Histogram using Numpy and OpenCV

Histogram Overview

Histogram can be defined as a graph or plot, which gives you an overall idea about the intensity distribution of an image. It is a plot with pixel values (ranging from 0 to 255, not always) in X-axis and corresponding number of pixels in the image on Y-axis.

Histogram Calculation in OpenCV

We can use cv2.calcHist() function to find the histogram. Let’s familiarize with the function and its parameters:

cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]])

• images : it is the source image of type uint8 or float32. it should be given in square brackets, ie, “[img]”.

• channels : it is also given in square brackets. It is the index of channel for which we calculate histogram. For example, if input is grayscale image, its value is [0]. For color image, you can pass [0], [1] or [2] to calculate histogram of blue, green or red channel respectively.

• mask : mask image. To find histogram of full image, it is given as “None”. But if you want to find histogram of particular region of image, you have to create a mask image for that and give it as mask.

• histSize : this represents our BIN count. Need to be given in square brackets. For full scale, we pass [256].say for example, you need to find the number of pixels lying between 0 to 15, then 16 to 31, …, 240 to 255. You will need only 16 values to represent the histogram. ranges : this is our RANGE. Normally, it is [0,256].

Let’s start with a sample image. You have to simply load an image in grayscale mode and find its full histogram. Here hist is a 256×1 array wherein each value corresponds to number of pixels in that image with its corresponding pixel value.

img = cv2.imread('home.jpg',0)

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

Histogram Calculation in Numpy

Numpy also provides you a function, np.histogram(). So instead of calcHist() function, you can use the below code :

hist,bins = np.histogram(img.ravel(),256,[0,256])

hist is same as we calculated before. But bins will have 257 elements, because Numpy calculates bins as 0-0.99, 1-1.99, 2-2.99 etc. So final range would be 255-255.99. To represent that, they also add 256 at end of bins. But we don’t need that 256 and upto 255 is sufficient.

Plotting Histograms

We can plot Histogram using Matplotlib plotting functions which is a short and quick way to plot it or we can also use OpenCV drawing functions to plot a Histogram. Let’s understand how to plot a Histogram.

Using Matplotlib

Let’s start with a sample image. You have to simply load an image in grayscale mode and find its full histogram. Here hist is a 256×1 array wherein each value corresponds to number of pixels in that image with its corresponding pixel value. Matplotlib comes with a histogram plotting function : matplotlib.pyplot.hist().It directly finds the histogram and plot it. You need not use calcHist() or np.histogram() function to find the histogram.

See the code below

import cv2

import numpy as np

from matplotlib import pyplot as plt

 img = cv2.imread('home.jpg',0)

 plt.hist(img.ravel(),256,[0,256]); plt.show()

Application of Mask

We used cv2.calcHist() to find the histogram of the full image. In case you want to find histograms of some regions of an image, you can create a mask image with white color on the region you want to find histogram and black otherwise. Then pass this as the mask.

img = cv2.imread('home.jpg',0)  '

# create a mask

 mask = np.zeros(img.shape[:2], np.uint8)

 mask[100:300, 100:400] = 255

masked_img = cv2.bitwise_and(img,img,mask = mask)

 # Calculate histogram with mask and without mask

 # Check third argument for mask

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

 hist_mask = cv2.calcHist([img],[0],mask,[256],[0,256])

plt.subplot(221), plt.imshow(img, 'gray')

 plt.subplot(222), plt.imshow(mask,'gray')

 plt.subplot(223), plt.imshow(masked_img, 'gray')

 plt.subplot(224), plt.plot(hist_full), plt.plot(hist_mask)

 plt.xlim([0,256])

plt.show()