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RealScaler.py
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# Standard library imports
import sys
from functools import cache
from time import sleep
from webbrowser import open as open_browser
from subprocess import run as subprocess_run
from shutil import rmtree as remove_directory
from timeit import default_timer as timer
from typing import Callable
from threading import Thread
from itertools import repeat
from multiprocessing.pool import ThreadPool
from multiprocessing import (
Process,
Queue as multiprocessing_Queue,
freeze_support as multiprocessing_freeze_support
)
from json import (
load as json_load,
dumps as json_dumps
)
from os import (
sep as os_separator,
devnull as os_devnull,
environ as os_environ,
makedirs as os_makedirs,
listdir as os_listdir,
remove as os_remove
)
from os.path import (
basename as os_path_basename,
dirname as os_path_dirname,
abspath as os_path_abspath,
join as os_path_join,
exists as os_path_exists,
splitext as os_path_splitext,
expanduser as os_path_expanduser
)
# Third-party library imports
from natsort import natsorted
from moviepy.video.io import ImageSequenceClip
from onnxruntime import InferenceSession
from PIL.Image import (
open as pillow_image_open,
fromarray as pillow_image_fromarray
)
from cv2 import (
CAP_PROP_FPS,
CAP_PROP_FRAME_COUNT,
CAP_PROP_FRAME_HEIGHT,
CAP_PROP_FRAME_WIDTH,
COLOR_BGR2RGB,
COLOR_GRAY2RGB,
COLOR_BGR2RGBA,
COLOR_RGB2GRAY,
IMREAD_UNCHANGED,
INTER_AREA,
VideoCapture as opencv_VideoCapture,
cvtColor as opencv_cvtColor,
imdecode as opencv_imdecode,
imencode as opencv_imencode,
addWeighted as opencv_addWeighted,
cvtColor as opencv_cvtColor,
resize as opencv_resize,
)
from numpy import (
ndarray as numpy_ndarray,
frombuffer as numpy_frombuffer,
concatenate as numpy_concatenate,
transpose as numpy_transpose,
full as numpy_full,
zeros as numpy_zeros,
expand_dims as numpy_expand_dims,
squeeze as numpy_squeeze,
clip as numpy_clip,
mean as numpy_mean,
repeat as numpy_repeat,
max as numpy_max,
float32,
float16,
uint8
)
# GUI imports
from tkinter import StringVar
from tkinter import DISABLED
from customtkinter import (
CTk,
CTkButton,
CTkEntry,
CTkFont,
CTkImage,
CTkLabel,
CTkOptionMenu,
CTkScrollableFrame,
CTkToplevel,
filedialog,
set_appearance_mode,
set_default_color_theme
)
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)
githubme = "https://github.com/Djdefrag/Realscaler"
telegramme = "https://linktr.ee/j3ngystudio"
app_name = "RealScaler"
version = "3.12"
app_name_color = "#FFA500"
dark_color = "#080808"
very_low_VRAM = 4
low_VRAM = 3
medium_VRAM = 2.2
very_high_VRAM = 0.6
AI_LIST_SEPARATOR = [ "----" ]
SRVGGNetCompact_models_list = [ 'RealESR_Gx4', 'RealSRx4_Anime' ]
RealESRGAN_models_list = [ 'RealESRGANx4', 'RealESRNetx4' ]
AI_models_list = ( SRVGGNetCompact_models_list + AI_LIST_SEPARATOR + RealESRGAN_models_list )
AI_multithreading_list = [ "1 threads", "2 threads", "3 threads", "4 threads", "5 threads", "6 threads"]
interpolation_list = [ "Disabled", "Low", "Medium", "High" ]
gpus_list = [ "Auto", "GPU 1", "GPU 2", "GPU 3", "GPU 4" ]
keep_frames_list = [ "Disabled", "Enabled" ]
image_extension_list = [ ".png", ".jpg", ".bmp", ".tiff" ]
video_extension_list = [ ".mp4 (x264)", ".mp4 (x265)", ".avi" ]
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}_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 = 25
MULTIPLE_FRAMES_TO_SAVE = 8
MULTIPLE_FRAMES_TO_SAVE_MULTITHREAD = MULTIPLE_FRAMES_TO_SAVE/2
COMPLETED_STATUS = "Completed"
ERROR_STATUS = "Error"
STOP_STATUS = "Stop"
if os_path_exists(FFMPEG_EXE_PATH):
print(f"[{app_name}] External ffmpeg.exe file found")
os_environ["IMAGEIO_FFMPEG_EXE"] = FFMPEG_EXE_PATH
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[0])
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_interpolation = json_data.get("default_interpolation", interpolation_list[1])
default_output_path = json_data.get("default_output_path", OUTPUT_PATH_CODED)
default_resize_factor = json_data.get("default_resize_factor", str(50))
default_VRAM_limiter = json_data.get("default_VRAM_limiter", str(4))
default_cpu_number = json_data.get("default_cpu_number", 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[0]
default_image_extension = image_extension_list[0]
