Overview

In this assignment you will create a simple image processing program. The operations that you implement will mostly be filters that take an input image, process the image, and produce an output image.

Getting Started

You should use the following skeleton code (1.zip or 1.tar.gz ) as a starting point for your assignment. We provide you with several files, but you should mainly change image.cpp.

• main.cpp: Parses the command line arguments, and calls the appropriate image functions.
• image.[cpp/h]: Image processing.
• pixel.[cpp/h]: Pixel processing.
• vector.[cpp/h]: Simple 2D vector class you may find useful.
• bmp.[cpp/h]: Functions to read and write Windows BMP files.
• Makefile: A Makefile suitable for UNIX platforms.
• image.[vcproj/sln]: Project file for Visual Studio .NET 2003 on Windows.

We provide several test images with the skeleton code, however any 24-bit BMP image should work.

After you copy the provided files to your directory, the first thing to do is compile the program. If you are developing on a Windows machine and have Visual Studio .NET 2003 (or later) installed, use the provided project files to build the program. If you are developing on a UNIX machine, type make in the program directory. In either case, an executable called image (or image.exe ) will be created.

How the Program Works

The user interface for this assignment was kept as simple as possible, so you can concentrate on the image processing issues. The program runs on the command line. It reads an image from the standard input, processes the image using the filters specified by the command line arguments, and writes the resulting image to the standard output. 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 -brightness 0.1 < in.bmp > out.bmp

For each available image filter there is a corresponding optional 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 found. For example,

% image -contrast 0.8 -scale 0.5 0.5 < in.bmp > 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. Another way of doing the same thing would be

% image -contrast 0.8 | image -scale 0.5 0.5 < in.bmp > out.bmp

which uses pipes instead of multiple options per command line. The ability to use pipes can be used to avoid creating temporary files. For example,

% image -contrast 0.8 | image -scale 0.5 0.5 < in.bmp | xv -

does the same thing as the previous command, but it redirects the final output to be the input of xv, which simply shows you the result on the screen, without creating any temporary files in your directory. Another advantage of this kind of interface is that you can combine different filters in shell scripts, and automate image processing steps, which can save you time.

Remember that if you wish to produce output to the console for debugging, you will have to use stderr instead of stdout (i.e., fprintf(stderr, ...))

What You Have to Do

The assignment is worth 20 points. The following is a list of features that you may implement (listed roughly from easiest to hardest). The number in front of the feature corresponds to how many points the feature is worth. The features in bold face are required. The other ones are optional. Refer to the examples web page for more details on the implementation of each filter and example output images.

• (1/2) Random noise: Add noise to an image.
• (1/2) Brighten: Individually scale the RGB channels of an image.
• (1/2) Contrast: Change the contrast of an image. See Graphica Obscura.
• (1/2) Saturation: Change the saturation of an image. See Graphica Obscura.
• (1/2) Crop: Extract a subimage specified by two corners.
• (1/2) Extract Channel: Leave specified channel intact and set all others to zero.
• (1) Quantize: Change the number of bits per channel of an image, using simple rounding.
• (1) Composite: Compose one image with a second image, using a third image as a matte.
• (2) Blur: Blur an image by convolving it with a Gaussian low-pass filter.
• (2) Edge detect: Detect edges in an image by convolving it with an edge detection kernel.
• (1) Random dither: Convert an image to a given number of bits per channel, using a random threshold.
• (2) Ordered dither: Convert an image to a given number of bits per channel, using a 4x4 ordered dithering matrix.
• (2) Floyd-Steinberg dither: Convert an image to a given number of bits per channel, using dithering with error diffusion.
• (2) Scale: Scale an image up or down by a real valued factor.
• (2) Rotate: Rotate an image by a given angle.
• (2) Fun: Warp an image using a non-linear mapping of your choice (examples are fisheye, sine, bulge, swirl).
• (3) Morph: Morph two images using the method in Beier & Neeley's "Feature-based Image Metamorphosis." We also provide a stand-alone utility for selecting line correspondences -- these are input to the morph. See this page for details.
• (up to 3) Nonphotorealism: Implement any non-trivial painterly filter. For inspiration, take a look at the effects available in programs like xv, PhotoShop, and Image Composer (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.

For any feature that involves resampling (i.e., scale, rotate, "fun," and morph), you have to provide three sampling methods: point sampling, bilinear sampling, and Gaussian sampling.

By implementing all the required features, you get 13 points. There are many ways to get more points:

• implementing the optional features listed above;
• (1) submitting one or more images for the art contest,
• (1) submitting a .mpeg or .avi movie animating the results of one or more filters with continuously varying parameters (e.g. the morph),
• (2) winning the art contest.

For images or movies that you submit, you also have to submit the sequence of commands used to created them, otherwise they will not be considered valid.

