SMB / README.md
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---
license: cc-by-nc-4.0
tags:
- music
- documents
- end-to-end
- full-page
- system-level
annotations_creators:
- manually expert-generated
pretty_name: Sheet Music Benchmark
size_categories:
- 1K<n<10K
task_categories:
- image-to-text
- image-segmentation
- text-retrieval
subtasks:
- document-retrieval
extra_gated_fields:
Affiliation: text
---
# ⚠️ Work in Progress! SMB: A Multi-Texture Sheet Music Recognition Benchmark ⚠️
## Overview
SMB (Sheet Music Benchmark) is a dataset of printed Common Western Modern Notation scores developed at the University of Alicante at the Pattern Recognition and Artificial Intelligence Group.
## Use Cases:
- Optical Music Recognition (OMR): system-level, full-page
- Image Segmentation: music regions
## Dataset Details
Each page includes the corresponding \**kern data for that specific page. Additionally, it provides detailed annotations for each region within the page.
### 1. Image
- **Type**: PNG
- **Description**: Encoded full-page image of the score.
### 2. Original Width
- **Type**: Integer
- **Description**: The width of the image in pixels.
### 3. Original Height
- **Type**: Integer
- **Description**: The height of the image in pixels.
### 4. Regions
- **Type**: List of JSON objects
- **Description**: Contains detailed information about regions on the page. Each JSON object includes:
- **bbox**:
- **x**: The vertical position on the page (in pixels).
- **y**: The horizontal position on the page (in pixels).
- **width**: Width of the region (in pixels).
- **height**: Height of the region (in pixels).
- **rotation**: Angle of rotation (in degrees) for the bounding box around its top-left corner. This angle defines how much the box is rotated clockwise from its default unrotated position.
- **raw**: The content extracted from the original dataset before any processing.
- **kern**: A standardized version of the content ready for rendering.
- **ekern**: A tokenized and standardized version of the content for enhanced processing.
### 5. Page Texture
- **Type**: String
- **Description**: The musical texture of the page.
- **Values**:
- "Pianoform"
- "Monophonic"
- "Other"
### 6. Page
- **Type**: JSON object
- **Description**: Metadata of the page. Fields include:
- **raw**: The unprocessed content extracted from the original dataset.
- **kern**: The content in a standardized format, ready to be rendered.
- **ekern**: The content in a tokenized and standardized format.
### 7. Score ID
- **Type**: String
- **Description**: Unique identifier for the original score to which the page belongs.
## SMB usage 📖
SMB is publicly available at [HuggingFace](https://huggingface.co/datasets/PRAIG/SMB).
To download from HuggingFace:
1. Gain access to the dataset and get your HF access token from: [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens).
2. Install dependencies and login HF:
- Install Python
- Run `pip install pillow datasets huggingface_hub[cli]`
- Login by `huggingface-cli login` and paste the HF access token. Check [here](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-login) for details.
3. Use the following code to load SMB and extract the regions:
```python
import math
from datasets import load_dataset
from PIL import ImageDraw
def draw_bounding_boxes(row):
"""
Draws bounding boxes on an image based on region data provided in the row.
Args:
row (dict): A row from the dataset.
Returns:
PIL.Image: An image with bounding boxes drawn.
"""
# Load the image
image = row["image"]
# Create a drawing context
draw = ImageDraw.Draw(image)
# Iterate through regions in the row
for index, region in enumerate(row["regions"]):
# Extract bounding box data
bbox = region["bbox"]
box_x = bbox["x"] / 100 * row["original_width"]
box_y = bbox["y"] / 100 * row["original_height"]
box_width = bbox["width"] / 100 * row["original_width"]
box_height = bbox["height"] / 100 * row["original_height"]
rotation = bbox["rotation"]
# Convert rotation to radians
rotation_rad = math.radians(rotation)
# Calculate the corners relative to the top-left corner (anchor point)
corners = [
(0, 0), # Top-left
(box_width, 0), # Top-right
(box_width, box_height), # Bottom-right
(0, box_height), # Bottom-left
]
# Apply rotation around the top-left corner
rotated_corners = []
for x, y in corners:
rotated_x = box_x + x * math.cos(rotation_rad) - y * math.sin(rotation_rad)
rotated_y = box_y + x * math.sin(rotation_rad) + y * math.cos(rotation_rad)
rotated_corners.append((rotated_x, rotated_y))
# Draw the rotated rectangle
draw.polygon(rotated_corners, outline="red", width=3)
# Show region data
print(f"\nRegion {index}:"
f"\nRotation (degrees): {rotation}"
f"\nkern: {region['kern']}")
return image
if __name__ == "__main__":
# Load dataset from Hugging Face
ds = load_dataset("PRAIG/SMB")
# Select a subset of the dataset
ds = ds["train"]
# Iterate through rows in the dataset
for row in ds:
# Draw bounding boxes on the image
image = draw_bounding_boxes(row)
# Show the image and wait for user to close it
image.show()
input("Close the image window and press Enter to continue...")
```
## Citation
If you use our work, please cite us:
```bibtex
@preprint{,
author = {,
title = {},
year = {}
}
```