gwlucastrig opened a new pull request #78:
URL: https://github.com/apache/commons-imaging/pull/78
Checking the Coverall reports, I spotted an unused area in DataReaderStrips
and DataReaderTiled. This block of code was attached to the 2012 performance
fixes. As far as I can tell, there are no TIFF configurations that would
exercise it. And, even if there were TIFF files that would use it, the new code
in this pull request would still work on them. The idea behind the old
performance fixes was that we dedicate custom blocks of code to frequently
used settings and let the generic code handle the rest. Because the custom code
could be tuned to the particular TIFF configuration, it would run faster.
I also noticed that the old code did not handle cases where RGB style files
were compressed and used a predictor. So I modified it slightly to handle the
predictor rather than allowing the code to fall through to the generic block.
I tested this on a image I have that is quite large 6000-by-4000 pixels and the
performance improved substantially. Below are the results using the
ApacheImagingSpeedAndMemory class that is in the commons-imaging example folder.
I tried to make the changes be as clear as possible to simplify inspection.
I will check Trevor later to make sure I managed to improve coverage (or at
least not degrade it).
```
Processing file: CONUS_LandWaterMask_LZW_RGB.tif (original)
image size: 6000 by 4000
time to load image -- memory
time ms avg ms -- used mb total mb
971.817 0.000 -- 213.592 252.000
921.690 0.000 -- 143.229 260.000
895.587 895.587 -- 96.234 174.000
899.227 897.407 -- 117.259 154.000
899.078 897.964 -- 134.200 184.000
889.602 895.873 -- 143.226 180.000
896.170 895.933 -- 128.183 188.000
894.250 895.652 -- 97.187 178.000
896.436 895.764 -- 103.226 186.000
891.540 895.236 -- 119.185 171.000
Processing file: CONUS_LandWaterMask_LZW_RGB.tif (with chamges)
image size: 6000 by 4000
time to load image -- memory
time ms avg ms -- used mb total mb
498.123 0.000 -- 212.589 252.000
423.136 0.000 -- 110.733 237.000
396.021 396.021 -- 100.735 164.000
400.435 398.228 -- 115.725 160.000
400.901 399.119 -- 114.726 162.000
395.092 398.112 -- 118.711 159.000
394.106 397.311 -- 118.710 159.000
400.866 397.903 -- 118.710 159.000
400.972 398.342 -- 115.710 160.000
397.218 398.201 -- 109.691 164.000
```
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