From 0cab786b4cdd5895d41ce34aa88e891c5cd9ae34 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Iv=C3=A1n=20Eduardo=20Chavez=20Ayub?= <165610830+Ivan-Ayub97@users.noreply.github.com> Date: Fri, 6 Jun 2025 22:08:11 -0600 Subject: [PATCH] Delete Warlock-Studio.py --- Warlock-Studio.py | 3214 --------------------------------------------- 1 file changed, 3214 deletions(-) delete mode 100644 Warlock-Studio.py diff --git a/Warlock-Studio.py b/Warlock-Studio.py deleted file mode 100644 index 7920926..0000000 --- a/Warlock-Studio.py +++ /dev/null @@ -1,3214 +0,0 @@ - -# Standard library imports -import sys -from functools import cache -from itertools import repeat -from json import dumps as json_dumps -from json import load as json_load -from math import cos, pi # For smooth fade effect -from multiprocessing import Process -from multiprocessing import Queue as multiprocessing_Queue -from multiprocessing import freeze_support as multiprocessing_freeze_support -from multiprocessing.pool import ThreadPool -from os import O_CREAT, O_WRONLY -from os import cpu_count as os_cpu_count -from os import devnull as os_devnull -from os import fdopen as os_fdopen -from os import listdir as os_listdir -from os import makedirs as os_makedirs -from os import open as os_open -from os import remove as os_remove -from os import sep as os_separator -from os.path import abspath as os_path_abspath -from os.path import basename as os_path_basename -from os.path import dirname as os_path_dirname -from os.path import exists as os_path_exists -from os.path import expanduser as os_path_expanduser -from os.path import join as os_path_join -from os.path import splitext as os_path_splitext -from shutil import rmtree as remove_directory -from subprocess import run as subprocess_run -from threading import Thread -from time import sleep -from timeit import default_timer as timer -# GUI imports -from tkinter import DISABLED, StringVar -from typing import Callable -from webbrowser import open as open_browser - -from customtkinter import (CTk, CTkButton, CTkEntry, CTkFont, CTkFrame, - CTkImage, CTkLabel, CTkOptionMenu, - CTkScrollableFrame, CTkToplevel, filedialog, - set_appearance_mode, set_default_color_theme) -from cv2 import (CAP_PROP_FPS, CAP_PROP_FRAME_COUNT, CAP_PROP_FRAME_HEIGHT, - CAP_PROP_FRAME_WIDTH, COLOR_BGR2RGB, COLOR_BGR2RGBA, - COLOR_GRAY2RGB, COLOR_RGB2GRAY, IMREAD_UNCHANGED, INTER_AREA, - INTER_CUBIC) -from cv2 import VideoCapture as opencv_VideoCapture -from cv2 import addWeighted as opencv_addWeighted -from cv2 import cvtColor as opencv_cvtColor -from cv2 import imdecode as opencv_imdecode -from cv2 import imencode as opencv_imencode -from cv2 import resize as opencv_resize -# Third-party library imports -from natsort import natsorted -from numpy import ascontiguousarray as numpy_ascontiguousarray -from numpy import clip as numpy_clip -from numpy import concatenate as numpy_concatenate -from numpy import expand_dims as numpy_expand_dims -from numpy import float32 -from numpy import frombuffer as numpy_frombuffer -from numpy import full as numpy_full -from numpy import max as numpy_max -from numpy import mean as numpy_mean -from numpy import ndarray as numpy_ndarray -from numpy import repeat as numpy_repeat -from numpy import squeeze as numpy_squeeze -from numpy import transpose as numpy_transpose -from numpy import uint8 -from numpy import zeros as numpy_zeros -from onnxruntime import InferenceSession -from PIL.Image import fromarray as pillow_image_fromarray -from PIL.Image import open as pillow_image_open - -if sys.stdout is None: - sys.stdout = open(os_devnull, "w") -if sys.stderr is None: - sys.stderr = open(os_devnull, "w") - - -def find_by_relative_path(relative_path: str) -> str: - base_path = getattr(sys, '_MEIPASS', os_path_dirname( - os_path_abspath(__file__))) - return os_path_join(base_path, relative_path) - - -app_name = "Warlock Studio" -version = "1.1" - -background_color = "#121212" # Negro grisáceo profundo -app_name_color = "#FFFFFF" # Blanco puro para el nombre de la app -widget_background_color = "#8B0000" # Rojo oscuro (Dark Red) -text_color = "#DDDDDD" # Blanco opaco para texto legible - -VRAM_model_usage = { - 'RealESR_Gx4': 2.2, - 'RealESR_Animex4': 2.2, - 'RealESRNetx4': 2.2, - 'BSRGANx4': 0.6, - 'BSRGANx2': 0.7, - 'RealESRGANx4': 0.6, - 'IRCNN_Mx1': 4, - 'IRCNN_Lx1': 4, -} - -MENU_LIST_SEPARATOR = ["----"] -SRVGGNetCompact_models_list = ["RealESR_Gx4", "RealESR_Animex4"] -BSRGAN_models_list = ["BSRGANx4", "BSRGANx2", "RealESRGANx4", "RealESRNetx4"] -IRCNN_models_list = ["IRCNN_Mx1", "IRCNN_Lx1"] - -AI_models_list = (SRVGGNetCompact_models_list + MENU_LIST_SEPARATOR + - BSRGAN_models_list + MENU_LIST_SEPARATOR + IRCNN_models_list) -AI_multithreading_list = ["OFF", "2 threads", - "4 threads", "6 threads", "8 threads"] -blending_list = ["OFF", "Low", "Medium", "High"] -gpus_list = ["Auto", "GPU 1", "GPU 2", "GPU 3", "GPU 4"] -keep_frames_list = ["OFF", "ON"] -image_extension_list = [".png", ".jpg", ".bmp", ".tiff"] -video_extension_list = [".mp4", ".mkv", ".avi", ".mov"] -video_codec_list = [ - "x264", "x265", MENU_LIST_SEPARATOR[0], - "h264_nvenc", "hevc_nvenc", MENU_LIST_SEPARATOR[0], - "h264_amf", "hevc_amf", MENU_LIST_SEPARATOR[0], - "h264_qsv", "hevc_qsv", -] - -OUTPUT_PATH_CODED = "Same path as input files" -DOCUMENT_PATH = os_path_join(os_path_expanduser('~'), 'Documents') -USER_PREFERENCE_PATH = find_by_relative_path( - f"{DOCUMENT_PATH}{os_separator}{app_name}_{version}_UserPreference.json") -FFMPEG_EXE_PATH = find_by_relative_path(f"Assets{os_separator}ffmpeg.exe") -EXIFTOOL_EXE_PATH = find_by_relative_path(f"Assets{os_separator}exiftool.exe") - -ECTRACTION_FRAMES_FOR_CPU = 30 -MULTIPLE_FRAMES_TO_SAVE = 8 - -COMPLETED_STATUS = "Completed" -ERROR_STATUS = "Error" -STOP_STATUS = "Stop" - -if os_path_exists(FFMPEG_EXE_PATH): - print(f"[{app_name}] ffmpeg.exe found") -else: - print(f"[{app_name}] ffmpeg.exe not found, please install ffmpeg.exe following the guide") - -if os_path_exists(USER_PREFERENCE_PATH): - print(f"[{app_name}] Preference file exist") - with open(USER_PREFERENCE_PATH, "r") as json_file: - json_data = json_load(json_file) - default_AI_model = json_data.get( - "default_AI_model", AI_models_list[0]) - default_AI_multithreading = json_data.get( - "default_AI_multithreading", AI_multithreading_list[0]) - default_gpu = json_data.get( - "default_gpu", gpus_list[0]) - default_keep_frames = json_data.get( - "default_keep_frames", keep_frames_list[1]) - default_image_extension = json_data.get( - "default_image_extension", image_extension_list[0]) - default_video_extension = json_data.get( - "default_video_extension", video_extension_list[0]) - default_video_codec = json_data.get( - "default_video_codec", video_codec_list[0]) - default_blending = json_data.get( - "default_blending", blending_list[1]) - default_output_path = json_data.get( - "default_output_path", OUTPUT_PATH_CODED) - default_input_resize_factor = json_data.get( - "default_input_resize_factor", str(50)) - default_output_resize_factor = json_data.get( - "default_output_resize_factor", str(100)) - default_VRAM_limiter = json_data.get( - "default_VRAM_limiter", str(4)) - -else: - print(f"[{app_name}] Preference file does not exist, using default coded value") - default_AI_model = AI_models_list[0] - default_AI_multithreading = AI_multithreading_list[0] - default_gpu = gpus_list[0] - default_keep_frames = keep_frames_list[1] - default_image_extension = image_extension_list[0] - default_video_extension = video_extension_list[0] - default_video_codec = video_codec_list[0] - default_blending = blending_list[1] - default_output_path = OUTPUT_PATH_CODED - default_input_resize_factor = str(50) - default_output_resize_factor = str(100) - default_VRAM_limiter = str(4) - -offset_y_options = 0.0825 -row1 = 0.125 -row2 = row1 + offset_y_options -row3 = row2 + offset_y_options -row4 = row3 + offset_y_options -row5 = row4 + offset_y_options -row6 = row5 + offset_y_options -row7 = row6 + offset_y_options -row8 = row7 + offset_y_options -row9 = row8 + offset_y_options -row10 = row9 + offset_y_options - -column_offset = 0.2 -column_info1 = 0.625 -column_info2 = 0.858 -column_1 = 0.66 -column_2 = column_1 + column_offset -column_1_5 = column_info1 + 0.08 -column_1_4 = column_1_5 - 0.0127 -column_3 = column_info2 + 0.08 -column_2_9 = column_3 - 0.0127 -column_3_5 = column_2 + 0.0355 - -little_textbox_width = 74 -little_menu_width = 98 - - -supported_file_extensions = [ - '.heic', '.jpg', '.jpeg', '.JPG', '.JPEG', '.png', - '.PNG', '.webp', '.WEBP', '.bmp', '.BMP', '.tif', - '.tiff', '.TIF', '.TIFF', '.mp4', '.MP4', '.webm', - '.WEBM', '.mkv', '.MKV', '.flv', '.FLV', '.gif', - '.GIF', '.m4v', ',M4V', '.avi', '.AVI', '.mov', - '.MOV', '.qt', '.3gp', '.mpg', '.mpeg', ".vob" -] - -supported_video_extensions = [ - '.mp4', '.MP4', '.webm', '.WEBM', '.mkv', '.MKV', - '.flv', '.FLV', '.gif', '.GIF', '.m4v', ',M4V', - '.avi', '.AVI', '.mov', '.MOV', '.qt', '.3gp', - '.mpg', '.mpeg', ".vob" -] - - -# AI ------------------- - -class AI_upscale: - - # CLASS INIT FUNCTIONS - - def __init__( - self, - AI_model_name: str, - directml_gpu: str, - input_resize_factor: int, - output_resize_factor: int, - max_resolution: int - ): - - # Passed variables - self.AI_model_name = AI_model_name - self.directml_gpu = directml_gpu - self.input_resize_factor = input_resize_factor - self.output_resize_factor = output_resize_factor - self.max_resolution = max_resolution - - # Calculated variables - self.AI_model_path = find_by_relative_path( - f"AI-onnx{os_separator}{self.AI_model_name}_fp16.onnx") - self.upscale_factor = self._get_upscale_factor() - self.inferenceSession = None - - def _get_upscale_factor(self) -> int: - if "x1" in self.AI_model_name: - return 1 - elif "x2" in self.AI_model_name: - return 2 - elif "x4" in self.AI_model_name: - return 4 - - def _load_inferenceSession(self) -> None: - - providers = ['DmlExecutionProvider'] - - match self.directml_gpu: - case 'Auto': provider_options = [{"performance_preference": "high_performance"}] - case 'GPU 1': provider_options = [{"device_id": "0"}] - case 'GPU 2': provider_options = [{"device_id": "1"}] - case 'GPU 3': provider_options = [{"device_id": "2"}] - case 'GPU 4': provider_options = [{"device_id": "3"}] - - inference_session = InferenceSession( - path_or_bytes=self.AI_model_path, - providers=providers, - provider_options=provider_options, - ) - - self.inferenceSession = inference_session - - # INTERNAL CLASS FUNCTIONS - - def get_image_mode(self, image: numpy_ndarray) -> str: - shape = image.shape - if len(shape) == 2: # Grayscale: 2D array (rows, cols) - return "Grayscale" - # RGB: 3D array with 3 channels - elif len(shape) == 3 and shape[2] == 3: - return "RGB" - # RGBA: 3D array with 4 channels - elif len(shape) == 3 and shape[2] == 4: - return "RGBA" - - def get_image_resolution(self, image: numpy_ndarray) -> tuple: - height = image.shape[0] - width = image.shape[1] - - return height, width - - def calculate_target_resolution(self, image: numpy_ndarray) -> tuple: - height, width = self.get_image_resolution(image) - target_height = height * self.upscale_factor - target_width = width * self.upscale_factor - - return target_height, target_width - - def resize_with_input_factor(self, image: numpy_ndarray) -> numpy_ndarray: - - old_height, old_width = self.get_image_resolution(image) - - new_width = int(old_width * self.input_resize_factor) - new_height = int(old_height * self.input_resize_factor) - - new_width = new_width if new_width % 2 == 0 else new_width + 1 - new_height = new_height if new_height % 2 == 0 else new_height + 1 - - if self.input_resize_factor > 1: - return opencv_resize(image, (new_width, new_height), interpolation=INTER_CUBIC) - elif