import cv2
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
from mediapipe.framework.formats import landmark_pb2
import numpy as np
import gradio as gr

# Mediapipe FaceLandmarker seçeneklerini belirleyin
base_options = python.BaseOptions(model_asset_path='face_landmarker.task')
options = vision.FaceLandmarkerOptions(
    base_options=base_options,
    output_face_blendshapes=True,
    output_facial_transformation_matrixes=True,
    num_faces=1
)
detector = vision.FaceLandmarker.create_from_options(options)

# Landmark noktalarını çizmek için fonksiyon
def draw_landmarks_on_image(rgb_image, detection_result):
    face_landmarks_list = detection_result.face_landmarks
    annotated_image = np.copy(rgb_image)
    
    for idx in range(len(face_landmarks_list)):
        face_landmarks = face_landmarks_list[idx]
        face_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
        face_landmarks_proto.landmark.extend([
            landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in face_landmarks
        ])

        mp.solutions.drawing_utils.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks_proto,
            connections=mp.solutions.face_mesh.FACEMESH_TESSELATION,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp.solutions.drawing_styles.get_default_face_mesh_tesselation_style())
        mp.solutions.drawing_utils.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks_proto,
            connections=mp.solutions.face_mesh.FACEMESH_CONTOURS,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp.solutions.drawing_styles.get_default_face_mesh_contours_style())
        mp.solutions.drawing_utils.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks_proto,
            connections=mp.solutions.face_mesh.FACEMESH_IRISES,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp.solutions.drawing_styles.get_default_face_mesh_iris_connections_style())
    return annotated_image

# Görseli işleyen fonksiyon
def process_image(image):
    # OpenCV görüntüsünü Mediapipe formatına dönüştür
    rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_image)

    # Yüz yer işaretlerini algıla
    detection_result = detector.detect(mp_image)

    # Çerçeveyi güncelle
    if detection_result.face_blendshapes:
        # İlk yüzün blendshape skorlarını al
        face_blendshapes = detection_result.face_blendshapes[0]

        # eyeBlinkLeft ve eyeBlinkRight blendshape skorlarını bul
        blink_left = next((bs.score for bs in face_blendshapes if bs.category_name == "eyeBlinkLeft"), 0)
        blink_right = next((bs.score for bs in face_blendshapes if bs.category_name == "eyeBlinkRight"), 0)

        # Göz durumunu belirle
        left_eye_status = "Kapalı" if blink_left > 0.5 else "Açık"
        right_eye_status = "Kapalı" if blink_right > 0.5 else "Açık"

        # Landmarkları çizin
        annotated_image = draw_landmarks_on_image(rgb_image, detection_result)

        return cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR), left_eye_status, right_eye_status
    else:
        return image, "Göz Tespiti Yok", "Göz Tespiti Yok"

# Gradio arayüzü
iface = gr.Interface(fn=process_image,
                     inputs=gr.Image(type="numpy", label="Görsel Yükleyin"),  # Giriş olarak görsel al
                     outputs=[gr.Image(label="Yüz Tespiti Sonucu"),
                              gr.Textbox(label="Sol Göz Durumu"),
                              gr.Textbox(label="Sağ Göz Durumu")])

# Gradio arayüzünü başlat
iface.launch(share=True)