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#!/usr/bin/env python
from __future__ import annotations
import argparse
import functools
import os
import pathlib
import sys
import tarfile
import gradio as gr
import huggingface_hub
import numpy as np
import torch
sys.path.insert(0, 'face_detection')
sys.path.insert(0, 'face_parsing')
sys.path.insert(0, 'roi_tanh_warping')
from ibug.face_detection import RetinaFacePredictor
from ibug.face_parsing.parser import WEIGHT, FaceParser
from ibug.face_parsing.utils import label_colormap
REPO_URL = 'https://github.com/hhj1897/face_parsing'
TITLE = 'hhj1897/face_parsing'
DESCRIPTION = f'This is a demo for {REPO_URL}.'
ARTICLE = None
TOKEN = os.environ['TOKEN']
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--theme', type=str)
parser.add_argument('--live', action='store_true')
parser.add_argument('--share', action='store_true')
parser.add_argument('--port', type=int)
parser.add_argument('--disable-queue',
dest='enable_queue',
action='store_false')
parser.add_argument('--allow-flagging', type=str, default='never')
parser.add_argument('--allow-screenshot', action='store_true')
return parser.parse_args()
def load_sample_images() -> list[pathlib.Path]:
image_dir = pathlib.Path('images')
if not image_dir.exists():
image_dir.mkdir()
dataset_repo = 'hysts/input-images'
filenames = ['000.tar', '001.tar']
for name in filenames:
path = huggingface_hub.hf_hub_download(dataset_repo,
name,
repo_type='dataset',
use_auth_token=TOKEN)
with tarfile.open(path) as f:
f.extractall(image_dir.as_posix())
return sorted(image_dir.rglob('*.jpg'))
def load_detector(device: torch.device) -> RetinaFacePredictor:
model = RetinaFacePredictor(
threshold=0.8,
device=device,
model=RetinaFacePredictor.get_model('mobilenet0.25'))
return model
def load_model(model_name: str, device: torch.device) -> FaceParser:
encoder, decoder, num_classes = model_name.split('-')
num_classes = int(num_classes)
model = FaceParser(device=device,
encoder=encoder,
decoder=decoder,
num_classes=num_classes)
model.num_classes = num_classes
return model
def predict(image: np.ndarray, model_name: str, max_num_faces: int,
detector: RetinaFacePredictor,
models: dict[str, FaceParser]) -> np.ndarray:
model = models[model_name]
colormap = label_colormap(model.num_classes)
# RGB -> BGR
image = image[:, :, ::-1]
faces = detector(image, rgb=False)
if len(faces) == 0:
raise RuntimeError('No face was found.')
faces = sorted(list(faces), key=lambda x: -x[4])[:max_num_faces][::-1]
masks = model.predict_img(image, faces, rgb=False)
mask_image = np.zeros_like(image)
for mask in masks:
temp = colormap[mask]
mask_image[temp > 0] = temp[temp > 0]
res = image.astype(float) * 0.5 + mask_image[:, :, ::-1] * 0.5
res = np.clip(np.round(res), 0, 255).astype(np.uint8)
return res[:, :, ::-1]
def main():
gr.close_all()
args = parse_args()
device = torch.device(args.device)
detector = load_detector(device)
model_names = list(WEIGHT.keys())
models = {name: load_model(name, device=device) for name in model_names}
func = functools.partial(predict, detector=detector, models=models)
func = functools.update_wrapper(func, predict)
image_paths = load_sample_images()
examples = [[path.as_posix(), model_names[1], 10] for path in image_paths]
gr.Interface(
func,
[
gr.inputs.Image(type='numpy', label='Input'),
gr.inputs.Radio(model_names,
type='value',
default=model_names[1],
label='Model'),
gr.inputs.Slider(
1, 20, step=1, default=10, label='Max Number of Faces'),
],
gr.outputs.Image(type='numpy', label='Output'),
examples=examples,
title=TITLE,
description=DESCRIPTION,
article=ARTICLE,
theme=args.theme,
allow_screenshot=args.allow_screenshot,
allow_flagging=args.allow_flagging,
live=args.live,
).launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
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