Capstone: Image as an Array

An image is simply a 3D NumPy array of shape (height, width, 3) holding uint8 values from 0 to 255 — one number per color channel (red, green, blue) for every pixel — which means every editing trick is really just array indexing, slicing, broadcasting, and aggregation. In this final capstone you'll build a synthetic image, set its channels, convert it to grayscale, flip and crop with slicing, invert its colors, and brighten it with clip — tying together everything from the whole course.

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A color image is a grid of pixels, and each pixel is three numbers — red, green, blue. NumPy stores that as a 3D array with shape (height, width, 3) and dtype uint8 (0–255). Create a blank black image with np.zeros .

The three axes are: axis 0 = rows (height), axis 1 = columns (width), axis 2 = color channels (R, G, B).

Expected output: shape (4, 6, 3) , dtype uint8 , and the first pixel [0 0 0] (pure black).

The last axis selects a channel: index 0 is red, 1 is green, 2 is blue. Assigning to a channel slice uses broadcasting to fill many pixels at once. Color cheat-sheet: [255, 0, 0] is red, [0, 255, 0] is green, [0, 0, 255] is blue, and [255, 255, 255] is white.

Expected output: a red pixel [255 0 0] and a green corner pixel [0 255 0] .

Grayscale collapses the 3 channels into a single brightness value per pixel using a weighted sum across the channel axis. The standard luminosity weights are 0.2989·R + 0.5870·G + 0.1140·B , which match how the human eye perceives color. np.dot applies them across the last axis.

Expected output: a 2D (2, 2) array of brightness values about 76.2 (red), 149.7 (green), 29.1 (blue), 255.0 (white).

Geometry operations are pure slicing. img[::-1] reverses the row axis to flip top-to-bottom; img[:, ::-1] flips left-to-right. Cropping is a slice of the grid: img[r0:r1, c0:c1] keeps a rectangle of pixels (with all channels).

Expected output: red values [0 10 20 30] top-to-bottom, reversed to [30 20 10 0] after the flip, and a crop of shape (2, 2, 3) .

Inverting colors is broadcasting a scalar across the whole array: 255 - img turns black to white and red to cyan. Brightening adds a value but must stay in range — use np.clip(..., 0, 255) so nothing overflows past 255.

Expected output: inverted pixels [245 235 225] and [55 5 155] ; the brightened pixel [240 255 140] — note 250 + 40 clips to 255, not a wrap-around.

Replace each ___ so the program builds a small blue image, then inverts it to yellow.

Expected output: [0 0 255] then [255 255 0] . (Answers: 3 , 2 , 255 .)

With uint8, 250 + 40 wraps to 34 instead of saturating, giving weird colors.

✅ Fix: compute in a wider type, then np.clip(result, 0, 255).astype(np.uint8) .

The shape is (height, width, 3) : rows come first, columns second.

✅ Fix: index as img[row, col] , and remember axis 2 is the color channel.

Build a gradient image, brighten it with clip, flip it, then report its average brightness — tying together shape, dtype, slicing, broadcasting, clip, and aggregation.

🎉 Capstone complete — you've finished the NumPy course!

You treated an image as a 3D array and used every core NumPy skill — shape and dtype, indexing and slicing, broadcasting, aggregation, and clip — to create, recolor, grayscale, flip, crop, invert, and brighten it. That is real image processing, powered entirely by arrays.

🚀 Congratulations! From your first np.array to a full image-editing pipeline, you now have a working command of NumPy. Go build something amazing with it.

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