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Table 12.2 Some results of applying convolution filters. The top row of the table shows the input images, the following rows show the result of a kernel and divider:bias combination (see the text for their meaning and the related command line options to the programs presented). A divider value of sum means that the sum of the kernel has been used, which is the default.
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sum: 0
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1 1 1 1 1
1 0 0 0 1
1 0 0 0 1
1 0 0 0 1
1 1 1 1 1
sum: 0
Edge Detect
1 1 1
1 8 1
1 1 1
1:255
Sharpen
2 2 2
2 48 2
2 2 2
sum: 0
Emboss
0 1 2
1 0 1
2 1 0
1;127
Shadow
4 3 0
3 1 3
0 3 4
sum: 255
There are several ways in which this program could be improved. First, you could accept an option that controls which of the three color components the filter acts on. A second improvement could be to implement more ways of dealing with edge conditions, and allowing the user to select which of these he wants to use. Another CONVOLUTION 223
improvement could be to preserve the transparency mask of the image, either by copying the original image s mask, or by applying the filter (separately) to an extracted mask, and inserting that into the output image. One disadvantage of the program presented is that it is slow, terribly slow. There is little we can do about the algorithm to speed things up, but we can affect the way it is implemented. To avoid the need for checking the coordinates for each single pixel that we look at (in the get_color_safe() subroutine), we could make sure that there is always a value. Before doing any work, we d increase the size of the image on the left and right by $x_offset pixels, and on the top and bottom by $y_offset pixels. The convolution would now run over only the pixels that were part of the original image, but wherever the window emerges over the edges of the old image, there are now the new pixels we put there. We would not have to call that subroutine for each single pixel, but we d have to do the preparation work (allocating a new image, copying the current image to the correct spot in that new image, filling the pixels on the outside), and some new calculations. The speed improvement would be largest for larger images. For smaller images the situation could actually turn out to be worse. 12.3.2
Using Image::Magick's Convolve() method
There is, for newer versions of Image::Magick, a faster way to apply convolution filters to images. As of version 5.2.0, Image::Magick includes a Convolve() method. The program presented in the previous section should probably never be used in a real production sense, but it can be valuable as an illustration to demonstrate how the individual pixels of an image can be manipulated with this module. Also, there are some limitations to the Convolve() method that do not exist in the preceding code, that might necessitate the use of a slower explicit method. I will discuss these later. A program using the Convolve() method doesn t have to do nearly as much work as the code presented earlier in this section, and can therefore be a lot simpler:
#!/usr/bin/perl -w use strict; use Getopt::Long; use Image::Magick; my $matrix_file; my $output_type; my $bias; GetOptions( "matrix-file=s" => \$matrix_file, "output-type=s" => \$output_type, "bias=i" => \$bias, ); my $kernel = read_kernel($matrix_file); my ($in_file, $out_file) = @ARGV; $in_file = '-' unless $in_file; $out_file = '-' unless $out_file;
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