Histogram computation in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to compute the histogram of a grayscale image using OpenCV.

Computer Vision

import cv2
image = cv2.imread('image.jpg', 0)
hist = cv2.calcHist([image], [0], None, [256], [0, [1]])

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

A256

B255

C257

D128

Attempts:

3 left

💡 Hint

Common Mistakes

Using 255 as the upper range causes the last pixel value to be excluded.

Using 128 limits the histogram to half the grayscale range.

The histogram range for pixel values in OpenCV should be from 0 to 256 to cover all 256 grayscale levels.

2fill in blank

medium

Complete the code to normalize the histogram so that the sum of all bins equals 1.

Computer Vision

hist_norm = hist / [1]

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Ahist.sum()

Bhist.max()

Clen(hist)

D256

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by max() scales the histogram incorrectly.

Dividing by length or fixed number does not normalize properly.

Dividing by the sum of all histogram bins normalizes the histogram to represent probabilities.

3fill in blank

hard

Fix the error in the code to compute a color histogram for a BGR image.

Computer Vision

image = cv2.imread('color.jpg')
hist = cv2.calcHist([image], [[1]], None, [256], [0, 256])

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A3

B0

C1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using channel 3 causes an index error because only 0,1,2 exist.

Using channel 1 or 2 is valid but not the fix requested.

Channel 0 corresponds to the Blue channel in a BGR image, which is valid for histogram calculation.

4fill in blank

hard

Fill both blanks to compute and normalize a histogram for the red channel of a color image.

Computer Vision

image = cv2.imread('color.jpg')
hist = cv2.calcHist([image], [[1]], None, [256], [0, 256])
hist_norm = hist / [2]

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A2

Bhist.sum()

C0

Dimage.size

Attempts:

3 left

💡 Hint

Common Mistakes

Using channel 0 or 1 selects wrong color channels.

Dividing by image.size does not normalize histogram counts correctly.

Channel 2 is red in BGR images. Normalizing by hist.sum() scales the histogram to probabilities.

5fill in blank

hard

Fill all three blanks to create a histogram for the green channel, normalize it, and then find the bin with the maximum frequency.

Computer Vision

image = cv2.imread('color.jpg')
hist = cv2.calcHist([image], [[1]], None, [256], [0, 256])
hist_norm = hist / [2]
max_bin = hist_norm.[3]()

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

A1

Bhist.sum()

Cargmax

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using max() returns the maximum value, not the bin index.

Using wrong channel index selects incorrect color channel.

Channel 1 is green. Normalizing by hist.sum() scales the histogram. argmax() finds the bin index with the highest frequency.