10.1.1 Vision Basics Roadmap: Pixels, Channels, Processing

Computer vision starts with input intuition. Before classification, detection, or segmentation, you need to know what an image looks like as numbers.

See the Image Pipeline First

The first mental model is simple: image = height × width × channels. Most later bugs come from shape, channel order, coordinates, or color-space confusion.

Run a Tiny Image Shape Check

This toy image has 2 rows, 3 columns, and RGB values.

image = [
    [[255, 0, 0], [0, 255, 0], [0, 0, 255]],
    [[255, 255, 255], [0, 0, 0], [128, 128, 128]],
]

height = len(image)
width = len(image[0])
channels = len(image[0][0])
top_left_pixel = image[0][0]

print("shape:", (height, width, channels))
print("top_left_pixel:", top_left_pixel)

Expected output:

shape: (2, 3, 3)
top_left_pixel: [255, 0, 0]

If your code reads a real image with the wrong shape or channel order, every later model result becomes harder to trust.

Learn in This Order

Step	Read	Practice Output
1	Image representation	Explain pixel, channel, height, width, RGB/BGR
2	OpenCV basics	Load, view, crop, resize, and save an image
3	Basic processing	Try grayscale, threshold, blur, edge, and simple filters

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Input Image

source image or synthetic image used in the run

Array Shape

width, height, channels, dtype, and coordinate convention

Processed Output

grayscale, crop, edge, threshold, or saved intermediate image

Failure Check

channel order, resize distortion, coordinate mistake, or over-processing

Expected Output

before/after image plus the printed shape or pixel values

Pass Check

You pass this chapter when you can inspect an image shape, crop a region by coordinates, explain channel order, and save one processed result for your README.

Check reasoning and explanation

1. A passing answer maps the task to the right visual output: class label, bounding box, mask, OCR text, embedding, or video event.
2. The evidence should include a rendered visual artifact and one metric or qualitative error note.
3. A good self-check names one visual failure mode such as class confusion, missed objects, bad masks, lighting shift, domain shift, or weak annotation quality.