Color transforms (brightness, contrast, hue) in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does adjusting brightness in an image do?

Adjusting brightness makes the whole image lighter or darker by adding or subtracting a constant value to all pixels.

beginner

How does changing contrast affect an image?

Changing contrast stretches or shrinks the difference between light and dark areas, making details stand out more or less.

beginner

What is hue in the context of color transforms?

Hue refers to the color type itself, like red, green, or blue. Changing hue shifts all colors around the color wheel.

intermediate

Why do we use color transforms like brightness, contrast, and hue in machine learning?

We use them to make models more robust by showing images in different lighting and color conditions, helping models learn better.

intermediate

How can you adjust brightness and contrast using simple math on pixel values?

Brightness is adjusted by adding a value to pixels; contrast is adjusted by multiplying pixel differences from a midpoint by a factor.

What happens when you increase the brightness of an image?

A The image colors shift around the color wheel

B The difference between light and dark areas increases

C The image becomes lighter overall

D The image becomes blurry

Increasing brightness adds a constant to all pixels, making the image lighter.

Which color transform changes the vividness of colors by stretching differences between light and dark?

A Saturation adjustment

B Brightness adjustment

C Hue adjustment

D Contrast adjustment

Contrast adjustment changes how much light and dark areas differ, affecting vividness.

What does changing the hue of an image do?

A Shifts all colors around the color wheel

B Makes the image lighter or darker

C Increases the sharpness

D Blurs the image

Hue shifts the colors to different points on the color wheel, changing their color type.

Why is adjusting brightness and contrast useful in training machine learning models?

A To create more varied training data

B To reduce image size

C To make images blurry

D To remove colors

Adjusting brightness and contrast helps create varied images so models learn to handle different lighting.

Which operation would you use to make an image darker?

A Add a positive value to pixel brightness

B Subtract a value from pixel brightness

C Shift hue values

D Multiply pixel values by a number greater than 1

Subtracting a value from pixel brightness makes the image darker.

Explain how brightness, contrast, and hue adjustments affect an image and why they are important in machine learning.

Think about how each transform changes the look of an image and why variety helps learning.

Describe a simple way to adjust brightness and contrast using pixel values.

Consider basic math operations on pixel numbers.

Practice

1. What does increasing the brightness of an image do to its pixel values?

easy

A. It makes pixel values larger, making the image lighter

B. It rotates the colors around the color wheel

C. It stretches the difference between pixel values

D. It converts the image to grayscale

Solution

1. Step 1: Understand brightness effect on pixels

   Brightness changes add or multiply pixel values to make them lighter or darker.

2. Step 2: Identify what increasing brightness means

   Increasing brightness means making pixel values larger, so the image looks lighter.

3. Final Answer:

   It makes pixel values larger, making the image lighter -> Option A

4. Quick Check:

   Brightness increase = lighter pixels [OK]

Hint: Brightness changes scale pixel values up or down [OK]

Common Mistakes:

• Confusing brightness with contrast
• Thinking brightness rotates colors
• Assuming brightness converts to grayscale

2. Which of the following is the correct way to increase contrast of an image using a simple formula?

easy

A. new_pixel = (old_pixel - 128) * 1.5 + 128

B. new_pixel = old_pixel + 50

C. new_pixel = old_pixel / 2

D. new_pixel = old_pixel * 0

Solution

1. Step 1: Recall contrast adjustment formula

   Contrast changes stretch pixel values around the midpoint (usually 128 for 8-bit images).

2. Step 2: Identify correct formula

   The formula (old_pixel - 128) * factor + 128 increases contrast by scaling differences from midpoint.

3. Final Answer:

   new_pixel = (old_pixel - 128) * 1.5 + 128 -> Option A

4. Quick Check:

   Contrast = scale around midpoint [OK]

Hint: Contrast scales difference from midpoint, not just add values [OK]

Common Mistakes:

• Adding a constant increases brightness, not contrast
• Dividing pixel values reduces brightness
• Multiplying by zero makes image black

3. Given this Python code snippet using OpenCV, what will be the effect on the image stored in img?

import cv2
import numpy as np
img = cv2.imread('image.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
hsv[:, :, 0] = (hsv[:, :, 0] + 30) % 180
img_shifted = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

medium

A. The image is converted to grayscale

B. The image contrast is increased by 30%

C. The image brightness is increased by 30 units

D. The image hue is shifted by 30 degrees on the color wheel

Solution

1. Step 1: Understand HSV color space and hue channel

   HSV separates color into Hue, Saturation, and Value. Hue channel controls color angle on the wheel.

2. Step 2: Analyze code effect on hue channel

   The code adds 30 to the hue channel and wraps around with modulo 180, shifting colors on the wheel.

3. Final Answer:

   The image hue is shifted by 30 degrees on the color wheel -> Option D

4. Quick Check:

   Hue shift = color rotation [OK]

Hint: Hue changes rotate colors in HSV space [OK]

Common Mistakes:

• Confusing hue shift with brightness or contrast
• Ignoring modulo wrap-around for hue
• Assuming color space conversion changes brightness

4. The following code attempts to increase image contrast but does not work as expected. What is the main error?

def increase_contrast(img):
    mean = img.mean()
    return (img - mean) * 1.2 + mean

result = increase_contrast(image)

medium

A. The image should be converted to grayscale first

B. The multiplication factor should be less than 1

C. The mean should be computed per channel, not overall

D. The code should add 1.2 instead of multiplying

Solution

1. Step 1: Understand contrast adjustment per channel

   Contrast should be adjusted per color channel to preserve color balance.

2. Step 2: Identify error in mean calculation

   The code calculates mean over entire image, mixing channels, causing wrong contrast scaling.

3. Final Answer:

   The mean should be computed per channel, not overall -> Option C

4. Quick Check:

   Contrast per channel = correct scaling [OK]

Hint: Calculate mean per channel for contrast adjustment [OK]

Common Mistakes:

• Using single mean for all channels
• Using factor less than 1 reduces contrast
• Adding constant changes brightness, not contrast

5. You want to apply a color transform pipeline to an image that first increases brightness by 20%, then shifts hue by 45 degrees, and finally increases contrast by 30%. Which order of operations is best to preserve natural colors?

hard

A. Increase contrast -> Shift hue -> Increase brightness

B. Increase brightness -> Increase contrast -> Shift hue

C. Shift hue -> Increase brightness -> Increase contrast

D. Shift hue -> Increase contrast -> Increase brightness

Solution

1. Step 1: Consider effect of brightness before contrast

   Increasing brightness first ensures pixel values are lifted before stretching differences with contrast.

2. Step 2: Apply hue shift last to avoid color distortion

   Hue shift should be last to rotate colors after brightness and contrast adjustments.

3. Step 3: Confirm order preserves natural colors

   Brightness -> Contrast -> Hue maintains natural look by adjusting lightness and contrast before color rotation.

4. Final Answer:

   Increase brightness -> Increase contrast -> Shift hue -> Option B

5. Quick Check:

   Brightness then contrast then hue = natural colors [OK]

Hint: Adjust brightness and contrast before hue shift [OK]

Common Mistakes:

• Shifting hue before brightness causes unnatural colors
• Increasing contrast before brightness can clip pixels
• Applying hue shift first distorts brightness and contrast