[Term Entry] Python Pillow - Image: .getextrema()

content/pillow/concepts/image/terms/getextrema/getextrema.md

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+---
+Title: '.getextrema()'
+Description: 'Returns the minimum and maximum pixel values for each band in an image.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Visualization'
+Tags: 
+  - 'Images'
+  - 'Methods'
+  - 'Pillow'
+  - 'Python'
+CatalogContent:
+  - '-learn-python-3'
+  - 'paths/computer-science'
+---
+
+The **`.getextrema()`** method in the Pillow library quickly finds the minimum and maximum (darkest and brightest) pixel values in an image, showing the range of pixel intensities present.
+
+## How It Works
+
+- **For single-band images (e.g., grayscale):** Returns a tuple (min, max) showing the darkest and brightest pixel values.
+- **For multi-band images (e.g., RGB or RGBA):** Returns a tuple of tuples like ((min_R, max_R), (min_G, max_G), (min_B, max_B)), providing the minimum and maximum values for each color channel separately.
+
+## Syntax
+
+```pseudo
+Image.getextrema()
+```
+
+## Example
+
+The image used in the example is as follows:
+
+![Funny Husky Image](https://raw.githubusercontent.com/Codecademy/docs/main/media/funny-husky.jpg)
+
+The following example demonstrates the usage of the `".getextrema()"` method:
+
+```py
+from PIL import Image
+
+# Open an image file
+cute_dog_image = Image.open("funny_husky.jpg")
+
+# Get extrema for the image
+extrema = cute_dog_image.getextrema()
+
+print("Extrema of the image:", extrema)
+```
+
+Here is the output generated by this code:
+
+```shell
+Extrema of the image: ((0, 255), (0, 255), (0, 255))
+```
+
+### Why It’s Useful
+
+- **Quick Overview of Pixel Value Range:**
+  - It provides a quick way to understand the overall contrast or brightness spread in an image. For example, if the extrema of a grayscale image are (10, 245), this means the darkest pixel has a value of 10, and the brightest pixel has a value of 245, giving you an insight into the image's contrast.
+
+- **Preprocessing and Analysis:**
+Knowing the pixel value range is useful for various image processing tasks, such as:
+  - Adjusting contrast to enhance image features.
+  - Performing thresholding to segment objects based on pixel intensity.
+  - Identifying potential saturation issues or underexposure in the image.
+