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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()
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