Hi! The files are located here:

https://drive.google.com/drive/folders/10YaiNbDPKLlLKZQW6tV0Q9Ni0BHtuodl?usp=sharing

Please write in c++

This assignment is divided into several parts as described below. Each part builds on top of the other. 1) Setup the project (5 points) For this assignment, you will need to work within an environment that allows compiling multiple files (e.g. g++, Code_:Blocks, repl.it, Geany). Download the hw04.zip from Canvas and extract them into your project directory (A Code::Blocks project file hw04.cbp is available if you would like to use it). 1. stb_image . h 2. stb_image_write. h 3. Image . h 4. Image . opp 5. ImageProcessor. h 6. ImageProcessor . opp (Note: the run (. . .) method in this class is where you will write your program) 7. main . opp Data used for this assignment is located in the /data/ directory. For example: 8. data/statue . png 9. data/dog . png 10. data/threshold filter. txt 11. data/blur_kernel . txt 12. data/ composite_filter . txt 13. etc . . . These source files enable us to read and write images to the filesystem. The Image.h file contains the interface for the Image class that you will be using. Familiarize yourself with the functions that are available in the Image.h class. Your application will be developed inside the ImageProcessor: : run ( . . .) method inside the ImageProcessor . opp file. #include "Image.h" at the top of Image Processor . epp for loading and saving images. 2) Work with images. (5 points) Note: you will need to understand the methods available in the Image.h file to know how to work with the Image and the Color classes. Inside your run (. . . ) method, load in "data/statue.png" using the Image class. Print out the width and height. Save a new file as "data/statue_copy.png". Verify that thenew image is a copy of the old one. Next create a new image by specifying the width = 256 and height = 256 in the constructor. Set each pixel color to Color(1, 0, 0, 1), which is red. Save the image as "data/red.png". Verify that the image created is completely red. Finally, create a gradient image from left to right (width = 256, height = 32) that increases the green intensity. So at the left the intensity is black and the right it is green. Save the image as "data/green_gradient.png". Verify the image looks similar to the following gradient: 3) Apply a threshold filter (10 points) Create a new class called ThresholdFilter using two files: ThresholdFilter . h - Contains the interface for the class ThresholdFilter . epp - Contains the implementation of the class Add a constructor for the Filter class that accepts a filename (e.g. "data/threshold_filter.txt"). Inside the constructor, read the floating point value stored in the file into a member variable. Create a method in the ThresholdFilter class called apply as follows: . void apply (const Image& original, Image& filtered) ; The apply method does the following steps to threshold an image: 1. Copy the original image into the filtered image. This allows us to make changes to the filtered image without changing the original image. 2. Loop through all the pixels and apply the following logic: if the luminance of the pixel value is less than the threshold value from the file: set the filtered pixel value to black [Color(0,0,0,1)] else set the filtered pixel value to white [Color(1, 1,1,1)] In the run() function in ImageProcessor.cpp, load in the "data/statue.png" image. Then create a ThresholdFilter by passing in "data/threshold_filter.txt" into the constructor.Then call the apply method on the original image to create a filtered image. Save the filtered image as "data/statue_threshold.png". Verify that "statue_threshold.png" looks like the right side of Figure 1. 4) Load in a convolution filter (10 points) Note: you will need to understand the methods available in the Image.h file to know how to work with the Kernel class. Create a new class called ConvolutionFilter using two files: . ConvolutionFilter . h - Contains the interface for the class . ConvolutionFilter . epp - Contains the implementation of the class A convolution filter is a bit more complex than the threshold filter, but once implemented it can be very powerful. Fortunately, inside Image.h is a Kernel class which does much of this work for you. A kemel in image processing is a square matrix with an odd number of rows and columns so that it has a middle value. The floating point values in the kernel are multiplied by the surrounding pixels and integrated into a single pixel value (See Figure 3). kernel Input output Figure 3 - For each pixel, a kernel integrates the surrounding pixels together to produce a final output pixel. Convolution, the mechanism that enables kernel integration, is accomplished by the following function: void Kernel: : convolv (const Images original, Images filtered) ; The task of the ConvolutionFilter class is to load in and store a kernel from a file. The format of the file is as follows:Format: kernel size matrix rows and columns separated by spaces and new lines Example: 3 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 Create a constructor inside ConvolutionFilter that reads in a file like the ThresholdFilter. The ConvolutionFilter reads in a kemel (e.g. data/blur_kernel.txt) and stores the kernel as a member variable. Inside the ConvolutionFilter create an apply method just like the ThresholdFilter. Inside the apply, the ConvolutionFilter should copy the original image to the filtered image and call the kernel's convolv (. . . ) method. Finally, use the ConvolutionFilter to apply a blur kernel ("data/blur_kernel.txt") to the "data/dog.png" image and save the filtered image as "data/dog_blur.png". Verify that the "data/dog_blur.png" looks similar to Figure 2. 5) Make the program dynamic based on command line arguments. (10 points) If no arguments are passed into the program from the command line, then execute all of the steps above. However, we want to be able to apply filters by passing in images and filters. Have another mode where users can pass in an image, filter information, and an output image. This mode is enabled if there are command line arguments as follows: Command line usage (Note: "/main" will be the name of your compiled program - which may be different across compilers): . /main

Examples: ./main dog.png threshold data/threshold_filter. txt data/dog_threshold.png /main statue. png convolution data/blur_kernel . txt data/statue_blur . png ./main convolution data/edge_detect_kernel. txt

./main convolution data/motion_blur_kernel. txt

The and

are your own files you would like to filter. Enjoy filtering images!Test your program using not only the example data above, but other cases as well. And revise your program until you are sure it is correct. When you are done, turn in the following by logging into gradescope and uploading your files for the "Homework 4" submission: ImageProcessor. h . . . . ImageProcessor . CPP ThresholdFilter. h ThresholdFilter . opp ConvolutionFilter . h . ConvolutionFilter . epp CompositeFilter . h CompositeFilter . cpp If you name your file incorrectly it will be unable to compile and run your code, so you will fail all test cases. Make sure to read the data from and save them to the directory ".data/" or it will have segment faults. You may submit files as many times as you want until the deadline to try and fix the code if you fail a test case. The good news is that we will be testing your ImageProcessor : : run (. . .) method and whether the correct images are created, meaning formatting within your main.cpp is not important for this assignment. Be free to be as creative! The data that is used for testing may be different from the city data set that is given with the assignment. Challenge Problem: Create a Composite Filter (5 points extra credit) This problem is optional and is extra credit. The composite filter applies multiple filters to the same image. It looks like the other filters, but it's data includes a set of filters and links to the corresponding filter data. Here is how to call the composite filter: /main composite

The data for the CompositeFilter (e.g. composite_filter.txt) is a list of filter types and corresponding links to the filter data as follows: Format: . . . Example: (composite_filter.txt) convolution data/blur_kernel . txt threshold data/threshold, txt Example: (composite?_filter.txt) convolution data/sharpen_kernel. txt composite data/composite_filter . txt Apply multiple filters with the following commands: ./main dog.png composite data/composite_filter. txt data/dog_composite. png ./main dog.png composite data/composite2_filter. txt data/dog_composite2. png The challenge program should be implemented in CompositeFilter.h and CompositeFilter . opp