Why Does Pillow's .convert('L') Not Modify Images In-Place and How to Get a 2D Grayscale Numpy Arra

Learn why Pillow's image conversion methods return new images instead of modifying in-place, and how to properly convert images to grayscale or black & white before converting to numpy arrays.
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Understanding Pillow’s .convert() Behavior

When working with Pillow (PIL) to convert images to grayscale or black & white, a common pitfall is expecting .convert() to change the original image object directly.

The Core Issue

Pillow's Image.convert() method does not modify the original image. Instead, it returns a new image object with the requested mode. If you call img.convert('L') without capturing the result, the original image remains unchanged.

Example mistake:

[[See Video to Reveal this Text or Code Snippet]]

Here, image_pil retains the 3-channel RGB shape because img was never replaced.

Correct Usage to Get a 2D Grayscale Numpy Array

To obtain a 2D numpy array representing a grayscale image:

Use .convert('L') and assign its result to a variable.

Convert that new image to a numpy array.

[[See Video to Reveal this Text or Code Snippet]]

What about .convert('1')?

L mode: 8-bit pixels, black and white

1 mode: 1-bit pixels, black and white, stored with 1 bit per pixel

The same principle applies: you must assign the converted image returned from .convert('1') to a variable.

Summary

Pillow's .convert() returns a new image; it does not alter the original image in-place.

Always assign the result of .convert() if you want the converted image.

Converting with .convert('L') or .convert('1') gives you single-channel images.

Converting those images to a numpy array yields a 2D array, reflecting greyscale or black & white pixels.

Correcting your code this way ensures you get the expected 2D numpy array shape instead of a 3D RGB array.