Update

.pre-commit-config.yaml

@@ -1,35 +1,60 @@
 repos:
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
-  hooks:
-  - id: check-executables-have-shebangs
-  - id: check-json
-  - id: check-merge-conflict
-  - id: check-shebang-scripts-are-executable
-  - id: check-toml
-  - id: check-yaml
-  - id: double-quote-string-fixer
-  - id: end-of-file-fixer
-  - id: mixed-line-ending
-    args: ['--fix=lf']
-  - id: requirements-txt-fixer
-  - id: trailing-whitespace
-- repo: https://github.com/myint/docformatter
-  rev: v1.4
-  hooks:
-  - id: docformatter
-    args: ['--in-place']
-- repo: https://github.com/pycqa/isort
-  rev: 5.12.0
-  hooks:
-    - id: isort
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.991
-  hooks:
-    - id: mypy
-      args: ['--ignore-missing-imports']
-- repo: https://github.com/google/yapf
-  rev: v0.32.0
-  hooks:
-  - id: yapf
-    args: ['--parallel', '--in-place']
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: check-executables-have-shebangs
+      - id: check-json
+      - id: check-merge-conflict
+      - id: check-shebang-scripts-are-executable
+      - id: check-toml
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+      - id: requirements-txt-fixer
+      - id: trailing-whitespace
+  - repo: https://github.com/myint/docformatter
+    rev: v1.7.5
+    hooks:
+      - id: docformatter
+        args: ["--in-place"]
+  - repo: https://github.com/pycqa/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.8.0
+    hooks:
+      - id: mypy
+        args: ["--ignore-missing-imports"]
+        additional_dependencies:
+          [
+            "types-python-slugify",
+            "types-requests",
+            "types-PyYAML",
+            "types-pytz",
+          ]
+  - repo: https://github.com/psf/black
+    rev: 24.2.0
+    hooks:
+      - id: black
+        language_version: python3.10
+        args: ["--line-length", "119"]
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.7.1
+    hooks:
+      - id: nbstripout
+        args:
+          [
+            "--extra-keys",
+            "metadata.interpreter metadata.kernelspec cell.metadata.pycharm",
+          ]
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.7.1
+    hooks:
+      - id: nbqa-black
+      - id: nbqa-pyupgrade
+        args: ["--py37-plus"]
+      - id: nbqa-isort
+        args: ["--float-to-top"]

.vscode/settings.json

@@ -0,0 +1,30 @@
+{
+    "editor.formatOnSave": true,
+    "files.insertFinalNewline": false,
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnType": true,
+        "editor.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        }
+    },
+    "[jupyter]": {
+        "files.insertFinalNewline": false
+    },
+    "black-formatter.args": [
+        "--line-length=119"
+    ],
+    "isort.args": ["--profile", "black"],
+    "flake8.args": [
+        "--max-line-length=119"
+    ],
+    "ruff.lint.args": [
+        "--line-length=119"
+    ],
+    "notebook.output.scrolling": true,
+    "notebook.formatOnCellExecution": true,
+    "notebook.formatOnSave.enabled": true,
+        "notebook.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        }
+}

README.md

@@ -4,7 +4,7 @@ emoji: 📊
 colorFrom: gray
 colorTo: red
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 4.19.2
 app_file: app.py
 pinned: false
 ---

app.py

@@ -15,54 +15,51 @@ import numpy as np
 import PIL.Image
 import torch
 
-sys.path.insert(0, 'yolov5_anime')
+sys.path.insert(0, "yolov5_anime")
 
 from models.yolo import Model
 from utils.datasets import letterbox
 from utils.general import non_max_suppression, scale_coords
 
-DESCRIPTION = '# [zymk9/yolov5_anime](https://github.com/zymk9/yolov5_anime)'
+DESCRIPTION = "# [zymk9/yolov5_anime](https://github.com/zymk9/yolov5_anime)"
 
-MODEL_REPO = 'public-data/yolov5_anime'
+MODEL_REPO = "public-data/yolov5_anime"
 
 
 def load_sample_image_paths() -> list[pathlib.Path]:
-    image_dir = pathlib.Path('images')
+    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')
+        dataset_repo = "hysts/sample-images-TADNE"
+        path = huggingface_hub.hf_hub_download(dataset_repo, "images.tar.gz", repo_type="dataset")
         with tarfile.open(path) as f:
             f.extractall()
-    return sorted(image_dir.glob('*'))
+    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,
-                                                 'yolov5x_anime.pth')
-    config_path = huggingface_hub.hf_hub_download(MODEL_REPO, 'yolov5x.yaml')
+    model_path = huggingface_hub.hf_hub_download(MODEL_REPO, "yolov5x_anime.pth")
+    config_path = huggingface_hub.hf_hub_download(MODEL_REPO, "yolov5x.yaml")
     state_dict = torch.load(model_path)
     model = Model(cfg=config_path)
     model.load_state_dict(state_dict)
     model.to(device)
-    if device.type != 'cpu':
+    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:
+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':
+    if device.type != "cpu":
         data = data.half()
 
     preds = model(data)[0]
@@ -71,12 +68,10 @@ def predict(image: PIL.Image.Image, score_threshold: float,
     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()
+            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))
+    detections = np.concatenate(detections) if detections else np.empty(shape=(0, 6))
 
     res = orig_image.copy()
     for det in detections:
@@ -88,34 +83,35 @@ def predict(image: PIL.Image.Image, score_threshold: float,
 image_paths = load_sample_image_paths()
 examples = [[path.as_posix(), 0.4, 0.5] for path in image_paths]
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 model = load_model(device)
 fn = functools.partial(predict, device=device, model=model)
 
-with gr.Blocks(css='style.css') as demo:
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
         with gr.Column():
-            image = gr.Image(label='Input', type='pil')
-            score_threshold = gr.Slider(label='Score Threshold',
-                                        minimum=0,
-                                        maximum=1,
-                                        step=0.05,
-                                        value=0.4)
-            iou_threshold = gr.Slider(label='IoU Threshold',
-                                      minimum=0,
-                                      maximum=1,
-                                      step=0.05,
-                                      value=0.5)
-            run_button = gr.Button('Run')
+            image = gr.Image(label="Input", type="pil")
+            score_threshold = gr.Slider(label="Score Threshold", minimum=0, maximum=1, step=0.05, value=0.4)
+            iou_threshold = gr.Slider(label="IoU Threshold", minimum=0, maximum=1, step=0.05, value=0.5)
+            run_button = gr.Button("Run")
         with gr.Column():
-            result = gr.Image(label='Output')
+            result = gr.Image(label="Output")
 
     inputs = [image, score_threshold, iou_threshold]
-    gr.Examples(examples=examples,
-                inputs=inputs,
-                outputs=result,
-                fn=fn,
-                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
-    run_button.click(fn=fn, inputs=inputs, outputs=result, api_name='predict')
-demo.queue(max_size=15).launch()
+    gr.Examples(
+        examples=examples,
+        inputs=inputs,
+        outputs=result,
+        fn=fn,
+        cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
+    )
+    run_button.click(
+        fn=fn,
+        inputs=inputs,
+        outputs=result,
+        api_name="predict",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=15).launch()

requirements.txt

@@ -1,4 +1,4 @@
-opencv-python-headless==4.7.0.72
-scipy==1.10.1
+opencv-python-headless==4.9.0.80
+scipy==1.12.0
 torch==2.0.1
 torchvision==0.15.2

style.css

@@ -1,3 +1,11 @@
 h1 {
   text-align: center;
+  display: block;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
 }