I need programming for this assignment
Project 6 Project 6 – Image manipulation CET 151 W19, revision 1.0 Due Apr 16, 9 pm 1 Introduction A grayscale image is represented by a computer as a two-dimensional matrix composed of individual pixels (a single cell in the matrix). For a grayscale image, a pixel is a single value on the range from 0 to 255 (8-bit unsigned integer, or uint8) where the value 0 represents black and 255 represents white. The values in the middle of the range represent shades of gray. A color image is similar to a grayscale image in that it is composed of individual pixels. However, as there is more information to be encoded, each pixel now includes the intensity of the three primary colors of light – red, blue, and green (RGB) – where each color layer is known as a "channel." Thus, a color image is represented as a three- dimensional matrix, where the dimensions are the height, width, and depth. Similar to the intensity value of a grayscale pixel, the intensity value for each color channel for a color pixel lies on a range from 0 to 255 (for uint8). Now, though, the number represents the intensity of that particular channel’s color as a contribution to the composite pixel’s color. For example, a uint8 value of 255 in the red channel matrix represents pure red at full intensity. Below are some examples. R255, G255, B255 R0, G255, B0 R0, G0, B0 In MATLAB, a color image is represented by a 3-D matrix, made up of three 2-D matrices: one for each color channel. The third dimension is the color channel. Index 1 is the red channel, index 2 is the green channel, and index 3 is the blue channel. 2 Project For this project, you will create “filters” to manipulate an image matrix to produce certain effects. In order to manipulate the image matrix of uint8, you need to first convert it to a matrix of doubles with the double function. Inversion: To apply an inversion filter to an image you must invert the individual intensities for each respective channel. This is formally represented by the following relationship: ?new= 255 − ?old where ?new represents the new intensity and ?old is the original intensity value. We subtract from 255 as that is the maximum intensity in the range. Grayscale: Converting a color image is essential for giving an authentically old look to an image, or for convincing others of a MiPhone user’s artistic appreciation. To convert an image to grayscale, you need to calculate a single intensity value to represent a pixel – this intensity value is a combination of the individual RGB channel intensities as specified by the mathematical relationship: ?GS = 0.299×?R + 0.587×?G + 0.114×?B Where ?GS is the intensity value of the grayscale and ?R, ?G, and ?B represent the R, G, and B channel intensities respectively. As a general note, this is one method to convert to grayscale – others exist which rely on various parameters, from different coefficients to using information specific to the image (min and max brightness). For the purpose of our application you should stick with the method specified above. Dimming: Sometimes the brightness of an image can be too much on the eyes and a muter version of the picture can produce an improved visual experience. To dim a picture, you need to scale the original range of 0 to 255 for the intensity to one that is between 0 and a new maximum value. (Note this is not the same as simply capping the brightness values.) Gaussian Blur: Where would we be without the blur filter? A simple blur can make sharp objects in an image appear less dangerous, or introduce the illusion of motion – which is a little frightening when it comes to cupcakes. In general, blurring is achieved by spreading the color information in each pixel across its neighboring pixels. To perform the blurring for our application, a square “window” matrix traverses the original image matrix, calculating the weighted average of the pixels within that window and storing the result in the center pixel of the new image – this needs to be done independently for each pixel and for each color channel. Pixels near the edges, where a full window would not fit, do not get changed. Using a larger window causes blurred part of the image to become blurrier. The weights for a given window are determined using the Gaussian distribution – hence the name Gaussian blur. To obtain the values for the Gaussian weights, call the user-defined function gaussWeights.p provided – do this from within the blurImage function. The gaussWeights function takes two inputs: the original image matrix (because a parameter within gaussWeights is based on its dimensions), and the number of rows/columns in the window, N. As a check, the matrix returned by gaussWeights should be the same size (NxN) as the window and all its elements should add up to one (1). 3 Files provided This program files are on canvas in the project6 folder. 4 Deliverables and grading You need to write four functions. The function names and definition lines must be invertImage.m function [inverted] = invertImage(orig) grayscaleImage.m function [gray] = grayscaleImage(orig)
dimImage.m function [dimmed] = dimImage(orig, new_max)
blurImage.m function [blurred] = blurImage(orig, window_size)
Your functions should never display an image directly. In other words, your functions should never call the imshow() or image() functions. You can view the output of your functions by displaying the resulting images with Matlab commands such as: imshow(uint8(invertImage(imagematrix))) Turn in the Matlab functions by 9 pm on the due date. This project is worth 80 points. Each filter function is 15 points for functionality and 5 points for readability and style. You may assume non-degenerate inputs. Images tested will be uint8 images with 3 color channels. You should not assume input images are square. In blurImage, window_size will be smaller than the smallest dimension of the image.