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In this program, we will draw an ellipse on an image in using the OpenCV library. We will use the OpenCV function ellipse() for the same.Original ImageAlgorithmStep 1: Import cv2. Step 2: Read the image using imread(). Step 3: Set the center coordinates. Step 4: Set the axes length. Step 5: Set the angle. Step 6: Set start and end angle. Step 6: Set the color. Step 7: Set the thickness. Step 8: Draw the ellipse by passing the above parameters in the cv2.ellipse function along with the original image. Step 9: Display the final output.Example Codeimport cv2 image = ... Read More
Using plt.legend() method, we can create a legend, and passing frameon would help to keep the border over there.StepsSet the X-axis label using plt.xlabel() method.Set the Y-axis label using plt.ylabel() method.Draw lines using plot() method.Location and legend drawn flags can help to find a location and make the flag True for the 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", "orange"], loc=0, frameon=legend_drawn_flag) plt.show()OutputRead 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
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
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
We can use the twiny() method to create a second X-axis. Similarly, using twinx, we can create a shared Y-axis.StepsCreate fig and ax variables using subplots method, where default nrows and ncols are 1.Plot line with lists passed in the argument of plot() method with color="red".Create a twin of Axes with a shared Y-axis but independent X-axis.Plot the line on ax2 that is created in step 3.Adjust the padding between and around subplots.To show the figure, use plt.show() method.Exampleimport matplotlib.pyplot as plt fig, ax1 = plt.subplots() ax1.plot([1, 2, 3, 4, 5], [3, 5, 7, 1, 9], color='red') ax2 = ax1.twiny() ... Read More
The shape property is usually used to get the current shape of an array, but it may also be used to reshape the array in-place by assigning a tuple of array dimensions to it.StepsGet an array Y using np.array method.Y.shape would return a tuple (4, ).Y.shape[0] method would return 4, i.e., the first element of the tuple.Exampleimport numpy as np Y = np.array([1, 2, 3, 4]) print("Output of .show method would be: ", Y.shape, " for ", Y) print("Output of .show[0] method would be: ", Y.shape[0], " for ", Y) print("Output for i in range(Y.shape[0]): ", end=" ") for ... Read 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