Update
Browse files- .pre-commit-config.yaml +35 -0
- .style.yapf +5 -0
- README.md +1 -29
- app.py +33 -64
.pre-commit-config.yaml
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.2.0
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hooks:
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- id: check-executables-have-shebangs
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- id: check-json
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- id: check-merge-conflict
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- id: check-shebang-scripts-are-executable
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- id: check-toml
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- id: check-yaml
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- id: double-quote-string-fixer
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- id: end-of-file-fixer
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- id: mixed-line-ending
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args: ['--fix=lf']
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- id: requirements-txt-fixer
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- id: trailing-whitespace
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- repo: https://github.com/myint/docformatter
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rev: v1.4
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hooks:
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- id: docformatter
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args: ['--in-place']
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- repo: https://github.com/pycqa/isort
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rev: 5.12.0
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hooks:
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- id: isort
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v0.991
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hooks:
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- id: mypy
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args: ['--ignore-missing-imports']
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- repo: https://github.com/google/yapf
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rev: v0.32.0
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hooks:
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- id: yapf
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args: ['--parallel', '--in-place']
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.style.yapf
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[style]
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based_on_style = pep8
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blank_line_before_nested_class_or_def = false
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spaces_before_comment = 2
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split_before_logical_operator = true
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README.md
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@@ -4,35 +4,7 @@ emoji: 🐢
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colorFrom: yellow
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.
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app_file: app.py
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pinned: false
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---
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# Configuration
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`title`: _string_
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Display title for the Space
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`emoji`: _string_
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Space emoji (emoji-only character allowed)
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`colorFrom`: _string_
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Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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`colorTo`: _string_
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Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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`sdk`: _string_
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Can be either `gradio`, `streamlit`, or `static`
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`sdk_version` : _string_
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Only applicable for `streamlit` SDK.
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See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
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`app_file`: _string_
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Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
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Path is relative to the root of the repository.
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`pinned`: _boolean_
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Whether the Space stays on top of your list.
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colorFrom: yellow
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.19.1
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app_file: app.py
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pinned: false
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---
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app.py
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from __future__ import annotations
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import argparse
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import functools
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import os
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import pathlib
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import sys
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import tarfile
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import urllib
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from typing import Callable
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import cv2
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TITLE = 'kanosawa/anime_face_landmark_detection'
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DESCRIPTION = 'This is an unofficial demo for https://github.com/kanosawa/anime_face_landmark_detection.'
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ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.anime_face_landmark_detection" alt="visitor badge"/></center>'
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MODEL_REPO = 'hysts/anime_face_landmark_detection'
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MODEL_FILENAME = 'checkpoint_landmark_191116.pth'
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CROP_SIZE = 128
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser()
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parser.add_argument('--device', type=str, default='cpu')
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parser.add_argument('--theme', type=str)
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parser.add_argument('--live', action='store_true')
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parser.add_argument('--share', action='store_true')
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parser.add_argument('--port', type=int)
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parser.add_argument('--disable-queue',
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dest='enable_queue',
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action='store_false')
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parser.add_argument('--allow-flagging', type=str, default='never')
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return parser.parse_args()
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def load_sample_image_paths() -> list[pathlib.Path]:
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image_dir = pathlib.Path('images')
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if not image_dir.exists():
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path = huggingface_hub.hf_hub_download(dataset_repo,
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'images.tar.gz',
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repo_type='dataset',
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use_auth_token=
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with tarfile.open(path) as f:
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f.extractall()
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return sorted(image_dir.glob('*'))
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def load_landmark_detector(device: torch.device) -> torch.nn.Module:
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path = huggingface_hub.hf_hub_download(MODEL_REPO,
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MODEL_FILENAME,
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use_auth_token=
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model = CFA(output_channel_num=NUM_LANDMARK + 1, checkpoint_name=path)
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model.to(device)
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model.eval()
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@torch.inference_mode()
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def detect(
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transform: Callable,
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landmark_detector: torch.nn.Module) -> np.ndarray:
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image = cv2.imread(
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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preds = face_detector.detectMultiScale(gray,
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scaleFactor=1.1,
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return res[:, :, ::-1]
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gr.
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title=TITLE,
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description=DESCRIPTION,
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article=ARTICLE,
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theme=args.theme,
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allow_flagging=args.allow_flagging,
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live=args.live,
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).launch(
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enable_queue=args.enable_queue,
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server_port=args.port,
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share=args.share,
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)
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if __name__ == '__main__':
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main()
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from __future__ import annotations
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import functools
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import os
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import pathlib
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import sys
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import tarfile
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import urllib.request
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from typing import Callable
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import cv2
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TITLE = 'kanosawa/anime_face_landmark_detection'
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DESCRIPTION = 'This is an unofficial demo for https://github.com/kanosawa/anime_face_landmark_detection.'
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HF_TOKEN = os.getenv('HF_TOKEN')
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MODEL_REPO = 'hysts/anime_face_landmark_detection'
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MODEL_FILENAME = 'checkpoint_landmark_191116.pth'
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CROP_SIZE = 128
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def load_sample_image_paths() -> list[pathlib.Path]:
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image_dir = pathlib.Path('images')
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if not image_dir.exists():
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path = huggingface_hub.hf_hub_download(dataset_repo,
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'images.tar.gz',
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repo_type='dataset',
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use_auth_token=HF_TOKEN)
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with tarfile.open(path) as f:
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f.extractall()
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return sorted(image_dir.glob('*'))
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def load_landmark_detector(device: torch.device) -> torch.nn.Module:
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path = huggingface_hub.hf_hub_download(MODEL_REPO,
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MODEL_FILENAME,
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use_auth_token=HF_TOKEN)
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model = CFA(output_channel_num=NUM_LANDMARK + 1, checkpoint_name=path)
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model.to(device)
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model.eval()
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@torch.inference_mode()
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def detect(image_path: str, face_detector: cv2.CascadeClassifier,
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device: torch.device, transform: Callable,
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landmark_detector: torch.nn.Module) -> np.ndarray:
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image = cv2.imread(image_path)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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preds = face_detector.detectMultiScale(gray,
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scaleFactor=1.1,
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return res[:, :, ::-1]
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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image_paths = load_sample_image_paths()
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examples = [[path.as_posix()] for path in image_paths]
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face_detector = load_face_detector()
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landmark_detector = load_landmark_detector(device)
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transform = T.Compose([
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T.ToTensor(),
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T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
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])
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func = functools.partial(detect,
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face_detector=face_detector,
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device=device,
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transform=transform,
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landmark_detector=landmark_detector)
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gr.Interface(
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fn=func,
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inputs=gr.Image(label='Input', type='filepath'),
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outputs=gr.Image(label='Output', type='numpy'),
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examples=examples,
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title=TITLE,
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description=DESCRIPTION,
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).queue().launch(show_api=False)
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