default_video_extension = video_extension_list[0]
default_interpolation = interpolation_list[1]
default_output_path = OUTPUT_PATH_CODED
default_resize_factor = str(50)
default_VRAM_limiter = str(4)
default_cpu_number = str(4)
offset_y_options = 0.105
row0_y = 0.52
row1_y = row0_y + offset_y_options
row2_y = row1_y + offset_y_options
row3_y = row2_y + offset_y_options
row4_y = row3_y + offset_y_options
offset_x_options = 0.28
column1_x = 0.5
column0_x = column1_x - offset_x_options
column2_x = column1_x + offset_x_options
column1_5_x = column1_x + offset_x_options/2
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:
# CLASS INIT FUNCTIONS
def __init__(
self,
AI_model_name: str,
directml_gpu: str,
resize_factor: int,
max_resolution: int
):
# Passed variables
self.AI_model_name = AI_model_name
self.directml_gpu = directml_gpu
self.resize_factor = 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 = self._load_inferenceSession()
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) -> InferenceSession:
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
)
return inference_session
# INTERNAL CLASS FUNCTIONS
def get_image_mode(self, image: numpy_ndarray) -> str:
match image.shape:
case (rows, cols):
return "Grayscale"
case (rows, cols, channels) if channels == 3:
return "RGB"
case (rows, cols, channels) if channels == 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_image_with_resize_factor(self, image: numpy_ndarray) -> numpy_ndarray:
old_height, old_width = self.get_image_resolution(image)
new_width = int(old_width * self.resize_factor)
new_height = int(old_height * self.resize_factor)
match self.resize_factor:
case factor if factor > 1:
return opencv_resize(image, (new_width, new_height))
case factor if factor < 1:
return opencv_resize(image, (new_width, new_height), interpolation = INTER_AREA)
case _:
return image
def resize_image_with_target_resolution(
self,
image: numpy_ndarray,
t_height: int,
t_width: int
) -> numpy_ndarray:
old_height, old_width = self.get_image_resolution(image)
old_resolution = old_height + old_width
new_resolution = t_height + t_width
if new_resolution > old_resolution:
return opencv_resize(image, (t_width, t_height))
else:
return opencv_resize(image, (t_width, t_height), interpolation = INTER_AREA)
# VIDEO CLASS FUNCTIONS
def calculate_multiframes_supported_by_gpu(self, video_frame_path: str) -> int:
resized_video_frame = self.resize_image_with_resize_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 video_need_tilling(self, video_frame_path: str) -> bool:
resized_video_frame = self.resize_image_with_resize_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
if image_pixels > max_supported_pixels:
return True
else:
return False
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.astype(float32)
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:
resized_image = self.resize_image_with_resize_factor(image)
if self.image_need_tilling(resized_image):
return self.AI_upscale_with_tilling(resized_image)
else:
return self.AI_upscale(resized_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)
self.after(10, self._create_widgets) # create widgets with slight delay, to avoid white flickering of background
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 = "#3399FF"
elif self._messageType == "error":
title_subtitle_text_color = "#FF3131"
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",
text_color = "#3399FF",
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,
corner_radius = 6,
anchor = 'w',
justify = "left",
text_color = "#C0C0C0",
fg_color = "#282828",
bg_color = "transparent",
font = bold12,
text = option_text)
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,
resize_factor = 0,
upscale_factor = 1,
**kwargs
) -> None:
super().__init__(master, **kwargs)
self.grid_columnconfigure(0, weight = 1)
self.file_list = selected_file_list
self.resize_factor = resize_factor
self.upscale_factor = upscale_factor
self.label_list = []
self._create_widgets()
def _destroy_(self) -> None:
self.file_list = []
self.destroy()
place_loadFile_section()
def _create_widgets(self) -> None:
self.add_clean_button()
index_row = 1
for file_path in self.file_list:
label = self.add_file_information(file_path, index_row)
