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# Adjust image brightness and contrast
This documentation page is also available as an interactive notebook. You can launch the notebook in
Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the
above links.
export const quartoRawHtml = [`
| image |
adjust_brightness |
adjust_brightness_1 |
|
|
|
`, `
`, `
| image |
low_contrast |
high_contrast |
|
|
|
|
|
|
|
|
|
`, `
| image |
desaturated |
saturated |
|
|
|
|
|
|
|
|
|
`];
## Problem
You need to fix inconsistent lighting across hundreds of
images—adjusting brightness, contrast, and color saturation.
## Solution
**What’s in this recipe:**
* Adjust brightness, contrast, and saturation
* Test adjustments before applying
* Process multiple images in batch
You adjust brightness, contrast, and saturation for images in your table
using custom UDFs that wrap Pillow’s `ImageEnhance` module (relies on
PIL/Pillow). This lets you control enhancement levels to match your
needs.
You can iterate on transformations before adding them to your table. Use
`.select()` with `.collect()` to preview results on sample
images—nothing is stored in your table. If you want to collect only the
first few rows, use `.head(n)` instead of `.collect()`. Once you’re
satisfied, use `.add_computed_column()` to apply the adjustments to all
images in your table.
For more on this workflow, see [Get fast feedback on
transformations](/howto/cookbooks/core/dev-iterative-workflow).
### Setup
```python theme={null}
%pip install -qU pixeltable
```
```python theme={null}
import pixeltable as pxt
from PIL import ImageEnhance
```
### Load images
```python theme={null}
# Create a fresh directory (drop existing if present)
pxt.drop_dir('image_demo', force=True)
pxt.create_dir('image_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'image\_demo'.
\
```python theme={null}
t = pxt.create_table('image_demo/enhancements', {'image': pxt.Image})
```
Created table 'enhancements'.
```python theme={null}
t.insert(
[
{
'image': 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images/000000000016.jpg'
},
{
'image': 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images/000000000049.jpg'
},
{
'image': 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images/000000000036.jpg'
},
]
)
```
Inserting rows into \`enhancements\`: 0 rows \[00:00, ? rows/s]Inserting rows into \`enhancements\`: 3 rows \[00:00, 601.16 rows/s]
Inserted 3 rows with 0 errors.
3 rows inserted, 6 values computed.
### Iterate: adjust brightness and contrast for a few images first
```python theme={null}
@pxt.udf
def adjust_brightness(img: pxt.Image, factor: float) -> pxt.Image:
"""Adjust brightness. factor < 1 = darker, > 1 = brighter."""
return ImageEnhance.Brightness(img).enhance(factor)
@pxt.udf
def adjust_contrast(img: pxt.Image, factor: float) -> pxt.Image:
"""Adjust contrast. factor < 1 = lower, > 1 = higher."""
return ImageEnhance.Contrast(img).enhance(factor)
@pxt.udf
def adjust_saturation(img: pxt.Image, factor: float) -> pxt.Image:
"""Adjust saturation. factor < 1 = less saturated, > 1 = more saturated."""
return ImageEnhance.Color(img).enhance(factor)
```
```python theme={null}
# Test brightness adjustments
t.select(
t.image,
adjust_brightness(t.image, 0.5),
adjust_brightness(t.image, 1.5),
).head(1)
```
### Add: adjust brightness and contrast for all images in your table
```python theme={null}
# Brightness adjustments (1.0 = original)
t.add_computed_column(darker=adjust_brightness(t.image, 0.5))
t.add_computed_column(brighter=adjust_brightness(t.image, 1.5))
# Contrast adjustments
t.add_computed_column(low_contrast=adjust_contrast(t.image, 0.5))
t.add_computed_column(high_contrast=adjust_contrast(t.image, 2.0))
# Color saturation
t.add_computed_column(desaturated=adjust_saturation(t.image, 0.3))
t.add_computed_column(saturated=adjust_saturation(t.image, 2.0))
```
Added 3 column values with 0 errors.
Added 3 column values with 0 errors.
Added 3 column values with 0 errors.
Added 3 column values with 0 errors.
Added 3 column values with 0 errors.
Added 3 column values with 0 errors.
3 rows updated, 3 values computed.
### View results
Compare different enhancement levels side-by-side.
```python theme={null}
# Compare brightness levels
t.select(t.image, t.darker, t.brighter).collect()
```
```python theme={null}
# Compare contrast levels
t.select(t.image, t.low_contrast, t.high_contrast).collect()
```
```python theme={null}
# Compare saturation levels
t.select(t.image, t.desaturated, t.saturated).collect()
```
## Explanation
**How the enhancement technique works:**
The UDFs wrap PIL’s `ImageEnhance` module to adjust visual properties of
images. Each enhancement type creates an enhancer object for the image,
then applies a multiplication factor. A factor of 1.0 leaves the image
unchanged, values below 1.0 decrease the property (darker, less
contrast, desaturated), and values above 1.0 increase it (brighter, more
contrast, saturated). You can apply different factors to the same image
to create multiple variations for comparison or different use cases.
**To customize the UDFs:**
* **Brightness factors**: Use 0.5 for darker images, 1.5 for brighter,
or adjust to match your lighting needs
* **Contrast factors**: Use 0.5 for lower contrast, 2.0 for higher
contrast, or fine-tune for image clarity
* **Saturation factors**: Use 0.3 for desaturated/muted colors, 2.0
for vibrant colors, or 0.0 for complete grayscale
* **Combine adjustments**: Apply multiple enhancements to create
complex transformations
**The Pixeltable workflow:**
In traditional databases, `.select()` just picks which columns to view.
In Pixeltable, `.select()` also lets you compute new transformations on
the fly—define new columns without storing them. This makes `.select()`
perfect for testing transformations before you commit them.
When you use `.select()`, you’re creating a query that doesn’t execute
until you call `.collect()`. You must use `.collect()` to execute the
query and return results—nothing is stored in your table. If you want to
collect only the first few rows, use `.head(n)` instead of `.collect()`
to test on a subset before processing your full dataset. Once satisfied,
use `.add_computed_column()` with the same expression to persist results
permanently.
For more on this workflow, see [Get fast feedback on
transformations](/howto/cookbooks/core/dev-iterative-workflow).
## See also
* [Test transformations with fast feedback
loops](/howto/cookbooks/core/dev-iterative-workflow)
* [Apply image
filters](/howto/cookbooks/images/img-apply-filters)
* *Pillow techniques from [Real Python: Image Processing With the
Python Pillow
Library](https://realpython.com/image-processing-with-the-python-pillow-library/)*
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