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from typing import Optional, Generator, Deque
from concurrent.futures import ThreadPoolExecutor
from collections import deque
import os
import platform
import subprocess
import cv2
import gradio
from tqdm import tqdm
import DeepFakeAI.globals
from DeepFakeAI import logger, wording
from DeepFakeAI.content_analyser import analyse_stream
from DeepFakeAI.typing import Frame, Face
from DeepFakeAI.face_analyser import get_average_face
from DeepFakeAI.processors.frame.core import get_frame_processors_modules
from DeepFakeAI.ffmpeg import open_ffmpeg
from DeepFakeAI.vision import normalize_frame_color, read_static_images
from DeepFakeAI.uis.typing import StreamMode, WebcamMode
from DeepFakeAI.uis.core import get_ui_component
WEBCAM_CAPTURE : Optional[cv2.VideoCapture] = None
WEBCAM_IMAGE : Optional[gradio.Image] = None
WEBCAM_START_BUTTON : Optional[gradio.Button] = None
WEBCAM_STOP_BUTTON : Optional[gradio.Button] = None
def get_webcam_capture() -> Optional[cv2.VideoCapture]:
global WEBCAM_CAPTURE
if WEBCAM_CAPTURE is None:
if platform.system().lower() == 'windows':
webcam_capture = cv2.VideoCapture(0, cv2.CAP_DSHOW)
else:
webcam_capture = cv2.VideoCapture(0)
if webcam_capture and webcam_capture.isOpened():
WEBCAM_CAPTURE = webcam_capture
return WEBCAM_CAPTURE
def clear_webcam_capture() -> None:
global WEBCAM_CAPTURE
if WEBCAM_CAPTURE:
WEBCAM_CAPTURE.release()
WEBCAM_CAPTURE = None
def render() -> None:
global WEBCAM_IMAGE
global WEBCAM_START_BUTTON
global WEBCAM_STOP_BUTTON
WEBCAM_IMAGE = gradio.Image(
label = wording.get('webcam_image_label')
)
WEBCAM_START_BUTTON = gradio.Button(
value = wording.get('start_button_label'),
variant = 'primary',
size = 'sm'
)
WEBCAM_STOP_BUTTON = gradio.Button(
value = wording.get('stop_button_label'),
size = 'sm'
)
def listen() -> None:
start_event = None
webcam_mode_radio = get_ui_component('webcam_mode_radio')
webcam_resolution_dropdown = get_ui_component('webcam_resolution_dropdown')
webcam_fps_slider = get_ui_component('webcam_fps_slider')
if webcam_mode_radio and webcam_resolution_dropdown and webcam_fps_slider:
start_event = WEBCAM_START_BUTTON.click(start, inputs = [ webcam_mode_radio, webcam_resolution_dropdown, webcam_fps_slider ], outputs = WEBCAM_IMAGE)
WEBCAM_STOP_BUTTON.click(stop, cancels = start_event)
source_image = get_ui_component('source_image')
if source_image:
for method in [ 'upload', 'change', 'clear' ]:
getattr(source_image, method)(stop, cancels = start_event)
def start(webcam_mode : WebcamMode, resolution : str, fps : float) -> Generator[Frame, None, None]:
DeepFakeAI.globals.face_selector_mode = 'one'
DeepFakeAI.globals.face_analyser_order = 'large-small'
source_frames = read_static_images(DeepFakeAI.globals.source_paths)
source_face = get_average_face(source_frames)
stream = None
if webcam_mode in [ 'udp', 'v4l2' ]:
stream = open_stream(webcam_mode, resolution, fps) # type: ignore[arg-type]
webcam_width, webcam_height = map(int, resolution.split('x'))
webcam_capture = get_webcam_capture()
if webcam_capture and webcam_capture.isOpened():
webcam_capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG')) # type: ignore[attr-defined]
webcam_capture.set(cv2.CAP_PROP_FRAME_WIDTH, webcam_width)
webcam_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, webcam_height)
webcam_capture.set(cv2.CAP_PROP_FPS, fps)
for capture_frame in multi_process_capture(source_face, webcam_capture, fps):
if webcam_mode == 'inline':
yield normalize_frame_color(capture_frame)
else:
try:
stream.stdin.write(capture_frame.tobytes())
except Exception:
clear_webcam_capture()
yield None
def multi_process_capture(source_face : Face, webcam_capture : cv2.VideoCapture, fps : float) -> Generator[Frame, None, None]:
with tqdm(desc = wording.get('processing'), unit = 'frame', ascii = ' =', disable = DeepFakeAI.globals.log_level in [ 'warn', 'error' ]) as progress:
with ThreadPoolExecutor(max_workers = DeepFakeAI.globals.execution_thread_count) as executor:
futures = []
deque_capture_frames : Deque[Frame] = deque()
while webcam_capture and webcam_capture.isOpened():
_, capture_frame = webcam_capture.read()
if analyse_stream(capture_frame, fps):
return
future = executor.submit(process_stream_frame, source_face, capture_frame)
futures.append(future)
for future_done in [ future for future in futures if future.done() ]:
capture_frame = future_done.result()
deque_capture_frames.append(capture_frame)
futures.remove(future_done)
while deque_capture_frames:
progress.update()
yield deque_capture_frames.popleft()
def stop() -> gradio.Image:
clear_webcam_capture()
return gradio.Image(value = None)
def process_stream_frame(source_face : Face, temp_frame : Frame) -> Frame:
for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
if frame_processor_module.pre_process('stream'):
temp_frame = frame_processor_module.process_frame(
source_face,
None,
temp_frame
)
return temp_frame
def open_stream(stream_mode : StreamMode, resolution : str, fps : float) -> subprocess.Popen[bytes]:
commands = [ '-f', 'rawvideo', '-pix_fmt', 'bgr24', '-s', resolution, '-r', str(fps), '-i', '-' ]
if stream_mode == 'udp':
commands.extend([ '-b:v', '2000k', '-f', 'mpegts', 'udp://localhost:27000?pkt_size=1316' ])
if stream_mode == 'v4l2':
try:
device_name = os.listdir('/sys/devices/virtual/video4linux')[0]
if device_name:
commands.extend([ '-f', 'v4l2', '/dev/' + device_name ])
except FileNotFoundError:
logger.error(wording.get('stream_not_loaded').format(stream_mode = stream_mode), __name__.upper())
return open_ffmpeg(commands)
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