facefusion/vision.py

import math
from functools import lru_cache
from typing import List, Optional, Tuple

import cv2
import numpy
from cv2.typing import Size

import facefusion.choices
from facefusion.common_helper import is_windows
from facefusion.filesystem import get_file_extension, is_image, is_video
from facefusion.thread_helper import thread_semaphore
from facefusion.types import Duration, Fps, Orientation, Resolution, VisionFrame
from facefusion.video_manager import get_video_capture

# === ДОБАВЬТЕ ЭТИ ИМПОРТЫ В НАЧАЛО ФАЙЛА ===
from PIL import Image
import tempfile
import os
import traceback
import time

# ==========================================


_png_cache = {}   # кэш для хранения путей к PNG файлам
_frame_cache = {} # кэш для фреймов
_is_processing = False  # Флаг для отслеживания процесса обработки

def start_processing():
    global _is_processing
    _is_processing = True

def end_processing():
    global _is_processing
    _is_processing = False

# Функции для работы с кэшем
def clear_caches():
    """
    Очищает все кэши
    """
    global _png_cache, _frame_cache
    _png_cache.clear()
    _frame_cache.clear()
    read_static_image.cache_clear()  # Очищаем LRU кэш

def get_cached_frame(image_path: str) -> Optional[VisionFrame]:
    """
    Получает кэшированный фрейм или читает его заново
    """
    if image_path in _frame_cache:
        return _frame_cache[image_path].copy()
    return None

def cache_frame(image_path: str, frame: VisionFrame) -> None:
    """
    Кэширует фрейм
    """
    _frame_cache[image_path] = frame.copy()
    

# Модифицируем read_static_image:
@lru_cache()
def read_static_image(image_path: str) -> Optional[VisionFrame]:
    # Сначала проверяем кэш фреймов
    cached_frame = get_cached_frame(image_path)
    if cached_frame is not None:
        print(f"[FaceFusion Cache] Using cached frame for '{os.path.basename(image_path)}'")
        return cached_frame

    frame = read_image(image_path)
    if frame is not None:
        cache_frame(image_path, frame)
    return frame


def read_static_images(image_paths : List[str]) -> List[VisionFrame]:
	frames = []

	if image_paths:
		for image_path in image_paths:
			frames.append(read_static_image(image_path))
	return frames


def get_or_create_png(original_path: str) -> str:
    """
    Возвращает путь к PNG версии файла, создавая его при необходимости.
    """
    if not original_path or not os.path.exists(original_path):
    #   print(f"[DEBUG] Invalid or non-existent path: {original_path}")
        return None
        
    if 'temp.png' in original_path or original_path.lower().endswith('.png'):
        return original_path
        
    file_name = os.path.basename(original_path)
    
    try:
        # Читаем через PIL сначала
        with Image.open(original_path) as img:
            if img.mode != 'RGB':
                img = img.convert('RGB')
            
            # Создаем временный файл
            temp_png_path = os.path.join(
                tempfile.gettempdir(),
                f'ff_temp_{hash(original_path)}_{os.getpid()}.png'
            )
            
            # Сохраняем через PIL
            img.save(temp_png_path, "PNG")
            print(f"[FaceFusion PNG Converter] Successfully created PNG for '{file_name}'")
            
            # Проверяем результат
            if os.path.exists(temp_png_path):
                return temp_png_path
                
    except Exception as e:
        print(f"[FaceFusion PNG Converter] Error converting '{file_name}': {e}")
        
