import cv2
import numpy as np
from typing import List, Iterable, Optional
from mediapipe.python.solutions.face_mesh import FaceMesh  # Correct import


def detect_landmarks(src: np.ndarray, is_stream: bool = False) -> Optional[List]:
    """
    Given an image `src`, retrieves the facial landmarks associated with it.
    Works with Mediapipe 0.10+.
    """
    with FaceMesh(
        static_image_mode=not is_stream,
        max_num_faces=1,
        refine_landmarks=True,
        min_detection_confidence=0.5,
        min_tracking_confidence=0.5
    ) as fm:
        results = fm.process(cv2.cvtColor(src, cv2.COLOR_BGR2RGB))
    
    if results.multi_face_landmarks:
        return results.multi_face_landmarks[0].landmark
    return None


def normalize_landmarks(landmarks, height: int, width: int, mask: Iterable = None) -> np.ndarray:
    normalized_landmarks = np.array([
        (int(landmark.x * width), int(landmark.y * height)) for landmark in landmarks
    ])
    if mask is not None:
        normalized_landmarks = normalized_landmarks[mask]
    return normalized_landmarks


def plot_landmarks(src: np.ndarray, landmarks: List, show: bool = False) -> np.ndarray:
    dst = src.copy()
    for x, y in landmarks:
        cv2.circle(dst, (x, y), 2, (0, 255, 0), cv2.FILLED)
    if show:
        print("Displaying image plotted with landmarks")
        cv2.imshow("Plotted Landmarks", dst)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
    return dst