Image Histogram Example with OpenCV in Python

      An image histogram represents a graphical distribution of pixel intensities in an image. Pixel range can be 0 to 255 that provides 256 possible intensity level for each color channel. Histogram provides an information about the overall contrast, brightness, and intensity and tonal distribution of an image. 

    By analyzing the distribution of pixel intensities, we can adjust image contrast, brightness, or apply techniques like histogram equalization to improve the overall quality of the image.

    OpenCV API provides functions to calculate image histogram and apply equalization techniques. In this tutorial, you'll briefly learn how to build image histogram and apply equalization method by using OpenCV in Python. The tutorial covers:

1. Grayscale histogram
2. Color histogram
3. Histogram equalization
4. Source code listing
   

    We'll start by loading the required libraries.

import cv2
from matplotlib import pyplot as plt   

 

Contour Detection Example with OpenCV in Python

     Image contour detection is a technique used to identify and extract the boundaries of objects or regions in an image. The process involves detecting edges in a grayscale image and grouping them to form closed contours. Contours are widely used in tasks such as object recognition, shape analysis, and image segmentation.

    In this tutorial, you'll briefly learn how to find and draw contours in image by using  OpenCV functions in Python. The tutorial covers:

1. Contour detection
   
2. Extracting contours
3. Source code listing
   

    We'll start by loading the required libraries.

import cv2
from matplotlib import pyplot as plt 

import numpy as np  

 

Edge Detection Example with OpenCV in Python

     Edge detection is a technique in image processing that includes identifying the edges of an image, which are the boundaries between areas with different color intensity. An edge is a sharp shift in color or intensity in a picture that may be used to spot details like object borders, shape, and textures.

    Edge detection algorithms calculate the gradient of an image which describes the changing rate of pixel intensity in an image, and applies different convolution masks to detect edges lines. There are several edge detection methods in image processing. 

    In this tutorial, you'll briefly learn some of the common edge detection methods like Sobel, Canny, and Laplacian. The tutorial covers:

1. Sobel edge detection
   
2. Canny edge detection
3. Laplacian edge detection
4. Source code listing
   

    We'll start by loading the required libraries.

import cv2
from matplotlib import pyplot as plt 

import numpy as np  

 

Thresholding Example with OpenCV in Python

     Image thresholding is a technique used in image processing to separate image into two or more parts based on pixel intensity values. When we use grayscale image, the process involves setting a threshold value for the pixel intensities, and then classifying the pixels as either "foreground" or "background" based on their intensity values.
    This technique is useful for various image processing tasks such as object detection, segmentation, and feature extraction.
    OpenCV provides several thresholding methods that can be used to convert a grayscale image to a binary image. In this tutorial, you will briefly learn how to use some of the most commonly used OpenCV thresholding methods in Python. The tutorial covers:

1. Binary thresholding
2. Otsu thresholding
3. Adaptive thresholding
4. Source code listing
   

    We'll start by loading the required libraries.

import cv2
import matplotlib.pyplot as plt 

 

Image Blurring Example with OpenCV in Python

    Image blur refers to the effect of making an image less sharp and more difficult to distinguish the details. Although blurring is typically a negative effect in photographs, it is useful method in image processing tasks. Image blur is used in image processing for a variety of reasons, such as image smoothing, edge detection, obscuring sensitive information, and preprocessing steps. 

    Image blur is achieved by convolving the image with a specific kernel or filter. A kernel is a small matrix that is applied to each pixel in the image. The value of each pixel in the output image is calculated by multiplying the corresponding values in the kernel with the values of the pixels in the neighborhood of the current pixel, and then summing the results.

    In this tutorial, you'll briefly learn how to blur image with OpenCV API in Python.   

    The tutorial covers;

1. Gaussian blur
   
2. Median blur
3. Bilateral filter method
4. Averaging method
   
5. Source code listing
   

    We'll start by loading the required libraries.

import cv2
import matplotlib.pyplot as plt 

 

Univariate Interpolation Examples in Python (part-2)

    In this tutorial, you'll briefly learn how to use SciPy API's BarycentricInterpolator and KroghInterpolator classes in Python.  

    Interpolation is a method of estimating unknown data points in a known range to make the curve smoother. Univariate interpolation is a type of curve fitting that seeks the curve that best fits a set of two-dimensional data points. Since the data points are sampled from a single variable function, it is called univariate interpolation.  

    The tutorial covers;

1. Preparing test data
   
2. BarycentricInterpolator  method
3. KroghInterpolator method
4. Source code listing
   

    We'll start by loading the required libraries.

from scipy import interpolate
import matplotlib.pyplot as plt
import numpy as np