    # Если PIL не сработал, пробуем через OpenCV
    try:
        if is_windows():
            image_buffer = numpy.fromfile(original_path, dtype=numpy.uint8)
            image = cv2.imdecode(image_buffer, cv2.IMREAD_COLOR)
        else:
            image = cv2.imread(original_path)
            
        if image is not None:
            temp_png_path = os.path.join(
                tempfile.gettempdir(),
                f'ff_temp_{hash(original_path)}_{os.getpid()}_cv.png'
            )
            cv2.imwrite(temp_png_path, image)
            return temp_png_path
            
    except Exception as e:
        print(f"[FaceFusion PNG Converter] Error with OpenCV fallback for '{file_name}': {e}")
        
    return None

# Добавим функцию очистки кэша:
def cleanup_png_cache():
    """
    Очищает временные PNG файлы и кэш.
    """
    for original_path, png_path in _png_cache.items():
        if png_path != original_path:  # Не удаляем оригинальные PNG файлы
            try:
                if os.path.exists(png_path):
                    os.remove(png_path)
                    print(f"[FaceFusion Cache] Cleaned up cached PNG for '{os.path.basename(original_path)}'")
            except Exception as e:
                print(f"[FaceFusion Cache] Warning: Could not remove '{png_path}'. Error: {e}")
    _png_cache.clear()
#--------------------- end PNG cashing ------

def read_image(image_path: str) -> Optional[VisionFrame]:
    """
    Читает изображение из файла.
    """
    if not is_image(image_path):
        return None
        
    try:
        if is_windows():
            image_buffer = numpy.fromfile(image_path, dtype=numpy.uint8)
            image = cv2.imdecode(image_buffer, cv2.IMREAD_COLOR)
        else:
            image = cv2.imread(image_path)
            
        if image is None:
        #   print(f"[DEBUG] Failed to read image: {image_path}")
            return None
            
        return image
    except Exception as e:
    #   print(f"[DEBUG] Error reading image: {e}")
        return None

def write_image(image_path: str, vision_frame: VisionFrame) -> bool:
#   print(f"[DEBUG] Writing image to: {image_path}")
#   print(f"[DEBUG] Frame shape: {vision_frame.shape if vision_frame is not None else 'None'}")
    
    if not image_path or vision_frame is None:
    #   print("[DEBUG] Invalid image path or frame")
        return False
        
    try:
        if is_windows():
        #   print("[DEBUG] Writing with Windows method")
            image_file_extension = get_file_extension(image_path)
            _, encoded_frame = cv2.imencode(image_file_extension, vision_frame)
            encoded_frame.tofile(image_path)
        else:
        #   print("[DEBUG] Writing with cv2.imwrite")
            cv2.imwrite(image_path, vision_frame)
            
        success = os.path.exists(image_path)
    #   print(f"[DEBUG] Write success: {success}")
        return success
    except Exception as e:
    #   print(f"[DEBUG] Error writing image: {e}")
        traceback.print_exc()
        return False


def detect_image_resolution(image_path : str) -> Optional[Resolution]:
	if is_image(image_path):
		image = read_image(image_path)
		if image is not None: # Добавлена проверка на None, так как read_image теперь может вернуть None
			height, width = image.shape[:2]

			if width > 0 and height > 0:
				return width, height
	return None


def restrict_image_resolution(image_path : str, resolution : Resolution) -> Resolution:
	if is_image(image_path):
		image_resolution = detect_image_resolution(image_path)
		if image_resolution and image_resolution < resolution: # Добавлена проверка на image_resolution
			return image_resolution
	return resolution


def create_image_resolutions(resolution : Resolution) -> List[str]:
	resolutions = []
	temp_resolutions = []

	if resolution:
		width, height = resolution
		temp_resolutions.append(normalize_resolution(resolution))
		for image_template_size in facefusion.choices.image_template_sizes:
			temp_resolutions.append(normalize_resolution((width * image_template_size, height * image_template_size)))
		temp_resolutions = sorted(set(temp_resolutions))
		for temp_resolution in temp_resolutions:
			resolutions.append(pack_resolution(temp_resolution))
	return resolutions


def read_video_frame(video_path : str, frame_number : int = 0) -> Optional[VisionFrame]:
	if is_video(video_path):
		video_capture = get_video_capture(video_path)