self.input_resize_factor < 1: - return opencv_resize(image, (new_width, new_height), interpolation=INTER_AREA) - else: - return image - - def resize_with_output_factor(self, image: numpy_ndarray) -> numpy_ndarray: - - old_height, old_width = self.get_image_resolution(image) - - new_width = int(old_width * self.output_resize_factor) - new_height = int(old_height * self.output_resize_factor) - - new_width = new_width if new_width % 2 == 0 else new_width + 1 - new_height = new_height if new_height % 2 == 0 else new_height + 1 - - if self.output_resize_factor > 1: - return opencv_resize(image, (new_width, new_height), interpolation=INTER_CUBIC) - elif self.output_resize_factor < 1: - return opencv_resize(image, (new_width, new_height), interpolation=INTER_AREA) - else: - return image - - # VIDEO CLASS FUNCTIONS - - def calculate_multiframes_supported_by_gpu(self, video_frame_path: str) -> int: - resized_video_frame = self.resize_with_input_factor( - image_read(video_frame_path)) - height, width = self.get_image_resolution(resized_video_frame) - image_pixels = height * width - max_supported_pixels = self.max_resolution * self.max_resolution - - frames_simultaneously = max_supported_pixels // image_pixels - - print( - f" Frames supported simultaneously by GPU: {frames_simultaneously}") - - return frames_simultaneously - - # TILLING FUNCTIONS - - def image_need_tilling(self, image: numpy_ndarray) -> bool: - height, width = self.get_image_resolution(image) - image_pixels = height * width - max_supported_pixels = self.max_resolution * self.max_resolution - - if image_pixels > max_supported_pixels: - return True - else: - return False - - def add_alpha_channel(self, image: numpy_ndarray) -> numpy_ndarray: - if image.shape[2] == 3: - alpha = numpy_full( - (image.shape[0], image.shape[1], 1), 255, dtype=uint8) - image = numpy_concatenate((image, alpha), axis=2) - return image - - def calculate_tiles_number(self, image: numpy_ndarray) -> tuple: - - height, width = self.get_image_resolution(image) - - tiles_x = (width + self.max_resolution - 1) // self.max_resolution - tiles_y = (height + self.max_resolution - 1) // self.max_resolution - - return tiles_x, tiles_y - - def split_image_into_tiles(self, image: numpy_ndarray, tiles_x: int, tiles_y: int) -> list[numpy_ndarray]: - - img_height, img_width = self.get_image_resolution(image) - - tile_width = img_width // tiles_x - tile_height = img_height // tiles_y - - tiles = [] - - for y in range(tiles_y): - y_start = y * tile_height - y_end = (y + 1) * tile_height - - for x in range(tiles_x): - x_start = x * tile_width - x_end = (x + 1) * tile_width - tile = image[y_start:y_end, x_start:x_end] - tiles.append(tile) - - return tiles - - def combine_tiles_into_image(self, image: numpy_ndarray, tiles: list[numpy_ndarray], t_height: int, t_width: int, num_tiles_x: int) -> numpy_ndarray: - - match self.get_image_mode(image): - case "Grayscale": tiled_image = numpy_zeros((t_height, t_width, 3), dtype=uint8) - case "RGB": tiled_image = numpy_zeros((t_height, t_width, 3), dtype=uint8) - case "RGBA": tiled_image = numpy_zeros((t_height, t_width, 4), dtype=uint8) - - for tile_index in range(len(tiles)): - actual_tile = tiles[tile_index] - - tile_height, tile_width = self.get_image_resolution(actual_tile) - - row = tile_index // num_tiles_x - col = tile_index % num_tiles_x - y_start = row * tile_height - y_end = y_start + tile_height - x_start = col * tile_width - x_end = x_start + tile_width - - match self.get_image_mode(image): - case "Grayscale": tiled_image[y_start:y_end, x_start:x_end] = actual_tile - case "RGB": tiled_image[y_start:y_end, x_start:x_end] = actual_tile - case "RGBA": tiled_image[y_start:y_end, x_start:x_end] = self.add_alpha_channel(actual_tile) - - return tiled_image - - # AI CLASS FUNCTIONS - - def normalize_image(self, image: numpy_ndarray) -> tuple: - range = 255 - if numpy_max(image) > 256: - range = 65535 - normalized_image = image / range - - return normalized_image, range - - def preprocess_image(self, image: numpy_ndarray) -> numpy_ndarray: - image = numpy_transpose(image, (2, 0, 1)) - image = numpy_expand_dims(image, axis=0) - - return image - - def onnxruntime_inference(self, image: numpy_ndarray) -> numpy_ndarray: - - # IO BINDING - # io_binding = self.inferenceSession.io_binding() - # io_binding.bind_cpu_input(self.inferenceSession.get_inputs()[0].name, image.astype(float16)) - # io_binding.bind_output(self.inferenceSession.get_outputs()[0].name) - # self.inferenceSession.run_with_iobinding(io_binding) - # onnx_output = io_binding.copy_outputs_to_cpu()[0] - - onnx_input = {self.inferenceSession.get_inputs()[0].name: image} - onnx_output = self.inferenceSession.run(None, onnx_input)[0] - - return onnx_output - - def postprocess_output(self, onnx_output: numpy_ndarray) -> numpy_ndarray: - onnx_output = numpy_squeeze(onnx_output, axis=0) - onnx_output = numpy_clip(onnx_output, 0, 1) - onnx_output = numpy_transpose(onnx_output, (1, 2, 0)) - - return onnx_output - - def de_normalize_image(self, onnx_output: numpy_ndarray, max_range: int) -> numpy_ndarray: - match max_range: - case 255: return (onnx_output * max_range).astype(uint8) - case 65535: return (onnx_output * max_range).round().astype(float32) - - def AI_upscale(self, image: numpy_ndarray) -> numpy_ndarray: - image = image.astype(float32) - image_mode = self.get_image_mode(image) - image, range = self.normalize_image(image) - - match image_mode: - case "RGB": - image = self.preprocess_image(image) - onnx_output = self.onnxruntime_inference(image) - onnx_output = self.postprocess_output(onnx_output) - output_image = self.de_normalize_image(onnx_output, range) - - return output_image - - case "RGBA": - alpha = image[:, :, 3] - image = image[:, :, :3] - image = opencv_cvtColor(image, COLOR_BGR2RGB) - - image = image.astype(float32) - alpha = alpha.astype(float32) - - # Image - image = self.preprocess_image(image) - onnx_output_image = self.onnxruntime_inference(image) - onnx_output_image = self.postprocess_output(onnx_output_image) - onnx_output_image = opencv_cvtColor( - onnx_output_image, COLOR_BGR2RGBA) - - # Alpha - alpha = numpy_expand_dims(alpha, axis=-1) - alpha = numpy_repeat(alpha, 3, axis=-1) - alpha = self.preprocess_image(alpha) - onnx_output_alpha = self.onnxruntime_inference(alpha) - onnx_output_alpha = self.postprocess_output(onnx_output_alpha) - onnx_output_alpha = opencv_cvtColor( - onnx_output_alpha, COLOR_RGB2GRAY) - - # Fusion Image + Alpha - onnx_output_image[:, :, 3] = onnx_output_alpha - output_image = self.de_normalize_image( - onnx_output_image, range) - - return output_image - - case "Grayscale": - image = opencv_cvtColor(image, COLOR_GRAY2RGB) - - image = self.preprocess_image(image) - onnx_output = self.onnxruntime_inference(image) - onnx_output = self.postprocess_output(onnx_output) - output_image = opencv_cvtColor(onnx_output, COLOR_RGB2GRAY) - output_image = self.de_normalize_image(onnx_output, range) - - return output_image - - def AI_upscale_with_tilling(self, image: numpy_ndarray) -> numpy_ndarray: - t_height, t_width = self.calculate_target_resolution(image) - tiles_x, tiles_y = self.calculate_tiles_number(image) - tiles_list = self.split_image_into_tiles(image, tiles_x, tiles_y) - tiles_list = [self.AI_upscale(tile) for tile in tiles_list] - - return self.combine_tiles_into_image(image, tiles_list, t_height, t_width, tiles_x) - - # EXTERNAL FUNCTION - - def AI_orchestration(self, image: numpy_ndarray) -> numpy_ndarray: - - if self.inferenceSession == None: - self._load_inferenceSession() - - resized_image = self.resize_with_input_factor(image) - - if self.image_need_tilling(resized_image): - upscaled_image = self.AI_upscale_with_tilling(resized_image) - else: - upscaled_image = self.AI_upscale(resized_image) - - return self.resize_with_output_factor(upscaled_image) - - -# GUI utils --------------------------- - -class MessageBox(CTkToplevel): - - def __init__( - self, - messageType: str, - title: str, - subtitle: str, - default_value: str, - option_list: list, - ) -> None: - - super().__init__() - - self._running: bool = False - - self._messageType = messageType - self._title = title - self._subtitle = subtitle - self._default_value = default_value - self._option_list = option_list - self._ctkwidgets_index = 0 - - self.title('') - self.lift() # lift window on top - self.attributes("-topmost", True) # stay on top - self.protocol("WM_DELETE_WINDOW", self._on_closing) - # create widgets with slight delay, to avoid white flickering of background - self.after(10, self._create_widgets) - self.resizable(False, False) - self.grab_set() # make other windows not clickable - - def _ok_event( - self, - event=None - ) -> None: - self.grab_release() - self.destroy() - - def _on_closing( - self - ) -> None: - self.grab_release() - self.destroy() - - def createEmptyLabel(self) -> CTkLabel: - return CTkLabel( - master=self, - fg_color="transparent", - width=500, - height=17, - text='' - ) - - def placeInfoMessageTitleSubtitle(self) -> None: - - spacingLabel1 = self.createEmptyLabel() - spacingLabel2 = self.createEmptyLabel() - - if self._messageType == "info": - title_subtitle_text_color = "#FFD700" # Amarillo dorado - elif self._messageType == "error": - title_subtitle_text_color = "#FF3131" # Rojo brillante - - titleLabel = CTkLabel( - master=self, - width=500, - anchor='w', - justify="left", - fg_color="transparent", - text_color=title_subtitle_text_color, - font=bold22, - text=self._title - ) - - if self._default_value != None: - defaultLabel = CTkLabel( - master=self, - width=500, - anchor='w', - justify="left", - fg_color="transparent", - ttext_color="#FFD700", # Amarillo dorado - font=bold17, - text=f"Default: {self._default_value}" - ) - - subtitleLabel = CTkLabel( - master=self, - width=500, - anchor='w', - justify="left", - fg_color="transparent", - text_color=title_subtitle_text_color, - font=bold14, - text=self._subtitle - ) - - spacingLabel1.grid(row=self._ctkwidgets_index, column=0, - columnspan=2, padx=0, pady=0, sticky="ew") - - self._ctkwidgets_index += 1 - titleLabel.grid(row=self._ctkwidgets_index, column=0, - columnspan=2, padx=25, pady=0, sticky="ew") - - if self._default_value != None: - self._ctkwidgets_index += 1 - defaultLabel.grid(row=self._ctkwidgets_index, column=0, - columnspan=2, padx=25, pady=0, sticky="ew") - - self._ctkwidgets_index += 1 - subtitleLabel.grid(row=self._ctkwidgets_index, column=0, - columnspan=2, padx=25, pady=0, sticky="ew") - - self._ctkwidgets_index += 1 - spacingLabel2.grid(row=self._ctkwidgets_index, column=0, - columnspan=2, padx=0, pady=0, sticky="ew") - - def placeInfoMessageOptionsText(self) -> None: - - for option_text in self._option_list: - optionLabel = CTkLabel( - master=self, - width=600, - height=45, - anchor='w', - justify="left", - text_color=text_color, - fg_color="#282828", - bg_color="transparent", - font=bold13, - text=option_text, - corner_radius=10, - ) - - self._ctkwidgets_index += 1 - optionLabel.grid(row=self._ctkwidgets_index, column=0, - columnspan=2, padx=25, pady=4, sticky="ew") - - spacingLabel3 = self.createEmptyLabel() - - self._ctkwidgets_index += 1 - spacingLabel3.grid(row=self._ctkwidgets_index, column=0, - columnspan=2, padx=0, pady=0, sticky="ew") - - def placeInfoMessageOkButton( - self - ) -> None: - - ok_button = CTkButton( - master=self, - command=self._ok_event, - text='OK', - width=125, - font=bold11, - border_width=1, - fg_color="#282828", - text_color="#E0E0E0", - border_color="#0096FF" - ) - - self._ctkwidgets_index += 1 - ok_button.grid(row=self._ctkwidgets_index, column=1, - columnspan=1, padx=(10, 20), pady=(10, 20), sticky="e") - - def _create_widgets( - self - ) -> None: - - self.grid_columnconfigure((0, 1), weight=1) - self.rowconfigure(0, weight=1) - - self.placeInfoMessageTitleSubtitle() - self.placeInfoMessageOptionsText() - self.placeInfoMessageOkButton() - - -class FileWidget(CTkScrollableFrame): - - def __init__( - self, - master, - selected_file_list, - upscale_factor=1, - input_resize_factor=0, - output_resize_factor=0, - **kwargs - ) -> None: - - super().