Histogram equalization in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read an image using OpenCV.

Computer Vision

import cv2

image = cv2.[1]('input.jpg', 0)

Drag options to blanks, or click blank then click option'

Aimread

Bimshow

Cimwrite

DcvtColor

Attempts:

3 left

💡 Hint

Common Mistakes

Using cv2.imshow instead of cv2.imread

Using cv2.imwrite which saves images

Using cv2.cvtColor which converts color spaces

The cv2.imread function reads an image from a file. The second argument 0 means read as grayscale.

2fill in blank

medium

Complete the code to apply histogram equalization on a grayscale image.

Computer Vision

equalized_image = cv2.[1](image)

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Athreshold

BequalizeHist

CcvtColor

Dblur

Attempts:

3 left

💡 Hint

Common Mistakes

Using cv2.cvtColor which changes color spaces

Using cv2.threshold which applies thresholding

Using cv2.blur which blurs the image

The cv2.equalizeHist function performs histogram equalization on a grayscale image.

3fill in blank

hard

Fix the error in the code to display the equalized image using OpenCV.

Computer Vision

cv2.[1]('Equalized Image', equalized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

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Aimread

Bimwrite

CequalizeHist

Dimshow

Attempts:

3 left

💡 Hint

Common Mistakes

Using cv2.imread which reads images

Using cv2.imwrite which saves images

Using cv2.equalizeHist which equalizes histograms

The cv2.imshow function displays an image in a window.

4fill in blank

hard

Fill both blanks to create a histogram equalization function for color images using OpenCV.

Computer Vision

def equalize_color_image(img):
    ycrcb = cv2.cvtColor(img, [1])
    ycrcb[:, :, 0] = cv2.[2](ycrcb[:, :, 0])
    return cv2.cvtColor(ycrcb, cv2.COLOR_YCrCb2BGR)

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ACOLOR_BGR2YCrCb

BCOLOR_BGR2GRAY

CequalizeHist

Dthreshold

Attempts:

3 left

💡 Hint

Common Mistakes

Using COLOR_BGR2GRAY which converts to grayscale

Applying threshold instead of equalizeHist

Not converting back to BGR color space

To equalize a color image, convert it to YCrCb color space (COLOR_BGR2YCrCb), equalize the Y channel using equalizeHist, then convert back.

5fill in blank

hard

Fill all three blanks to compute and plot the histogram of a grayscale image using matplotlib.

Computer Vision

import matplotlib.pyplot as plt

hist = cv2.calcHist([image], [[1]], None, [256], [0, 256])
plt.plot(hist)
plt.title('[2] Histogram')
plt.xlabel('[3]')
plt.ylabel('Frequency')
plt.show()

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A0

BGrayscale

CPixel Intensity

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using channel 1 which is invalid for grayscale

Wrong plot title or axis labels

Not calling plt.show() to display the plot

For grayscale images, the channel index is 0. The plot title is 'Grayscale Histogram' and the x-axis label is 'Pixel Intensity'.

Practice

1. What is the main purpose of histogram equalization in image processing?

easy

A. To improve image contrast by spreading out brightness levels

B. To reduce the image size without losing quality

C. To convert a color image to grayscale

D. To blur the image for noise reduction

Solution

1. Step 1: Understand histogram equalization

   Histogram equalization redistributes pixel brightness to use the full range of intensities.

2. Step 2: Identify the effect on image contrast

   This redistribution improves contrast, making details clearer in dark or bright areas.

3. Final Answer:

   To improve image contrast by spreading out brightness levels -> Option A

4. Quick Check:

   Histogram equalization = Contrast improvement [OK]

Hint: Think: histogram equalization spreads brightness evenly [OK]

Common Mistakes:

• Confusing it with image resizing
• Thinking it changes image color
• Assuming it blurs the image

2. Which OpenCV function is used to apply histogram equalization on a grayscale image?

easy

A. cv2.cvtColor()

B. cv2.GaussianBlur()

C. cv2.equalizeHist()

D. cv2.resize()

Solution

1. Step 1: Recall OpenCV functions for image processing

   cv2.equalizeHist() is designed specifically for histogram equalization on grayscale images.

2. Step 2: Differentiate from other functions

   cv2.cvtColor() changes color spaces, cv2.GaussianBlur() blurs images, and cv2.resize() changes image size.

3. Final Answer:

   cv2.equalizeHist() -> Option C

4. Quick Check:

   Histogram equalization function = cv2.equalizeHist() [OK]

Hint: Remember: 'equalizeHist' means histogram equalization [OK]

Common Mistakes:

• Using cv2.cvtColor() for equalization
• Confusing with blur or resize functions
• Trying to apply equalization on color images directly

3. What will be the output image type after applying cv2.equalizeHist() on a grayscale image?

medium

A. A binary (black and white) image

B. A color image with enhanced colors

C. A blurred grayscale image

D. A grayscale image with improved contrast

Solution

1. Step 1: Understand input and output of cv2.equalizeHist()

   The function takes a grayscale image and returns a grayscale image with adjusted pixel intensities.

2. Step 2: Identify the effect on image type

   The output remains grayscale but with better contrast, not color or binary or blurred.

3. Final Answer:

   A grayscale image with improved contrast -> Option D

4. Quick Check:

   EqualizeHist output = Grayscale with better contrast [OK]

Hint: EqualizeHist keeps grayscale, just improves contrast [OK]

Common Mistakes:

• Expecting color image output
• Thinking it creates a binary image
• Assuming it blurs the image

4. Consider this code snippet:

import cv2
img = cv2.imread('image.jpg')
equalized = cv2.equalizeHist(img)
cv2.imshow('Equalized', equalized)
cv2.waitKey(0)

What is the main error here?

medium

A. cv2.imread() does not load images

B. cv2.equalizeHist() requires a grayscale image, but 'img' is color

C. cv2.waitKey() needs an argument of 1, not 0

D. cv2.imshow() cannot display images

Solution

1. Step 1: Check input type for cv2.equalizeHist()

   cv2.equalizeHist() only works on single-channel grayscale images, but 'img' is loaded as color (3 channels).

2. Step 2: Identify the fix

   Convert 'img' to grayscale using cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) before equalization.

3. Final Answer:

   cv2.equalizeHist() requires a grayscale image, but 'img' is color -> Option B

4. Quick Check:

   EqualizeHist needs grayscale input [OK]

Hint: EqualizeHist only accepts grayscale images [OK]

Common Mistakes:

• Ignoring image color channels
• Misunderstanding cv2.waitKey argument
• Thinking cv2.imshow() can't display images

5. You have a very dark grayscale image with pixel values mostly between 0 and 50. After applying histogram equalization, what is the expected effect on the pixel value distribution?

hard

A. Pixel values will spread across the full 0 to 255 range

B. Pixel values will remain mostly between 0 and 50

C. Pixel values will cluster around 128 only

D. Pixel values will become binary, only 0 or 255

Solution

1. Step 1: Understand histogram equalization effect on pixel distribution

   It redistributes pixel intensities to use the full available range, enhancing contrast.

2. Step 2: Apply to dark image pixel range

   Since original pixels are mostly low (0-50), equalization spreads them across 0-255 to improve visibility.

3. Final Answer:

   Pixel values will spread across the full 0 to 255 range -> Option A

4. Quick Check:

   Equalization spreads pixel values fully [OK]

Hint: Equalization stretches pixel values to full range [OK]

Common Mistakes:

• Thinking pixel values stay in original range
• Assuming values cluster at mid-gray
• Confusing equalization with thresholding