NumPy Image Arrays

Every image you have ever opened is a grid of numbers, and NumPy is how Python holds that grid. Once you know the shape convention and the right data type, creating a blank canvas, reading a pixel, or isolating a colour channel all become ordinary array operations.

Create a 210x210 black image with np.zeros((210, 210, 3), dtype=np.uint8). The height-width-channels rule, why uint8 matters, and how to address pixels and…

Part of the free Numpy course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

We will start with the question people most often arrive at NumPy asking — how do I make a 210×210 black image? — and build out from there.

What You'll Learn in This Lesson

1 The Shape Rule: Height, Width, Channels

• Grayscale — (height, width) . One brightness value per pixel.
• RGB — (height, width, 3) . Red, green and blue per pixel.
• RGBA — (height, width, 4) . RGB plus an alpha transparency channel.

So a 210×210 image is (210, 210, 3) — square, so the swap does not bite here, which is exactly why square examples hide the bug until you try a rectangle.

2 Why uint8 — and the np.ones() Trap

Each channel stores a whole number from 0 (none) to 255 (full) . That is precisely one byte, which is what np.uint8 gives you — and what every image format and viewer expects.

The memory difference is not trivial. A 4K RGB frame is about 25 MB as uint8, and nearly 200 MB as the float64 NumPy would otherwise default to — eight times the memory for data that only ever holds whole numbers 0-255.

To make any flat colour, fill with a three-value list. Remember the order is red, green, blue — so pure red is [255, 0, 0] .

3 Pixels, Regions and Channels

Because it is just an array, ordinary indexing does everything:

• img[y, x] — one pixel, as a 3-value array.
• img[y0:y1, x0:x1] = colour — fill a rectangle.
• img[:, :, 0] — the whole red plane.
• img[::-1] — flip vertically; img[:, ::-1] flips horizontally.

Slicing returns a view , not a copy, so assigning into a slice edits the original image directly — which is exactly what you want when drawing.

4 Two Bugs That Turn Images Black

1. uint8 overflow. Brightening with img + 50 looks sensible until a channel already at 230 wraps past 255 and comes back near zero — bright areas turn dark and speckled. Do the arithmetic in a wider type and clip:

np.clip(img.astype(np.int16) + 50, 0, 255).astype(np.uint8)

2. Float-to-uint8 without scaling. Many libraries hand back floats in the range 0–1. Casting straight to uint8 truncates every one of them to 0 — a perfectly black image. Multiply by 255 first.

5 🎯 Your Turn

Build a small flag: a black canvas, a white horizontal band across the middle, and a red square in the top-left corner. Then confirm the shape and a couple of pixels.

❓ Frequently Asked Questions

Lesson complete — images are just arrays now!

You can create a canvas at any size, pick the right dtype, address pixels and channels by index, and recognise the two dtype bugs that turn an image unexpectedly black.

🚀 Up next: Array Data Types — go deeper on dtypes, precision and memory.

Practice quiz

Continue this course

• Previous: Creating Arrays
• Next: Array Data Types (dtypes)