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from typing import Optional, List, Tuple
from functools import lru_cache
import cv2
from facefusion.typing import Frame, Resolution
from facefusion.choices import video_template_sizes
from facefusion.filesystem import is_image, is_video
def get_video_frame(video_path : str, frame_number : int = 0) -> Optional[Frame]:
if is_video(video_path):
video_capture = cv2.VideoCapture(video_path)
if video_capture.isOpened():
frame_total = video_capture.get(cv2.CAP_PROP_FRAME_COUNT)
video_capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
has_frame, frame = video_capture.read()
video_capture.release()
if has_frame:
return frame
return None
def count_video_frame_total(video_path : str) -> int:
if is_video(video_path):
video_capture = cv2.VideoCapture(video_path)
if video_capture.isOpened():
video_frame_total = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
video_capture.release()
return video_frame_total
return 0
def detect_video_fps(video_path : str) -> Optional[float]:
if is_video(video_path):
video_capture = cv2.VideoCapture(video_path)
if video_capture.isOpened():
video_fps = video_capture.get(cv2.CAP_PROP_FPS)
video_capture.release()
return video_fps
return None
def detect_video_resolution(video_path : str) -> Optional[Tuple[float, float]]:
if is_video(video_path):
video_capture = cv2.VideoCapture(video_path)
if video_capture.isOpened():
width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)
height = video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
video_capture.release()
return width, height
return None
def create_video_resolutions(video_path : str) -> Optional[List[str]]:
temp_resolutions = []
video_resolutions = []
video_resolution = detect_video_resolution(video_path)
if video_resolution:
width, height = video_resolution
temp_resolutions.append(normalize_resolution(video_resolution))
for template_size in video_template_sizes:
if width > height:
temp_resolutions.append(normalize_resolution((template_size * width / height, template_size)))
else:
temp_resolutions.append(normalize_resolution((template_size, template_size * height / width)))
temp_resolutions = sorted(set(temp_resolutions))
for temp in temp_resolutions:
video_resolutions.append(pack_resolution(temp))
return video_resolutions
return None
def normalize_resolution(resolution : Tuple[float, float]) -> Resolution:
width, height = resolution
if width and height:
normalize_width = round(width / 2) * 2
normalize_height = round(height / 2) * 2
return normalize_width, normalize_height
return 0, 0
def pack_resolution(resolution : Tuple[float, float]) -> str:
width, height = normalize_resolution(resolution)
return str(width) + 'x' + str(height)
def unpack_resolution(resolution : str) -> Resolution:
width, height = map(int, resolution.split('x'))
return width, height
def resize_frame_resolution(frame : Frame, max_width : int, max_height : int) -> Frame:
height, width = frame.shape[:2]
if height > max_height or width > max_width:
scale = min(max_height / height, max_width / width)
new_width = int(width * scale)
new_height = int(height * scale)
return cv2.resize(frame, (new_width, new_height))
return frame
def normalize_frame_color(frame : Frame) -> Frame:
return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
@lru_cache(maxsize = 128)
def read_static_image(image_path : str) -> Optional[Frame]:
return read_image(image_path)
def read_static_images(image_paths : List[str]) -> Optional[List[Frame]]:
frames = []
if image_paths:
for image_path in image_paths:
frames.append(read_static_image(image_path))
return frames
def read_image(image_path : str) -> Optional[Frame]:
if is_image(image_path):
return cv2.imread(image_path)
return None
def write_image(image_path : str, frame : Frame) -> bool:
if image_path:
return cv2.imwrite(image_path, frame)
return False
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