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app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import functools
+import os
+import pathlib
+import sys
+import tarfile
+import urllib.request
+from typing import Callable
+
+import cv2
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import PIL.Image
+import torch
+import torchvision.transforms as T
+
+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)'
+
+NUM_LANDMARK = 24
+CROP_SIZE = 128
+
+
+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')
+        with tarfile.open(path) as f:
+            f.extractall()
+    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')
+    if not path.exists():
+        urllib.request.urlretrieve(url, path.as_posix())
+    return cv2.CascadeClassifier(path.as_posix())
+
+
+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')
+    model = CFA(output_channel_num=NUM_LANDMARK + 1, checkpoint_name=path)
+    model.to(device)
+    model.eval()
+    return model
+
+
+@torch.inference_mode()
+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))
+
+    image_h, image_w = image.shape[:2]
+    pil_image = PIL.Image.fromarray(image[:, :, ::-1].copy())
+
+    res = image.copy()
+    for x_orig, y_orig, w_orig, h_orig in preds:
+
+        x0 = round(max(x_orig - w_orig / 8, 0))
+        x1 = round(min(x_orig + w_orig * 9 / 8, image_w))
+        y0 = round(max(y_orig - h_orig / 4, 0))
+        y1 = y_orig + h_orig
+        w = x1 - x0
+        h = y1 - y0
+
+        temp = pil_image.crop((x0, y0, x1, y1))
+        temp = temp.resize((CROP_SIZE, CROP_SIZE), PIL.Image.BICUBIC)
+        data = transform(temp)
+        data = data.to(device).unsqueeze(0)
+
+        heatmaps = landmark_detector(data)
+        heatmaps = heatmaps[-1].cpu().numpy()[0]
+
+        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)
+            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 = 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')
+
+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:
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        with gr.Column():
+            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()

gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

gitmodules (1)

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+[submodule "anime_face_landmark_detection"]
+	path = anime_face_landmark_detection
+	url = https://github.com/kanosawa/anime_face_landmark_detection

pre-commit-config (1).yaml

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+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']

requirements.txt

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+opencv-python-headless>=4.7.0.72
+torch==2.0.1
+torchvision==0.15.2

style (1).yapf

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+[style]
+based_on_style = pep8
+blank_line_before_nested_class_or_def = false
+spaces_before_comment = 2
+split_before_logical_operator = true

style.css

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+h1 {
+  text-align: center;
+}