# Distribution of annotations based on their size relative

To create a chart showing the distribution of annotations based on their size relative to their image, along with additional information about the average size of images containing annotations of each size category compared to the average dataset image size, you can follow these steps:

1. **Calculate Annotation Size**: Determine the size of each annotation relative to its corresponding image. This could be measured in terms of area, bounding box dimensions, or any other relevant metric.
2. **Group Annotations by Size Category**: Group the annotations into size categories based on their relative size compared to the image. For example, you could categorize annotations as small, medium, or large based on predefined thresholds.
3. **Calculate Average Image Size**: Calculate the average size of images containing annotations for each size category. This can be done by averaging the dimensions (e.g., width and height) of images with annotations in each size category.
4. **Visualize Distribution**: Create a chart (e.g., a bar chart) to visualize the distribution of annotations across size categories. Each bar represents a size category, and the height of the bar corresponds to the percentage of annotations in that category relative to the total number of annotations.
5. **Add Additional Information**: Overlay additional information on the chart to provide context. For example, you can add blue bars to represent the average size of images containing annotations in each size category compared to the average dataset image size. Additionally, you can include cyan bars to show the percentage of images containing annotations in each size category.

<figure><img src="/files/wreM0a4qQ99Wu8OPg2fJ" alt=""><figcaption></figcaption></figure>

Here's an example of how you might implement this visualization using Python and Matplotlib:

```python
import matplotlib.pyplot as plt
import numpy as np

# Sample data (replace with your actual data)
size_categories = ['Small', 'Medium', 'Large']
annotation_counts = [100, 200, 150]  # Number of annotations in each size category
avg_image_sizes = [0.5, 0.7, 0.9]  # Average image size relative to dataset size
image_percentage = [30, 50, 20]  # Percentage of images containing annotations in each size category

# Create figure and axis objects
fig, ax = plt.subplots()

# Plot annotation distribution
ax.bar(size_categories, annotation_counts, color='skyblue', label='Annotation Count')

# Plot average image size
ax.bar(size_categories, avg_image_sizes, color='blue', alpha=0.5, label='Avg. Image Size')

# Plot image percentage
ax.bar(size_categories, image_percentage, color='cyan', alpha=0.5, label='Image Percentage')

# Add labels and legend
ax.set_xlabel('Size Category')
ax.set_ylabel('Count / Size')
ax.set_title('Annotation Size Distribution')
ax.legend()

# Show plot
plt.show()
```

In this example, `size_categories` represent the different size categories, `annotation_counts` represent the number of annotations in each category, `avg_image_sizes` represent the average size of images containing annotations in each category relative to the dataset size, and `image_percentage` represent the percentage of images containing annotations in each category. You should replace these sample values with your actual data.


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