, 0), -1)
    
    # Display results
    print("Geometry:", head_model.geometry_params)
    print("Expression:", head_model.expression_params)
    print("Pose:", head_model.pose_params)
    
    cv2.imshow("3D Head Tracking", image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()
'''