Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,381 Bytes
a038241 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
#!/usr/bin/env python
from __future__ import annotations
import argparse
import functools
import pathlib
import sys
import urllib.request
from typing import Union
import cv2
import gradio as gr
import numpy as np
import torch
import torch.nn as nn
sys.path.insert(0, 'face_detection')
from ibug.face_detection import RetinaFacePredictor, S3FDPredictor
REPO_URL = 'https://github.com/ibug-group/face_detection'
TITLE = 'ibug-group/face_detection'
DESCRIPTION = f'This is a demo for {REPO_URL}.'
ARTICLE = None
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--face-score-slider-step', type=float, default=0.05)
parser.add_argument('--face-score-threshold', type=float, default=0.8)
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_model(
model_name: str, threshold: float,
device: torch.device) -> Union[RetinaFacePredictor, S3FDPredictor]:
if model_name == 's3fd':
model = S3FDPredictor(threshold=threshold, device=device)
else:
model_name = model_name.replace('retinaface_', '')
model = RetinaFacePredictor(
threshold=threshold,
device=device,
model=RetinaFacePredictor.get_model(model_name))
return model
def detect(image: np.ndarray, model_name: str, face_score_threshold: float,
detectors: dict[str, nn.Module]) -> np.ndarray:
model = detectors[model_name]
model.threshold = face_score_threshold
# RGB -> BGR
image = image[:, :, ::-1]
preds = model(image, rgb=False)
res = image.copy()
for pred in preds:
box = np.round(pred[:4]).astype(int)
line_width = max(2, int(3 * (box[2:] - box[:2]).max() / 256))
cv2.rectangle(res, tuple(box[:2]), tuple(box[2:]), (0, 255, 0),
line_width)
if len(pred) == 15:
pts = pred[5:].reshape(-1, 2)
for pt in np.round(pts).astype(int):
cv2.circle(res, tuple(pt), line_width, (0, 255, 0), cv2.FILLED)
return res[:, :, ::-1]
def main():
gr.close_all()
args = parse_args()
device = torch.device(args.device)
model_names = [
'retinaface_mobilenet0.25',
'retinaface_resnet50',
's3fd',
]
detectors = {
name: load_model(name,
threshold=args.face_score_threshold,
device=device)
for name in model_names
}
func = functools.partial(detect, detectors=detectors)
func = functools.update_wrapper(func, detect)
image_path = pathlib.Path('selfie.jpg')
if not image_path.exists():
url = 'https://raw.githubusercontent.com/peiyunh/tiny/master/data/demo/selfie.jpg'
urllib.request.urlretrieve(url, image_path)
examples = [[image_path.as_posix(), model_names[1], 0.8]]
gr.Interface(
func,
[
gr.inputs.Image(type='numpy', label='Input'),
gr.inputs.Radio(model_names,
type='value',
default='retinaface_resnet50',
label='Model'),
gr.inputs.Slider(0,
1,
step=args.face_score_slider_step,
default=args.face_score_threshold,
label='Face Score Threshold'),
],
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()
|