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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
We can use the attribute sharex = "ax1", and then, use the subplot method to zoom the subplots together.StepsAdd a subplot to the current figure with (nrow = 1, ncols = 2, index = 1).Add line on the current subplot with (nrow = 1, ncols = 2, index = 1).Add a subplot to the current figure with (nrow = 1, ncols = 2, index = 2).Add line on the current subplot with (nrow = 1, ncols = 2, index = 2), where sharex can help to share the x or y `~matplotlib.axis` with sharex and/or sharey. The axis will have ... Read More
In this article, we will learn how to read and display images using the OpenCV library.OpenCV is a library of programming functions mainly aimed at real time computer vision. Before reading an image, make sure that the image is in the same directory as your program.AlgorithmStep 1: Import OpenCV. Step 2: Read an image using imread(). Step 3: Display the image using imshow().Example Codeimport cv2 as cv image = cv.imread ('ronaldo.jpg') cv.imshow('Cristiano Ronaldo', image)Output
In this program, we will write an image or save an image to a file using OpenCV.AlgorithmStep 1: Import cv2 Step 2: Read the image using opencv.imread() Step 3: Save the image using opencv.imwrite(filename, image)Example Codeimport cv2 import os image = cv2.imread('testimage.jpg') directory = r'C:\Users\prasa\Desktop' os.chdir(directory) cv2.imwrite('CAMERAMAN.jpg', image)OutputThis program will save the image in the directory as same as the original image directoryExplanationEnsure that you have set the proper directory in order for the program to execute without errors.
Using Pandas, we will create a dataframe and set the vertical lines on the created axes, using axvline lines.StepsUsing panda we can create a data frame.Creating a data frame would help to create help.Using axvline(), add a vertical line across the axes, where color is green, linestyle="dashed".Using axvline(), add a vertical line across the axes, where color is red, linestyle="dashed".Using plt.show(), show the plot.Exampleimport pandas as pd from matplotlib import pyplot as plt df = pd.DataFrame(index=pd.date_range("2019-07-01", "2019-07-31")) df["y"] = 1 ax = df.plot() ax.axvline("2019-07-24", color="green", linestyle="dashed") ax.axvline("2019-07-31", color="red", linestyle="dashed") plt.show()OutputRead More
In this article, we can take a program code to show how we can make a 3D plot interactive using Jupyter Notebook.StepsCreate a new figure, or activate an existing figure.Create fig and ax variables using subplots method, where default nrows and ncols are 1, projection=’3d”.Get x, y and z using np.cos and np.sin function.Plot the 3D wireframe, using x, y, z and color="red".Set a title to the current axis.To show the figure, use plt.show() method.Exampleimport matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_subplot(111, projection='3d') u, v = np.mgrid[0:2 * np.pi:30j, 0:np.pi:20j] x = np.cos(u) * ... Read More
In this program, we will change the color scheme of an image from rgb to grayscaleAlgorithmStep 1: Import OpenCV. Step 2: Read the original image using imread(). Step 3: Convert to grayscale using cv2.cvtcolor() function.Example Codeimport cv2 image = cv2.imread('colourful.jpg') cv2.imshow('Original',image) grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imshow('Grayscale', grayscale)OutputOriginal Image:Grayscale Image:
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
In this article, we will draw a circle on an image using the OpenCV function circle().Original ImageAlgorithmStep 1: Import OpenCV. Step 2: Define the radius of circle. Step 3: Define the center coordinates of the circle. Step 4: Define the color of the circle. Step 5: Define the thickness. Step 6: Pass the above arguments into cv2.circle() along with the image. Step 7: Display the output.Example Codeimport cv2 image = cv2.imread('testimage.jpg') radius = 100 center = (350, 175) color = (255,255,0) thickness = 15 image = cv2.circle(image, center, radius, color, thickness) cv2.imshow('Circle', image)Output
In the following code, we will see how to 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 X-axis but independent Y-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.twinx() ax2.plot([11, 12, 31, 41, 15], [13, 51, ... Read More