It is possible to get more than 20 points. However, after 20 points, each point is divided by 2, and after 22 points, each point is divided by 4. If your raw score is 19, your final score will be 19. If the raw score is 23, you'll get 21.25. For a raw score of 26, you'll get 22.

Extra credit points cannot replace the required features (bold items). Your final score will be calculated by adding 13 to the number of extra credit points you achieve, applying the above formula for diminishing returns, and then subtracting the number of required points that you missed.

What to Submit

You should submit one archive (zip or tar file) containing:

• the complete source code, along with an updated Visual Studio project file (if you added any source files);
• the .mpeg or .avi movie for the movie feature (optional);
• the images for the art contest (optional); and
• a writeup.

The writeup should be a HTML document called assignment1.html which may include other documents or pictures. It should be brief, describing what you have implemented, what works and what doesn't, how you created the composite image, how you created the art contest images, and instructions on how to run the fun filters you have implemented.

NOTE: Please convert your art contest and writeup images to JPEG format before submitting them (to save space). You can use the img2img utility detailed here.

Should you choose to create a video of your morph there are many options for video encoders available, however we have found that FFMPEG works well and runs on both Windows and linux. MEncoder is another option.

Make sure the source code compiles on the workstations in Friend 017.

Always remember the late policy and the collaboration policy.

Hints

A few hints:

• Do the simplest filters first!
• Look at the example page.
• There are functions to manipulate pixel components and pixels in image.[cpp/h]. You may find them helpful while writing your filters.
• There are functions to manipulate 2D vectors in vector.[cpp/h]. You may find them helpful while writing the morphing functions.
• Send mail to the cos426 staff.

FAQ

• The compose image parameters don't make sense. Why?
  We are using PixelOver operator for composing images.  Unfortunately, the bmp format doesn't store alpha values, so we need a way of getting alpha values for both the top and bottom image.  The way we are doing this is by loading another image and treating its blue channel as the alpha value (see the function SetImageMask in main.cpp).  So... we need 4 images: top, top_mask, bottom, bottom_mask.  top is the current image that's already loaded, so the ComposeImage option takes the remaining 3 filenames as input.  In the parameter section of main.cpp these 4 images are put together to create two images (main.cpp, lines 148-170) with the alpha values filled in, which is then passed to the ComposeImage function that you write in image.cpp.  The three parameters of ComposeImage are stylistic, you could get away with two, or return an image pointer, or something equivalent.

• For the quantization function, can we safely assume that the image channels for input are always 8 bit and not any greater?
  Yes.

• For the quantization function, should we assume that the number of bits to which we're quantizing is always a multiple of 3?
  No. The parameter is the number of bits per channel, not per pixel.

• I get error messages and a core dump when the program tries to read in my bmp files. What should I do?
  Make sure your files are 24-bit, uncompressed bitmaps. The provided code does not read any other kind of files. If you are using xv: right-click to open the xv controls window, then click on the 24/8 Bit menu-button, then select the 24-bit mode option. Alt-8 toggles between 8-bit and 24-bit mode. Under windows, the error msg:

  Assertion failed: bmih.biSizeImage == (DWORD) lineLength * (DWORD) bmih.biHeight, file c:\temp\one\1\bmp.cpp, line 252

  means the image is not correctly formatted. One solution is to open the image, save it a .jpg then save as back to .bmp.

• When doing convolutions, each pixel becomes a weighted average of its neighbors. What do I do when the pixel is close to the border, and therefore doesn't have some of its neighbors?
  There are a few things you could do. First of all, here is what what not to do: don't just skip those pixels. One thing you could do is pretend that the image is "toroidal" -- the right side is connected to the left side, and the top to the bottom. So, the pixel just to the right of the rightmost pixel is the leftmost pixel. Another thing you could do is adjust the convolution matrix for the edge and corner points. When you adjust the matrix, take care that the entries in the matrix still sum to 1.0. For example, if you are applying the edge detection matrix

  |-1 -1 -1|
  |-1 +8 -1|
  |-1 -1 -1|

  to a pixel in the middle of the right column of pixels, you would use

  |-1 -1 0|
  |-1 +5 0|
  |-1 -1 0|
  
  In Floyd-Steinberg, you won't be able to transfer any error from the bottom row, but this doesn't matter.

• Is it ok to just do nothing if the filter argument in a convolution is larger than the size of the image.
  Yes, but a warning message would be nice.

• My blur filter is darkening the image. What is wrong?
  Make sure that the entries in your kernel add up to 1. Be especially careful with rounding errors: although small in each entry, collectively they can be large enough to cause this undesired darkening effect.

• Can I assume my functions will always be called with valid arguments?
  No, but bailing out is acceptable (print a message and exit). Please avoid segmentation faults, core dumps, blue screens of death, etc.