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Articles by Prasad Naik
Page 2 of 4
Eroding an image using the OpenCV function erode()
In this program, we will erode an image using the OpenCV function erode(). Erosion of image means to shrink the image. If any of the pixels in a kernel is 0, then all the pixels in the kernel are set to 0. One condition before applying an erosion function on image is that the image should be a grayscale image.Original ImageAlgorithmStep 1: Import cv2 Step 2: Import numpy. Step 3: Read the image using imread(). Step 4: Define the kernel size using numpy ones. Step 5: Pass the image and kernel to the erode function. Step 6: Display the output.Example ...
Read MoreDraw a filled polygon using the OpenCV function fillPoly()
In this program, we will draw a filled polygon using the opencv function fillPoly(). The function takes in an image and the endpoints of the polygon.AlgorithmStep 1: Import cv2 and numpy. Step 2: Define the endpoints. Step 3: Define the image using zeros. Step 4: Draw the polygon using the fillpoly() function. Step 5: Display the output.Example Codeimport cv2 import numpy as np contours = np.array([[50,50], [50,150], [150,150], [150,50]]) image = np.zeros((200,200)) cv2.fillPoly(image, pts = [contours], color =(255,255,255)) cv2.imshow("filledPolygon", image)Output
Read MoreBlurring an image using the OpenCV function blur()
In this program, we will blur an image using the opencv function blur().AlgorithmStep 1: Import OpenCV. Step 2: Import the image. Step 3: Set the kernel size. Step 4: Call the blur() function and pass the image and kernel size as parameters. Step 5: Display the results.Original ImageExample Codeimport cv2 image = cv2.imread("testimage.jpg") kernel_size = (7,7) image = cv2.blur(image, kernel_size) cv2.imshow("blur", image)OutputBlurred ImageExplanationThe kernel size is used to blur only a small part of an image. The kernel moves across the entire image and blurs the pixels it covers.
Read MoreHow to create a simple screen using Tkinter?
We will create a simple screen using the Tkinter library.AlgorithmStep 1: Import tkinter. Step 2: Create an object of the tkinter class. Step 3: Display the screen.Example Codeimport tkinter as tk window = tk.Tk()Output
Read MoreAdding textures to graphs using Matplotlib
In this program, we will plot a bar graph using the matplotlib library. The most important Step in solving matplotlib related problems using the matplotlib library is importing the matplotlib library. The syntax is:import matplotlib.pyplot as pltPyplot is a collection of command style functions that make Matplotlib work like MATLAB. In addition to plotting the bar graphs, we will also add some textures to the graphs. The 'hatch' parameter in the bar() function is used to define the texture of the barAlgorithmStep 1: Define a list of values. Step 2: Use the bar() function and define parameters like xaxis, yaxis, ...
Read MorePandas program to convert a string of date into time
In this program, we will convert a date string like "24 August 2020" to 2020-08-24 00:00:00. We will use the to_datetime() function in pandas library to solve this task.AlgorithmStep 1: Define a Pandas series containing date string. Step 2: Convert these date strings into date time format using the to_datetime format(). Step 3: Print the results.Example Codeimport pandas as pd series = pd.Series(["24 August 2020", "25 December 2020 20:05"]) print("Series: ", series) datetime = pd.to_datetime(series) print("DateTime Format: ", datetime)OutputSeries: 0 24 August 2020 1 25 December 2020 20:05 dtype: object DateTime Format: 0 2020-08-24 00:00:00 1 2020-12-25 20:05:00 dtype: datetime64[ns]
Read MoreHow to use regular expressions (Regex) to filter valid emails in a Pandas series?
A regular expression is a sequence of characters that define a search pattern. In this program, we will use these regular expressions to filter valid and invalid emails.We will define a Pandas series with different emails and check which email is valid. We will also use a python library called re which is used for regex purposes.AlgorithmStep 1: Define a Pandas series of different email ids. Step 2: Define a regex for checking validity of emails. Step 3: Use the re.search() function in the re library for checking the validity of the email.Example Codeimport pandas as pd import re ...
Read MoreHow to get the nth percentile of a Pandas series?
A percentile is a term used in statistics to express how a score compares to other scores in the same set. In this program, we have to find nth percentile of a Pandas series.AlgorithmStep 1: Define a Pandas series. Step 2: Input percentile value. Step 3: Calculate the percentile. Step 4: Print the percentile.Example Codeimport pandas as pd series = pd.Series([10, 20, 30, 40, 50]) print("Series:", series) n = int(input("Enter the percentile you want to calculate: ")) n = n/100 percentile = series.quantile(n) print("The {} percentile of the given series is: {}".format(n*100, percentile))OutputSeries: 0 10 1 ...
Read MoreHow to calculate the frequency of each item in a Pandas series?
In this program, we will calculate the frequency of each element in a Pandas series. The function value_counts() in the pandas library helps us to find the frequency of elements.AlgorithmStep 1: Define a Pandas series. Step 2: Print the frequency of each item using the value_counts() function.Example Codeimport pandas as pd series = pd.Series([10,10,20,30,40,30,50,10,60,50,50]) print("Series:", series) frequency = series.value_counts() print("Frequency of elements:", frequency)OutputSeries: 0 10 1 10 2 20 3 30 4 40 5 30 6 50 7 10 8 60 9 50 10 50 dtype: int64 Frequency of elements: 50 3 10 3 30 2 20 1 40 1 60 1 dtype: int64
Read MoreFinding the multiples of a number in a given list using NumPy
In this program, we will find the index position at which a multiple of a given number exists. We will use both the Numpy and the Pandas library for this task.AlgorithmStep 1: Define a Pandas series. Step 2: Input a number n from the user. Step 3: Find the multiples of that number from the series using argwhere() function in the numpy library.Example Codeimport numpy as np listnum = np.arange(1, 20) multiples = [] print("NumList:", listnum) n = int(input("Enter the number you want to find multiples of: ")) for num in listnum: if num % n == ...
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