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https://issues.apache.org/jira/browse/IMAGING-267?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17199709#comment-17199709
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Gary Lucas commented on IMAGING-267:
------------------------------------
Thank you for posting the images. They may help to answer some issues I've
wondered about for awhile. I've inspected their content and I have a few
questions for you.
First off, this TIFF appears to be geophysical data. But there are no GeoTIFF
tags bundled with the image. Judging from the ModelTiepointTag, it appears that
the original image was configured using a projected coordinate system, maybe a
UTM zone. It might help me understand this better if there were more location
information attached to the image. Do you have that information?
Secondly, I note that this TIFF file is not strictly-speaking an image , but is
actually numerical data stored using the TIFF standard floating-point raster
format. My guess is that it's probably elevations. The ability to process TIFF
files containing floating point data was introduced in the most recent release
of Commons Imaging.
One challenge of floating point images is how to map the range of values to
gray scale. In this case, the following text gives the TIFF Tags attached to
the image:
Directory 0 Has TIFF Image Data, description: Root
256 (0x100: ImageWidth): 601 (1 Long)
257 (0x101: ImageLength): 410 (1 Long)
258 (0x102: BitsPerSample): 32 (1 Short)
259 (0x103: Compression): 1 (1 Short)
262 (0x106: PhotometricInterpretation): 1 (Indicates "zero is black")
277 (0x115: SamplesPerPixel): 1 (1 Short)
278 (0x116: RowsPerStrip): 8 (1 Long)
339 (0x153: SampleFormat): 3 (Indicates float-point format)
33550 (0x830e: ModelPixelScaleTag): 25.0, 25.0 (2 Double)
33922 (0x8482: ModelTiepointTag): 0.0, 0.0, 0.0, 262846.525725, 4464275.0, 0.0
(6 Double)
42113 (0xa481: GDALNoData): 45, 51, 50, 55, 54, 56, 46, 48 (8 Byte)
Upon inspection, I find that the values in the image range from 514 to 2410.
Commons Imaging does have an API element called the
custom-photometric-interpreter that lets an application specify how colors (or
gray tones) are assigned to elevations. So in this case, I was able to render
the data by specifying the following lines:
{{ HashMap<String, Object> params = new HashMap<>(); }}
{{ PhotometricInterpreterFloat pi = }}
{{ new PhotometricInterpreterFloat(514.0f, 2410.0f);}}
{{ params.put(TiffConstants.PARAM_KEY_CUSTOM_PHOTOMETRIC_INTERPRETER, pi);}}
{{ BufferedImage bImage = Imaging.getBufferedImage(target, params);}}
{{ ImageIO.write(bImage, "JPG", output);}}
I've attached the image I produced (ISSUE_267.JPG). However, to create it, I
had to know before hand what the range of values was. So my application does a
few extra steps that I did not show in the example above. I was wondering how
the software you used handles this issue. Is it all automatic?
Also, a second question I had is that the PhotometricInterpretation tag given
with your image is 1, which means "0 is black". In other words, the palette
should range from the darkest shading for the lowest numerical values to the
lightest shading for the highest values. However, in looking at your image I
notice that the lowest value pixels are drawn in the lightest colors, which
seems to contradict the setting in the source TIFF file. In the image I've
attached, the lowest value pixels are draw in the darkest colors, which is
consistent with the specification in the TIFF image. Is there some setting in
the application you used that overwrites the settings from the TIFF file?
!ISSUE_267.JPG!
> Colorful rendering of b/w Monoband TIF
> --------------------------------------
>
> Key: IMAGING-267
> URL: https://issues.apache.org/jira/browse/IMAGING-267
> Project: Commons Imaging
> Issue Type: Bug
> Reporter: edgar soldin
> Priority: Major
> Attachments: ISSUE_267.JPG, mdt25a-commons.png, mdt25a-sextante.png,
> mdt25a.tif
>
>
> see attached images.
> mdt25a.tif - the original tif
> mdt25a-commons.png - as rendered/read with Commons Imaging
> mdt25a-sextante.png - as rendered /read properly with ImageIO-Core from
> https://github.com/jai-imageio/jai-imageio-core
> thanks!.. ede
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