demo.py

import argparse
import os
import gradio as gr
import pickle
from PIL import Image
from torchvision import transforms
from detector.model import *
from detector import config
from font_dataset.font import load_fonts
from huggingface_hub import hf_hub_download

parser = argparse.ArgumentParser()
parser.add_argument(
    "-d",
    "--device",
    type=int,
    default=0,
    help="GPU devices to use (default: 0), -1 for CPU",
)
parser.add_argument(
    "-c",
    "--checkpoint",
    type=str,
    default=None,
    help="Trainer checkpoint path (default: None). Use link as huggingface://<user>/<repo>/<file> for huggingface.co models, currently only supports model file in the root directory.",
)
parser.add_argument(
    "-m",
    "--model",
    type=str,
    default="resnet18",
    choices=["resnet18", "resnet34", "resnet50", "resnet101", "deepfont"],
    help="Model to use (default: resnet18)",
)
parser.add_argument(
    "-f",
    "--font-classification-only",
    action="store_true",
    help="Font classification only (default: False)",
)
parser.add_argument(
    "-z",
    "--size",
    type=int,
    default=512,
    help="Model feature image input size (default: 512)",
)
parser.add_argument(
    "-s",
    "--share",
    action="store_true",
    help="Get public link via Gradio (default: False)",
)
parser.add_argument(
    "-p",
    "--port",
    type=int,
    default=7860,
    help="Port to use for Gradio (default: 7860)",
)
parser.add_argument(
    "-a",
    "--address",
    type=str,
    default="127.0.0.1",
    help="Address to use for Gradio (default: 127.0.0.1)",
)

args = parser.parse_args()

config.INPUT_SIZE = args.size
device = torch.device("cpu") if args.device == -1 else torch.device("cuda", args.device)

regression_use_tanh = False

if args.model == "resnet18":
    model = ResNet18Regressor(regression_use_tanh=regression_use_tanh)
elif args.model == "resnet34":
    model = ResNet34Regressor(regression_use_tanh=regression_use_tanh)
elif args.model == "resnet50":
    model = ResNet50Regressor(regression_use_tanh=regression_use_tanh)
elif args.model == "resnet101":
    model = ResNet101Regressor(regression_use_tanh=regression_use_tanh)
elif args.model == "deepfont":
    assert args.pretrained is False
    assert args.size == 105
    assert args.font_classification_only is True
    model = DeepFontBaseline()
else:
    raise NotImplementedError()

if torch.__version__ >= "2.0" and os.name == "posix":
    model = torch.compile(model)
    torch._dynamo.config.suppress_errors = True


if str(args.checkpoint).startswith("huggingface://"):
    args.checkpoint = args.checkpoint[14:]
    user, repo, file = args.checkpoint.split("/")
    repo = f"{user}/{repo}"
    args.checkpoint = hf_hub_download(repo, file)

detector = FontDetector(
    model=model,
    lambda_font=1,
    lambda_direction=1,
    lambda_regression=1,
    font_classification_only=args.font_classification_only,
    lr=1,
    betas=(1, 1),
    num_warmup_iters=1,
    num_iters=1e9,
    num_epochs=1e9,
)
detector.load_from_checkpoint(
    args.checkpoint,
    map_location=device,
    model=model,
    lambda_font=1,
    lambda_direction=1,
    lambda_regression=1,
    font_classification_only=args.font_classification_only,
    lr=1,
    betas=(1, 1),
    num_warmup_iters=1,
    num_iters=1e9,
    num_epochs=1e9,
)
detector = detector.to(device)
detector.eval()


transform = transforms.Compose(
    [
        transforms.Resize((512, 512)),
        transforms.ToTensor(),
    ]
)


def prepare_fonts(cache_path="font_demo_cache.bin"):
    print("Preparing fonts ...")
    if os.path.exists(cache_path):
        return pickle.load(open(cache_path, "rb"))

    font_list, exclusion_rule = load_fonts()

    font_list = list(filter(lambda x: not exclusion_rule(x), font_list))
    font_list.sort(key=lambda x: x.path)

    for i in range(len(font_list)):
        font_list[i].path = font_list[i].path[18:]  # remove ./dataset/fonts/./ prefix

    with open(cache_path, "wb") as f:
        pickle.dump(font_list, f)
    return font_list


font_list = prepare_fonts()

font_demo_images = []

for i in range(len(font_list)):
    font_demo_images.append(Image.open(f"demo_fonts/{i}.jpg").convert("RGB"))


def recognize_font(image):
    transformed_image = transform(image)
    with torch.no_grad():
        transformed_image = transformed_image.to(device)
        output = detector(transformed_image.unsqueeze(0))
        prob = output[0][: config.FONT_COUNT].softmax(dim=0)

        indicies = torch.topk(prob, 9)[1]

        return [
            {font_list[i].path: float(prob[i]) for i in range(config.FONT_COUNT)},
            *[gr.Image.update(value=font_demo_images[indicies[i]]) for i in range(9)],
            *[
                gr.Markdown.update(
                    value=f"**Font Name**: {font_list[indicies[i]].path}"
                )
                for i in range(9)
            ],
        ]


def generate_grid(num_columns, num_rows):
    ret_images, ret_labels = [], []
    with gr.Column():
        for _ in range(num_rows):
            with gr.Row():
                for _ in range(num_columns):
                    with gr.Column():
                        ret_labels.append(gr.Markdown("**Font Name**"))
                        ret_images.append(gr.Image())
    return ret_images, ret_labels


# fmt: off
intro = \
"""
<div align="center">
<h1>✨ Font Recognition 字体检测 ✨</h1>
</div>

Project page 项目地址: [https://github.com/JeffersonQin/YuzuMarker.FontDetection](https://github.com/JeffersonQin/YuzuMarker.FontDetection)

Upload an image to detect the font used in the image. Please make sure the text occupies most of the image area to achieve higher recognition accuracy.

上传图片以检测字体，尽量使得图片中的文字占据图片的大部分区域以获得更高的识别准确率。

Click "Run" to start the demo after uploading an image.

上传完成之后点击“Run”开始识别。
"""
# fmt: on

with gr.Blocks() as demo:
    with gr.Column():
        intro = gr.Markdown(intro)
        with gr.Row():
            inp = gr.Image(type="pil", label="Input Image")
            out = gr.Label(num_top_classes=9, label="Output Font")
        font_demo_images_blocks, font_demo_labels_blocks = generate_grid(3, 3)

    submit_button = gr.Button(label="Submit")
    submit_button.click(
        fn=recognize_font,
        inputs=inp,
        outputs=[out, *font_demo_images_blocks, *font_demo_labels_blocks],
    )


demo.launch(share=args.share, server_port=args.port, server_name=args.address)