# Colour Spaces — Computer Vision

Source: https://www.skillbyai.com/en/computer-vision/i-color

> RGB, BGR, grayscale and HSV.

## Different coordinates for colour

RGB stores red, green and blue intensities. **OpenCV loads images in BGR order** by historical convention, so a mistaken order swaps red and blue. **Grayscale** keeps only brightness, often enough for shapes and text. **HSV** separates **hue** (colour), **saturation** (purity) and **value** (brightness), which makes colour-based selection more robust to lighting than raw RGB thresholds. LAB and YCrCb are other spaces used for perceptual differences and skin or compression tasks.

## Converting colour spaces and masking by hue, run

I ran this on CPU with Python 3, OpenCV 5.0.0, scikit-image 0.26.0, PyTorch 2.14.1 and torchvision 0.29.1, using scikit-image's bundled sample photos and torchvision's published pretrained weights. The coffee photo converts from RGB to grayscale and HSV; a hue and saturation mask marks 70.1% of pixels as strongly red or orange (the photo is dominated by warm tones). The first pixel reads [21, 13, 8] in RGB and [8, 13, 21] in BGR order.

```python
import cv2, numpy as np
from skimage import data
rgb = data.coffee()
gray = cv2.cvtColor(rgb, cv2.COLOR_RGB2GRAY)
hsv = cv2.cvtColor(rgb, cv2.COLOR_RGB2HSV)
print("RGB", rgb.shape, "-> gray", gray.shape, "-> HSV", hsv.shape)
# select strongly red/orange pixels by hue and saturation (OpenCV hue range 0-179)
mask = ((hsv[..., 0] < 15) | (hsv[..., 0] > 165)) & (hsv[..., 1] > 120) & (hsv[..., 2] > 60)
print(f"share of strongly red/orange pixels: {mask.mean():.1%}")
bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
print("first pixel RGB", rgb[0, 0].tolist(), "vs same pixel in BGR order", bgr[0, 0].tolist())
```

Output:

```
RGB (400, 600, 3) -> gray (400, 600) -> HSV (400, 600, 3)
share of strongly red/orange pixels: 70.1%
first pixel RGB [21, 13, 8] vs same pixel in BGR order [8, 13, 21]
```

## Convert BGR right after cv2.imread

If the rest of your pipeline expects RGB, convert immediately after loading with OpenCV and keep one convention everywhere.

**Quiz:** Why is HSV useful for colour selection?

- [ ] It removes all noise
- [ ] It uses fewer pixels
- [ ] It is the format neural networks require
- [x] It separates colour (hue) from brightness, so masks are more robust to lighting

*Answer:* It separates colour (hue) from brightness, so masks are more robust to lighting. Hue stays similar when brightness changes.