Rohit Kochikkat Francis
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from typing import Optional, List, Tuple
from functools import lru_cache
import cv2
import numpy
from cv2.typing import Size
from facefusion.typing import VisionFrame, Resolution, Fps
from facefusion.choices import image_template_sizes, video_template_sizes
from facefusion.filesystem import is_image, is_video
@lru_cache(maxsize = 128)
def read_static_image(image_path : str) -> Optional[VisionFrame]:
return read_image(image_path)
def read_static_images(image_paths : List[str]) -> Optional[List[VisionFrame]]:
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[VisionFrame]:
if is_image(image_path):
return cv2.imread(image_path)
return None
def write_image(image_path : str, vision_frame : VisionFrame) -> bool:
if image_path:
return cv2.imwrite(image_path, vision_frame)
return False
def detect_image_resolution(image_path : str) -> Optional[Resolution]:
if is_image(image_path):
image = read_image(image_path)
height, width = image.shape[:2]
return width, height
return None
def restrict_image_resolution(image_path : str, resolution : Resolution) -> Resolution:
if is_image(image_path):
image_resolution = detect_image_resolution(image_path)
if image_resolution < resolution:
return image_resolution
return resolution
def get_video_frame(video_path : str, frame_number : int = 0) -> Optional[VisionFrame]:
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_vision_frame, vision_frame = video_capture.read()
video_capture.release()
if has_vision_frame:
return vision_frame
return None
def create_image_resolutions(resolution : Resolution) -> List[str]:
resolutions = []
temp_resolutions = []
if resolution:
width, height = resolution
temp_resolutions.append(normalize_resolution(resolution))
for template_size in image_template_sizes:
temp_resolutions.append(normalize_resolution((width * template_size, height * template_size)))
temp_resolutions = sorted(set(temp_resolutions))
for temp_resolution in temp_resolutions:
resolutions.append(pack_resolution(temp_resolution))
return resolutions
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 restrict_video_fps(video_path : str, fps : Fps) -> Fps:
if is_video(video_path):
video_fps = detect_video_fps(video_path)
if video_fps < fps:
return video_fps
return fps
def detect_video_resolution(video_path : str) -> Optional[Resolution]:
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 int(width), int(height)
return None
def restrict_video_resolution(video_path : str, resolution : Resolution) -> Resolution:
if is_video(video_path):
video_resolution = detect_video_resolution(video_path)
if video_resolution < resolution:
return video_resolution
return resolution
def create_video_resolutions(resolution : Resolution) -> List[str]:
resolutions = []
temp_resolutions = []
if resolution:
width, height = resolution
temp_resolutions.append(normalize_resolution(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_resolution in temp_resolutions:
resolutions.append(pack_resolution(temp_resolution))
return resolutions
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 : Resolution) -> 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(vision_frame : VisionFrame, max_resolution : Resolution) -> VisionFrame:
height, width = vision_frame.shape[:2]
max_width, max_height = max_resolution
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(vision_frame, (new_width, new_height))
return vision_frame
def normalize_frame_color(vision_frame : VisionFrame) -> VisionFrame:
return cv2.cvtColor(vision_frame, cv2.COLOR_BGR2RGB)
def create_tile_frames(vision_frame : VisionFrame, size : Size) -> Tuple[List[VisionFrame], int, int]:
vision_frame = numpy.pad(vision_frame, ((size[1], size[1]), (size[1], size[1]), (0, 0)))
tile_width = size[0] - 2 * size[2]
pad_size_bottom = size[2] + tile_width - vision_frame.shape[0] % tile_width
pad_size_right = size[2] + tile_width - vision_frame.shape[1] % tile_width
pad_vision_frame = numpy.pad(vision_frame, ((size[2], pad_size_bottom), (size[2], pad_size_right), (0, 0)))
pad_height, pad_width = pad_vision_frame.shape[:2]
row_range = range(size[2], pad_height - size[2], tile_width)
col_range = range(size[2], pad_width - size[2], tile_width)
tile_vision_frames = []
for row_vision_frame in row_range:
top = row_vision_frame - size[2]
bottom = row_vision_frame + size[2] + tile_width
for column_vision_frame in col_range:
left = column_vision_frame - size[2]
right = column_vision_frame + size[2] + tile_width
tile_vision_frames.append(pad_vision_frame[top:bottom, left:right, :])
return tile_vision_frames, pad_width, pad_height
def merge_tile_frames(tile_vision_frames : List[VisionFrame], temp_width : int, temp_height : int, pad_width : int, pad_height : int, size : Size) -> VisionFrame:
merge_vision_frame = numpy.zeros((pad_height, pad_width, 3)).astype(numpy.uint8)
tile_width = tile_vision_frames[0].shape[1] - 2 * size[2]
tiles_per_row = min(pad_width // tile_width, len(tile_vision_frames))
for index, tile_vision_frame in enumerate(tile_vision_frames):
tile_vision_frame = tile_vision_frame[size[2]:-size[2], size[2]:-size[2]]
row_index = index // tiles_per_row
col_index = index % tiles_per_row
top = row_index * tile_vision_frame.shape[0]
bottom = top + tile_vision_frame.shape[0]
left = col_index * tile_vision_frame.shape[1]
right = left + tile_vision_frame.shape[1]
merge_vision_frame[top:bottom, left:right, :] = tile_vision_frame
merge_vision_frame = merge_vision_frame[size[1] : size[1] + temp_height, size[1]: size[1] + temp_width, :]
return merge_vision_frame