Add files

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import pathlib
+import subprocess
+import tarfile
+
+# workaround for https://github.com/gradio-app/gradio/issues/483
+command = 'pip install -U gradio==2.7.0'
+subprocess.call(command.split())
+
+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()

requirements.txt

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+opencv-python-headless>=4.5.5.62
+torch>=1.10.1
+torchvision>=0.11.2