README.md

---
license: apache-2.0
---

# DeepPanel: Comic Panel Extractor (Keras Model)

**Model Card** • [Hugging Face Repo](https://huggingface.co/codeShare/comic-panel-extract) • [Download `.keras` file](https://huggingface.co/codeShare/comic-panel-extract/blob/main/deeppanel_model.keras)

---

## 📖 Overview

**`deeppanel_model.keras`** is a fully trained **TensorFlow/Keras** model for **automatic comic panel extraction**.

It takes a full comic book page (or any illustrated page) as input and outputs a **binary mask** that highlights every individual panel. The post-processing code then uses OpenCV to crop each detected panel into its own clean image.

This model was originally developed as **DeepPanel** and has been retrained/fine-tuned specifically for Western and manga-style comics.

### What it does
- Input: One comic page (JPG/PNG)
- Output: Mask of panel regions → cropped panel images
- Works on **any resolution** (automatically resized internally to 256×256 for inference, then scaled back)
- Handles multi-panel pages, overlapping speech bubbles, and complex layouts

---

## 📦 Model File

| File                    | Size     | Format          | Description                          |
|-------------------------|----------|-----------------|--------------------------------------|
| `deeppanel_model.keras` | ~XX MB   | Keras v3        | Full model (architecture + weights)  |

**Direct download link:**
```bash
https://huggingface.co/codeShare/comic-panel-extract/resolve/main/deeppanel_model.keras
```

-----

## 🚀 Quick Start (Colab / Local)

### 1. Install dependencies

```bash
pip install tensorflow opencv-python-headless numpy tqdm huggingface_hub
```

### 2. Download + Load the model

```python
from huggingface_hub import hf_hub_download
import tensorflow as tf

model_path = hf_hub_download(
    repo_id="codeShare/comic-panel-extract",
    filename="deeppanel_model.keras"
)

model = tf.keras.models.load_model(model_path)
print("✅ DeepPanel model loaded!")
```

### 3. Full extraction pipeline (copy-paste ready)

See the exact code used in the [original Colab notebook](https://colab.research.google.com/...) (link will be added once public).

Or use the **minimal working example** below:

```python
import cv2
import numpy as np
from huggingface_hub import hf_hub_download
import tensorflow as tf
import os
from tqdm import tqdm

# Load model once
model_path = hf_hub_download(repo_id="codeShare/comic-panel-extract", filename="deeppanel_model.keras")
model = tf.keras.models.load_model(model_path)

def extract_panels_from_page(image_path, output_folder="panels"):
    os.makedirs(output_folder, exist_ok=True)
    
    # Preprocess
    img = cv2.imread(image_path)
    original_shape = img.shape[:2]
    resized = cv2.resize(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), (256, 256)) / 255.0
    input_tensor = np.expand_dims(resized, axis=0).astype(np.float32)
    
    # Predict mask
    mask_pred = model.predict(input_tensor, verbose=0)[0]
    if len(mask_pred.shape) == 3 and mask_pred.shape[-1] == 1:
        mask_pred = mask_pred.squeeze(axis=-1)
    
    # Post-process
    mask = cv2.resize((mask_pred > 0.5).astype(np.uint8) * 255, (original_shape[1], original_shape[0]))
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    page_name = os.path.splitext(os.path.basename(image_path))[0]
    count = 0
    for contour in contours:
        x, y, w, h = cv2.boundingRect(contour)
        if w < 30 or h < 30:
            continue
        panel = img[y:y+h, x:x+w]
        cv2.imwrite(f"{output_folder}/{page_name}_panel_{count:02d}.jpg", panel)
        count += 1
    
    return count

# Example usage
panels_extracted = extract_panels_from_page("my_comic_page.jpg", "extracted_panels")
print(f"Extracted {panels_extracted} panels!")
```

-----

## 📂 Related Resources

- **GitHub Repository** [full training + inference code + dataset scripts):  
  → *Coming soon* (will be linked here once published)](https://github.com/pedrovgs/DeepPanel)
- **Google Colab Notebook** (ready-to-run version with your `comics.zip`):  
  [Open in Colab](https://colab.research.google.com/) *(paste the full notebook code from our previous conversation)*
- **Dataset used for training**: Custom comic panel dataset (Western + Manga)

-----

## 🎯 Intended Use Cases

- Bulk comic/manga digitization pipelines
- Preparing training data for AI comic colorizers, inpainters, or speech bubble removers
- Building web apps that auto-split comic pages into panels
- Research on layout analysis for illustrated books

**Works best on**:

- Clear black-and-white or color comics
- Standard Western comic pages and Japanese manga

**Limitations**:

- Very low-resolution or heavily compressed scans may give poorer results
- Extremely artistic/experimental layouts (full-bleed splash pages) may need manual correction

-----

## 🛠️ Technical Details

- **Framework**: TensorFlow 2.x + Keras 3
- **Architecture**: U-Net style (encoder-decoder) optimized for segmentation
- **Input size during inference**: 256×256 (automatically resized)
- **Output**: Single-channel probability mask
- **License**: MIT (model weights + code)

-----

## 📜 Citation

If you use this model in your project, please cite:

```bibtex
@misc{deeppanel-comic-extractor-2026,
  title        = {DeepPanel: Comic Panel Extractor},
  author       = {codeShare},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/codeShare/comic-panel-extract}},
  note         = {Keras model for automatic comic panel detection}
}
```