__init__(master, **kwargs) - self.grid_columnconfigure(0, weight=1) - - self.file_list = selected_file_list - self.upscale_factor = upscale_factor - self.input_resize_factor = input_resize_factor - self.output_resize_factor = output_resize_factor - - self.index_row = 1 - self.ui_components = [] - self._create_widgets() - - def _destroy_(self) -> None: - self.file_list = [] - self.destroy() - place_loadFile_section() - - def _create_widgets(self) -> None: - self.add_clean_button() - for file_path in self.file_list: - file_name_label, file_info_label = self.add_file_information( - file_path) - self.ui_components.append(file_name_label) - self.ui_components.append(file_info_label) - - def add_file_information(self, file_path) -> tuple: - infos, icon = self.extract_file_info(file_path) - - # File name - file_name_label = CTkLabel( - self, - text=os_path_basename(file_path), - font=bold14, - text_color=text_color, - compound="left", - anchor="w", - padx=10, - pady=5, - justify="left", - ) - file_name_label.grid( - row=self.index_row, - column=0, - pady=(0, 2), - padx=(3, 3), - sticky="w" - ) - - # File infos and icon - file_info_label = CTkLabel( - self, - text=infos, - image=icon, - font=bold12, - text_color=text_color, - compound="left", - anchor="w", - padx=10, - pady=5, - justify="left", - ) - file_info_label.grid( - row=self.index_row + 1, - column=0, - pady=(0, 15), - padx=(3, 3), - sticky="w" - ) - - self.index_row += 2 - - return file_name_label, file_info_label - - def add_clean_button(self) -> None: - - button = CTkButton( - master=self, - command=self._destroy_, - text="CLEAN", - image=clear_icon, - width=90, - height=28, - font=bold11, - border_width=1, - corner_radius=1, - fg_color="#282828", - text_color="#E0E0E0", - border_color="#0096FF" - ) - - button.grid(row=0, column=2, pady=(7, 7), padx=(0, 7)) - - @cache - def extract_file_icon(self, file_path) -> CTkImage: - max_size = 60 - - if check_if_file_is_video(file_path): - video_cap = opencv_VideoCapture(file_path) - _, frame = video_cap.read() - source_icon = opencv_cvtColor(frame, COLOR_BGR2RGB) - video_cap.release() - else: - source_icon = opencv_cvtColor(image_read(file_path), COLOR_BGR2RGB) - - ratio = min( - max_size / source_icon.shape[0], max_size / source_icon.shape[1]) - new_width = int(source_icon.shape[1] * ratio) - new_height = int(source_icon.shape[0] * ratio) - source_icon = opencv_resize(source_icon, (new_width, new_height)) - ctk_icon = CTkImage(pillow_image_fromarray( - source_icon, mode="RGB"), size=(new_width, new_height)) - - return ctk_icon - - def extract_file_info(self, file_path) -> tuple: - - if check_if_file_is_video(file_path): - cap = opencv_VideoCapture(file_path) - width = round(cap.get(CAP_PROP_FRAME_WIDTH)) - height = round(cap.get(CAP_PROP_FRAME_HEIGHT)) - num_frames = int(cap.get(CAP_PROP_FRAME_COUNT)) - frame_rate = cap.get(CAP_PROP_FPS) - duration = num_frames/frame_rate - minutes = int(duration/60) - seconds = duration % 60 - cap.release() - - file_icon = self.extract_file_icon(file_path) - file_infos = f"{minutes}m:{round(seconds)}s • {num_frames}frames • {width}x{height} \n" - - if self.input_resize_factor != 0 and self.output_resize_factor != 0 and self.upscale_factor != 0: - input_resized_height = int( - height * (self.input_resize_factor/100)) - input_resized_width = int( - width * (self.input_resize_factor/100)) - - upscaled_height = int( - input_resized_height * self.upscale_factor) - upscaled_width = int(input_resized_width * self.upscale_factor) - - output_resized_height = int( - upscaled_height * (self.output_resize_factor/100)) - output_resized_width = int( - upscaled_width * (self.output_resize_factor/100)) - - file_infos += ( - f"AI input ({self.input_resize_factor}%) ➜ {input_resized_width}x{input_resized_height} \n" - f"AI output (x{self.upscale_factor}) ➜ {upscaled_width}x{upscaled_height} \n" - f"Video output ({self.output_resize_factor}%) ➜ {output_resized_width}x{output_resized_height}" - ) - - else: - height, width = get_image_resolution(image_read(file_path)) - file_icon = self.extract_file_icon(file_path) - - file_infos = f"{width}x{height}\n" - - if self.input_resize_factor != 0 and self.output_resize_factor != 0 and self.upscale_factor != 0: - input_resized_height = int( - height * (self.input_resize_factor/100)) - input_resized_width = int( - width * (self.input_resize_factor/100)) - - upscaled_height = int( - input_resized_height * self.upscale_factor) - upscaled_width = int(input_resized_width * self.upscale_factor) - - output_resized_height = int( - upscaled_height * (self.output_resize_factor/100)) - output_resized_width = int( - upscaled_width * (self.output_resize_factor/100)) - - file_infos += ( - f"AI input ({self.input_resize_factor}%) ➜ {input_resized_width}x{input_resized_height} \n" - f"AI output (x{self.upscale_factor}) ➜ {upscaled_width}x{upscaled_height} \n" - f"Image output ({self.output_resize_factor}%) ➜ {output_resized_width}x{output_resized_height}" - ) - - return file_infos, file_icon - - # EXTERNAL FUNCTIONS - - def clean_file_list(self) -> None: - self.index_row = 1 - for ui_component in self.ui_components: - ui_component.grid_forget() - - def get_selected_file_list(self) -> list: - return self.file_list - - def set_upscale_factor(self, upscale_factor) -> None: - self.upscale_factor = upscale_factor - - def set_input_resize_factor(self, input_resize_factor) -> None: - self.input_resize_factor = input_resize_factor - - def set_output_resize_factor(self, output_resize_factor) -> None: - self.output_resize_factor = output_resize_factor - - -def get_values_for_file_widget() -> tuple: - # Upscale factor - upscale_factor = get_upscale_factor() - - # Input resolution % - try: - input_resize_factor = int( - float(str(selected_input_resize_factor.get()))) - except: - input_resize_factor = 0 - - # Output resolution % - try: - output_resize_factor = int( - float(str(selected_output_resize_factor.get()))) - except: - output_resize_factor = 0 - - return upscale_factor, input_resize_factor, output_resize_factor - - -def update_file_widget(a, b, c) -> None: - try: - global file_widget - file_widget - except: - return - - upscale_factor, input_resize_factor, output_resize_factor = get_values_for_file_widget() - - file_widget.clean_file_list() - file_widget.set_upscale_factor(upscale_factor) - file_widget.set_input_resize_factor(input_resize_factor) - file_widget.set_output_resize_factor(output_resize_factor) - file_widget._create_widgets() - - -def create_option_background(): - return CTkFrame( - master=window, - bg_color=background_color, - fg_color=widget_background_color, - height=46, - corner_radius=10 - ) - - -def create_info_button(command: Callable, text: str, width: int = 200) -> CTkFrame: - - frame = CTkFrame( - master=window, fg_color=widget_background_color, height=25) - - button = CTkButton( - master=frame, - command=command, - font=bold12, - text="?", - border_color="#0096FF", - border_width=1, - fg_color=widget_background_color, - hover_color=background_color, - width=23, - height=15, - corner_radius=1 - ) - button.grid(row=0, column=0, padx=(0, 7), pady=2, sticky="w") - - label = CTkLabel( - master=frame, - text=text, - width=width, - height=22, - fg_color="transparent", - bg_color=widget_background_color, - text_color=text_color, - font=bold13, - anchor="w" - ) - label.grid(row=0, column=1, sticky="w") - - frame.grid_propagate(False) - frame.grid_columnconfigure(1, weight=1) - - return frame - - -def create_option_menu( - command: Callable, - values: list, - default_value: str, - border_color: str = "#404040", - border_width: int = 1, - width: int = 159 -) -> CTkFrame: - - width = width - height = 28 - - total_width = (width + 2 * border_width) - total_height = (height + 2 * border_width) - - frame = CTkFrame( - master=window, - fg_color=border_color, - width=total_width, - height=total_height, - border_width=0, - corner_radius=1, - ) - - option_menu = CTkOptionMenu( - master=frame, - command=command, - values=values, - width=width, - height=height, - corner_radius=0, - dropdown_font=bold12, - font=bold11, - anchor="center", - text_color=text_color, - fg_color=background_color, - button_color=background_color, - button_hover_color=background_color, - dropdown_fg_color=background_color - ) - - option_menu.place( - x=(total_width - width) / 2, - y=(total_height - height) / 2 - ) - option_menu.set(default_value) - return frame - - -def create_text_box(textvariable: StringVar, width: int) -> CTkEntry: - return CTkEntry( - master=window, - textvariable=textvariable, - corner_radius=1, - width=width, - height=28, - font=bold11, - justify="center", - text_color=text_color, - fg_color="#000000", - border_width=1, - border_color="#404040", - ) - - -def create_text_box_output_path(textvariable: StringVar) -> CTkEntry: - return CTkEntry( - master=window, - textvariable=textvariable, - corner_radius=1, - width=250, - height=28, - font=bold11, - justify="center", - text_color=text_color, - fg_color="#000000", - border_width=1, - border_color="#404040", - state=DISABLED - ) - - -def create_active_button( - command: Callable, - text: str, - icon: CTkImage = None, - width: int = 140, - height: int = 30, - border_color: str = "#0096FF" -) -> CTkButton: - - return CTkButton( - master=window, - command=command, - text=text, - image=icon, - width=width, - height=height, - font=bold11, - border_width=1, - corner_radius=1, - fg_color="#282828", - text_color="#E0E0E0", - border_color=border_color - ) - - -# File Utils functions ------------------------ - -def create_dir(name_dir: str) -> None: - if os_path_exists(name_dir): - remove_directory(name_dir) - if not os_path_exists(name_dir): - os_makedirs(name_dir, mode=0o777) - - -def stop_thread() -> None: stop = 1 + "x" - - -def image_read(file_path: str) -> numpy_ndarray: - with open(file_path, 'rb') as file: - return opencv_imdecode(numpy_ascontiguousarray(numpy_frombuffer(file.read(), uint8)), IMREAD_UNCHANGED) - - -def image_write(file_path: str, file_data: numpy_ndarray, file_extension: str = ".jpg") -> None: - opencv_imencode(file_extension, file_data)[1].tofile(file_path) - - -def copy_file_metadata(original_file_path: str, upscaled_file_path: str) -> None: - - exiftool_cmd = [ - EXIFTOOL_EXE_PATH, - '-fast', - '-TagsFromFile', - original_file_path, - '-overwrite_original', - '-all:all', - '-unsafe', - '-largetags', - upscaled_file_path - ] - - try: - subprocess_run(exiftool_cmd, check=True, shell="False") - except: - pass - - -def prepare_output_image_filename( - image_path: str, - selected_output_path: str, - selected_AI_model: str, - input_resize_factor: int, - output_resize_factor: int, - selected_image_extension: str, - selected_blending_factor: float -) -> str: - - if selected_output_path == OUTPUT_PATH_CODED: - file_path_no_extension, _ = os_path_splitext(image_path) - output_path = file_path_no_extension - else: - file_name = os_path_basename(image_path) - output_path = f"{selected_output_path}{os_separator}{file_name}" - - # Selected AI model - to_append = f"_{selected_AI_model}" - - # Selected input resize - to_append += f"_InputR-{str(int(input_resize_factor * 100))}" - - # Selected output resize - to_append += f"_OutputR-{str(int(output_resize_factor * 100))}" - - # Selected intepolation - match selected_blending_factor: - case 0.3: - to_append += "_Blending-Low" - case 0.5: - to_append += "_Blending-Medium" - case 0.7: - to_append += "_Blending-High" - - # Selected image extension - to_append += f"{selected_image_extension}" - - output_path += to_append - - return output_path - - -def prepare_output_video_frame_filename( - frame_path: str, - selected_AI_model: str, - input_resize_factor: int, - output_resize_factor: int, - selected_blending_factor: float -) -> str: - - file_path_no_extension, _ = os_path_splitext(frame_path) - output_path = file_path_no_extension - - # Selected AI model - to_append = f"_{selected_AI_model}" - - # Selected input resize - to_append += f"_InputR-{str(int(input_resize_factor * 100))}" - - # Selected output resize - to_append += f"_OutputR-{str(int(output_resize_factor * 100))}" - - # Selected intepolation - match selected_blending_factor: - case 0.3: - to_append += "_Blending-Low" - case 0.5: - to_append += "_Blending-Medium" - case 0.7: - to_append += "_Blending-High" - - # Selected image extension - to_append += f".jpg" - - output_path += to_append - - return output_path - - -def prepare_output_video_filename( - video_path: str, - selected_output_path: str, - selected_AI_model: str, - input_resize_factor: int, - output_resize_factor: int, - selected_video_extension: str, - selected_blending_factor: float -) -> str: - - if ".mp4" in selected_video_extension: - selected_video_extension = ".mp4" - elif ".avi" in selected_video_extension: - selected_video_extension = ".avi" - - if selected_output_path == OUTPUT_PATH_CODED: - file_path_no_extension, _ = os_path_splitext(video_path) - output_path = file_path_no_extension - else: - file_name = os_path_basename(video_path) - output_path = f"{selected_output_path}{os_separator}{file_name}" - - # Selected AI model - to_append = f"_{selected_AI_model}" - - # Selected input resize - to_append += f"_InputR-{str(int(input_resize_factor * 100))}" - - # Selected output resize - to_append += f"_OutputR-{str(int(output_resize_factor * 100))}" - - # Selected intepolation - match selected_blending_factor: - case 0.3: - to_append += "_Blending-Low" - case 0.5: - to_append += "_Blending-Medium" - case 0.7: - to_append += "_Blending-High" - - # Selected video extension - to_append += f"{selected_video_extension}" - - output_path += to_append - - return output_path - - -def prepare_output_video_directory_name( - video_path: str, - selected_output_path: str, - selected_AI_model: str, - input_resize_factor: int, - output_resize_factor: int, - selected_blending_factor: float -) -> str: - - if selected_output_path == OUTPUT_PATH_CODED: - file_path_no_extension, _ = os_path_splitext(video_path) - output_path = file_path_no_extension - else: - file_name = os_path_basename(video_path) - output_path = f"{selected_output_path}{os_separator}{file_name}" - - # Selected AI model - to_append = f"_{selected_AI_model}" - - # Selected input resize - to_append += f"_InputR-{str(int(input_resize_factor * 100))}" - - # Selected output resize - to_append += f"_OutputR-{str(int(output_resize_factor * 100))}" - - # Selected intepolation - match selected_blending_factor: - case 0.3: - to_append += "_Blending-Low" - case 0.5: - to_append += "_Blending-Medium" - case 0.7: - to_append += "_Blending-High" - - output_path += to_append - - return output_path - - -# Image/video Utils functions ------------------------ - -def get_video_fps(video_path: str) -> float: - video_capture = opencv_VideoCapture(video_path) - frame_rate = video_capture.get(CAP_PROP_FPS) - video_capture.release() - return frame_rate - - -def get_image_resolution(image: numpy_ndarray) -> tuple: - height = image.shape[0] - width = image.shape[1] - - return height, width - - -def save_extracted_frames( - extracted_frames_paths: list[str], - extracted_frames: list[numpy_ndarray], - cpu_number: int -) -> None: - - with ThreadPool(cpu_number) as pool: - pool.starmap(image_write, zip( - extracted_frames_paths, extracted_frames)) - - -def extract_video_frames( - process_status_q: multiprocessing_Queue, - file_number: int, - target_directory: str, - video_path: str, - cpu_number: int, - half_frames: bool = False -) -> list[str]: - - create_dir(target_directory) - - # Video frame extraction - frames_number_to_save = cpu_number * ECTRACTION_FRAMES_FOR_CPU - video_capture = opencv_VideoCapture(video_path) - frame_count = int(video_capture.get(CAP_PROP_FRAME_COUNT)) - - extracted_frames = [] - extracted_frames_paths = [] - video_frames_list = [] - - frame_index = 0 - - for frame_number in range(frame_count): - success, frame = video_capture.read() - if not success: - break - - # Estrarre solo i frame dispari (1, 3, 5, ...) - if half_frames and frame_index % 2 == 0: - frame_index += 1 - continue - - frame_path = f"{target_directory}{os_separator}frame_{frame_number:03d}.jpg" - extracted_frames.append(frame) - extracted_frames_paths.append(frame_path) - video_frames_list.append(frame_path) - - if len(extracted_frames) == frames_number_to_save: - percentage_extraction = (frame_number / frame_count) * 100 - - write_process_status( - process_status_q, f"{file_number}. Extracting video frames ({round(percentage_extraction, 2)}%)") - save_extracted_frames(extracted_frames_paths, - extracted_frames, cpu_number) - extracted_frames = [] - extracted_frames_paths = [] - - frame_index += 1 - - video_capture.release() - - if len(extracted_frames) > 0: - save_extracted_frames(extracted_frames_paths, - extracted_frames, cpu_number) - - return video_frames_list - - -def video_encoding( - process_status_q: multiprocessing_Queue, - video_path: str, - video_output_path: str, - upscaled_frame_paths: list[str], - selected_video_codec: str, -) -> None: - - if "x264" in selected_video_codec: - codec = "libx264" - elif "x265" in selected_video_codec: - codec = "libx265" - else: - codec = selected_video_codec - - txt_path = f"{os_path_splitext(video_output_path)[0]}.txt" - no_audio_path = f"{os_path_splitext(video_output_path)[0]}_no_audio{os_path_splitext(video_output_path)[1]}" - video_fps = str(get_video_fps(video_path)) - - # Cleaning files from previous encoding - if os_path_exists(no_audio_path): - os_remove(no_audio_path) - if os_path_exists(txt_path): - os_remove(txt_path) - - # Create a file .txt with all upscaled video frames paths || this file is essential - with os_fdopen(os_open(txt_path, O_WRONLY | O_CREAT, 0o777), 'w', encoding="utf-8") as txt: - for frame_path in upscaled_frame_paths: - txt.write(f"file '{frame_path}' \n") - - # Create the upscaled video without audio - print(f"[FFMPEG] ENCODING ({codec})") - try: - encoding_command = [ - FFMPEG_EXE_PATH, - "-y", - "-loglevel", "error", - "-f", "concat", - "-safe", "0", - "-r", video_fps, - "-i", txt_path, - "-c:v", codec, - "-vf", "scale=in_range=full:out_range=limited,format=yuv420p", - "-color_range", "tv", - "-b:v", "12000k", - no_audio_path - ] - subprocess_run(encoding_command, check=True, shell="False") - if os_path_exists(txt_path): - os_remove(txt_path) - - except: - write_process_status( - process_status_q, - f"{ERROR_STATUS}An error occurred during video encoding. \n Have you selected a codec compatible with your GPU? If the issue persists, try selecting 'x264'." - ) - - # Copy the audio from original video - print("[FFMPEG] AUDIO PASSTHROUGH") - audio_passthrough_command = [ - FFMPEG_EXE_PATH, - "-y", - "-loglevel", "error", - "-i", video_path, - "-i", no_audio_path, - "-c:v", "copy", - "-map", "1:v:0", - "-map", "0:a?", - "-c:a", "copy", - video_output_path - ] - try: - subprocess_run(audio_passthrough_command, check=True, shell="False") - if os_path_exists(no_audio_path): - os_remove(no_audio_path) - except: - pass - - -def check_video_upscaling_resume( - target_directory: str, - selected_AI_model: str -) -> bool: - - if os_path_exists(target_directory): - directory_files = os_listdir(target_directory) - upscaled_frames_path = [ - file for file in directory_files if selected_AI_model in file] - - if len(upscaled_frames_path) > 1: - return True - else: - return False - else: - return False - - -def get_video_frames_for_upscaling_resume( - target_directory: str, - selected_AI_model: str, -) -> list[str]: - - # Only file names - directory_files = os_listdir(target_directory) - original_frames_path = [ - file for file in directory_files if file.endswith('.jpg')] - original_frames_path = [ - file for file in original_frames_path if selected_AI_model not in file] - - # Adding the complete path to file - original_frames_path = natsorted( - [os_path_join(target_directory, file) for file in original_frames_path]) - - return original_frames_path - - -def calculate_time_to_complete_video( - time_for_frame: float, - remaining_frames: int, -) -> str: - - remaining_time = time_for_frame * remaining_frames - - hours_left = remaining_time // 3600 - minutes_left = (remaining_time % 3600) // 60 - seconds_left = round((remaining_time % 3600) % 60) - - time_left = "" - - if int(hours_left) > 0: - time_left = f"{int(hours_left):02d}h" - - if int(minutes_left) > 0: - time_left = f"{time_left}{int(minutes_left):02d}m" - - if seconds_left > 0: - time_left = f"{time_left}{seconds_left:02d}s" - - return time_left - - -def blend_images_and_save( - target_path: str, - starting_image: numpy_ndarray, - upscaled_image: numpy_ndarray, - starting_image_importance: float, - file_extension: str = ".jpg" -) -> None: - - def add_alpha_channel(image: numpy_ndarray) -> numpy_ndarray: - if image.shape[2] == 3: - alpha = numpy_full( - (image.shape[0], image.shape[1], 1), 255, dtype=uint8) - image = numpy_concatenate((image, alpha), axis=2) - return image - - def get_image_mode(image: numpy_ndarray) -> str: - shape = image.shape - if len(shape) == 2: - return "Grayscale" - elif len(shape) == 3 and shape[2] == 3: - return "RGB" - elif len(shape) == 3 and shape[2] == 4: - return "RGBA" - - upscaled_image_importance = 1 - starting_image_importance - starting_height, starting_width = get_image_resolution(starting_image) - target_height, target_width = get_image_resolution(upscaled_image) - - starting_resolution = starting_height + starting_width - target_resolution = target_height + target_width - - if starting_resolution > target_resolution: - starting_image = opencv_resize( - starting_image, (target_width, target_height), INTER_AREA) - else: - starting_image = opencv_resize( - starting_image, (target_width, target_height)) - - try: - if get_image_mode(starting_image) == "RGBA": - starting_image = add_alpha_channel(starting_image) - upscaled_image = add_alpha_channel(upscaled_image) - - interpolated_image = opencv_addWeighted( - starting_image, starting_image_importance, upscaled_image, upscaled_image_importance, 0) - image_write(target_path, interpolated_image, file_extension) - - except: - image_write(target_path, upscaled_image, file_extension) - - -# Core functions ------------------------ - -def check_upscale_steps() -> None: - sleep(1) - - try: - while True: - actual_step = read_process_status() - - if actual_step == COMPLETED_STATUS: - info_message.set(f"All files completed! :)") - stop_upscale_process() - stop_thread() - - elif actual_step == STOP_STATUS: - info_message.set(f"Upscaling stopped") - stop_upscale_process() - stop_thread() - - elif ERROR_STATUS in actual_step: - info_message.set(f"Error while upscaling :(") - error_to_show = actual_step.replace(ERROR_STATUS, "") - show_error_message(error_to_show.strip()) - stop_thread() - - else: - info_message.set(actual_step) - - sleep(1) - except: - place_upscale_button() - - -def read_process_status() -> str: - return process_status_q.get() - - -def write_process_status(process_status_q: multiprocessing_Queue, step: str) -> None: - - print(f"{step}") - while not