self.label_list.append(label)
index_row +=1
def add_file_information(self, file_path, index_row) -> CTkLabel:
infos, icon = self.extract_file_info(file_path)
label = CTkLabel(
self,
text = infos,
image = icon,
font = bold12,
text_color = "#C0C0C0",
compound = "left",
anchor = "w",
padx = 10,
pady = 5,
justify = "left",
)
label.grid(
row = index_row,
column = 0,
pady = (3, 3),
padx = (3, 3),
sticky = "w")
return label
def add_clean_button(self) -> None:
button = CTkButton(
self,
image = clear_icon,
font = bold11,
text = "CLEAN",
compound = "left",
width = 100,
height = 28,
border_width = 1,
fg_color = "#282828",
text_color = "#E0E0E0",
border_color = "#0096FF"
)
button.configure(command=lambda: self._destroy_())
button.grid(row = 0, column=2, pady=(7, 7), padx = (0, 7))
@cache
def extract_file_icon(self, file_path) -> CTkImage:
max_size = 50
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()
video_name = str(file_path.split("/")[-1])
file_icon = self.extract_file_icon(file_path)
file_infos = (f"{video_name}\n"
f"Resolution {width}x{height} • {minutes}m:{round(seconds)}s • {num_frames}frames\n")
if self.resize_factor != 0 and self.upscale_factor != 0:
resized_height = int(height * (self.resize_factor/100))
resized_width = int(width * (self.resize_factor/100))
upscaled_height = int(resized_height * self.upscale_factor)
upscaled_width = int(resized_width * self.upscale_factor)
file_infos += (f"AI input {self.resize_factor}% ➜ {resized_width}x{resized_height} \n"
f"AI output x{self.upscale_factor} ➜ {upscaled_width}x{upscaled_height}")
else:
image_name = str(file_path.split("/")[-1])
height, width = get_image_resolution(image_read(file_path))
file_icon = self.extract_file_icon(file_path)
file_infos = (f"{image_name}\n"
f"Resolution {width}x{height}\n")
if self.resize_factor != 0 and self.upscale_factor != 0:
resized_height = int(height * (self.resize_factor/100))
resized_width = int(width * (self.resize_factor/100))
upscaled_height = int(resized_height * self.upscale_factor)
upscaled_width = int(resized_width * self.upscale_factor)
file_infos += (f"AI input {self.resize_factor}% ➜ {resized_width}x{resized_height} \n"
f"AI output x{self.upscale_factor} ➜ {upscaled_width}x{upscaled_height}")
return file_infos, file_icon
# EXTERNAL FUNCTIONS
def clean_file_list(self) -> None:
for label in self.label_list:
label.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_resize_factor(self, resize_factor) -> None:
self.resize_factor = resize_factor
def update_file_widget(a, b, c) -> None:
try:
global file_widget
file_widget
except:
return
upscale_factor = get_upscale_factor()
try:
resize_factor = int(float(str(selected_resize_factor.get())))
except:
resize_factor = 0
file_widget.clean_file_list()
file_widget.set_resize_factor(resize_factor)
file_widget.set_upscale_factor(upscale_factor)
file_widget._create_widgets()
def create_info_button(
command: Callable,
text: str,
width: int = 150
) -> CTkButton:
return CTkButton(
master = window,
command = command,
text = text,
fg_color = "transparent",
hover_color = "#181818",
text_color = "#C0C0C0",
anchor = "w",
corner_radius = 10,
height = 22,
width = width,
font = bold12,
image = info_icon
)
def create_option_menu(
command: Callable,
values: list,
default_value: str
) -> CTkOptionMenu:
option_menu = CTkOptionMenu(
master = window,
command = command,
values = values,
width = 150,
height = 30,
corner_radius = 5,
dropdown_font = bold11,
font = bold11,
anchor = "center",
text_color = "#C0C0C0",
fg_color = "#000000",
button_color = "#000000",
button_hover_color = "#000000",
dropdown_fg_color = "#000000"
)
option_menu.set(default_value)
return option_menu
def create_text_box(textvariable: StringVar) -> CTkEntry:
return CTkEntry(
master = window,
textvariable = textvariable,
corner_radius = 5,
width = 150,
height = 30,
font = bold11,
justify = "center",
text_color = "#C0C0C0",
fg_color = "#000000",
border_width = 1,
border_color = "#404040",
)
def create_text_box_output_path(textvariable: StringVar) -> CTkEntry:
return CTkEntry(
master = window,
textvariable = textvariable,
border_width = 1,
corner_radius = 5,
width = 300,
height = 30,
font = bold11,
justify = "center",
text_color = "#C0C0C0",
fg_color = "#000000",
border_color = "#404040",
state = DISABLED
)