Histogram computation in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of grayscale histogram computation

What is the output of this code that computes a grayscale histogram of a 3x3 image?

Computer Vision

import numpy as np
image = np.array([[0, 128, 255], [128, 128, 0], [255, 0, 128]], dtype=np.uint8)
histogram, bins = np.histogram(image, bins=4, range=(0, 256))
print(histogram.tolist())

A [3, 3, 0, 3]

B [2, 3, 2, 2]

C [3, 0, 4, 2]

D [2, 2, 3, 3]

💡 Hint

Bins divide the pixel values into ranges: 0-63, 64-127, 128-191, 192-255.

The histogram counts pixels in these ranges: 0-63 has 3 pixels (0s), 64-127 has 0 pixels, 128-191 has 4 pixels (128s), 192-255 has 2 pixels (255s).

🧠 Conceptual

intermediate

Purpose of histogram equalization

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

A To increase the contrast of an image by spreading out the most frequent intensity values.

B To reduce the image size by compressing pixel values.

C To convert a color image into grayscale by averaging channels.

D To blur the image by averaging neighboring pixels.

💡 Hint

Think about how the image looks after equalization compared to before.

Histogram equalization redistributes pixel intensities to enhance contrast, making details more visible.

❓ Metrics

advanced

Interpreting histogram similarity metrics

Which metric is best to compare two image histograms to measure similarity?

A Histogram intersection

B Mean Squared Error (MSE)

C Euclidean distance

D Correlation coefficient

💡 Hint

Consider which metric measures overlap between histograms.

Histogram intersection measures the common area under two histograms, indicating similarity.

🔧 Debug

advanced

Bug in histogram calculation code

What error does this code raise? import cv2 import numpy as np image = np.array([[10, 20], [30, 40]], dtype=np.uint8) hist = cv2.calcHist([image], [1], None, [256], [0, 256])

A TypeError: argument must be a sequence

B IndexError: list index out of range

C ValueError: channels must be 0 or 1

D No error, returns histogram

💡 Hint

Check the channel index used for a single-channel image.

The channel index is 1, but the image has only one channel (index 0), causing IndexError.

❓ Model Choice

expert

Choosing histogram features for image classification

You want to classify images of fruits by color distribution. Which histogram feature approach is best?

A Use grayscale histogram with 256 bins

B Use histogram of oriented gradients (HOG)

C Use binary threshold histogram

D Use RGB histograms concatenated for each channel

💡 Hint

Color information is important for fruit classification.

RGB histograms capture color distribution across channels, useful for distinguishing fruits by color.

Practice

1. What does a histogram represent in image processing?

easy

A. The file format of the image

B. The count of pixels for each brightness or color value

C. The number of edges detected in the image

D. The size of the image in pixels

Solution

1. Step 1: Understand what a histogram measures

   A histogram counts how many pixels fall into each brightness or color range in an image.

2. Step 2: Compare options with this definition

   Only The count of pixels for each brightness or color value correctly describes this counting of pixels by brightness or color.

3. Final Answer:

   The count of pixels for each brightness or color value -> Option B

4. Quick Check:

   Histogram = pixel counts by brightness/color [OK]

Hint: Histogram counts pixels per brightness/color range [OK]

Common Mistakes:

• Confusing histogram with image size
• Thinking histogram shows edges
• Mixing histogram with file format

2. Which of the following is the correct way to call OpenCV's calcHist function for a grayscale image stored in variable img?

easy

A. cv2.calcHist([img], [0], None, [256], [0,256])

B. cv2.calcHist(img, 0, None, 256, 0, 256)

C. cv2.calcHist(img, [0], None, [256], [0,256])

D. cv2.calcHist([img], 0, None, 256, [0,256])

Solution

1. Step 1: Recall the correct syntax of cv2.calcHist

   The function requires the image inside a list, channels as a list, mask (None if no mask), histogram size as a list, and ranges as a list.

2. Step 2: Match options to this syntax

   Only cv2.calcHist([img], [0], None, [256], [0,256]) correctly uses lists for image, channels, histogram size, and ranges.

3. Final Answer:

   cv2.calcHist([img], [0], None, [256], [0,256]) -> Option A

4. Quick Check:

   Use lists for parameters in calcHist [OK]

Hint: Always wrap image and channels in lists for calcHist [OK]

Common Mistakes:

• Passing image directly without list
• Using integers instead of lists for channels or bins
• Incorrect number of arguments

3. What will be the output shape of the histogram computed by this code snippet?

hist = cv2.calcHist([img], [0], None, [128], [0,256])
print(hist.shape)

medium

A. (128, 1)

B. (256, 1)

C. (1, 128)

D. (128,)

Solution

1. Step 1: Understand the bins parameter in calcHist

   The bins parameter is [128], so the histogram will have 128 bins.

2. Step 2: Check the shape of the returned histogram

   OpenCV returns a 2D array with shape (bins, 1), so shape is (128, 1).

3. Final Answer:

   (128, 1) -> Option A

4. Quick Check:

   Bins = 128 means shape (128, 1) [OK]

Hint: Histogram shape = (bins, 1) in OpenCV [OK]

Common Mistakes:

• Assuming shape is (bins,) 1D array
• Confusing bins with range size
• Expecting (1, bins) shape

4. Identify the error in this code snippet for computing a color histogram of an image img:

hist = cv2.calcHist([img], [0, 1, 2], None, [256], [0,256])

medium

A. The ranges parameter should be a single number, not a list

B. The image should not be inside a list

C. The bins parameter should be a list with one value per channel

D. The mask parameter cannot be None

Solution

1. Step 1: Check the channels and bins parameters

   Channels are [0,1,2] for 3 color channels, so bins must be a list with 3 values, one per channel.

2. Step 2: Identify the mistake in bins argument

   Bins is given as [256], a single value, which is incorrect for 3 channels.

3. Final Answer:

   The bins parameter should be a list with one value per channel -> Option C

4. Quick Check:

   Bins list length = channels count [OK]

Hint: Bins list length must match channels count [OK]

Common Mistakes:

• Using single bins value for multiple channels
• Not wrapping image in list
• Misusing ranges parameter

5. You want to compare the brightness distribution of two grayscale images using histograms. Which approach is best to make the comparison fair and meaningful?

hard

A. Compute histograms with different bin sizes to capture details

B. Use histograms without normalization to keep original counts

C. Compare raw pixel values directly without histograms

D. Compute histograms with the same number of bins and normalize them before comparing

Solution

1. Step 1: Understand the need for fair comparison

   To compare brightness distributions, histograms must be computed with the same bin count to align ranges.

2. Step 2: Importance of normalization

   Normalizing histograms removes effects of image size differences, making comparison meaningful.

3. Final Answer:

   Compute histograms with the same number of bins and normalize them before comparing -> Option D

4. Quick Check:

   Same bins + normalization = fair histogram comparison [OK]

Hint: Normalize histograms with same bins for fair comparison [OK]

Common Mistakes:

• Using different bin sizes for each image
• Comparing raw counts without normalization
• Ignoring histogram and comparing pixels directly