Phase 0 · ToolsWorking with data

Project: images are arrays

About 45 minLooking ahead: some prerequisites aren't done yet

Why this matters

Every image a vision model sees is an array like the ones you'll build here, and every image tool you've used (filters, crops, thumbnails) is array math underneath. This project makes NumPy concrete: you'll see slicing, broadcasting and masks as pictures. It also gives you a handful of functions you'll actually use, from thumbnails to watermarks to color palettes.

Treat a picture as a (height, width, 3) array of numbers from 0 to 255
Draw shapes with boolean masks, then flip, crop and turn an image gray with slicing and math
Brighten an image without numbers wrapping around past 255
Use Pillow to add text and save a file, and find an image's most common colors

The brief

The practice shop wants product shots processed before they go on the website: turned gray for a print catalog, brightened, cropped square for thumbnails, stamped with a watermark, and summed up as a color palette. You'll draw a test image in code, then write one function for each job.

A picture is a grid of numbers

A color image is a 3-D array with the shape (height, width, 3). Each pixel holds three numbers: how much red, green and blue light it gives off, from 0 (none) to 255 (full). The type is uint8, an "unsigned 8-bit integer", which holds exactly the whole numbers 0 to 255.

import matplotlib.pyplot as plt
import numpy as np

tiny = np.array([
    [[255, 0, 0], [0, 255, 0], [0, 0, 255]],
    [[255, 255, 255], [128, 128, 128], [0, 0, 0]],
], dtype=np.uint8)

print(tiny.shape, tiny.dtype)
print(tiny[0, 2])          # row 0, column 2: the blue pixel

fig, ax = plt.subplots(figsize=(3, 2))
ax.imshow(tiny)
ax.axis("off")

Row 0 is the top of the picture. Red, green and blue at full strength make white; all three at 0 make black; equal amounts in between make gray. imshow draws an array as an image, and axis("off") hides the tick marks, which mean nothing for a photo.

Drawing a scene with masks

To draw a shape, make a mask: a True/False array with one value per pixel, True inside the shape. Assigning to img[mask] colors every True pixel at once, with no loop over pixels at all. The trick for building masks is a column of row numbers and a row of column numbers. Broadcasting compares them into a full grid:

import matplotlib.pyplot as plt
import numpy as np

def make_scene(h=120, w=160):
    img = np.zeros((h, w, 3), dtype=np.uint8)
    rows = np.arange(h)[:, None]            # shape (h, 1): 0, 1, 2 ... down the side
    cols = np.arange(w)[None, :]            # shape (1, w): 0, 1, 2 ... along the top
    img[:, :] = (150, 200, 235)             # sky everywhere
    img[h * 2 // 3:, :] = (70, 130, 60)     # grass on the bottom third
    sun = (rows - 25) ** 2 + (cols - 130) ** 2 <= 12 ** 2
    img[sun] = (250, 215, 80)
    tent = (rows >= 30) & (rows <= 100) & (abs(cols - 70) <= (rows - 30) * 0.8)
    img[tent] = (230, 120, 40)
    return img

scene = make_scene()
print(scene.shape)
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(scene)
ax.axis("off")

The sun is every pixel within 12 of a center point, the formula for a circle. The tent is a triangle: the further down a row is, the wider the band of columns it covers. Both masks have the shape (120, 160), one value per pixel, so img[sun] picks exactly the pixels to paint.