OpenCV Python Program to analyze an image using Histogram

In this article, we will learn how to analyze an image using histograms with OpenCV and Matplotlib in Python. A histogram represents the distribution of pixel intensity values in an image, helping us understand brightness, contrast and overall image composition.

Before getting started, make sure you have the following installed:

• OpenCV (cv2)
• Matplotlib (matplotlib.pyplot)

Importing image data

For demonstration, we will use a 24-bit RGB PNG image (8 bits for each of R, G, B channels). Matplotlib supports image arrays in float32 and uint8 formats.

Output

Explanation: plt.imread() loads the image as an array, plt.imshow() displays it, plt.title() adds a title, and plt.show() renders the output.

Creating Histogram using Numpy & Matplotlib

To analyze intensity distribution, we can flatten (ravel()) the image into a 1D array and use plt.hist() to compute the histogram.

• For grayscale images, this shows the spread of pixel values (0–255).
• For RGB images, we can plot separate histograms for the Red, Green and Blue channels.

Output

Explanation:

• cv2.imread() loads the image, cv2.cvtColor() makes it grayscale, plt.imshow() displays it, and plt.hist() shows its intensity histogram.
• A loop over ('r','g','b') uses cv2.calcHist() and plt.plot() to display RGB channel histograms.

Histogram Calculation using OpenCV

OpenCV provides an in-built function cv2.calcHist() for histogram calculation. It is more efficient and widely used in image processing tasks.

Output

Explanation: cv2.imread() loads the grayscale image, cv2.calcHist() computes its histogram and plt.plot() displays it with title and labels.

Histogram for Color Images

For color images, histograms can be calculated separately for Blue, Green and Red channels.

Output

Explanation: cv2.imread() loads the image, a loop over ('b','g','r') uses cv2.calcHist() to compute each channel’s histogram and plt.plot() displays them with axis labels and title.

Plotting Histograms

Apart from analyzing images with NumPy and OpenCV, we can also plot histograms using two common methods:

1. Using OpenCV (cv2.calcHist())

cv2.calcHist() calculate histograms efficiently. It gives full control over the number of bins, intensity range and even allows applying masks for region-based histogram analysis.

Input:

Output:

Explanation: cv2.imread() loads the image in grayscale, cv2.calcHist() computes its intensity histogram and plt.plot() displays it.

2. Using plt.hist()

plt.hist(), an image is flattened into a 1D array and its pixel intensity distribution is quickly visualized, making it ideal for simple exploration tasks.

Output:

Explanation: Here, img.ravel() flattens the image into a 1D array and plt.hist() plots its pixel intensity distribution as a histogram.