		if video_capture.isOpened():
			frame_total = video_capture.get(cv2.CAP_PROP_FRAME_COUNT)

			with thread_semaphore():
				video_capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
				has_vision_frame, vision_frame = video_capture.read()

			if has_vision_frame:
				return vision_frame

	return None


def count_video_frame_total(video_path : str) -> int:
	if is_video(video_path):
		video_capture = get_video_capture(video_path)

		if video_capture.isOpened():
			with thread_semaphore():
				video_frame_total = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
				return video_frame_total

	return 0


def predict_video_frame_total(video_path : str, fps : Fps, trim_frame_start : int, trim_frame_end : int) -> int:
	if is_video(video_path):
		video_fps = detect_video_fps(video_path)
		extract_frame_total = count_trim_frame_total(video_path, trim_frame_start, trim_frame_end) * fps / video_fps
		return math.floor(extract_frame_total)
	return 0


def detect_video_fps(video_path : str) -> Optional[float]:
	if is_video(video_path):
		video_capture = get_video_capture(video_path)

		if video_capture.isOpened():
			with thread_semaphore():
				video_fps = video_capture.get(cv2.CAP_PROP_FPS)
				return video_fps

	return None


def restrict_video_fps(video_path : str, fps : Fps) -> Fps:
	if is_video(video_path):
		video_fps = detect_video_fps(video_path)
		if video_fps < fps:
			return video_fps
	return fps


def detect_video_duration(video_path : str) -> Duration:
	video_frame_total = count_video_frame_total(video_path)
	video_fps = detect_video_fps(video_path)

	if video_frame_total and video_fps:
		return video_frame_total / video_fps
	return 0


def count_trim_frame_total(video_path : str, trim_frame_start : Optional[int], trim_frame_end : Optional[int]) -> int:
	trim_frame_start, trim_frame_end = restrict_trim_frame(video_path, trim_frame_start, trim_frame_end)

	return trim_frame_end - trim_frame_start


def restrict_trim_frame(video_path : str, trim_frame_start : Optional[int], trim_frame_end : Optional[int]) -> Tuple[int, int]:
	video_frame_total = count_video_frame_total(video_path)

	if isinstance(trim_frame_start, int):
		trim_frame_start = max(0, min(trim_frame_start, video_frame_total))
	if isinstance(trim_frame_end, int):
		trim_frame_end = max(0, min(trim_frame_end, video_frame_total))

	if isinstance(trim_frame_start, int) and isinstance(trim_frame_end, int):
		return trim_frame_start, trim_frame_end
	if isinstance(trim_frame_start, int):
		return trim_frame_start, video_frame_total
	if isinstance(trim_frame_end, int):
		return 0, trim_frame_end

	return 0, video_frame_total


def detect_video_resolution(video_path : str) -> Optional[Resolution]:
	if is_video(video_path):
		video_capture = get_video_capture(video_path)

		if video_capture.isOpened():
			with thread_semaphore():
				width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)
				height = video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
				return int(width), int(height)

	return None


def restrict_video_resolution(video_path : str, resolution : Resolution) -> Resolution:
	if is_video(video_path):
		video_resolution = detect_video_resolution(video_path)
		if video_resolution < resolution:
			return video_resolution
	return resolution


def create_video_resolutions(resolution : Resolution) -> List[str]:
	resolutions = []
	temp_resolutions = []

	if resolution:
		width, height = resolution
		temp_resolutions.append(normalize_resolution(resolution))
		for video_template_size in facefusion.choices.video_template_sizes:
			if width > height:
				temp_resolutions.append(normalize_resolution((video_template_size * width / height, video_template_size)))
			else:
				temp_resolutions.append(normalize_resolution((video_template_size, video_template_size * height / width)))
		temp_resolutions = sorted(set(temp_resolutions))
		for temp_resolution in temp_resolutions:
			resolutions.append(pack_resolution(temp_resolution))
	return resolutions