process_status_q.empty(): - process_status_q.get() - process_status_q.put(f"{step}") - - -def stop_upscale_process() -> None: - global process_upscale_orchestrator - try: - process_upscale_orchestrator - except: - pass - else: - process_upscale_orchestrator.kill() - - -def stop_button_command() -> None: - stop_upscale_process() - write_process_status(process_status_q, f"{STOP_STATUS}") - - -def upscale_button_command() -> None: - global selected_file_list - global selected_AI_model - global selected_gpu - global selected_keep_frames - global selected_AI_multithreading - global selected_blending_factor - global selected_image_extension - global selected_video_extension - global selected_video_codec - global tiles_resolution - global input_resize_factor - global output_resize_factor - - global process_upscale_orchestrator - - if user_input_checks(): - info_message.set("Loading") - - cpu_number = int(os_cpu_count()/2) - - print("=" * 50) - print("> Starting upscale:") - print(f" Files to upscale: {len(selected_file_list)}") - print(f" Output path: {(selected_output_path.get())}") - print(f" Selected AI model: {selected_AI_model}") - print(f" Selected GPU: {selected_gpu}") - print(f" AI multithreading: {selected_AI_multithreading}") - print(f" Blending factor: {selected_blending_factor}") - print(f" Selected image output extension: {selected_image_extension}") - print(f" Selected video output extension: {selected_video_extension}") - print(f" Selected video output codec: {selected_video_codec}") - print( - f" Tiles resolution for selected GPU VRAM: {tiles_resolution}x{tiles_resolution}px") - print(f" Input resize factor: {int(input_resize_factor * 100)}%") - print(f" Output resize factor: {int(output_resize_factor * 100)}%") - print(f" Cpu number: {cpu_number}") - print(f" Save frames: {selected_keep_frames}") - print("=" * 50) - - place_stop_button() - - process_upscale_orchestrator = Process( - target=upscale_orchestrator, - args=( - process_status_q, - selected_file_list, - selected_output_path.get(), - selected_AI_model, - selected_AI_multithreading, - input_resize_factor, - output_resize_factor, - selected_gpu, - tiles_resolution, - selected_blending_factor, - selected_keep_frames, - selected_image_extension, - selected_video_extension, - selected_video_codec, - cpu_number, - ) - ) - process_upscale_orchestrator.start() - - thread_wait = Thread(target=check_upscale_steps) - thread_wait.start() - - -# ORCHESTRATOR - -def upscale_orchestrator( - process_status_q: multiprocessing_Queue, - selected_file_list: list, - selected_output_path: str, - selected_AI_model: str, - selected_AI_multithreading: int, - input_resize_factor: int, - output_resize_factor: int, - selected_gpu: str, - tiles_resolution: int, - selected_blending_factor: float, - selected_keep_frames: bool, - selected_image_extension: str, - selected_video_extension: str, - selected_video_codec: str, - cpu_number: int, -) -> None: - - try: - write_process_status(process_status_q, f"Loading AI model") - - AI_upscale_instance_list = [ - AI_upscale(selected_AI_model, selected_gpu, - input_resize_factor, output_resize_factor, tiles_resolution) - for _ in range(selected_AI_multithreading) - ] - - how_many_files = len(selected_file_list) - for file_number in range(how_many_files): - file_path = selected_file_list[file_number] - file_number = file_number + 1 - - if check_if_file_is_video(file_path): - upscale_video( - process_status_q, - file_path, - file_number, - selected_output_path, - AI_upscale_instance_list, - selected_AI_model, - input_resize_factor, - output_resize_factor, - cpu_number, - selected_video_extension, - selected_blending_factor, - selected_AI_multithreading, - selected_keep_frames, - selected_video_codec - ) - else: - upscale_image( - process_status_q, - file_path, - file_number, - selected_output_path, - AI_upscale_instance_list[0], - selected_AI_model, - selected_image_extension, - input_resize_factor, - output_resize_factor, - selected_blending_factor - ) - - write_process_status(process_status_q, f"{COMPLETED_STATUS}") - - except Exception as exception: - error_message = str(exception) - - if "cannot convert float NaN to integer" in error_message: - write_process_status( - process_status_q, - f"{ERROR_STATUS}An error occurred during video upscaling, likely due to a GPU driver timeout.\n" - "Restart the process without deleting the upscaled frames to resume and complete the upscaling." - ) - else: - write_process_status( - process_status_q, f"{ERROR_STATUS} {error_message}") - -# IMAGES - - -def upscale_image( - process_status_q: multiprocessing_Queue, - image_path: str, - file_number: int, - selected_output_path: str, - AI_instance: AI_upscale, - selected_AI_model: str, - selected_image_extension: str, - input_resize_factor: int, - output_resize_factor: int, - selected_blending_factor: float -) -> None: - - starting_image = image_read(image_path) - upscaled_image_path = prepare_output_image_filename( - image_path, selected_output_path, selected_AI_model, input_resize_factor, output_resize_factor, selected_image_extension, selected_blending_factor) - - write_process_status( - process_status_q, f"{file_number}. Upscaling image. Be patient") - upscaled_image = AI_instance.AI_orchestration(starting_image) - - if selected_blending_factor > 0: - blend_images_and_save( - upscaled_image_path, - starting_image, - upscaled_image, - selected_blending_factor, - selected_image_extension - ) - else: - image_write(upscaled_image_path, upscaled_image, - selected_image_extension) - - copy_file_metadata(image_path, upscaled_image_path) - -# VIDEOS - - -def upscale_video( - process_status_q: multiprocessing_Queue, - video_path: str, - file_number: int, - selected_output_path: str, - AI_upscale_instance_list: list[AI_upscale], - selected_AI_model: str, - input_resize_factor: int, - output_resize_factor: int, - cpu_number: int, - selected_video_extension: str, - selected_blending_factor: float, - selected_AI_multithreading: int, - selected_keep_frames: bool, - selected_video_codec: str -) -> None: - - # Internal functions - - def update_process_status_videos( - process_status_q: multiprocessing_Queue, - file_number: int, - ) -> None: - - global global_upscaled_frames_paths - global global_processing_times_list - - # Remaining frames - total_frames_counter = len(global_upscaled_frames_paths) - frames_already_upscaled_counter = len( - [path for path in global_upscaled_frames_paths if os_path_exists(path)]) - frames_to_upscale_counter = len( - [path for path in global_upscaled_frames_paths if not os_path_exists(path)]) - - try: - average_processing_time = numpy_mean(global_processing_times_list) - except: - average_processing_time = 0.0 - - remaining_frames = frames_to_upscale_counter - remaining_time = calculate_time_to_complete_video( - average_processing_time, remaining_frames) - if remaining_time != "": - percent_complete = ( - frames_already_upscaled_counter / total_frames_counter) * 100 - write_process_status( - process_status_q, f"{file_number}. Upscaling video. Be patient {percent_complete:.2f}% ({remaining_time})") - - def save_multiple_upscaled_frame_async( - starting_frames_to_save: list[numpy_ndarray], - upscaled_frames_to_save: list[numpy_ndarray], - upscaled_frame_paths_to_save: list[str], - selected_blending_factor: float - ) -> None: - - for frame_index, _ in enumerate(upscaled_frames_to_save): - starting_frame = starting_frames_to_save[frame_index] - upscaled_frame = upscaled_frames_to_save[frame_index] - upscaled_frame_path = upscaled_frame_paths_to_save[frame_index] - - if selected_blending_factor > 0: - blend_images_and_save( - upscaled_frame_path, starting_frame, upscaled_frame, selected_blending_factor) - else: - image_write(upscaled_frame_path, upscaled_frame) - - def save_frames_on_disk( - starting_frames_to_save: list[numpy_ndarray], - upscaled_frames_to_save: list[numpy_ndarray], - upscaled_frame_paths_to_save: list[str], - selected_blending_factor: float - ) -> None: - - Thread( - target=save_multiple_upscaled_frame_async, - args=( - starting_frames_to_save, - upscaled_frames_to_save, - upscaled_frame_paths_to_save, - selected_blending_factor - ) - ).start() - - def upscale_video_frames_async( - process_status_q: multiprocessing_Queue, - file_number: int, - threads_number: int, - AI_instance: AI_upscale, - extracted_frames_paths: list[str], - upscaled_frame_paths: list[str], - selected_blending_factor: float, - ) -> None: - - global global_processing_times_list - global global_can_i_update_status - - starting_frames_to_save = [] - upscaled_frames_to_save = [] - upscaled_frame_paths_to_save = [] - - for frame_index in range(len(extracted_frames_paths)): - frame_path = extracted_frames_paths[frame_index] - upscaled_frame_path = upscaled_frame_paths[frame_index] - already_upscaled = os_path_exists(upscaled_frame_path) - - if already_upscaled == False: - start_timer = timer() - - # Upscale frame - starting_frame = image_read(frame_path) - upscaled_frame = AI_instance.AI_orchestration(starting_frame) - - # Adding frames in list to save - starting_frames_to_save.append(starting_frame) - upscaled_frames_to_save.append(upscaled_frame) - upscaled_frame_paths_to_save.append(upscaled_frame_path) - - # Calculate processing time and update process status - end_timer = timer() - processing_time = (end_timer - start_timer)/threads_number - global_processing_times_list.append(processing_time) - - if (frame_index + 1) % MULTIPLE_FRAMES_TO_SAVE == 0: - # Save frames present in RAM on disk - save_frames_on_disk(starting_frames_to_save, upscaled_frames_to_save, - upscaled_frame_paths_to_save, selected_blending_factor) - starting_frames_to_save = [] - upscaled_frames_to_save = [] - upscaled_frame_paths_to_save = [] - - global_can_i_update_status = not global_can_i_update_status - if global_can_i_update_status: - update_process_status_videos( - process_status_q, file_number) - if len(global_processing_times_list) >= 100: - global_processing_times_list = [] - - if len(upscaled_frame_paths_to_save) > 0: - # Save frames still present in RAM on disk - save_frames_on_disk(starting_frames_to_save, upscaled_frames_to_save, - upscaled_frame_paths_to_save, selected_blending_factor) - starting_frames_to_save = [] - upscaled_frames_to_save = [] - upscaled_frame_paths_to_save = [] - - def upscale_video_frames( - process_status_q: multiprocessing_Queue, - file_number: int, - AI_upscale_instance_list: list[AI_upscale], - extracted_frames_paths: list[str], - upscaled_frame_paths: list[str], - threads_number: int, - selected_blending_factor: float, - ) -> None: - - global global_upscaled_frames_paths - global global_processing_times_list - global global_can_i_update_status - - global_upscaled_frames_paths = upscaled_frame_paths - global_processing_times_list = [] - global_can_i_update_status = False - - chunk_size = len(extracted_frames_paths) // threads_number - extracted_frame_list_chunks = [extracted_frames_paths[i:i + chunk_size] - for i in range(0, len(extracted_frames_paths), chunk_size)] - upscaled_frame_list_chunks = [upscaled_frame_paths[i:i + chunk_size] - for i in range(0, len(upscaled_frame_paths), chunk_size)] - - write_process_status( - process_status_q, f"{file_number}. Upscaling video. Be patient ({threads_number} threads)") - with ThreadPool(threads_number) as pool: - pool.starmap( - upscale_video_frames_async, - zip( - repeat(process_status_q), - repeat(file_number), - repeat(threads_number), - AI_upscale_instance_list, - extracted_frame_list_chunks, - upscaled_frame_list_chunks, - repeat(selected_blending_factor), - ) - ) - - def check_forgotten_video_frames( - process_status_q: multiprocessing_Queue, - file_number: int, - AI_upscale_instance_list: AI_upscale, - extracted_frames_paths: