README.md

metadata

title: Layout Non-Alignment
emoji: 🌍
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.36.1
app_file: app.py
pinned: false

Layout Non-Alignment

Description

The Layout Non-Alignment metric quantifies the extent of spatial non-alignment between layout elements. This metric evaluates layouts by detecting elements that break alignment patterns, providing insights into layout organization quality and visual coherence.

What It Measures

This metric computes non-alignment scores that measure:

• Alignment violations: Elements that don't align with others along edges or centers
• Spatial organization: How consistently elements follow grid or alignment patterns
• Visual disorder: Degree of positional inconsistency between elements

The metric comes from PosterLayout (Hsu et al., CVPR 2023) and AC-GAN (Li et al., TVCG 2021) research, specifically designed for evaluating poster and graphic design layouts.

Metric Details

• Analyzes element edge positions (left, right, top, bottom) to detect alignment patterns
• Computes delta (minimum distance) between element edges
• Applies logarithmic transformation to penalize near-misses more than obvious non-alignments
• Lower scores indicate better overall alignment (less non-alignment)

Usage

Installation

pip install evaluate

Basic Example

import evaluate
import numpy as np

# Load the metric with canvas dimensions
metric = evaluate.load(
    "creative-graphic-design/layout-non-alignment",
    canvas_width=360,
    canvas_height=504
)

# Single layout processing
predictions = np.random.rand(1, 25, 4)  # (batch, max_elements, coordinates)
gold_labels = np.random.randint(0, 4, size=(1, 25))  # (batch, max_elements)
score = metric.compute(predictions=predictions, gold_labels=gold_labels)
print(score)

Batch Processing Example

import evaluate
import numpy as np

# Load the metric
metric = evaluate.load(
    "creative-graphic-design/layout-non-alignment",
    canvas_width=360,
    canvas_height=504
)

# Batch processing
batch_size = 128
predictions = np.random.rand(batch_size, 25, 4)
gold_labels = np.random.randint(0, 4, size=(batch_size, 25))
score = metric.compute(predictions=predictions, gold_labels=gold_labels)
print(score)

Parameters

Initialization Parameters

• canvas_width (int, required): Width of the canvas in pixels
• canvas_height (int, required): Height of the canvas in pixels

Computation Parameters

• predictions (list of lists of float): Normalized bounding boxes in ltrb (left-top-right-bottom) format
• gold_labels (list of lists of int): Class labels for each element (0 = padding/invalid)

Note: Elements with gold_labels == 0 are treated as padding and excluded from computation. Very small elements (< 0.1% of canvas area) are also filtered out.

Returns

Returns a float value representing the non-alignment score.

Interpretation

• Lower is better: Less non-alignment indicates better spatial organization
• Value of 0: Perfect alignment across all elements (rare in practice)
• Typical range: Varies based on layout complexity and density
• Higher values: More alignment violations, less organized layout

Use Cases

• Layout quality assessment: Evaluate how well elements follow alignment principles
• Generative model evaluation: Compare alignment quality between different generation methods
• Design feedback: Identify layouts with poor spatial organization

Key Insights

• Professional designs typically have lower non-alignment scores
• Grid-based layouts naturally achieve better alignment
• Dense layouts may have higher scores due to increased element interactions
• Small violations (near-alignments) are penalized more than obvious non-alignments

Citations

@inproceedings{hsu2023posterlayout,
  title{Posterlayout: A new benchmark and approach for content-aware visual-textual presentation layout},
  author={Hsu, Hsiao Yuan and He, Xiangteng and Peng, Yuxin and Kong, Hao and Zhang, Qing},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6018--6026},
  year={2023}
}

@article{li2020attribute,
  title={Attribute-conditioned layout gan for automatic graphic design},
  author={Li, Jianan and Yang, Jimei and Zhang, Jianming and Liu, Chang and Wang, Christina and Xu, Tingfa},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  volume={27},
  number={10},
  pages={4039--4048},
  year={2020},
  publisher={IEEE}
}

References

• Paper: PosterLayout (Hsu et al., CVPR 2023)
• Paper: Attribute-Conditioned Layout GAN (Li et al., TVCG 2021)
• Reference Implementation: PosterLayout eval.py

Related Metrics

• Layout Alignment: Measures positive alignment between elements
• Layout Validity: Checks basic validity constraints
• Layout Overlay: Measures element overlap (excluding underlay)