Add files

.gitmodules

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

anime_face_landmark_detection

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+Subproject commit 95231f3884fc531273c731ce4d8f583b61e5530d

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import pathlib
+import sys
+import tarfile
+import urllib
+from typing import Callable
+
+sys.path.insert(0, 'anime_face_landmark_detection')
+
+import cv2
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import PIL.Image
+import torch
+import torchvision.transforms as T
+from CFA import CFA
+
+TOKEN = os.environ['TOKEN']
+
+MODEL_REPO = 'hysts/anime_face_landmark_detection'
+MODEL_FILENAME = 'checkpoint_landmark_191116.pth'
+
+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')
+    parser.add_argument('--allow-screenshot', action='store_true')
+    return parser.parse_args()
+
+
+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',
+                                               use_auth_token=TOKEN)
+        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(MODEL_REPO,
+                                           MODEL_FILENAME,
+                                           use_auth_token=TOKEN)
+    model = CFA(output_channel_num=NUM_LANDMARK + 1, checkpoint_name=path)
+    model.to(device)
+    model.eval()
+    return model
+
+
+@torch.inference_mode()
+def detect(image, face_detector: cv2.CascadeClassifier, device: torch.device,
+           transform: Callable,
+           landmark_detector: torch.nn.Module) -> np.ndarray:
+    image = cv2.imread(image.name)
+    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)
+
+    res = cv2.cvtColor(res, cv2.COLOR_BGR2RGB)
+    return res
+
+
+def main():
+    gr.close_all()
+
+    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)
+
+    repo_url = 'https://github.com/kanosawa/anime_face_landmark_detection'
+    title = 'kanosawa/anime_face_landmark_detection'
+    description = f'A demo for {repo_url}'
+    article = None
+
+    gr.Interface(
+        func,
+        gr.inputs.Image(type='file', label='Input'),
+        gr.outputs.Image(label='Output'),
+        theme=args.theme,
+        title=title,
+        description=description,
+        article=article,
+        examples=examples,
+        allow_screenshot=args.allow_screenshot,
+        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()

requirements.txt

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+opencv-python-headless>=4.5.5.62
+torch>=1.10.1
+torchvision>=0.11.2