Add files

.gitmodules

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+[submodule "yolov5_anime"]
+	path = yolov5_anime
+	url = https://github.com/zymk9/yolov5_anime

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import pathlib
+import sys
+import tarfile
+
+sys.path.insert(0, 'yolov5_anime')
+
+import cv2
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import PIL.Image
+import torch
+from models.yolo import Model
+from utils.datasets import letterbox
+from utils.general import non_max_suppression, scale_coords
+
+TOKEN = os.environ['TOKEN']
+
+MODEL_REPO = 'hysts/yolov5_anime'
+MODEL_FILENAME = 'yolov5x_anime.pth'
+CONFIG_FILENAME = 'yolov5x.yaml'
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cpu')
+    parser.add_argument('--score-slider-step', type=float, default=0.05)
+    parser.add_argument('--score-threshold', type=float, default=0.4)
+    parser.add_argument('--iou-slider-step', type=float, default=0.05)
+    parser.add_argument('--iou-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) -> torch.nn.Module:
+    torch.set_grad_enabled(False)
+    model_path = huggingface_hub.hf_hub_download(MODEL_REPO,
+                                                 MODEL_FILENAME,
+                                                 use_auth_token=TOKEN)
+    config_path = huggingface_hub.hf_hub_download(MODEL_REPO,
+                                                  CONFIG_FILENAME,
+                                                  use_auth_token=TOKEN)
+    state_dict = torch.load(model_path)
+    model = Model(cfg=config_path)
+    model.load_state_dict(state_dict)
+    model.to(device)
+    if device.type != 'cpu':
+        model.half()
+    model.eval()
+    return model
+
+
+@torch.inference_mode()
+def predict(image: PIL.Image.Image, score_threshold: float,
+            iou_threshold: float, device: torch.device,
+            model: torch.nn.Module) -> np.ndarray:
+    orig_image = np.asarray(image)
+
+    image = letterbox(orig_image, new_shape=640)[0]
+    data = torch.from_numpy(image.transpose(2, 0, 1)).float() / 255
+    data = data.to(device).unsqueeze(0)
+    if device.type != 'cpu':
+        data = data.half()
+
+    preds = model(data)[0]
+    preds = non_max_suppression(preds, score_threshold, iou_threshold)
+
+    detections = []
+    for pred in preds:
+        if pred is not None and len(pred) > 0:
+            pred[:, :4] = scale_coords(data.shape[2:], pred[:, :4],
+                                       orig_image.shape).round()
+            # (x0, y0, x1, y0, conf, class)
+            detections.append(pred.cpu().numpy())
+    detections = np.concatenate(detections) if detections else np.empty(
+        shape=(0, 6))
+
+    res = orig_image.copy()
+    for det in detections:
+        x0, y0, x1, y1 = det[:4].astype(int)
+        cv2.rectangle(res, (x0, y0), (x1, y1), (0, 255, 0), 3)
+    return res
+
+
+def main():
+    gr.close_all()
+
+    args = parse_args()
+    device = torch.device(args.device)
+
+    image_paths = load_sample_image_paths()
+    examples = [[path.as_posix(), args.score_threshold, args.iou_threshold]
+                for path in image_paths]
+
+    model = load_model(device)
+
+    func = functools.partial(predict, device=device, model=model)
+    func = functools.update_wrapper(func, predict)
+
+    repo_url = 'https://github.com/zymk9/yolov5_anime'
+    title = 'zymk9/yolov5_anime'
+    description = f'A demo for {repo_url}'
+    article = None
+
+    gr.Interface(
+        func,
+        [
+            gr.inputs.Image(type='pil', label='Input'),
+            gr.inputs.Slider(0,
+                             1,
+                             step=args.score_slider_step,
+                             default=args.score_threshold,
+                             label='Score Threshold'),
+            gr.inputs.Slider(0,
+                             1,
+                             step=args.iou_slider_step,
+                             default=args.iou_threshold,
+                             label='IoU Threshold'),
+        ],
+        gr.outputs.Image(label='Output'),
+        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
+scipy>=1.7.3
+torch>=1.10.1
+torchvision>=0.11.2

yolov5_anime

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+Subproject commit 8b50add22dbd8224904221be3173390f56046794