Assignment 1 – Image Processing Solution

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Description

Overview:

In this assignment you will implement the basic methods involved in 2D graphics. The focus will be mostly on implementing filters that take an input image, process it, and produces a modified output image.

 

Getting Started:

You should use the skeleton code on the course webpage as a starting point for the assignment. There are several files that are provided for you to setup the framework and read/write image files, but you should only change image.cpp.

The files that need to be in your project are:

  • main.cpp – Parses the command line arguments, calls the appropriate image

functions

  • stb_image.h – Reads images files (.bmp, .jpg/.jpeg, .png, .tga)
  • stb_image_write.h – Write images files (.bmp, .jpg/.jpeg, .png, .tga)
  • pixel.[h/cpp] – Pixel processing
  • image.[h/cpp] – Image Processing (The only file you need to edit!)

 

A small number of starter images are included with the code, but you can find more

in your personal collection or online (https://www.google.com/imghp). The images

you use must be one of the supported formats (.bmp, .jpg/.jpeg, .png, .tga).

 

How the Program Works:

The UI in this assignment was kept as simple as possible, so you can

concentrate on the image processing. The program runs on the command line and performs operation in the order that they appear in the arguments. For example, to increase the brightness of the image in.bmp by 10%, and save the result in the image out.bmp, you would type:

 

image -input in.bmp -brightness 1.1 -output out.bmp

 

Notice the input parameter must appear first. Remember, everything happens in the order specified. First the input, then the brightness change, then the writing to the specified output file.

 

For several of the filters, there is more than one corresponding argument. To see the

complete list of options, type:

image –help

 

If you specify more than one option, the options are processed in the order that they

are encountered. For example,

image -input in.bmp -contrast 0.8 -scale 0.5 0.5 –output out.bmp

would first decrease the contrast of the input image by 20%, and then scale down the result by 50% in both x and y directions. It is also possible to specify -output multiple times, to save out intermediate results:

 

image -input in.bmp -blur 5 -output blurred.bmp -edgeDetect

-output edges.bmp -rotate 30 -output allCombined.bmp

 

What You Have To Do:

The following is a list of features that you may implement (roughly by difficulty). The

number in front corresponds to how many points the feature is worth:

  • (1) Brighten: This filter is done for you and you can use it as a starting point.
  • (3) Random noise: Add noise to an image.
  • (3) Crop: Extract a subimage specified by a corner and area.
  • (3) Extract Channel: Leave specified channel intact and set all others to zero.
  • (3) Contrast: Change the contrast of an image.
  • (3) Saturation: Change the saturation of an image.
  • (4) Sharpen: Sharpen an image by extrapolating from a blurred version.
  • (5) Quantize: Change the number of bits per channel of an image, using

simple rounding.

  • (5) Random dither: Convert an image to a given number of bits per channel,

using a randomized threshold.

  • (10) Blur: Blur an image by convolving it with a Gaussian low-pass filter.
  • (10) Edge detect: Detect edges in an image by convolving it with an edge

detection kernel.

  • (10) Ordered dither: Convert an image to a given number of bits per channel,

using a 4×4 ordered dithering matrix.

  • (10) Floyd-Steinberg dither: Convert an image to a given number of bits per

channel, using dithering with error diffusion.

  • (10) Scale: Scale an image up or down in size by a real valued factor.
  • (10) Rotate: Rotate an image by a given angle.
  • (10) Fun: Warp an image using a non-linear mapping of your choice (examples are fisheye, sine, bulge, swirl).

 

For any feature that involves resampling (e.g. scale, rotate, fun), you must provide

three sampling methods: point sampling, bilinear sampling, and Gaussian sampling.

                                                       

Extra Credit – 

In addition the above required filters you have two options for extra credit. Both are somewhat harder to implement than the above features but very rewarding to complete. These are independent and optional; feel free to do either, both, or neither.

 

  • Nonphotorealism (up to 10 extra): Implement any non-trivial painterly filter. For inspiration, take a look at the effects available in programs like The Gimp or PhotoShop, (e.g., impressionist, charcoal, stained glass, etc.). The points

awarded for this feature will depend on the creativity and difficulty of the filter. At most one such filter will receive points.

  • Lossy Compression (up to 10 extra): Design your own compressed image format. Similar to jpeg, this should be a lossy (not lossless) format. The image you save will be slightly different from the original, but the saved image and original image should look very similar. You must write your own code both to save an image to a file in your format and to read it back. Points will be awarded based on how well images are compressed (i.e., low file size and high image quality), the explanation of your approach, and the analysis of your results.

 

Submission Details:

Submission should be in the form of a link to a webpage. The web page must contain:

  • Your modified image.cpp
  • At least one result image for each implemented filter, along with the

command-line arguments used to make the image

  • At least one triplet of images showing the differences between different

sampling methods

  • A write-up of your experiences, difficulties and successes (~1 paragraph)
  • Submission of an artistically interesting image to the art contest (this is

optional, but this is probably the easiest assignment to do it for…)

 

Hints:

  • Do the simplest filters first!
  • Look at pixel.[cpp/h]. You may find helpful functions for manipulating pixels

there (e.g. Interpolation)

  • The brighten filter is implemented for you. This should get you started and

show you how to access and modify pixels in an image.