ragavsachdeva
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README.md
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---
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language:
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- en
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tags:
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- Manga
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- Object Detection
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- OCR
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- Clustering
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- Diarisation
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---
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<style>
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.title-container {
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display: flex;
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flex-direction: column; /* Stack elements vertically */
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justify-content: center;
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align-items: center;
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}
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.title {
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font-size: 2em;
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text-align: center;
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color: #333;
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font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */
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text-transform: uppercase;
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letter-spacing: 0.1em;
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padding: 0.5em 0 0.2em;
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background: transparent;
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}
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.title span {
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background: -webkit-linear-gradient(45deg, #6495ED, #4169E1); /* Blue gradient */
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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}
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.subheading {
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font-size: 1.5em; /* Adjust the size as needed */
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text-align: center;
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color: #555; /* Adjust the color as needed */
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font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */
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}
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.authors {
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font-size: 1em; /* Adjust the size as needed */
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text-align: center;
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color: #777; /* Adjust the color as needed */
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font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */
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padding-top: 1em;
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}
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.affil {
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font-size: 1em; /* Adjust the size as needed */
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text-align: center;
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color: #777; /* Adjust the color as needed */
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font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */
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}
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</style>
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<div class="title-container">
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<div class="title">
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Ta<span>il</span>s Tell Ta<span>le</span>s
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</div>
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<div class="subheading">
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Chapter-Wide Manga Transcriptions With Character Names
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</div>
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<div class="authors">
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Ragav Sachdeva, Gyungin Shin and Andrew Zisserman
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</div>
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<div class="affil">
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University of Oxford
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</div>
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<div style="display: flex;">
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<a href="https://arxiv.org/abs/2408.00298"><img alt="Static Badge" src="https://img.shields.io/badge/arXiv-2408.00298-blue"></a>
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<img alt="Dynamic JSON Badge" src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2Fragavsachdeva%2Fmagiv2%3Fexpand%255B%255D%3Ddownloads%26expand%255B%255D%3DdownloadsAllTime&query=%24.downloadsAllTime&label=%F0%9F%A4%97%20Downloads">
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</div>
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</div>
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/630852d2f0dc38fb47c347a4/OQW4r_A3aA9RrWpG6Wkve.png)
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# Usage
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```python
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from PIL import Image
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import numpy as np
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from transformers import AutoModel
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import torch
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model = AutoModel.from_pretrained("ragavsachdeva/magiv2", trust_remote_code=True).cuda().eval()
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def read_image(path_to_image):
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with open(path_to_image, "rb") as file:
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image = Image.open(file).convert("L").convert("RGB")
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image = np.array(image)
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return image
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chapter_pages = ["page1.png", "page2.png", "page3.png" ...]
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character_bank = {
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"images": ["char1.png", "char2.png", "char3.png", "char4.png" ...],
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"names": ["Luffy", "Sanji", "Zoro", "Ussop" ...]
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}
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chapter_pages = [read_image(x) for x in chapter_pages]
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character_bank["images"] = [read_image(x) for x in character_bank["images"]]
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with torch.no_grad():
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per_page_results = model.do_chapter_wide_prediction(chapter_pages, character_bank, use_tqdm=True, do_ocr=True)
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transcript = []
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for i, (image, page_result) in enumerate(zip(chapter_pages, per_page_results)):
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model.visualise_single_image_prediction(image, page_result, f"page_{i}.png")
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speaker_name = {
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text_idx: page_result["character_names"][char_idx] for text_idx, char_idx in page_result["text_character_associations"]
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}
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for j in range(len(page_result["ocr"])):
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if not page_result["is_essential_text"][j]:
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continue
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name = speaker_name.get(j, "unsure")
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transcript.append(f"<{name}>: {page_result['ocr'][j]}")
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with open(f"transcript.txt", "w") as fh:
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for line in transcript:
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fh.write(line + "\n")
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```
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# License and Citation
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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.
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My contact information can be found on my website: ragavsachdeva [dot] github [dot] io
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```
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@misc{magiv2,
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title={Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names},
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author={Ragav Sachdeva and Gyungin Shin and Andrew Zisserman},
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year={2024},
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eprint={2408.00298},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2408.00298},
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}
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```
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