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FluidFrames.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 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 as 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,
IMREAD_UNCHANGED,
INTER_LINEAR,
INTER_AREA,
VideoCapture as opencv_VideoCapture,
cvtColor as opencv_cvtColor,
imdecode as opencv_imdecode,
imencode as opencv_imencode,
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,
expand_dims as numpy_expand_dims,
squeeze as numpy_squeeze,
clip as numpy_clip,
mean as numpy_mean,
float32,
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)
app_name = "FluidFrames"
version = "3.12"
dark_color = "#080808"
githubme = "https://github.com/Djdefrag/FluidFrames.RIFE"
telegramme = "https://linktr.ee/j3ngystudio"
AI_models_list = [ "RIFE", "RIFE_Lite" ]
generation_options_list = [ "x2", "x4", "x8", "Slowmotion x2", "Slowmotion x4", "Slowmotion x8" ]
gpus_list = [ "Auto", "GPU 1", "GPU 2", "GPU 3", "GPU 4" ]
image_extension_list = [ ".jpg", ".png" ]
video_extension_list = [ ".mp4 (x264)", ".mp4 (x265)", ".avi" ]
keep_frames_list = [ "Enabled", "Disabled" ]
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 = 16
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_generation_option = json_data.get("default_generation_option", generation_options_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_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_generation_option = generation_options_list[0]
default_gpu = gpus_list[0]
default_image_extension = image_extension_list[0]
default_video_extension = video_extension_list[0]
default_keep_frames = keep_frames_list[0]
default_output_path = OUTPUT_PATH_CODED
default_resize_factor = str(50)
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
if sys.stdout is None: sys.stdout = open(os_devnull, "w")
if sys.stderr is None: sys.stderr = open(os_devnull, "w")
supported_file_extensions = [
".mp4", ".MP4", ".webm", ".WEBM", ".mkv", ".MKV",
".flv", ".FLV", ".gif", ".GIF", ".m4v", ".M4V",
".avi", ".AVI", ".mov", ".MOV", ".qt", ".3gp",
".mpg", ".mpeg", ".vob", ".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", ".VOB"
]
# AI -------------------
class AI:
# CLASS INIT FUNCTIONS
def __init__(
self,
AI_model_name: str,
directml_gpu: str,
resize_factor: int,
frame_gen_factor: int
):
# Passed variables
self.AI_model_name = AI_model_name
self.directml_gpu = directml_gpu
self.resize_factor = resize_factor
self.frame_gen_factor = frame_gen_factor
# Calculated variables
self.AI_model_path = find_by_relative_path(f"AI-onnx{os_separator}{self.AI_model_name}_fp32.onnx")
self.inferenceSession = self._load_inferenceSession()
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 resize_image(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), interpolation = INTER_LINEAR)
case factor if factor < 1:
return opencv_resize(image, (new_width, new_height), interpolation = INTER_AREA)
case _:
return image
# AI CLASS FUNCTIONS
def concatenate_images(self, image1: numpy_ndarray, image2: numpy_ndarray) -> numpy_ndarray:
image1 = image1 / 255
image2 = image2 / 255
concateneted_image = numpy_concatenate((image1, image2), axis=2)
return concateneted_image
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)
# io_binding.bind_output(self.inferenceSession.get_outputs()[0].name, element_type = float32)
# 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_interpolation(self, image1: numpy_ndarray, image2: numpy_ndarray) -> numpy_ndarray:
image = self.concatenate_images(image1, image2).astype(float32)
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, 255)
return output_image
# EXTERNAL FUNCTION
def AI_orchestration(self, image1: numpy_ndarray, image2: numpy_ndarray) -> tuple[list[numpy_ndarray], list[numpy_ndarray]]:
generated_images = []
if self.frame_gen_factor == 2: # Generate 1 image [image1 / image_A / image2]
image_A = self.AI_interpolation(image1, image2)
generated_images.append(image_A)
elif self.frame_gen_factor == 4: # Generate 3 images [image1 / image_A / image_B / image_C / image2]
image_B = self.AI_interpolation(image1, image2)
image_A = self.AI_interpolation(image1, image_B)
image_C = self.AI_interpolation(image_B, image2)
generated_images.append(image_A)
generated_images.append(image_B)
generated_images.append(image_C)
elif self.frame_gen_factor == 8: # Generate 7 images [image1 / image_A / image_B / image_C / image_D / image_E / image_F / image_G / image2]
image_D = self.AI_interpolation(image1, image2)
image_B = self.AI_interpolation(image1, image_D)
image_A = self.AI_interpolation(image1, image_B)
image_C = self.AI_interpolation(image_B, image_D)
image_F = self.AI_interpolation(image_D, image2)
image_E = self.AI_interpolation(image_D, image_F)
image_G = self.AI_interpolation(image_F, image2)
generated_images.append(image_A)
generated_images.append(image_B)
generated_images.append(image_C)
generated_images.append(image_D)
generated_images.append(image_E)
generated_images.append(image_F)
generated_images.append(image_G)
return generated_images
# 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()
self.attributes("-topmost", True)
self.protocol("WM_DELETE_WINDOW", self._on_closing)
self.after(10, self._create_widgets)
self.resizable(False, False)
self.grab_set()
def _ok_event(self) -> 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,
frame_generation_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.frame_generation_factor = frame_generation_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"{width}x{height} • {round(frame_rate, 2)} fps • {minutes}m:{round(seconds)}s • {num_frames} frames\n")
if self.resize_factor != 0:
