from typing import Any, List, Callable
import cv2
import threading
from gfpgan.utils import GFPGANer

import DeepFakeAI.globals
import DeepFakeAI.processors.frame.core as frame_processors
from DeepFakeAI import wording
from DeepFakeAI.core import update_status
from DeepFakeAI.face_analyser import get_many_faces
from DeepFakeAI.typing import Frame, Face
from DeepFakeAI.utilities import conditional_download, resolve_relative_path, is_image, is_video

FRAME_PROCESSOR = None
THREAD_SEMAPHORE = threading.Semaphore()
THREAD_LOCK = threading.Lock()
NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_ENHANCER'


def get_frame_processor() -> Any:
	global FRAME_PROCESSOR

	with THREAD_LOCK:
		if FRAME_PROCESSOR is None:
			model_path = resolve_relative_path('../.assets/models/GFPGANv1.4.pth')
			FRAME_PROCESSOR = GFPGANer(
				model_path = model_path,
				upscale = 1,
				device = frame_processors.get_device()
			)
	return FRAME_PROCESSOR


def clear_frame_processor() -> None:
	global FRAME_PROCESSOR

	FRAME_PROCESSOR = None


def pre_check() -> bool:
	download_directory_path = resolve_relative_path('../.assets/models')
	conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth'])
	return True


def pre_process() -> bool:
	if not is_image(DeepFakeAI.globals.target_path) and not is_video(DeepFakeAI.globals.target_path):
		update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
		return False
	return True


def post_process() -> None:
	clear_frame_processor()


def enhance_face(target_face : Face, temp_frame : Frame) -> Frame:
	start_x, start_y, end_x, end_y = map(int, target_face['bbox'])
	padding_x = int((end_x - start_x) * 0.5)
	padding_y = int((end_y - start_y) * 0.5)
	start_x = max(0, start_x - padding_x)
	start_y = max(0, start_y - padding_y)
	end_x = max(0, end_x + padding_x)
	end_y = max(0, end_y + padding_y)
	crop_frame = temp_frame[start_y:end_y, start_x:end_x]
	if crop_frame.size:
		with THREAD_SEMAPHORE:
			_, _, crop_frame = get_frame_processor().enhance(
				crop_frame,
				paste_back = True
			)
		temp_frame[start_y:end_y, start_x:end_x] = crop_frame
	return temp_frame


def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
	many_faces = get_many_faces(temp_frame)
	if many_faces:
		for target_face in many_faces:
			temp_frame = enhance_face(target_face, temp_frame)
	return temp_frame


def process_frames(source_path : str, temp_frame_paths : List[str], update: Callable[[], None]) -> None:
	for temp_frame_path in temp_frame_paths:
		temp_frame = cv2.imread(temp_frame_path)
		result_frame = process_frame(None, None, temp_frame)
		cv2.imwrite(temp_frame_path, result_frame)
		if update:
			update()


def process_image(source_path : str, target_path : str, output_path : str) -> None:
	target_frame = cv2.imread(target_path)
	result_frame = process_frame(None, None, target_frame)
	cv2.imwrite(output_path, result_frame)


def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
	DeepFakeAI.processors.frame.core.process_video(None, temp_frame_paths, process_frames)