Color transforms (brightness, contrast, hue) in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of brightness adjustment code

What will be the pixel value of the center pixel after applying this brightness increase code on a grayscale image with pixel value 100?

Computer Vision

import numpy as np
image = np.full((3,3), 100, dtype=np.uint8)
brightness_increase = 50
result = np.clip(image.astype(np.int16) + brightness_increase, 0, 255).astype(np.uint8)
center_pixel = result[1,1]
print(center_pixel)

A 150

B 100

C 50

D 255

💡 Hint

Adding brightness increases pixel values but caps at 255.

The original pixel value is 100. Adding 50 results in 150, which is within the 0-255 range, so the center pixel becomes 150.

🧠 Conceptual

intermediate

Effect of contrast adjustment on pixel values

If you multiply all pixel values in an image by 1.5 and then clip to 255, what happens to pixels originally at 200?

A They become 300

B They become 255 due to clipping

C They remain 200

D They become 133

💡 Hint

Multiplying 200 by 1.5 gives 300, but pixel values cannot exceed 255.

200 * 1.5 = 300, but pixel values are clipped to 255, so the final value is 255.

❓ Metrics

advanced

Measuring hue shift effect

Which metric best measures the difference in hue between two images after a hue shift?

A Cosine similarity on HSV hue channel

B Mean Squared Error on RGB channels

C Structural Similarity Index (SSIM)

D Pixel-wise absolute difference on grayscale

💡 Hint

Hue is a circular value best compared with angular similarity.

Cosine similarity on the hue channel in HSV color space captures angular differences in hue effectively.

🔧 Debug

advanced

Bug in contrast adjustment code

What error will this code raise? import numpy as np image = np.array([[50, 100], [150, 200]], dtype=np.uint8) contrast_factor = 1.2 result = image * contrast_factor print(result)

A OverflowError due to exceeding 255

B TypeError due to multiplying uint8 by float

C No error, but result is float array

D ValueError due to shape mismatch

💡 Hint

Multiplying a uint8 numpy array by a float returns a float array.

NumPy automatically upcasts the result to float64, so no error occurs but the result is float type.

❓ Model Choice

expert

Choosing model for color transform robustness

Which model architecture is best suited to learn robustness to brightness, contrast, and hue changes in images?

A Simple fully connected network without normalization

B Linear regression on raw pixel values

C Recurrent Neural Network processing pixel sequences

D Convolutional Neural Network with batch normalization and data augmentation

💡 Hint

Robustness to color changes benefits from convolution and normalization.

CNNs capture spatial features well, batch normalization stabilizes training, and data augmentation with color transforms improves robustness.

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