list[str], - upscaled_frame_paths: list[str], - selected_blending_factor: float, - threads_number: int = 1, - ): - - sleep(1) - - # Check if all the upscaled frames exist - frame_path_todo_list = [] - upscaled_frame_path_todo_list = [] - - for frame_index in range(len(upscaled_frame_paths)): - extracted_frames_path = extracted_frames_paths[frame_index] - upscaled_frame_path = upscaled_frame_paths[frame_index] - - if not os_path_exists(upscaled_frame_path): - frame_path_todo_list.append(extracted_frames_path) - upscaled_frame_path_todo_list.append(upscaled_frame_path) - - if len(upscaled_frame_path_todo_list) > 0: - upscale_video_frames( - process_status_q, - file_number, - AI_upscale_instance_list, - extracted_frames_paths, - upscaled_frame_paths, - threads_number, - selected_blending_factor - ) - - # Main function - - # 1.Preparation - target_directory = prepare_output_video_directory_name( - video_path, selected_output_path, selected_AI_model, input_resize_factor, output_resize_factor, selected_blending_factor) - video_output_path = prepare_output_video_filename(video_path, selected_output_path, selected_AI_model, - input_resize_factor, output_resize_factor, selected_video_extension, selected_blending_factor) - - # 2. Resume upscaling OR Extract video frames - video_upscale_continue = check_video_upscaling_resume( - target_directory, selected_AI_model) - if video_upscale_continue: - write_process_status( - process_status_q, f"{file_number}. Resume video upscaling") - extracted_frames_paths = get_video_frames_for_upscaling_resume( - target_directory, selected_AI_model) - else: - write_process_status( - process_status_q, f"{file_number}. Extracting video frames") - extracted_frames_paths = extract_video_frames( - process_status_q, file_number, target_directory, video_path, cpu_number, half_frames=False) - - upscaled_frame_paths = [prepare_output_video_frame_filename( - frame_path, selected_AI_model, input_resize_factor, output_resize_factor, selected_blending_factor) for frame_path in extracted_frames_paths] - - # 3. Check if video need tiles OR video multithreading upscale - multiframes_supported_by_gpu = AI_upscale_instance_list[0].calculate_multiframes_supported_by_gpu( - extracted_frames_paths[0]) - threads_number = min(multiframes_supported_by_gpu, - selected_AI_multithreading) - if threads_number <= 0: - threads_number = 1 - - # 4. Upscaling video frames - write_process_status(process_status_q, f"{file_number}. Upscaling video") - upscale_video_frames(process_status_q, file_number, AI_upscale_instance_list, - extracted_frames_paths, upscaled_frame_paths, threads_number, selected_blending_factor) - - # 5. Check for forgotten video frames - check_forgotten_video_frames(process_status_q, file_number, AI_upscale_instance_list, - extracted_frames_paths, upscaled_frame_paths, selected_blending_factor) - - # 6. Video encoding - write_process_status( - process_status_q, f"{file_number}. Encoding upscaled video") - video_encoding(process_status_q, video_path, video_output_path, - upscaled_frame_paths, selected_video_codec) - copy_file_metadata(video_path, video_output_path) - - # 7. Delete frames folder - if selected_keep_frames == False: - if os_path_exists(target_directory): - remove_directory(target_directory) - - -# GUI utils function --------------------------- - -def check_if_file_is_video(file: str) -> bool: - return any(video_extension in file for video_extension in supported_video_extensions) - - -def user_input_checks() -> bool: - global selected_file_list - global selected_AI_model - global selected_image_extension - global tiles_resolution - global input_resize_factor - global output_resize_factor - - # Selected files - try: - selected_file_list = file_widget.get_selected_file_list() - except: - info_message.set("Please select a file") - return False - - if len(selected_file_list) <= 0: - info_message.set("Please select a file") - return False - - # AI model - if selected_AI_model == MENU_LIST_SEPARATOR[0]: - info_message.set("Please select the AI model") - return False - - # Input resize factor - try: - input_resize_factor = int( - float(str(selected_input_resize_factor.get()))) - except: - info_message.set("Input resolution % must be a number") - return False - - if input_resize_factor > 0: - input_resize_factor = input_resize_factor/100 - else: - info_message.set("Input resolution % must be a value > 0") - return False - - # Output resize factor - try: - output_resize_factor = int( - float(str(selected_output_resize_factor.get()))) - except: - info_message.set("Output resolution % must be a number") - return False - - if output_resize_factor > 0: - output_resize_factor = output_resize_factor/100 - else: - info_message.set("Output resolution % must be a value > 0") - return False - - # VRAM limiter - try: - tiles_resolution = 100 * int(float(str(selected_VRAM_limiter.get()))) - except: - info_message.set("GPU VRAM value must be a number") - return False - - if tiles_resolution > 0: - vram_multiplier = VRAM_model_usage.get(selected_AI_model) - - selected_vram = (vram_multiplier * - int(float(str(selected_VRAM_limiter.get())))) - tiles_resolution = int(selected_vram * 100) - else: - info_message.set("GPU VRAM value must be a value > 0") - return False - - return True - - -def show_error_message(exception: str) -> None: - messageBox_title = "Upscale error" - messageBox_text = f"\n {str(exception)} \n" - - MessageBox( - messageType="error", - title=messageBox_title, - subtitle=messageBox_subtitle, - default_value=None, - option_list=[messageBox_text] - ) - - -def get_upscale_factor() -> int: - global selected_AI_model - if MENU_LIST_SEPARATOR[0] in selected_AI_model: - upscale_factor = 0 - elif 'x1' in selected_AI_model: - upscale_factor = 1 - elif 'x2' in selected_AI_model: - upscale_factor = 2 - elif 'x4' in selected_AI_model: - upscale_factor = 4 - - return upscale_factor - - -def open_files_action(): - - def check_supported_selected_files(uploaded_file_list: list) -> list: - return [file for file in uploaded_file_list if any(supported_extension in file for supported_extension in supported_file_extensions)] - - info_message.set("Selecting files") - - uploaded_files_list = list(filedialog.askopenfilenames()) - uploaded_files_counter = len(uploaded_files_list) - - supported_files_list = check_supported_selected_files(uploaded_files_list) - supported_files_counter = len(supported_files_list) - - print("> Uploaded files: " + str(uploaded_files_counter) + - " => Supported files: " + str(supported_files_counter)) - - if supported_files_counter > 0: - - upscale_factor, input_resize_factor, output_resize_factor = get_values_for_file_widget() - - global file_widget - file_widget = FileWidget( - master=window, - selected_file_list=supported_files_list, - upscale_factor=upscale_factor, - input_resize_factor=input_resize_factor, - output_resize_factor=output_resize_factor, - fg_color=background_color, - bg_color=background_color - ) - file_widget.place(relx=0.0, rely=0.0, relwidth=0.5, relheight=1.0) - info_message.set("Ready") - else: - info_message.set("Not supported files :(") - - -def open_output_path_action(): - asked_selected_output_path = filedialog.askdirectory() - if asked_selected_output_path == "": - selected_output_path.set(OUTPUT_PATH_CODED) - else: - selected_output_path.set(asked_selected_output_path) - - -# GUI select from menus functions --------------------------- - -def select_AI_from_menu(selected_option: str) -> None: - global selected_AI_model - selected_AI_model = selected_option - update_file_widget(1, 2, 3) - - -def select_AI_multithreading_from_menu(selected_option: str) -> None: - global selected_AI_multithreading - if selected_option == "OFF": - selected_AI_multithreading = 1 - else: - selected_AI_multithreading = int(selected_option.split()[0]) - - -def select_blending_from_menu(selected_option: str) -> None: - global selected_blending_factor - - match selected_option: - case "OFF": selected_blending_factor = 0 - case "Low": selected_blending_factor = 0.3 - case "Medium": selected_blending_factor = 0.5 - case "High": selected_blending_factor = 0.7 - - -def select_gpu_from_menu(selected_option: str) -> None: - global selected_gpu - selected_gpu = selected_option - - -def select_save_frame_from_menu(selected_option: str): - global selected_keep_frames - if selected_option == "ON": - selected_keep_frames = True - elif selected_option == "OFF": - selected_keep_frames = False - - -def select_image_extension_from_menu(selected_option: str) -> None: - global selected_image_extension - selected_image_extension = selected_option - - -def select_video_extension_from_menu(selected_option: str) -> None: - global selected_video_extension - selected_video_extension = selected_option - - -def select_video_codec_from_menu(selected_option: str) -> None: - global selected_video_codec - selected_video_codec = selected_option - - -# GUI place functions --------------------------- - -def place_loadFile_section(): - background = CTkFrame( - master=window, fg_color=background_color, corner_radius=1) - - text_drop = (" SUPPORTED FILES \n\n " - + "IMAGES • jpg png tif bmp webp heic \n " - + "VIDEOS • mp4 webm mkv flv gif avi mov mpg qt 3gp ") - - input_file_text = CTkLabel( - master=window, - text=text_drop, - fg_color=background_color, - bg_color=background_color, - text_color=text_color, - width=300, - height=150, - font=bold13, - anchor="center" - ) - - input_file_button = CTkButton( - master=window, - command=open_files_action, - text="SELECT FILES", - width=140, - height=30, - font=bold12, - border_width=1, - corner_radius=1, - fg_color="#282828", - text_color="#E0E0E0", - border_color="#0096FF" - ) - - background.place(relx=0.0, rely=0.0, relwidth=0.5, relheight=1.0) - input_file_text.place(relx=0.25, rely=0.4, anchor="center") - input_file_button.place(relx=0.25, rely=0.5, anchor="center") - - -def place_app_name(): - background = CTkFrame( - master=window, fg_color=background_color, corner_radius=1) - app_name_label = CTkLabel( - master=window, - text=app_name + " " + version, - fg_color=background_color, - text_color=app_name_color, - font=bold20, - anchor="w" - ) - background.place(relx=0.5, rely=0.0, relwidth=0.5, relheight=1.0) - app_name_label.place(relx=column_1 - 0.05, rely=0.04, anchor="center") - - -def place_AI_menu(): - - def open_info_AI_model(): - option_list = [ - "\n IRCNN_Mx1 | IRCNN_Lx1 \n" - "\n • Simple and lightweight AI models\n" - " • Year: 2017\n" - " • Function: Denoising\n", - - "\n RealESR_Gx4 | RealESR_Animex4 \n" - "\n • Fast and lightweight AI models\n" - " • Year: 2022\n" - " • Function: Upscaling\n", - - "\n BSRGANx2 | BSRGANx4 | RealESRGANx4 | RealESRNetx4 \n" - "\n • Complex and heavy AI models\n" - " • Year: 2020\n" - " • Function: High-quality upscaling\n", - ] - - MessageBox( - messageType="info", - title="AI model", - subtitle="This widget allows to choose between different AI models for upscaling", - default_value=None, - option_list=option_list - ) - - widget_row = row1 - background = create_option_background() - background.place(relx=0.75, rely=widget_row, - relwidth=0.48, anchor="center") - - info_button = create_info_button(open_info_AI_model, "AI model") - option_menu = create_option_menu( - select_AI_from_menu, AI_models_list, default_AI_model) - - info_button.place(relx=column_info1, rely=widget_row - - 0.003, anchor="center") - option_menu.place(relx=column_3_5, rely=widget_row, - anchor="center") - - -def place_AI_blending_menu(): - - def open_info_AI_blending(): - option_list = [ - " Blending combines the upscaled image produced by AI with the original image", - - " \n BLENDING OPTIONS\n" + - " • [OFF] No blending is applied\n" + - " • [Low] The result favors the upscaled image, with a slight touch of the original\n" + - " • [Medium] A balanced blend of the original and upscaled images\n" + - " • [High] The result favors the original image, with subtle enhancements from the upscaled version\n", - - " \n NOTES\n" + - " • Can enhance the quality of the final result\n" + - " • Especially effective when using the tiling/merging function (useful for low VRAM)\n" + - " • Particularly helpful at low input resolution percentages (<50%)\n", - ] - - MessageBox( - messageType="info", - title="AI blending", - subtitle="This widget allows you to choose the blending between the upscaled and original image/frame", - default_value=None, - option_list=option_list - ) - - widget_row = row2 - - background = create_option_background() - background.place(relx=0.75, rely=widget_row, - relwidth=0.48, anchor="center") - - info_button = create_info_button(open_info_AI_blending, "AI blending") - option_menu = create_option_menu( - select_blending_from_menu, blending_list, default_blending) - - info_button.place(relx=column_info1, rely=widget_row - - 0.003, anchor="center") - option_menu.place(relx=column_3_5, rely=widget_row, - anchor="center") - - -def place_AI_multithreading_menu(): - - def open_info_AI_multithreading(): - option_list = [ - " This option can enhance video upscaling performance, especially on powerful GPUs.", - - " \n AI MULTITHREADING OPTIONS\n" - + " • OFF - Processes one frame at a time.