Histogram computation in Computer Vision - ML Experiment: Train & Evaluate

Experiment - Histogram computation

Problem:You want to compute the color histogram of images to understand the distribution of pixel intensities. Currently, the histogram is computed but it does not correctly represent the image colors, leading to poor feature extraction for further tasks.

Current Metrics:Histogram bins do not sum up to the total number of pixels; color channels are mixed; histogram shape is inconsistent with expected color distribution.

Issue:The histogram computation mixes color channels and does not normalize counts, causing inaccurate representation of image color distribution.

Your Task

Compute separate histograms for each color channel (Red, Green, Blue) of an image, normalize them so that the sum of bins equals 1, and verify the histogram sums and shapes are correct.

Use only OpenCV and NumPy libraries.

Do not change the input image format (assume BGR as loaded by OpenCV).

Histogram bins must be 256 for each channel.

Hint 1

Hint 2

Hint 3

Solution

Computer Vision

import cv2
import numpy as np

# Load image in BGR format
image = cv2.imread('sample.jpg')

# Check if image is loaded
if image is None:
    raise ValueError('Image not found or path is incorrect')

# Number of bins
bins = 256

# Calculate histogram for each channel separately
hist_b = cv2.calcHist([image], [0], None, [bins], [0, 256])
hist_g = cv2.calcHist([image], [1], None, [bins], [0, 256])
hist_r = cv2.calcHist([image], [2], None, [bins], [0, 256])

# Normalize histograms by total number of pixels
num_pixels = image.shape[0] * image.shape[1]
hist_b_norm = hist_b / num_pixels
hist_g_norm = hist_g / num_pixels
hist_r_norm = hist_r / num_pixels

# Verify sums
sum_b = hist_b_norm.sum()
sum_g = hist_g_norm.sum()
sum_r = hist_r_norm.sum()

print(f'Sum of normalized Blue histogram bins: {sum_b:.4f}')
print(f'Sum of normalized Green histogram bins: {sum_g:.4f}')
print(f'Sum of normalized Red histogram bins: {sum_r:.4f}')

Computed histograms separately for each color channel (B, G, R) instead of mixing channels.

Normalized each histogram by dividing by total pixel count to get probability distribution.

Added checks to verify that the sum of each normalized histogram is 1.

Results Interpretation

Before: Histograms mixed color channels and were not normalized, sums were not 1, leading to inaccurate color distribution representation.

After: Separate histograms per channel normalized to sum to 1, accurately representing the color intensity distribution in the image.

Computing separate normalized histograms per color channel provides a clear and accurate representation of image color distribution, which is essential for tasks like image classification or enhancement.

Bonus Experiment

Try computing histograms for grayscale images and compare the histogram shape with color channel histograms.

💡 Hint

Convert the image to grayscale using cv2.cvtColor and compute a single histogram with 256 bins. Observe how the distribution differs from color histograms.

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