Spaces:
Runtime error
Runtime error
File size: 5,389 Bytes
ec93f77 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
#!/usr/bin/env python
from __future__ import annotations
import argparse
import functools
import os
import pathlib
import subprocess
import tarfile
try:
import detectron2
except:
command = 'pip install git+https://github.com/facebookresearch/detectron2@v0.6'
subprocess.call(command.split())
try:
import adet
except:
command = 'pip install git+https://github.com/aim-uofa/AdelaiDet@7bf9d87'
subprocess.call(command.split())
import gradio as gr
import huggingface_hub
import numpy as np
import torch
from adet.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.engine.defaults import DefaultPredictor
from detectron2.utils.visualizer import Visualizer
TOKEN = os.environ['TOKEN']
MODEL_REPO = 'hysts/Yet-Another-Anime-Segmenter'
MODEL_FILENAME = 'SOLOv2.pth'
CONFIG_FILENAME = 'SOLOv2.yaml'
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--class-score-slider-step', type=float, default=0.05)
parser.add_argument('--class-score-threshold', type=float, default=0.1)
parser.add_argument('--mask-score-slider-step', type=float, default=0.05)
parser.add_argument('--mask-score-threshold', type=float, default=0.5)
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_image_paths() -> list[pathlib.Path]:
image_dir = pathlib.Path('images')
if not image_dir.exists():
dataset_repo = 'hysts/sample-images-TADNE'
path = huggingface_hub.hf_hub_download(dataset_repo,
'images.tar.gz',
repo_type='dataset',
use_auth_token=TOKEN)
with tarfile.open(path) as f:
f.extractall()
return sorted(image_dir.glob('*'))
def load_model(device: torch.device) -> DefaultPredictor:
config_path = huggingface_hub.hf_hub_download(MODEL_REPO,
CONFIG_FILENAME,
use_auth_token=TOKEN)
model_path = huggingface_hub.hf_hub_download(MODEL_REPO,
MODEL_FILENAME,
use_auth_token=TOKEN)
cfg = get_cfg()
cfg.merge_from_file(config_path)
cfg.MODEL.WEIGHTS = model_path
cfg.MODEL.DEVICE = device.type
cfg.freeze()
return DefaultPredictor(cfg)
def predict(image, class_score_threshold: float, mask_score_threshold: float,
model: DefaultPredictor) -> tuple[np.ndarray, np.ndarray]:
model.score_threshold = class_score_threshold
model.mask_threshold = mask_score_threshold
image = read_image(image.name, format='BGR')
preds = model(image)
instances = preds['instances'].to('cpu')
visualizer = Visualizer(image[:, :, ::-1])
vis = visualizer.draw_instance_predictions(predictions=instances)
vis = vis.get_image()
masked = image.copy()[:, :, ::-1]
mask = instances.pred_masks.cpu().numpy().astype(int).max(axis=0)
masked[mask == 0] = 255
return vis, masked
def main():
gr.close_all()
args = parse_args()
device = torch.device(args.device)
image_paths = load_sample_image_paths()
examples = [[
path.as_posix(), args.class_score_threshold, args.mask_score_threshold
] for path in image_paths]
model = load_model(device)
func = functools.partial(predict, model=model)
func = functools.update_wrapper(func, predict)
repo_url = 'https://github.com/zymk9/Yet-Another-Anime-Segmenter'
title = 'zymk9/Yet-Another-Anime-Segmenter'
description = f'A demo for {repo_url}'
article = None
gr.Interface(
func,
[
gr.inputs.Image(type='file', label='Input'),
gr.inputs.Slider(0,
1,
step=args.class_score_slider_step,
default=args.class_score_threshold,
label='Class Score Threshold'),
gr.inputs.Slider(0,
1,
step=args.mask_score_slider_step,
default=args.mask_score_threshold,
label='Mask Score Threshold'),
],
[
gr.outputs.Image(label='Instances'),
gr.outputs.Image(label='Masked'),
],
theme=args.theme,
title=title,
description=description,
article=article,
examples=examples,
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()
|