Multiple peaks with peak_local_max
Forest Applied Remote Sensing RG (FARS)
fars.rg at gmail.com
Fri Apr 10 04:25:11 EDT 2015
Josh,
My data is originally a bmp image exported from ArcGIS. The image is
georeferenced. So every pixel has a 3D coordinate, coordinate East and West
(Planar), and the third coordinate height (x, y, z).
Basically I want to import the image, run the peak_local_max algorithm, get
the local maxima and export the points with the original 3D coordinates in
a txt file. So far I was able to do everything except the export part. That
is where I have problems.
Em quinta-feira, 9 de abril de 2015 20:51:11 UTC+2, Josh Warner escreveu:
>
> NumPy exclusively uses zero-indexed integers for indexing. What format
> does your raw data come from which has the coordinates?
>
> However, assuming this is a regularly sampled array you should be able to
> map the raw integer coordinate indices to true coordinates. This should be
> a fairly simple operation, but complicated somewhat if rotation is included.
>
> Less efficient in terms of memory, you could separate out known x/y
> coordinates as two separate NumPy arrays. Then directly index those with
> the raw coordinates to return your known good, calibrated values.
>
> Josh
>
> On Thursday, April 9, 2015 at 10:43:47 AM UTC-5, Forest Applied Remote
> Sensing RG (FARS) wrote:
>>
>> Thank you for you answer Josh,
>>
>> these red dots are actually an array, where each cell has a coordinate x
>> and y.
>> To be honest I wanted to export this red dots with the following
>> structure:
>>
>> 590600,00 6890408,00 1019,04
>>
>> This image I'm using each pixel has a geographic coordinate. But, at the
>> moment I use the image in the scrip, the coordinates are lost and remains
>> only basic pixel coordinates (i. e. 40, 412, 210).
>> I'm quite new at scikit and python. So I'm trying to learn things with
>> practice.
>>
>> Thanks for your attention
>>
>>
>> Em quinta-feira, 9 de abril de 2015 17:22:51 UTC+2, Josh Warner escreveu:
>>>
>>> @FARS - My recommendation was going to be applying some blur first, I'm
>>> glad that worked for you.
>>>
>>> How have you labeled the red points in the image above? If they are in a
>>> separate - possibly boolean - array, you can extract the coordinate indices
>>> directly via `np.where` or `np.nonzero`. If not, we'll need a little more
>>> information about those red dots to advise.
>>>
>>> Josh
>>>
>>>
>>> On Thursday, April 9, 2015 at 10:12:29 AM UTC-5, Forest Applied Remote
>>> Sensing RG (FARS) wrote:
>>>>
>>>> Stefan,
>>>>
>>>> Thanks for your help, but I end up solving the problem. I combined the
>>>> gaussin filter plus the max filter. The result now is much better.
>>>>
>>>> Now I'm strugling to export the local maxima points. Is there a
>>>> function to export the points from the local maxima?
>>>>
>>>> Cheers,
>>>>
>>>> JP
>>>>
>>>
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