def normalize_resolution(resolution : Tuple[float, float]) -> Resolution:
	width, height = resolution

	if width > 0 and height > 0:
		normalize_width = round(width / 2) * 2
		normalize_height = round(height / 2) * 2
		return normalize_width, normalize_height
	return 0, 0


def pack_resolution(resolution: Resolution) -> str:
    if resolution is None:
    #   print("[DEBUG] Warning: Received None resolution in pack_resolution")
        return "1024x1024"  # возвращаем значение по умолчанию вместо "0x0"
    try:
        width, height = normalize_resolution(resolution)
        return str(width) + 'x' + str(height)
    except Exception as e:
    #   print(f"[DEBUG] Error in pack_resolution: {e}")
        return "1024x1024"  # возвращаем значение по умолчанию


def unpack_resolution(resolution : str) -> Resolution:
	width, height = map(int, resolution.split('x'))
	return width, height


def detect_frame_orientation(vision_frame : VisionFrame) -> Orientation:
	height, width = vision_frame.shape[:2]

	if width > height:
		return 'landscape'
	return 'portrait'


def restrict_frame(vision_frame : VisionFrame, resolution : Resolution) -> VisionFrame:
	height, width = vision_frame.shape[:2]
	restrict_width, restrict_height = resolution

	if height > restrict_height or width > restrict_width:
		scale = min(restrict_height / height, restrict_width / width)
		new_width = int(width * scale)
		new_height = int(height * scale)
		return cv2.resize(vision_frame, (new_width, new_height))
	return vision_frame


def fit_frame(vision_frame : VisionFrame, resolution: Resolution) -> VisionFrame:
	fit_width, fit_height = resolution
	height, width = vision_frame.shape[:2]
	scale = min(fit_height / height, fit_width / width)
	new_width = int(width * scale)
	new_height = int(height * scale)
	paste_vision_frame = cv2.resize(vision_frame, (new_width, new_height))
	x_pad = (fit_width - new_width) // 2
	y_pad = (fit_height - new_height) // 2
	temp_vision_frame = numpy.pad(paste_vision_frame, ((y_pad, fit_height - new_height - y_pad), (x_pad, fit_width - new_width - x_pad), (0, 0)))
	return temp_vision_frame


def normalize_frame_color(vision_frame: VisionFrame) -> VisionFrame:
    if vision_frame is None:
    #   print("[DEBUG] Warning: Received None vision_frame in normalize_frame_color")
        return None
    try:
        return cv2.cvtColor(vision_frame, cv2.COLOR_BGR2RGB)
    except Exception as e:
    #   print(f"[DEBUG] Error in normalize_frame_color: {e}")
        return vision_frame


def conditional_match_frame_color(source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame:
	histogram_factor = calc_histogram_difference(source_vision_frame, target_vision_frame)
	target_vision_frame = blend_vision_frames(target_vision_frame, match_frame_color(source_vision_frame, target_vision_frame), histogram_factor)
	return target_vision_frame


def match_frame_color(source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame:
	color_difference_sizes = numpy.linspace(16, target_vision_frame.shape[0], 3, endpoint = False)

	for color_difference_size in color_difference_sizes:
		source_vision_frame = equalize_frame_color(source_vision_frame, target_vision_frame, normalize_resolution((color_difference_size, color_difference_size)))
	target_vision_frame = equalize_frame_color(source_vision_frame, target_vision_frame, target_vision_frame.shape[:2][::-1])
	return target_vision_frame


