Magi，漫画解析大师

待办事项：

上传Magiv2模型，
提供新的注释，
开源PopCharacters数据集，
发布评估脚本。

Magiv1

模型可在🤗 HuggingFace模型仓库获取。
您可以通过这个🤗 HuggingFace Spaces演示亲自尝试（无GPU，速度较慢）。
以下提供基本的模型使用方法。更多信息请查看此文件。

Magi_预览图

v1 使用方法

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

images = [
        "图片1路径.jpg",
        "图片2路径.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"图片_{i}.png")
    model.generate_transcript_for_single_image(results[i], ocr_results[i], filename=f"文字记录_{i}.txt")

Magiv2

模型可在🤗 HuggingFace模型仓库获取。
您可以通过这个🤗 HuggingFace Spaces演示亲自尝试（有GPU支持，感谢HF团队！）。
以下提供基本的模型使用方法。更多信息请查看此文件。

magiv2

v2 使用方法

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

model = AutoModel.from_pretrained("ragavsachdeva/magiv2", trust_remote_code=True).cuda().eval()


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

chapter_pages = ["页面1.png", "页面2.png", "页面3.png" ...]
character_bank = {
    "images": ["角色1.png", "角色2.png", "角色3.png", "角色4.png" ...],
    "names": ["路飞", "山治", "索隆", "乌索普" ...]
}

chapter_pages = [read_image(x) for x in chapter_pages]
character_bank["images"] = [read_image(x) for x in character_bank["images"]]

with torch.no_grad():
    per_page_results = model.do_chapter_wide_prediction(chapter_pages, character_bank, use_tqdm=True, do_ocr=True)

transcript = []
for i, (image, page_result) in enumerate(zip(chapter_pages, per_page_results)):
    model.visualise_single_image_prediction(image, page_result, f"页面_{i}.png")
    speaker_name = {
        text_idx: page_result["character_names"][char_idx] for text_idx, char_idx in page_result["text_character_associations"]
    }
    for j in range(len(page_result["ocr"])):
        if not page_result["is_essential_text"][j]:
            continue
        name = speaker_name.get(j, "未知") 
        transcript.append(f"<{name}>: {page_result['ocr'][j]}")
with open(f"文字记录.txt", "w") as fh:
    for line in transcript:
        fh.write(line + "\n")

许可和引用

提供的模型和数据集可在个人、研究、非商业和非营利性项目中无限制使用。对于任何其他使用场景，请通过电子邮件联系我，详细描述您的需求，以建立定制的许可安排。我的联系信息可在我的网站上找到。

@InProceedings{magiv1,
    author    = {Sachdeva, Ragav and Zisserman, Andrew},
    title     = {The Manga Whisperer: Automatically Generating Transcriptions for Comics},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {12967-12976}
}

@misc{magiv2,
      title={Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names}, 
      author={Ragav Sachdeva and Gyungin Shin and Andrew Zisserman},
      year={2024},
      eprint={2408.00298},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.00298}, 
}