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"]

.style.yapf

@@ -1,5 +0,0 @@
-[style]
-based_on_style = pep8
-blank_line_before_nested_class_or_def = false
-spaces_before_comment = 2
-split_before_logical_operator = true

.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: yellow
 colorTo: indigo
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 4.19.2
 app_file: app.py
 pinned: false
 ---

app.py

@@ -18,31 +18,29 @@ import PIL.Image
 import torch
 import torchvision.transforms as T
 
-sys.path.insert(0, 'anime_face_landmark_detection')
+sys.path.insert(0, "anime_face_landmark_detection")
 
 from CFA import CFA
 
-DESCRIPTION = '# [kanosawa/anime_face_landmark_detection](https://github.com/kanosawa/anime_face_landmark_detection)'
+DESCRIPTION = "# [kanosawa/anime_face_landmark_detection](https://github.com/kanosawa/anime_face_landmark_detection)"
 
 NUM_LANDMARK = 24
 CROP_SIZE = 128
 
 
 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_face_detector() -> cv2.CascadeClassifier:
-    url = 'https://raw.githubusercontent.com/nagadomi/lbpcascade_animeface/master/lbpcascade_animeface.xml'
-    path = pathlib.Path('lbpcascade_animeface.xml')
+    url = "https://raw.githubusercontent.com/nagadomi/lbpcascade_animeface/master/lbpcascade_animeface.xml"
+    path = pathlib.Path("lbpcascade_animeface.xml")
     if not path.exists():
         urllib.request.urlretrieve(url, path.as_posix())
     return cv2.CascadeClassifier(path.as_posix())
@@ -50,8 +48,8 @@ def load_face_detector() -> cv2.CascadeClassifier:
 
 def load_landmark_detector(device: torch.device) -> torch.nn.Module:
     path = huggingface_hub.hf_hub_download(
-        'public-data/anime_face_landmark_detection',
-        'checkpoint_landmark_191116.pth')
+        "public-data/anime_face_landmark_detection", "checkpoint_landmark_191116.pth"
+    )
     model = CFA(output_channel_num=NUM_LANDMARK + 1, checkpoint_name=path)
     model.to(device)
     model.eval()
@@ -59,15 +57,16 @@ def load_landmark_detector(device: torch.device) -> torch.nn.Module:
 
 
 @torch.inference_mode()
-def detect(image_path: str, face_detector: cv2.CascadeClassifier,
-           device: torch.device, transform: Callable,
-           landmark_detector: torch.nn.Module) -> np.ndarray:
+def detect(
+    image_path: str,
+    face_detector: cv2.CascadeClassifier,
+    device: torch.device,
+    transform: Callable,
+    landmark_detector: torch.nn.Module,
+) -> np.ndarray:
     image = cv2.imread(image_path)
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    preds = face_detector.detectMultiScale(gray,
-                                           scaleFactor=1.1,
-                                           minNeighbors=5,
-                                           minSize=(24, 24))
+    preds = face_detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(24, 24))
 
     image_h, image_w = image.shape[:2]
     pil_image = PIL.Image.fromarray(image[:, :, ::-1].copy())
@@ -93,48 +92,59 @@ def detect(image_path: str, face_detector: cv2.CascadeClassifier,
         cv2.rectangle(res, (x0, y0), (x1, y1), (0, 255, 0), 2)
 
         for i in range(NUM_LANDMARK):
-            heatmap = cv2.resize(heatmaps[i], (CROP_SIZE, CROP_SIZE),
-                                 interpolation=cv2.INTER_CUBIC)
+            heatmap = cv2.resize(heatmaps[i], (CROP_SIZE, CROP_SIZE), interpolation=cv2.INTER_CUBIC)
             pty, ptx = np.unravel_index(np.argmax(heatmap), heatmap.shape)
-            pt_crop = np.round(np.array([ptx * w, pty * h]) /
-                               CROP_SIZE).astype(int)
+            pt_crop = np.round(np.array([ptx * w, pty * h]) / CROP_SIZE).astype(int)
             pt = np.array([x0, y0]) + pt_crop
             cv2.circle(res, tuple(pt), 2, (0, 0, 255), cv2.FILLED)
 
     return res[:, :, ::-1]
 
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 image_paths = load_sample_image_paths()
 examples = [[path.as_posix()] for path in image_paths]
 
 face_detector = load_face_detector()
 landmark_detector = load_landmark_detector(device)
-transform = T.Compose([
-    T.ToTensor(),
-    T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
-])
-
-fn = functools.partial(detect,
-                       face_detector=face_detector,
-                       device=device,
-                       transform=transform,
-                       landmark_detector=landmark_detector)
-
-with gr.Blocks(css='style.css') as demo:
+transform = T.Compose(
+    [
+        T.ToTensor(),
+        T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    ]
+)
+
+fn = functools.partial(
+    detect,
+    face_detector=face_detector,
+    device=device,
+    transform=transform,
+    landmark_detector=landmark_detector,
+)
+
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
         with gr.Column():
-            image = gr.Image(label='Input', type='filepath')
-            run_button = gr.Button('Run')
+            image = gr.Image(label="Input", type="filepath")
+            run_button = gr.Button("Run")
         with gr.Column():
-            result = gr.Image(label='Result')
-
-    gr.Examples(examples=examples,
-                inputs=image,
-                outputs=result,
-                fn=fn,
-                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
-    run_button.click(fn=fn, inputs=image, outputs=result, api_name='predict')
-demo.queue(max_size=15).launch()
+            result = gr.Image(label="Result")
+
+    gr.Examples(
+        examples=examples,
+        inputs=image,
+        outputs=result,
+        fn=fn,
+        cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
+    )
+    run_button.click(
+        fn=fn,
+        inputs=image,
+        outputs=result,
+        api_name="predict",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=15).launch()

requirements.txt

@@ -1,3 +1,3 @@
-opencv-python-headless>=4.7.0.72
+opencv-python-headless>=4.9.0.80
 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;
 }