The Problem: You are coding a graphics filter that process each of an image through a filter. Each pixel is defined a triplet of real numbers (R,G,B) each of which is between 0 and 255. The filtering algorithms need to work on real numbers in order to do their job. But examining the program you discover that they only need two digits of accuracy. Any additional digits is just overkill.

The Problem: You are coding a graphics filter that process each of an image through a filter. Each pixel is defined a triplet of real numbers (R,G,B) each of which is between 0 and 255. The filtering algorithms need to work on real numbers in order to do their job. But examining the program you discover that they only need two digits of accuracy. Any additional digits is just overkill.

C++ Programming: From Problem Analysis to Program Design

8th Edition

ISBN:9781337102087

Author:D. S. Malik

Publisher:D. S. Malik

Chapter8: Arrays And Strings

Section: Chapter Questions

Problem 24PE

See similar textbooks

Similar questions

Computer Science For an image represented by a given large 3d numpy array, use numpy to get a neighborhood filter using the mean of the pixels. For example, look through each of the image’s 3×3 window of pixels and replace outlier pixels with the mean value of the others in the 3×3 window. Set the resulting pixel value to be the mean value of a pixel and its eight neighbors. For pixels on an edge, use the mean value of the pixel and its five neighbors. For pixels on corners, use the mean value of the pixel and its three neighbors. If you loop over every pixel then you must store your input and output arrays separately
Implement the"paint fill"function that one might see on many image editing programs. That is, given a screen (represented by a two-dimensional array of colors), a point, and a new color, fill in the surrounding area until the color changes from the original color.
Write a program to reduce the number of gray levels L in a PGM image from L=256 to: (i) L=128,(ii) L=32, (iii) L=8, and (iv) L=2. Show your results using the “lenna” and “peppers” images. Forcomparison purposes, subsample the gray-levels to better visualize the quantized images (e.g.,if L=128, you can use 0, 2, 4, …, 254 with Q=255 in the PGM file instead of 0, 1, 2, …, 127 withQ=127; similarly, if L=2, you can use 0, 127 with Q=255 instead of 0, 1 with Q=1).
Write a program that reads three edges for atriangle and computes the perimeter if the input is valid. Otherwise, display that theinput is invalid. The input is valid if the sum of every pair of two edges is greaterthan the remaining edge.
Write a python code that implements LSB steganography on an image(any format) that lets the user enter the hidden message data?
Please using python to implement the following task. Given a RGB color image ‘test.jpg’ noised by salt and pepper noise, first choose a good filter to remove noise on each channel, then based on the gray image of this new RGB color image, use canny edge detection to get edges. Finally show the original image and its edge image.
Modify the SmallO.java program (image attached) so that it: a) Keeps and displays a running total and the average so far of the enterednumbers. b) Displays the largest and the smallest numbers so far of the enterednumbers.
I already saw a solution to this problem here, but it is not correct The edge-detection function (detectEdges) described in Chapter 7 and shown below returns a black and white image. Think of a similar way to transform color values so that the new image is still in its original colors but the outlines within it are merely sharpened. def detectEdges(image, amount): """Builds and returns a new image in which the edges of the argument image are highlighted and the colors are reduced to black and white.""" def average(triple): (r, g, b) = triple return (r + g + b) // 3 blackPixel = (0, 0, 0) whitePixel = (255, 255, 255) new = image.clone() for y in range(image.getHeight() - 1): for x in range(1, image.getWidth()): oldPixel = image.getPixel(x, y) leftPixel = image.getPixel(x - 1, y) bottomPixel = image.getPixel(x, y + 1) oldLum = average(oldPixel) leftLum = average(leftPixel)…
The question is on the attached image. grayscale function code: from images import Image def grayscale1(image): """Converts an image to grayscale using the psychologically accurate transformations.""" for y inrange(image.getHeight()): for x inrange(image.getWidth()): (r, g, b) = image.getPixel(x, y) r = int(r * 0.299) g = int(g * 0.587) b = int(b * 0.114) lum = r + g + b image.setPixel(x, y, (lum, lum, lum))
Write out possible test paths based on the image given
Implement the "paint fill"feature seen in several picture editing products.To put it another way, if you have a screen (represented by a two-dimensional array of colours), a point, and a new colour, Fill in the surrounding area until the colour shifts away from the original.
Write a small piece of pseudo-code for generating a histogram H[L] for an intensity image I[YDIM][XDIM], where L is the total level of intensities of the image, H is the histogram array, XDIM * YDIM are the size of the image, and I[i][j] is the intensity value of the pixel at location (i,j), with i = 1, …, YDIM, and j = 1,…, XDIM. You might choose to use a certain program language that you are familiar with to write the code. You don’t have to compile the code.