def equalize_frame_color(source_vision_frame : VisionFrame, target_vision_frame : VisionFrame, size : Size) -> VisionFrame:
	source_frame_resize = cv2.resize(source_vision_frame, size, interpolation = cv2.INTER_AREA).astype(numpy.float32)
	target_frame_resize = cv2.resize(target_vision_frame, size, interpolation = cv2.INTER_AREA).astype(numpy.float32)
	color_difference_vision_frame = numpy.subtract(source_frame_resize, target_frame_resize)
	color_difference_vision_frame = cv2.resize(color_difference_vision_frame, target_vision_frame.shape[:2][::-1], interpolation = cv2.INTER_CUBIC)
	target_vision_frame = numpy.add(target_vision_frame, color_difference_vision_frame).clip(0, 255).astype(numpy.uint8)
	return target_vision_frame


def calc_histogram_difference(source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> float:
	histogram_source = cv2.calcHist([cv2.cvtColor(source_vision_frame, cv2.COLOR_BGR2HSV)], [ 0, 1 ], None, [ 50, 60 ], [ 0, 180, 0, 256 ])
	histogram_target = cv2.calcHist([cv2.cvtColor(target_vision_frame, cv2.COLOR_BGR2HSV)], [ 0, 1 ], None, [ 50, 60 ], [ 0, 180, 0, 256 ])
	histogram_difference = float(numpy.interp(cv2.compareHist(histogram_source, histogram_target, cv2.HISTCMP_CORREL), [ -1, 1 ], [ 0, 1 ]))
	return histogram_difference


def blend_vision_frames(source_vision_frame : VisionFrame, target_vision_frame : VisionFrame, blend_factor : float) -> VisionFrame:
	blend_vision_frame = cv2.addWeighted(source_vision_frame, 1 - blend_factor, target_vision_frame, blend_factor, 0)
	return blend_vision_frame


def create_tile_frames(vision_frame : VisionFrame, size : Size) -> Tuple[List[VisionFrame], int, int]:
	vision_frame = numpy.pad(vision_frame, ((size[1], size[1]), (size[1], size[1]), (0, 0)))
	tile_width = size[0] - 2 * size[2]
	pad_size_bottom = size[2] + tile_width - vision_frame.shape[0] % tile_width
	pad_size_right = size[2] + tile_width - vision_frame.shape[1] % tile_width
	pad_vision_frame = numpy.pad(vision_frame, ((size[2], pad_size_bottom), (size[2], pad_size_right), (0, 0)))
	pad_height, pad_width = pad_vision_frame.shape[:2]
	row_range = range(size[2], pad_height - size[2], tile_width)
	col_range = range(size[2], pad_width - size[2], tile_width)
	tile_vision_frames = []

	for row_vision_frame in row_range:
		top = row_vision_frame - size[2]
		bottom = row_vision_frame + size[2] + tile_width

		for column_vision_frame in col_range:
			left = column_vision_frame - size[2]
			right = column_vision_frame + size[2] + tile_width
			tile_vision_frames.append(pad_vision_frame[top:bottom, left:right, :])

	return tile_vision_frames, pad_width, pad_height


def merge_tile_frames(tile_vision_frames : List[VisionFrame], temp_width : int, temp_height : int, pad_width : int, pad_height : int, size : Size) -> VisionFrame:
	merge_vision_frame = numpy.zeros((pad_height, pad_width, 3)).astype(numpy.uint8)
	tile_width = tile_vision_frames[0].shape[1] - 2 * size[2]
	tiles_per_row = min(pad_width // tile_width, len(tile_vision_frames))

	for index, tile_vision_frame in enumerate(tile_vision_frames):
		tile_vision_frame = tile_vision_frame[size[2]:-size[2], size[2]:-size[2]]
		row_index = index // tiles_per_row
		col_index = index % tiles_per_row
		top = row_index * tile_vision_frame.shape[0]
		bottom = top + tile_vision_frame.shape[0]
		left = col_index * tile_vision_frame.shape[1]
		right = left + tile_vision_frame.shape[1]
		merge_vision_frame[top:bottom, left:right, :] = tile_vision_frame

	merge_vision_frame = merge_vision_frame[size[1] : size[1] + temp_height, size[1]: size[1] + temp_width, :]
	return merge_vision_frame