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By creating a 3D projection on the axis and iterating that axis for different angles using view_init(), we can rotate the output diagram.StepsCreate a new figure, or activate an existing figure.Add an `~.axes.Axes` to the figure as part of a subplot arrangement with nrow = 1, ncols = 1, index = 1, and projection = '3d'.Use the method, get_test_data to return a tuple X, Y, Z with a test dataset.Plot a 3D wireframe with data test data x, y, and z.To make it rotatable, we can set the elevation and azimuth of the axes in degrees (not radians), using view_init() ... Read More
First, we can create bars using plt.bar and using xticks. Then, we can align the labels by setting the “vertical” or “horizontal” attributes in the “rotation” key.StepsMake lists, bars_heights, and bars_label, with numbers.Make a bar plot using bar() method, with bars_heights and length of bars_label.Get or set the current tick locations and labels of the X-axis, using xticks() with rotation='vertical' and bars_label.To show the plot, use plt.show() method.Examplefrom matplotlib import pyplot as plt bars_heights = [14, 8, 10] bars_label = ["A label", "B label", "C label"] plt.bar(range(len(bars_label)), bars_heights) plt.xticks(range(len(bars_label)), bars_label, rotation='vertical') plt.show()OutputRead More
Use the plot method of matplotlib and set the legend with different sets of colors.StepsSet the X-axis label using plt.xlabel() method.Set the Y-axis label using plt.ylabel() method.Plot the lines using plt.plot() method with [9, 5], [2, 5] and [4, 7, 8] array.Initialize two variables; location = 0 for the best location and border_drawn_flag = True (True, if border to be drawn for legend. False, if border is not drawn).Use plt.legend() method for the legend and set the location and border_drawn_flag accordingly to get the perfect legend in the diagram.Show the figure using plt.show() method.Exampleimport matplotlib.pyplot as plt plt.ylabel("Y-axis ") ... Read More
In this program, we will down sample an image. Downsampling is decreasing the spatial resolution while keeping the 2D representation of an image. It is typically used for zooming out of an image. We will use the pyrdown() function in the openCV library to complete this task.Original ImageAlgorithmStep 1: Fead the image. Step 2: Pass the image as a parameter to the pyrdown() function. Step 3: Display the output.Example Codeimport cv2 image = cv2.imread('testimage.jpg') print("Size of image before pyrDown: ", image.shape) image = cv2.pyrDown(image) print("Size of image after pyrDown: ", image.shape) cv2.imshow('DownSample', image)OutputSize of image before pyrDown: (350, ... Read More
Using the savefig method of the pyplot package, we can save the figure remotely by specifying the location of the figure.StepsTo use a different backend, set it using matplotlib.use('Agg') method.Plot the lines using plot() method.Using savefig() method, we can save the image remotely, just putting the directory.To show the figure, use plt.show().Exampleimport matplotlib matplotlib.use('Agg') from matplotlib import pyplot as plt plt.plot([1, 2, 3]) plt.savefig("remotely_fig.png")Output
In this program, we will calculate the mean of all the pixels in each channel using the Pillow library. There are a total three channels in an image and therefore, we will get a list of three values.Original ImageAlgorithmStep 1: Import the Image and ImageStat libraries. Step 2: Open the image. Step 3: Pass the image to the stat function of the imagestat class. Step 4: Print the mean of the pixels.Example Codefrom PIL import Image, ImageStat im = Image.open('image_test.jpg') stat = ImageStat.Stat(im) print(stat.mean)Output[76.00257724463832, 69.6674300254453, 64.38017448200654]
To plot multiple lines in a diagram, we can use the cycler that could help to set a new color from the given list of colors. (Here, ‘r’ => ‘red’, ‘g’ => ‘green’, ‘y’ => ‘yellow’, ‘b’ => ‘blue’).StepsUse a cycler to set the color for the group of lines. The color list consists of ‘r’ for red, ‘g’ for green, ‘b’ for blue, and ‘y’ for yellow.The cycler class helps to create a new Cycler object from a single positional argument, a pair of positional arguments, or the combination of keyword arguments.Plot the number of lines with different colors.Use ... Read More
In this program, we will up sample an image. Up sampling is increasing the spatial resolution while keeping the 2D representation of an image. It is typically used for zooming in on a small region of an image. We will use the pyrup() function in the openCV library to complete this task.Original ImageAlgorithmStep 1: Read the image. Step 2: Pass the image as a parameter to the pyrup() function. Step 3: Display the output.Example Codeimport cv2 image = cv2.imread('testimage.jpg') print("Size of image before pyrUp: ", image.shape) image = cv2.pyrUp(image) print("Size of image after pyrUp: ", image.shape) cv2.imshow('UpSample', image)OutputSize ... Read More
In this program, we will perform the Blackhat operation on an image using OpenCV. BlackHat transform is used to enhance dark objects of interest in a bright background. We will use the morphologyEx(image, cv2.MORPH_BLACKHAT, kernel) function.Original ImageAlgorithmStep 1: Import cv2. Step 2: Read the image. Step 3: Define the kernel size. Step 4: Pass the image and kernel to the cv2.morphologyex() function. Step 5: Display the output.Example Codeimport cv2 image = cv2.imread('image_test.jpg') filter_size = (5,5) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, filter_size) image = cv2.morphologyEx(image, cv2.MORPH_BLACKHAT, kernel) cv2.imshow('BlackHat', image)Output
In this program, we will perform the TopHat operation on images. TopHat operation is a morphological operation that is used to extract small elements and details from given images. TopHat is used to enhance bright objects in a dark background. We will use the morphologyEx(image, cv2.MORPH_TOPHAT, kernel) functionOriginal ImageAlgorithmStep 1: Import cv2. Step 2: Read the image. Step 3: Define the kernel size. Step 4: Pass the image and kernel to the cv2.morphologyex() function. Step 5: Display the output.Example Codeimport cv2 image = cv2.imread('tophat.jpg') filter_size = (5, 5) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, filter_size) image = cv2.morphologyEx(image, cv2.MORPH_TOPHAT, kernel) cv2.imshow('TopHat', image)OutputExplanationAs ... Read More