resized_height = int(height * (self.resize_factor/100))
resized_width = int(width * (self.resize_factor/100))
if "x2" in self.frame_generation_factor: generation_factor = 2
elif "x4" in self.frame_generation_factor: generation_factor = 4
elif "x8" in self.frame_generation_factor: generation_factor = 8
if "Slowmotion" in self.frame_generation_factor: slowmotion = True
else: slowmotion = False
if slowmotion:
duration_slowmotion = (num_frames/frame_rate) * generation_factor
minutes_slowmotion = int(duration_slowmotion/60)
seconds_slowmotion = duration_slowmotion % 60
file_infos += (f"AI input {self.resize_factor}% ➜ {resized_width}x{resized_height} • {round(frame_rate, 2)} fps \n"
f"AI output x{generation_factor}-slowmo ➜ {resized_width}x{resized_height} • {round(frame_rate, 2)} fps • {minutes_slowmotion}m:{round(seconds_slowmotion)}s")
else:
fps_frame_generated = frame_rate * generation_factor
file_infos += (f"AI input {self.resize_factor}% ➜ {resized_width}x{resized_height} • {round(frame_rate, 2)} fps \n"
f"AI output x{generation_factor} ➜ {resized_width}x{resized_height} • {round(fps_frame_generated, 2)} fps")
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_frame_generation_factor(self, frame_generation_factor) -> None:
self.frame_generation_factor = frame_generation_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
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_frame_generation_factor(selected_generation_option)
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",
height = 22,
width = width,
corner_radius = 10,
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 = 6,
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 = 6,
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 = 6,
width = 300,
height = 30,
font = bold11,
justify = "center",
text_color = "#C0C0C0",
fg_color = "#000000",
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,
fg_color = "#282828",
text_color = "#E0E0E0",
border_color = border_color
)
# File Utils functions ------------------------
def image_write(file_path: str, file_data: numpy_ndarray) -> None:
_, file_extension = os_path_splitext(file_path)
opencv_imencode(file_extension, file_data)[1].tofile(file_path)
def image_read(file_path: str) -> numpy_ndarray:
with open(file_path, 'rb') as file:
return opencv_imdecode(numpy_frombuffer(file.read(), uint8), IMREAD_UNCHANGED)
def remove_dir(name_dir: str) -> None:
if os_path_exists(name_dir): remove_directory(name_dir)
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)
# 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 save_extracted_frames(
extracted_frames_paths: list[str],
extracted_frames: list[numpy_ndarray],
cpu_number: int
) -> None:
pool = ThreadPool(cpu_number)
pool.starmap(image_write, zip(extracted_frames_paths, extracted_frames))
pool.close()
pool.join()
def extract_video_frames(
processing_queue: multiprocessing_Queue,
file_number: int,
target_directory: str,
AI_instance: AI,
video_path: str,
selected_image_extension: str,
cpu_number: int
) -> tuple[list[str], str]:
create_dir(target_directory)
# Video frame extraction + resize
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 = []
for frame_number in range(frame_count):
success, frame = video_capture.read()
if success:
frame_path = f"{target_directory}{os_separator}frame_{frame_number:03d}{selected_image_extension}"
frame = AI_instance.resize_image(frame)
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(processing_queue, 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 = []
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(
video_path: str,
video_output_path: str,
total_frames_paths: list,
frame_gen_factor: int,
slowmotion: bool,
cpu_number: int,
selected_video_extension: str
) -> None:
match selected_video_extension:
case ".mp4 (x264)": codec = "libx264"
case ".mp4 (x265)": codec = "libx265"
case ".avi": codec = "png"
no_audio_path = f"{os_path_splitext(video_output_path)[0]}_no_audio{os_path_splitext(video_output_path)[1]}"
video_fps = get_video_fps(video_path) if slowmotion else get_video_fps(video_path) * frame_gen_factor
video_clip = ImageSequenceClip.ImageSequenceClip(sequence = total_frames_paths, fps = video_fps)
if slowmotion:
video_clip.write_videofile(
filename = video_output_path,
fps = video_fps,
codec = codec,
threads = cpu_number,
logger = None,
audio = None,
bitrate = "12M",
preset = "ultrafast"
)
else:
video_clip.write_videofile(
filename = no_audio_path,
fps = video_fps,
codec = codec,
threads = cpu_number,
logger = None,
audio = None,
bitrate = "12M",
preset = "ultrafast"
)
# Copy the audio from original video
audio_passthrough_command = [
FFMPEG_EXE_PATH,
"-y",
"-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_frame_generation_resume(
target_directory: str,
selected_AI_model: str,
selected_image_extension: str
) -> bool:
if os_path_exists(target_directory):
directory_files = os_listdir(target_directory)
generated_frames_paths = [file for file in directory_files if selected_AI_model in file]
generated_frames_paths = [file for file in generated_frames_paths if file.endswith(selected_image_extension)]
if len(generated_frames_paths) > 1:
return True
else:
return False
else:
return False
def get_video_frames_for_frame_generation_resume(
target_directory: str,
selected_AI_model: str,
selected_image_extension: str
) -> list[str]:
# Only file names
directory_files = os_listdir(target_directory)
original_frames_path = [file for file in directory_files if file.endswith(selected_image_extension)]
original_frames_path = [file for file in original_frames_path if selected_AI_model not in file]
# Adding the complete path to files
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: