In this program, we will dilate an image using the dilate function in the OpenCV library. Dilation adds pixels to the boundaries of objects in an image, i.e., it expands the image on all sides.Original ImageAlgorithmStep 1: Import cv2 and numpy. Step 2: Read the image using opencv.imread(). Step 3: Define the kernel using np.ones() function. Step 4: Pass the image and kernel to the dilate() function. Step 5: Display the imageExample Codeimport cv2 import numpy as np image = cv2.imread('testimage.jpg') kernel = np.ones((3, 3), np.uint8) image = cv2.dilate(image, kernel) cv2.imshow('Dilated Image', image)OutputExplanationAs you can see, the image ... Read More

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 More

To test for the significance of proportion between two categorical columns of an R data frame, we first need to find the contingency table using those columns and then apply the chi square test for independence using chisq.test. For example, if we have a data frame called df that contains two categorical columns say C1 and C2 then the test for significant relationship can be done by using the command chisq.test(table(df$C1,df$C2))Example Live Demox1

In this article, we can create a pie chart to show our daily activities, i.e., sleeping, eating, working, and playing. Using plt.pie() method, we can create a pie chart with the given different data sets for different activities.StepsCreate a list of days, i.e., [1, 2, 3, 4, 5]. Similarly, make lists for sleeping, eating, playing, and working. There is an activities list that keeps “sleeping”, “eating”, “working” and “playing”.Make a list of colors.Use plt.pie() method to draw the pie chart, where slices, activities, colors as cols, etc. are passed.Set a title for the axes, i.e., “Pie Chart”.To show the figure ... Read More

Using plt.legend(), we can add or show certain items just by putting the values in the list.StepsSet the X-axis label using plt.xlabel() method.Set the Y-axis label using plt.ylabel() method.Plot the lines using the lists that are passed in the plot() method argument.Location and legend_drawn flags can help to find a location and make the flag True for border.Set the legend with “blue” and “orange” elements.To show the figure use plt.show() method.Exampleimport matplotlib.pyplot as plt plt.ylabel("Y-axis ") plt.xlabel("X-axis ") plt.plot([9, 5], [2, 5], [4, 7, 8]) location = 0 # For the best location legend_drawn_flag = True plt.legend(["blue", ... Read More

First, we can create fig, ax using subplots() and then, we can plot the lines. After that, using ax.yaxis.set_minor_locator(tck.AutoMinorLocator()), we can turn on the minor ticks.StepsCreate fig and ax variables using subplots method, where default nrows and ncols are 1.Plot the line using two lists.Set the locator of the minor ticker.Dynamically find minor tick positions based on the positions of major ticks. The scale must be linear with major ticks evenly spaced.Using plt.show() method, we can show the figure.Exampleimport matplotlib.pyplot as plt import matplotlib.ticker as tck fig, ax = plt.subplots() plt.plot([0, 2, 4], [3, 6, 1]) ax.yaxis.set_minor_locator(tck.AutoMinorLocator()) plt.show()OutputRead More

To replace a particular value in R data frame with a new value, we can use ifelse function where the new value will be placed after the condition and if the column values do not match the condition then the same column will be placed. For example, if we have a data frame called df that contains a column x having 20 values and some of them are 5 and if we want to replace 5 with 2 then we can use the command df$x

In this program, will blur an image using the openCV function GaussianBlur(). Gaussian blur is the process of blurring an image using the gaussian function. It is widely used in graphics software to remove noise from the image and reduce detail.AlgorithmStep 1: Import cv2. Step 2: Read the original image. Step 3: Apply gaussian blur function. Pass the image and the kernel size as parameter. Step 4: Display the image.Original ImageExample Codeimport cv2 image = cv2.imread("testimage.jpg") Gaussian = cv2.GaussianBlur(image, (7,7), 0) cv2.imshow("Gaussian Blur", Gaussian)OutputGaussian 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.

In this program, we will write text on an image using the opencv function putText(). This function takes in the image, font, coordinates of where to put the text, color, thickness, etc.Original ImageAlgorithmStep 1: Import cv2 Step 2: Define the parameters for the puttext( ) function. Step 3: Pass the parameters in to the puttext() function. Step 4: Display the image.Example Codeimport cv2 image = cv2.imread("testimage.jpg") text = "TutorialsPoint" coordinates = (100,100) font = cv2.FONT_HERSHEY_SIMPLEX fontScale = 1 color = (255,0,255) thickness = 2 image = cv2.putText(image, text, coordinates, font, fontScale, color, thickness, cv2.LINE_AA) cv2.imshow("Text", image)Output