from typing import Any, List, Literal
from argparse import ArgumentParser
import cv2
import numpy

import DeepFakeAI.globals
import DeepFakeAI.processors.frame.core as frame_processors
from DeepFakeAI import wording
from DeepFakeAI.face_analyser import get_one_face, get_average_face, get_many_faces, find_similar_faces, clear_face_analyser
from DeepFakeAI.face_store import get_reference_faces
from DeepFakeAI.content_analyser import clear_content_analyser
from DeepFakeAI.typing import Face, FaceSet, Frame, Update_Process, ProcessMode
from DeepFakeAI.vision import read_image, read_static_image, read_static_images, write_image
from DeepFakeAI.face_helper import warp_face
from DeepFakeAI.face_masker import create_static_box_mask, create_occlusion_mask, create_region_mask, clear_face_occluder, clear_face_parser
from DeepFakeAI.processors.frame import globals as frame_processors_globals, choices as frame_processors_choices

NAME = __name__.upper()


def get_frame_processor() -> None:
	pass


def clear_frame_processor() -> None:
	pass


def get_options(key : Literal['model']) -> None:
	pass


def set_options(key : Literal['model'], value : Any) -> None:
	pass


def register_args(program : ArgumentParser) -> None:
	program.add_argument('--face-debugger-items', help = wording.get('face_debugger_items_help').format(choices = ', '.join(frame_processors_choices.face_debugger_items)), default = [ 'kps', 'face-mask' ], choices = frame_processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')


def apply_args(program : ArgumentParser) -> None:
	args = program.parse_args()
	frame_processors_globals.face_debugger_items = args.face_debugger_items


def pre_check() -> bool:
	return True


def pre_process(mode : ProcessMode) -> bool:
	return True


def post_process() -> None:
	clear_frame_processor()
	clear_face_analyser()
	clear_content_analyser()
	clear_face_occluder()
	clear_face_parser()
	read_static_image.cache_clear()


def debug_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
	primary_color = (0, 0, 255)
	secondary_color = (0, 255, 0)
	bounding_box = target_face.bbox.astype(numpy.int32)
	if 'bbox' in frame_processors_globals.face_debugger_items:
		cv2.rectangle(temp_frame, (bounding_box[0], bounding_box[1]), (bounding_box[2], bounding_box[3]), secondary_color, 2)
	if 'face-mask' in frame_processors_globals.face_debugger_items:
		crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, 'arcface_128_v2', (128, 512))
		inverse_matrix = cv2.invertAffineTransform(affine_matrix)
		temp_frame_size = temp_frame.shape[:2][::-1]
		crop_mask_list = []
		if 'box' in DeepFakeAI.globals.face_mask_types:
			crop_mask_list.append(create_static_box_mask(crop_frame.shape[:2][::-1], 0, DeepFakeAI.globals.face_mask_padding))
		if 'occlusion' in DeepFakeAI.globals.face_mask_types:
			crop_mask_list.append(create_occlusion_mask(crop_frame))
		if 'region' in DeepFakeAI.globals.face_mask_types:
			crop_mask_list.append(create_region_mask(crop_frame, DeepFakeAI.globals.face_mask_regions))
		crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
		crop_mask = (crop_mask * 255).astype(numpy.uint8)
		inverse_mask_frame = cv2.warpAffine(crop_mask, inverse_matrix, temp_frame_size)
		inverse_mask_frame_edges = cv2.threshold(inverse_mask_frame, 100, 255, cv2.THRESH_BINARY)[1]
		inverse_mask_frame_edges[inverse_mask_frame_edges > 0] = 255
		inverse_mask_contours = cv2.findContours(inverse_mask_frame_edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0]
		cv2.drawContours(temp_frame, inverse_mask_contours, -1, primary_color, 2)
	if bounding_box[3] - bounding_box[1] > 60 and bounding_box[2] - bounding_box[0] > 60:
		if 'kps' in frame_processors_globals.face_debugger_items:
			kps = target_face.kps.astype(numpy.int32)
			for index in range(kps.shape[0]):
				cv2.circle(temp_frame, (kps[index][0], kps[index][1]), 3, primary_color, -1)
		if 'score' in frame_processors_globals.face_debugger_items:
			score_text = str(round(target_face.score, 2))
			score_position = (bounding_box[0] + 10, bounding_box[1] + 20)
			cv2.putText(temp_frame, score_text, score_position, cv2.FONT_HERSHEY_SIMPLEX, 0.5, secondary_color, 2)
	return temp_frame


def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
	pass


def process_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
	if 'reference' in DeepFakeAI.globals.face_selector_mode:
		similar_faces = find_similar_faces(temp_frame, reference_faces, DeepFakeAI.globals.reference_face_distance)
		if similar_faces:
			for similar_face in similar_faces:
				temp_frame = debug_face(source_face, similar_face, temp_frame)
	if 'one' in DeepFakeAI.globals.face_selector_mode:
		target_face = get_one_face(temp_frame)
		if target_face:
			temp_frame = debug_face(source_face, target_face, temp_frame)
	if 'many' in DeepFakeAI.globals.face_selector_mode:
		many_faces = get_many_faces(temp_frame)
		if many_faces:
			for target_face in many_faces:
				temp_frame = debug_face(source_face, target_face, temp_frame)
	return temp_frame


def process_frames(source_paths : List[str], temp_frame_paths : List[str], update_progress : Update_Process) -> None:
	source_frames = read_static_images(source_paths)
	source_face = get_average_face(source_frames)
	reference_faces = get_reference_faces() if 'reference' in DeepFakeAI.globals.face_selector_mode else None
	for temp_frame_path in temp_frame_paths:
		temp_frame = read_image(temp_frame_path)
		result_frame = process_frame(source_face, reference_faces, temp_frame)
		write_image(temp_frame_path, result_frame)
		update_progress()


def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
	source_frames = read_static_images(source_paths)
	source_face = get_average_face(source_frames)
	target_frame = read_static_image(target_path)
	reference_faces = get_reference_faces() if 'reference' in DeepFakeAI.globals.face_selector_mode else None
	result_frame = process_frame(source_face, reference_faces, target_frame)
	write_image(output_path, result_frame)


def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
	frame_processors.multi_process_frames(source_paths, temp_frame_paths, process_frames)