ragavsachdeva/magi

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<div class="title-container"> <div class="title"> The <span>Ma</span>n<span>g</span>a Wh<span>i</span>sperer </div> <div class="subheading"> Automatically Generating Transcriptions for Comics </div> <div class="authors"> Ragav Sachdeva and Andrew Zisserman </div> <div class="affil"> University of Oxford </div> <div style="display: flex;"> <a href="https://arxiv.org/abs/2401.10224"><img alt="Static Badge" src="https://img.shields.io/badge/arXiv-2401.10224-blue"></a> &emsp; <img alt="Dynamic JSON Badge" src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2Fragavsachdeva%2Fmagi%3Fexpand%255B%255D%3Ddownloads%26expand%255B%255D%3DdownloadsAllTime&query=%24.downloadsAllTime&label=%F0%9F%A4%97%20Downloads"> </div> </div>

Usage

from transformers import AutoModel
import numpy as np
from PIL import Image
import torch
import os

images = [
        "path_to_image1.jpg",
        "path_to_image2.png",
    ]

def read_image_as_np_array(image_path):
    with open(image_path, "rb") as file:
        image = Image.open(file).convert("L").convert("RGB")
        image = np.array(image)
    return image

images = [read_image_as_np_array(image) for image in images]

model = AutoModel.from_pretrained("ragavsachdeva/magi", trust_remote_code=True).cuda()
with torch.no_grad():
    results = model.predict_detections_and_associations(images)
    text_bboxes_for_all_images = [x["texts"] for x in results]
    ocr_results = model.predict_ocr(images, text_bboxes_for_all_images)

for i in range(len(images)):
    model.visualise_single_image_prediction(images[i], results[i], filename=f"image_{i}.png")
    model.generate_transcript_for_single_image(results[i], ocr_results[i], filename=f"transcript_{i}.txt")

License and Citation

The provided model and datasets are available for unrestricted use in personal, research, non-commercial, and not-for-profit endeavors. For any other usage scenarios, kindly contact me via email, providing a detailed description of your requirements, to establish a tailored licensing arrangement. My contact information can be found on my website: ragavsachdeva [dot] github [dot] io

@misc{sachdeva2024manga,
      title={The Manga Whisperer: Automatically Generating Transcriptions for Comics}, 
      author={Ragav Sachdeva and Andrew Zisserman},
      year={2024},
      eprint={2401.10224},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}