In this program, we will calculate the rms (root mean square) 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 root mean square of the pixels.Example Codefrom PIL import Image, ImageStat im = Image.open('image_test.jpg') stat = ImageStat.Stat(im) print(stat.rms)Output[104.86876722259062, 96.13661429330132, 91.8480515464677]

In this program, we will calculate the variance 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 variance of the pixels.Example Codefrom PIL import Image, ImageStat im = Image.open('image_test.jpg') stat = ImageStat.Stat(im) print(stat.var)Output[5221.066590958682, 4388.697801428673, 4291.257706548981]

In this program, we will calculate the standard deviation of all the pixels in each channel using the Pillow library. There are total 3 channels in an image and therefore we will get a list of three values.Original ImageAlgorithmStep 1: Import 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 standard deviation of the pixels.Example Codefrom PIL import Image, ImageStat im = Image.open('image_test.jpg') stat = ImageStat.Stat(im) print(stat.stddev)Output[72.25694839223894, 66.24724750077299, 65.50769196475312]

In this program, we will blur an image using a rank filter. The ImageFilter class in the pillow library contains a function called RankFilter() which helps to apply the rank filter. It takes two parameters, size of the kernel and rank. Rank is 0 for a min filter, size*size/2 for a median filter and size*size-1 for a max filter.Original ImageAlgorithmStep 1: Import Image and ImageFilter from Pillow. Step 2: Open the image. Step 3: Call the rankfilter() method and specify the size and rank. Step 4: Display the output.Example Codefrom PIL import Image, ImageFilter im = Image.open('image_test.jpg') im1 = ... Read More

In this program, we will blur an image using a Box filter. The ImageFilter class in the pillow library contains a function called BoxBlur() which helps to apply the box blur filter. It takes only one parameter that is blur radius.Original ImageAlgorithmStep 1: Import Image and ImageFilter from Pillow. Step 2: Open the image. Step 3: Call the boxblur() method and specify the radius. Step 4: Display the output.Example Codefrom PIL import Image, ImageFilter im = Image.open('image_test.jpg') im1 = im.filter(ImageFilter.BoxBlur(radius = 7)) im1.show()Output

In this program, we will blur an image using a Gaussian filter. The ImageFilter class in the pillow library contains a function called GaussianBlur() which helps to apply the gaussian blur filter. It takes only one parameter that is blur radius.Original ImageAlgorithmStep 1: Import Image and ImageFilter from Pillow. Step 2: Open the image. Step 3: Call the gaussianblur() method and specify the radius Step 4: Display the output.Example Codefrom PIL import Image, ImageFilter im = Image.open('image_test.jpg') im1 = im.filter(ImageFilter.GaussianBlur(radius = 9)) im1.show()Output

In this program, we will apply a minimum filter on an image using the pillow library. In median filtering, the value of each pixel in a selected window of the image is replaced by the median of that window. The filter function is used to apply different filters using the pillow library.Original ImageAlgorithmStep 1: Import Image from Pillow. Step 2: Open the image. Step 3: Call the filter function and specify the median filter. Step 4: Display the output.Example Codefrom PIL import Image, ImageFilter im = Image.open('testimage.jpg') im1 = im.filter(ImageFilter.MedianFilter(size = 7)) im1.show()Output

In this program, we will apply a minimum filter on an image using the pillow library. In mode filtering, the value of each pixel in a selected window of the image is replaced by the mode of that window. The filter function is used to apply different filters using the pillow library.Original ImageAlgorithmStep 1: Import Image from Pillow. Step 2: Open the image. Step 3: Call the filter function and specify modefilter. Step 4: Display the output.Example Codefrom PIL import Image, ImageFilter im = Image.open('testimage.jpg') im1 = im.filter(ImageFilter.ModeFilter(size = 7)) im1.show()Output

In this program, we will apply a minimum filter on an image using the pillow library. In maximum filtering, the value of each pixel in a selected window of the image is replaced by the maximum pixel of that window. The filter function is used to apply different filters using the pillow library.Original ImageAlgorithmStep 1: Import Image from Pillow. Step 2: Open the image. Step 3: Call the filter function and specify maxfilter. Step 4: Display the output.Example Codefrom PIL import Image, ImageFilter im = Image.open('testimage.jpg') im1 = im.filter(ImageFilter.MaxFilter(size = 7)) im1.show()Output

In this program, we will apply a minimum filter on an image using the pillow library. In minimum filtering, the value of each pixel in a selected window of the image is replaced by the minimum pixel of that window. The filter function is used to apply different filters using the pillow library.Original ImageAlgorithmStep 1: Import Image from Pillow. Step 2: Open the image. Step 3: Call the filter function and specify minfilter. Step 4: Display the output.Example Codefrom PIL import Image, ImageFilter im = Image.open('testimage.jpg') im1 = im.filter(ImageFilter.MinFilter(size = 7)) im1.show()Output