from typing import Optional, List from functools import lru_cache import cv2 from DeepFakeAI.typing import Frame def get_video_frame(video_path : str, frame_number : int = 0) -> Optional[Frame]: if 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 detect_fps(video_path : str) -> Optional[float]: if video_path: video_capture = cv2.VideoCapture(video_path) if video_capture.isOpened(): return video_capture.get(cv2.CAP_PROP_FPS) return None def count_video_frame_total(video_path : str) -> int: if 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 normalize_frame_color(frame : Frame) -> Frame: return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) def resize_frame_dimension(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 @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 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