Update

.pre-commit-config.yaml

@@ -0,0 +1,35 @@
+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']

.style.yapf

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

README.md

@@ -4,35 +4,7 @@ emoji: 🐢
 colorFrom: yellow
 colorTo: indigo
 sdk: gradio
-sdk_version: 3.0.5
+sdk_version: 3.19.1
 app_file: app.py
 pinned: false
 ---
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

app.py

@@ -2,13 +2,12 @@
 
 from __future__ import annotations
 
-import argparse
 import functools
 import os
 import pathlib
 import sys
 import tarfile
-import urllib
+import urllib.request
 from typing import Callable
 
 import cv2
@@ -25,9 +24,8 @@ from CFA import CFA
 
 TITLE = 'kanosawa/anime_face_landmark_detection'
 DESCRIPTION = 'This is an unofficial demo for https://github.com/kanosawa/anime_face_landmark_detection.'
-ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.anime_face_landmark_detection" alt="visitor badge"/></center>'
 
-TOKEN = os.environ['TOKEN']
+HF_TOKEN = os.getenv('HF_TOKEN')
 MODEL_REPO = 'hysts/anime_face_landmark_detection'
 MODEL_FILENAME = 'checkpoint_landmark_191116.pth'
 
@@ -35,20 +33,6 @@ NUM_LANDMARK = 24
 CROP_SIZE = 128
 
 
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    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')
-    return parser.parse_args()
-
-
 def load_sample_image_paths() -> list[pathlib.Path]:
     image_dir = pathlib.Path('images')
     if not image_dir.exists():
@@ -56,7 +40,7 @@ def load_sample_image_paths() -> list[pathlib.Path]:
         path = huggingface_hub.hf_hub_download(dataset_repo,
                                                'images.tar.gz',
                                                repo_type='dataset',
-                                               use_auth_token=TOKEN)
+                                               use_auth_token=HF_TOKEN)
         with tarfile.open(path) as f:
             f.extractall()
     return sorted(image_dir.glob('*'))
@@ -73,7 +57,7 @@ def load_face_detector() -> cv2.CascadeClassifier:
 def load_landmark_detector(device: torch.device) -> torch.nn.Module:
     path = huggingface_hub.hf_hub_download(MODEL_REPO,
                                            MODEL_FILENAME,
-                                           use_auth_token=TOKEN)
+                                           use_auth_token=HF_TOKEN)
     model = CFA(output_channel_num=NUM_LANDMARK + 1, checkpoint_name=path)
     model.to(device)
     model.eval()
@@ -81,10 +65,10 @@ def load_landmark_detector(device: torch.device) -> torch.nn.Module:
 
 
 @torch.inference_mode()
-def detect(image, face_detector: cv2.CascadeClassifier, device: torch.device,
-           transform: Callable,
+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.name)
+    image = cv2.imread(image_path)
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     preds = face_detector.detectMultiScale(gray,
                                            scaleFactor=1.1,
@@ -126,44 +110,29 @@ def detect(image, face_detector: cv2.CascadeClassifier, device: torch.device,
     return res[:, :, ::-1]
 
 
-def main():
-    args = parse_args()
-    device = torch.device(args.device)
-
-    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]),
-    ])
-
-    func = functools.partial(detect,
-                             face_detector=face_detector,
-                             device=device,
-                             transform=transform,
-                             landmark_detector=landmark_detector)
-    func = functools.update_wrapper(func, detect)
-
-    gr.Interface(
-        func,
-        gr.inputs.Image(type='file', label='Input'),
-        gr.outputs.Image(type='numpy', label='Output'),
-        examples=examples,
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        theme=args.theme,
-        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()
+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]),
+])
+
+func = functools.partial(detect,
+                         face_detector=face_detector,
+                         device=device,
+                         transform=transform,
+                         landmark_detector=landmark_detector)
+
+gr.Interface(
+    fn=func,
+    inputs=gr.Image(label='Input', type='filepath'),
+    outputs=gr.Image(label='Output', type='numpy'),
+    examples=examples,
+    title=TITLE,
+    description=DESCRIPTION,
+).queue().launch(show_api=False)