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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
In this program, we will draw a rectangle using the OpenCV function rectangle(). This function takes some parameters like starting coordinates, ending coordinates, color and thickness and the image itself.Original ImageAlgorithmStep 1: Import cv2. Step 2: Read the image using imread(). Step 3: Define the starting coordinates. Step 5: Define the ending coordinates. Step 6: Define the color and the thickness. Step 7: Draw the rectangle using the cv2.reactangle() function. Step 8: Display the rectangle.Example Codeimport cv2 image = cv2.imread('testimage.jpg') height, width, channels = image.shape start_point = (0, 0) end_point = (width, height) color = (0, 0, 255) thickness ... Read More
Using Pandas, we can create a dataframe and can create a figure and axes variable using subplot() method. After that, we can use the ax.scatter() method to get the required plot.StepsMake a list of the number of students.Make a list of marks that have been obtained by the students.To represent the color of each scattered point, we can have a list of colors.Using Pandas, we can have a list representing the axes of the data frame.Create fig and ax variables using subplots method, where default nrows and ncols are 1.Set the “Students count” label using plt.xlabel() method.Set the “Obtained marks” ... Read More
To prevent scientific notation, we must pass style='plain' in the ticklabel_format method.StepsPass two lists to draw a line using plot() method.Using ticklabel_format() method with style='plain'. If a parameter is not set, the corresponding property of the formatter is left unchanged. Style='plain' turns off scientific notation.To show the figure, use plt.show() method.Examplefrom matplotlib import pyplot as plt plt.plot([1, 2, 3, 4, 5], [11, 12, 13, 14, 15]) plt.ticklabel_format(style='plain') # to prevent scientific notation. plt.show()Output