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

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]

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 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

The following program code shows how you can plot a newline in matplotlib label with Tex.StepsSetup X-axis and Y-axis labels for the diagram with to plot a newline in the labels.Set the current .rcParams for axes facecolor; the group is axed.Use 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 ... Read More

Using the FuncAnimation method, we can create a film. We will create a user-defined method, update, to keep on changing the position of particles and at the end, the method would return the scatter instance.StepsGet the particles initial position, velocity, force, and size.Create a new figure, or activate an existing figure with figsize = (7, 7).Add an axes to the current figure and make it the current axes, with xlim and ylim.Plot the scatter for initial position of the particles.Makes an animation by repeatedly calling a function *func*. We can pass a user-defined method that helps to change the position ... Read More

Using style='plain' in the ticklabel_format() method, we can restrict the value being changed into exponential form.StepsPass two lists to draw a line using plot() method.Use 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

In this program, we will calculate the MEDIAN of all the pixels in each channel using the Pillow library. There are a total 3 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 median of the pixels.Example Codefrom PIL import Image, ImageStat im = Image.open('image_test.jpg') stat = ImageStat.Stat(im) print(stat.median)Output[41, 43, 40]