from __future__ import annotations

import os
import pathlib
import shlex
import subprocess
import tarfile

if os.environ.get('SYSTEM') == 'spaces':
    subprocess.call(shlex.split('pip uninstall -y opencv-python'))
    subprocess.call(shlex.split('pip uninstall -y opencv-python-headless'))
    subprocess.call(
        shlex.split('pip install opencv-python-headless==4.5.5.64'))

import gradio as gr
import huggingface_hub
import mediapipe as mp
import numpy as np

mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_face_mesh = mp.solutions.face_mesh

TITLE = 'MediaPipe Face Mesh'
DESCRIPTION = 'https://google.github.io/mediapipe/'

HF_TOKEN = os.getenv('HF_TOKEN')


def load_sample_images() -> list[pathlib.Path]:
    image_dir = pathlib.Path('images')
    if not image_dir.exists():
        image_dir.mkdir()
        dataset_repo = 'hysts/input-images'
        filenames = ['001.tar', '005.tar']
        for name in filenames:
            path = huggingface_hub.hf_hub_download(dataset_repo,
                                                   name,
                                                   repo_type='dataset',
                                                   use_auth_token=HF_TOKEN)
            with tarfile.open(path) as f:
                f.extractall(image_dir.as_posix())
    return sorted(image_dir.rglob('*.jpg'))


def run(
    image: np.ndarray,
    max_num_faces: int,
    min_detection_confidence: float,
    show_tesselation: bool,
    show_contours: bool,
    show_irises: bool,
) -> np.ndarray:
    with mp_face_mesh.FaceMesh(
            static_image_mode=True,
            max_num_faces=max_num_faces,
            refine_landmarks=True,
            min_detection_confidence=min_detection_confidence) as face_mesh:
        results = face_mesh.process(image)

    res = image[:, :, ::-1].copy()
    if results.multi_face_landmarks is not None:
        for face_landmarks in results.multi_face_landmarks:
            if show_tesselation:
                mp_drawing.draw_landmarks(
                    image=res,
                    landmark_list=face_landmarks,
                    connections=mp_face_mesh.FACEMESH_TESSELATION,
                    landmark_drawing_spec=None,
                    connection_drawing_spec=mp_drawing_styles.
                    get_default_face_mesh_tesselation_style())
            if show_contours:
                mp_drawing.draw_landmarks(
                    image=res,
                    landmark_list=face_landmarks,
                    connections=mp_face_mesh.FACEMESH_CONTOURS,
                    landmark_drawing_spec=None,
                    connection_drawing_spec=mp_drawing_styles.
                    get_default_face_mesh_contours_style())
            if show_irises:
                mp_drawing.draw_landmarks(
                    image=res,
                    landmark_list=face_landmarks,
                    connections=mp_face_mesh.FACEMESH_IRISES,
                    landmark_drawing_spec=None,
                    connection_drawing_spec=mp_drawing_styles.
                    get_default_face_mesh_iris_connections_style())

    return res[:, :, ::-1]


image_paths = load_sample_images()
examples = [[path.as_posix(), 5, 0.5, True, True, True]
            for path in image_paths]

gr.Interface(
    fn=run,
    inputs=[
        gr.Image(label='Input', type='numpy'),
        gr.Slider(label='Max Number of Faces',
                  minimum=0,
                  maximum=10,
                  step=1,
                  value=5),
        gr.Slider(label='Minimum Detection Confidence',
                  minimum=0,
                  maximum=1,
                  step=0.05,
                  value=0.5),
        gr.Checkbox(label='Show Tesselation', value=True),
        gr.Checkbox(label='Show Contours', value=True),
        gr.Checkbox(label='Show Irises', value=True),
    ],
    outputs=gr.Image(label='Output', type='numpy'),
    examples=examples,
    title=TITLE,
    description=DESCRIPTION,
).launch(show_api=False)