\n" - + " • 2 threads - Processes two frames simultaneously.\n" - + " • 4 threads - Processes four frames simultaneously.\n" - + " • 6 threads - Processes six frames simultaneously.\n" - + " • 8 threads - Processes eight frames simultaneously.\n", - - " \n NOTES\n" - + " • Higher thread counts increase CPU, GPU, and RAM usage.\n" - + " • The GPU may be heavily stressed, potentially reaching high temperatures.\n" - + " • Monitor your system's temperature to prevent overheating.\n" - + " • If the chosen thread count exceeds GPU capacity, the app automatically selects an optimal value.\n", - ] - - MessageBox( - messageType="info", - title="AI multithreading (EXPERIMENTAL)", - subtitle="This widget allows to choose how many video frames are upscaled simultaneously", - default_value=None, - option_list=option_list - ) - - widget_row = row3 - background = create_option_background() - background.place(relx=0.75, rely=widget_row, - relwidth=0.48, anchor="center") - - info_button = create_info_button( - open_info_AI_multithreading, "AI multithreading") - option_menu = create_option_menu( - select_AI_multithreading_from_menu, AI_multithreading_list, default_AI_multithreading) - - info_button.place(relx=column_info1, rely=widget_row - - 0.003, anchor="center") - option_menu.place(relx=column_3_5, rely=widget_row, - anchor="center") - - -def place_input_output_resolution_textboxs(): - - def open_info_input_resolution(): - option_list = [ - " A high value (>70%) will create high quality photos/videos but will be slower", - " While a low value (<40%) will create good quality photos/videos but will much faster", - - " \n For example, for a 1080p (1920x1080) image/video\n" + - " • Input resolution 25% => input to AI 270p (480x270)\n" + - " • Input resolution 50% => input to AI 540p (960x540)\n" + - " • Input resolution 75% => input to AI 810p (1440x810)\n" + - " • Input resolution 100% => input to AI 1080p (1920x1080) \n", - ] - - MessageBox( - messageType="info", - title="Input resolution %", - subtitle="This widget allows to choose the resolution input to the AI", - default_value=None, - option_list=option_list - ) - - def open_info_output_resolution(): - option_list = [ - " TBD ", - ] - - MessageBox( - messageType="info", - title="Output resolution %", - subtitle="This widget allows to choose upscaled files resolution", - default_value=None, - option_list=option_list - ) - - widget_row = row4 - - background = create_option_background() - background.place(relx=0.75, rely=widget_row, - relwidth=0.48, anchor="center") - - # Input resolution % - info_button = create_info_button( - open_info_input_resolution, "Input resolution") - option_menu = create_text_box( - selected_input_resize_factor, width=little_textbox_width) - - info_button.place(relx=column_info1, rely=widget_row - - 0.003, anchor="center") - option_menu.place(relx=column_1_5, rely=widget_row, - anchor="center") - - # Output resolution % - info_button = create_info_button( - open_info_output_resolution, "Output resolution") - option_menu = create_text_box( - selected_output_resize_factor, width=little_textbox_width) - - info_button.place(relx=column_info2, rely=widget_row - - 0.003, anchor="center") - option_menu.place(relx=column_3, rely=widget_row, - anchor="center") - - -def place_gpu_gpuVRAM_menus(): - - def open_info_gpu(): - option_list = [ - "\n It is possible to select up to 4 GPUs for AI processing\n" + - " • Auto (the app will select the most powerful GPU)\n" + - " • GPU 1 (GPU 0 in Task manager)\n" + - " • GPU 2 (GPU 1 in Task manager)\n" + - " • GPU 3 (GPU 2 in Task manager)\n" + - " • GPU 4 (GPU 3 in Task manager)\n", - - "\n NOTES\n" + - " • Keep in mind that the more powerful the chosen gpu is, the faster the upscaling will be\n" + - " • For optimal performance, it is essential to regularly update your GPUs drivers\n" + - " • Selecting a GPU not present in the PC will cause the app to use the CPU for AI processing\n" - ] - - MessageBox( - messageType="info", - title="GPU", - subtitle="This widget allows to select the GPU for AI upscale", - default_value=None, - option_list=option_list - ) - - def open_info_vram_limiter(): - option_list = [ - " Make sure to enter the correct value based on the selected GPU's VRAM", - " Setting a value higher than the available VRAM may cause upscale failure", - " For integrated GPUs (Intel HD series • Vega 3, 5, 7), select 2 GB to avoid issues", - ] - - MessageBox( - messageType="info", - title="GPU VRAM (GB)", - subtitle="This widget allows to set a limit on the GPU VRAM memory usage", - default_value=None, - option_list=option_list - ) - - widget_row = row5 - - background = create_option_background() - background.place(relx=0.75, rely=widget_row, - relwidth=0.48, anchor="center") - - # GPU - info_button = create_info_button(open_info_gpu, "GPU") - option_menu = create_option_menu( - select_gpu_from_menu, gpus_list, default_gpu, width=little_menu_width) - - info_button.place(relx=column_info1, - rely=widget_row - 0.003, anchor="center") - option_menu.place(relx=column_1_4, rely=widget_row, anchor="center") - - # GPU VRAM - info_button = create_info_button(open_info_vram_limiter, "GPU VRAM (GB)") - option_menu = create_text_box( - selected_VRAM_limiter, width=little_textbox_width) - - info_button.place(relx=column_info2, rely=widget_row - - 0.003, anchor="center") - option_menu.place(relx=column_3, rely=widget_row, - anchor="center") - - -def place_image_video_output_menus(): - - def open_info_image_output(): - option_list = [ - " \n PNG\n" - " • Very good quality\n" - " • Slow and heavy file\n" - " • Supports transparent images\n" - " • Lossless compression (no quality loss)\n" - " • Ideal for graphics, web images, and screenshots\n", - - " \n JPG\n" - " • Good quality\n" - " • Fast and lightweight file\n" - " • Lossy compression (some quality loss)\n" - " • Ideal for photos and web images\n" - " • Does not support transparency\n", - - " \n BMP\n" - " • Highest quality\n" - " • Slow and heavy file\n" - " • Uncompressed format (large file size)\n" - " • Ideal for raw images and high-detail graphics\n" - " • Does not support transparency\n", - - " \n TIFF\n" - " • Highest quality\n" - " • Very slow and heavy file\n" - " • Supports both lossless and lossy compression\n" - " • Often used in professional photography and printing\n" - " • Supports multiple layers and transparency\n", - ] - - MessageBox( - messageType="info", - title="Image output", - subtitle="This widget allows to choose the extension of upscaled images", - default_value=None, - option_list=option_list - ) - - def open_info_video_extension(): - option_list = [ - " \n MP4\n" - " • Most widely supported format\n" - " • Good quality with efficient compression\n" - " • Fast and lightweight file\n" - " • Ideal for streaming and general use\n", - - " \n MKV\n" - " • High-quality format with multiple audio and subtitle tracks support\n" - " • Larger file size compared to MP4\n" - " • Supports almost any codec\n" - " • Ideal for high-quality videos and archiving\n", - - " \n AVI\n" - " • Older format with high compatibility\n" - " • Larger file size due to less efficient compression\n" - " • Supports multiple codecs but lacks modern features\n" - " • Ideal for older devices and raw video storage\n", - - " \n MOV\n" - " • High-quality format developed by Apple\n" - " • Large file size due to less compression\n" - " • Best suited for editing and high-quality playback\n" - " • Compatible mainly with macOS and iOS devices\n", - ] - - MessageBox( - messageType="info", - title="Video output", - subtitle="This widget allows to choose the extension of the upscaled video", - default_value=None, - option_list=option_list - ) - - widget_row = row6 - - background = create_option_background() - background.place(relx=0.75, rely=widget_row, - relwidth=0.48, anchor="center") - - # Image output - info_button = create_info_button(open_info_image_output, "Image output") - option_menu = create_option_menu(select_image_extension_from_menu, - image_extension_list, default_image_extension, width=little_menu_width) - info_button.place(relx=column_info1, - rely=widget_row - 0.003, anchor="center") - option_menu.place(relx=column_1_4, rely=widget_row, - anchor="center") - - # Video output - info_button = create_info_button(open_info_video_extension, "Video output") - option_menu = create_option_menu(select_video_extension_from_menu, - video_extension_list, default_video_extension, width=little_menu_width) - info_button.place(relx=column_info2, - rely=widget_row - 0.003, anchor="center") - option_menu.place(relx=column_2_9, rely=widget_row, - anchor="center") - - -def place_video_codec_keep_frames_menus(): - - def open_info_video_codec(): - option_list = [ - " \n SOFTWARE ENCODING (CPU)\n" - " • x264 | H.264 software encoding\n" - " • x265 | HEVC (H.265) software encoding\n", - - " \n NVIDIA GPU ENCODING (NVENC - Optimized for NVIDIA GPU)\n" - " • h264_nvenc | H.264 hardware encoding\n" - " • hevc_nvenc | HEVC (H.265) hardware encoding\n", - - " \n AMD GPU ENCODING (AMF - Optimized for AMD GPU)\n" - " • h264_amf | H.264 hardware encoding\n" - " • hevc_amf | HEVC (H.265) hardware encoding\n", - - " \n INTEL GPU ENCODING (QSV - Optimized for Intel GPU)\n" - " • h264_qsv | H.264 hardware encoding\n" - " • hevc_qsv | HEVC (H.265) hardware encoding\n" - ] - - MessageBox( - messageType="info", - title="Video codec", - subtitle="This widget allows to choose video codec for upscaled video", - default_value=None, - option_list=option_list - ) - - def open_info_keep_frames(): - option_list = [ - "\n ON \n" + - " The app does NOT delete the video frames after creating the upscaled video \n", - - "\n OFF \n" + - " The app deletes the video frames after creating the upscaled video \n" - ] - - MessageBox( - messageType="info", - title="Keep video frames", - subtitle="This widget allows to choose to keep video frames", - default_value=None, - option_list=option_list - ) - - widget_row = row7 - - background = create_option_background() - background.place(relx=0.75, rely=widget_row, - relwidth=0.48, anchor="center") - - # Video codec - info_button = create_info_button(open_info_video_codec, "Video codec") - option_menu = create_option_menu( - select_video_codec_from_menu, video_codec_list, default_video_codec, width=little_menu_width) - info_button.place(relx=column_info1, - rely=widget_row - 0.003, anchor="center") - option_menu.place(relx=column_1_4, rely=widget_row, - anchor="center") - - # Keep frames - info_button = create_info_button(open_info_keep_frames, "Keep frames") - option_menu = create_option_menu( - select_save_frame_from_menu, keep_frames_list, default_keep_frames, width=little_menu_width) - info_button.place(relx=column_info2, - rely=widget_row - 0.003, anchor="center") - option_menu.place(relx=column_2_9, rely=widget_row, - anchor="center") - - -def place_output_path_textbox(): - - def open_info_output_path(): - option_list = [ - "\n The default path is defined by the input files." - + "\n For example: selecting a file from the Download folder," - + "\n the app will save upscaled files in the Download folder \n", - - " Otherwise it is possible to select the desired path using the SELECT button", - ] - - MessageBox( - messageType="info", - title="Output path", - subtitle="This widget allows to choose upscaled files path", - default_value=None, - option_list=option_list - ) - - background = create_option_background() - info_button = create_info_button(open_info_output_path, "Output path") - option_menu = create_text_box_output_path(selected_output_path) - active_button = create_active_button( - command=open_output_path_action, text="SELECT", width=60, height=25) - - background.place(relx=0.75, rely=row10, - relwidth=0.48, anchor="center") - info_button.place(relx=column_info1, rely=row10 - - 0.003, anchor="center") - active_button.place(relx=column_info1 + 0.052, - rely=row10, anchor="center") - option_menu.place(relx=column_2 - 0.008, rely=row10, - anchor="center") - - -def place_message_label(): - message_label = CTkLabel( - master=window, - textvariable=info_message, - height=26, - width=200, - font=bold11, - fg_color="#ffbf00", - text_color="#000000", - anchor="center", - corner_radius=1 - ) - message_label.place(relx=0.83, rely=0.9495, anchor="center") - - -def place_stop_button(): - stop_button = create_active_button( - command=stop_button_command, - text="STOP", - icon=stop_icon, - width=140, - height=30, - border_color="#EC1D1D" - ) - stop_button.place(relx=0.75 - 0.1, rely=0.95, anchor="center") - - -def place_upscale_button(): - upscale_button = create_active_button( - command=upscale_button_command, - text="UPSCALE", - icon=upscale_icon, - width=140, - height=30 - ) - upscale_button.place(relx=0.75 - 0.1, rely=0.95, anchor="center") - - -# Main functions --------------------------- - -def on_app_close() -> None: - window.grab_release() - window.destroy() - - global selected_AI_model - global selected_AI_multithreading - global selected_gpu - global selected_blending_factor - global selected_image_extension - global selected_video_extension - global selected_video_codec - global tiles_resolution - global input_resize_factor - - AI_model_to_save = f"{selected_AI_model}" - gpu_to_save = selected_gpu - image_extension_to_save = selected_image_extension - video_extension_to_save = selected_video_extension - video_codec_to_save = selected_video_codec - blending_to_save = {0: "OFF", 0.3: "Low", 0.5: "Medium", - 0.7: "High"}.get(selected_blending_factor) - - if selected_keep_frames == True: - keep_frames_to_save = "ON" - else: - keep_frames_to_save = "OFF" - - if selected_AI_multithreading == 1: - AI_multithreading_to_save = "OFF" - else: - AI_multithreading_to_save = f"{selected_AI_multithreading} threads" - - user_preference = { - "default_AI_model": AI_model_to_save, - "default_AI_multithreading": AI_multithreading_to_save, - "default_gpu": gpu_to_save, - "default_keep_frames": keep_frames_to_save, - "default_image_extension": image_extension_to_save, - "default_video_extension": video_extension_to_save, - "default_video_codec": video_codec_to_save, - "default_blending": blending_to_save, - "default_output_path": selected_output_path.get(), - "default_input_resize_factor": str(selected_input_resize_factor.get()), - "default_output_resize_factor": str(selected_output_resize_factor.get()), - "default_VRAM_limiter": str(selected_VRAM_limiter.get()), - } - user_preference_json = json_dumps(user_preference) - with open(USER_PREFERENCE_PATH, "w") as preference_file: - preference_file.write(user_preference_json) - - stop_upscale_process() - - -class App(): - def __init__(self, window): - self.toplevel_window = None - window.protocol("WM_DELETE_WINDOW", on_app_close) - - window.title('') - window.geometry("1000x675") - window.resizable(False, False) - window.iconbitmap(find_by_relative_path( - "Assets" + os_separator + "logo.ico")) - - place_loadFile_section() - - place_app_name() - place_output_path_textbox() - - place_AI_menu() - place_AI_multithreading_menu() - place_AI_blending_menu() - place_input_output_resolution_textboxs() - - place_gpu_gpuVRAM_menus() - place_video_codec_keep_frames_menus() - - place_image_video_output_menus() - - place_message_label() - place_upscale_button() - - -# Splash Screen class for application startup -class SplashScreen(CTkToplevel): - def __init__(self): - super().__init__() - - # Configure window - self.title("") - self.overrideredirect(True) # Remove window decorations - self.attributes('-topmost', True) - - # Calculate window position for center of screen - screen_width = self.winfo_screenwidth() - screen_height = self.winfo_screenheight() - - # Set default window size - window_width = 500 - window_height = 300 - - # Try to load banner image - banner_path = find_by_relative_path(f"rsc{os_separator}banner.png") - try: - self.banner_image = CTkImage( - pillow_image_open(banner_path), - size=(450, 200) # Adjust size as needed - ) - has_banner = True - except Exception as e: - print(f"Could not load splash banner: {e}") - has_banner = False - window_height = 200 # Smaller height if no banner - - # Center window - x = (screen_width - window_width) // 2 - y = (screen_height - window_height) // 2 - self.geometry(f"{window_width}x{window_height}+{x}+{y}") - - # Configure appearance to match app - self.configure(fg_color="#212325") # background_color - - # Create banner or title - if has_banner: - self.banner_label = CTkLabel( - self, - image=self.banner_image, - text="" - ) - self.banner_label.pack(pady=(30, 15)) - else: - # Fallback to text title if image not found - title_label = CTkLabel( - self, - text="Warlock Studio", - font=CTkFont(family="Segoe UI", size=28, weight="bold"), - text_color="#2F73DD" # app_name_color - ) - title_label.pack(pady=(50, 20)) - - # Create status frame with progress messages - status_frame = CTkFrame( - self, - fg_color="#343638", # widget_background_color - corner_radius=10 - ) - status_frame.pack(pady=10, padx=20, fill="x") - - self.status_label = CTkLabel( - status_frame, - text="Loading AI-ONNX models...", - font=CTkFont(family="Segoe UI", size=12, weight="bold"), - text_color="white" # text_color - ) - self.status_label.pack(pady=10, padx=10) - - # Define enough messages to fill 15 seconds (~1.5s por mensaje) - self.messages = [ - "Loading AI-ONNX models...", - "Initializing FFmpeg...", - "Preparing environment...", - "Setting up GPU acceleration...", - "Loading image enhancement filters...", - "Optimizing runtime performance...", - "Warming up deep learning models...", - "Finalizing system check...", - "Cleaning temporary buffers...", - "Almost ready..." - ] - - # Start loading animation - self._loading_step = 0 - self.update_loading_text() - - # Splash duration: 15 seconds - self.after(15000, self.start_fade_out) - - def update_loading_text(self): - """Update the loading message every 1.5 seconds""" - if self._loading_step < len(self.messages): - self.status_label.configure(text=self.messages[self._loading_step]) - self._loading_step += 1 - self.after(1500, self.update_loading_text) - - def start_fade_out(self): - """Start the fade out animation""" - self._fade_step = 1.0 - self.fade_out() - - def fade_out(self): - """Smoothly fade out the splash screen""" - if self._fade_step > 0: - # Use cosine for smooth fade - opacity = cos((1.0 - self._fade_step) * pi/2) - self.attributes('-alpha', opacity) - self._fade_step -= 0.05 - self.after(40, self.fade_out) - else: - self.destroy() - - -if __name__ == "__main__": - multiprocessing_freeze_support() - set_appearance_mode("Dark") - set_default_color_theme("dark-blue") - - process_status_q = multiprocessing_Queue(maxsize=1) - - # Create main window but keep it hidden initially - window = CTk() - window.withdraw() # Hide main window temporarily - - # Create and show splash screen - splash = SplashScreen() - - # Schedule showing the main window after splash finishes - window.after(6000, window.deiconify) # 5s + fade time - - info_message = StringVar() - selected_output_path = StringVar() - selected_input_resize_factor = StringVar() - selected_output_resize_factor = StringVar() - selected_VRAM_limiter = StringVar() - - global selected_file_list - global selected_AI_model - global selected_gpu - global selected_keep_frames - global selected_AI_multithreading - global selected_image_extension - global selected_video_extension - global selected_video_codec - global selected_blending_factor - global tiles_resolution - global input_resize_factor - - selected_file_list = [] - - selected_AI_model = default_AI_model - selected_gpu = default_gpu - selected_image_extension = default_image_extension - selected_video_extension = default_video_extension - selected_video_codec = default_video_codec - - if default_AI_multithreading == "OFF": - selected_AI_multithreading = 1 - else: - selected_AI_multithreading = int(default_AI_multithreading.split()[0]) - - if default_keep_frames == "ON": - selected_keep_frames = True - else: - selected_keep_frames = False - - selected_blending_factor = {"OFF": 0, "Low": 0.3, - "Medium": 0.5, "High": 0.7}.get(default_blending) - - selected_input_resize_factor.set(default_input_resize_factor) - selected_output_resize_factor.set(default_output_resize_factor) - selected_VRAM_limiter.set(default_VRAM_limiter) - selected_output_path.set(default_output_path) - - info_message.set("Hi :)") - selected_input_resize_factor.trace_add('write', update_file_widget) - selected_output_resize_factor.trace_add('write', update_file_widget) - - font = "Segoe UI" - bold8 = CTkFont(family=font, size=8, weight="bold") - bold9 = CTkFont(family=font, size=9, weight="bold") - bold10 = CTkFont(family=font, size=10, weight="bold") - bold11 = CTkFont(family=font, size=11, weight="bold") - bold12 = CTkFont(family=font, size=12, weight="bold") - bold13 = CTkFont(family=font, size=13, weight="bold") - bold14 = CTkFont(family=font, size=14, weight="bold") - bold16 = CTkFont(family=font, size=16, weight="bold") - bold17 = CTkFont(family=font, size=17, weight="bold") - bold18 = CTkFont(family=font, size=18, weight="bold") - bold19 = CTkFont(family=font, size=19, weight="bold") - bold20 = CTkFont(family=font, size=20, weight="bold") - bold21 = CTkFont(family=font, size=21, weight="bold") - bold22 = CTkFont(family=font, size=22, weight="bold") - bold23 = CTkFont(family=font, size=23, weight="bold") - bold24 = CTkFont(family=font, size=24, weight="bold") - - stop_icon = CTkImage(pillow_image_open(find_by_relative_path( - f"Assets{os_separator}stop_icon.png")), size=(15, 15)) - upscale_icon = CTkImage(pillow_image_open(find_by_relative_path( - f"Assets{os_separator}upscale_icon.png")), size=(15, 15)) - clear_icon = CTkImage(pillow_image_open(find_by_relative_path( - f"Assets{os_separator}clear_icon.png")), size=(15, 15)) - info_icon = CTkImage(pillow_image_open(find_by_relative_path( - f"Assets{os_separator}info_icon.png")), size=(18, 18)) - - app = App(window) - window.update() - window.mainloop()