I have used sextractor in the past : http://terapix.iap.fr/rubrique.php?id_rubrique=91/
Johann On 05/29/2010 08:29 PM, Wayne Watson wrote: > I managed to find some FORTRAN code that is relevant. Algorithms for CCD > Stellar Photometry,K. Mighell, Kitt Peak, > <http://www.adass.org/adass/proceedings/adass98/mighellkj/>. He > discusses PSF and provides a few "starter" programs, centroid and peak > determination. He discusses sky background (, correction, cleanup?) and > points to his photometry package). > > My chip is not big at all, about 1/2". No large chips for what I'm > doing. I don't mind starting with simple methods to see how far they > get me. That's a learning experience that often provides a better > understanding of the whole process. At this point, this is about > exploration to see how setting up an image to analyze can be > accomplished. Once done, then well known methods are available for my > purpose. > > Where this is going at the moment is to do source extraction. Towards > that end I found an interesting source that does a good job of > summarizing how one does it. Here's an excerpt. > ===================================== > Star-Extraction Algorithms > The conventional star-extraction algorithms[4,5] widely used for > ground-based data reductions are comprised of two basic steps. The first > step is to fit the sky background in order to detect the star pixels > from the background. The second is to search for pixels that belong to > the same star, and group them together to compute the center coordinates > of the star. A typical implementation of such an algorithm is described > as below. > 1) > Background Estimation > First, the sky background in each pixel is estimated in order to > seperate the object signal from the background signal[4]. The image is > divided into many grid cells, each including a number of pixels. In each > grid cell, an estimate of the background level is made and is assigned > to the center pixel as its nominal value. A relatively "coarse" mesh of > the sky background distribution is thus constructed. The value at any > other pixel can be obtained from 2-dimensional spline-fitting > interpolation of the mesh. > 2) > Searching for Star Pixels > After the interpolated sky background is subtracted from the image, the > sky contribution to any star pixel becomes negligible. Only small > residues remain in the sky pixels and the mean residue value can be > taken as the standard deviation of the background. After convolution > with a Gaussian filter, the image is traversed to search for pixels > whose values are N times higher than the background standard deviation. > Each group of connected high-value pixels are joined together to form an > extracted star. > The conventional algorithms described above are time-consuming, although > they have the advantage of detecting sources with very low > signal-to-noise ratios (SNRs). They are not suitable for satellite > onboard data reductions since the onboard computers are usually several > hundred times slower than even a normal personal computer available on > the ground. > > SciPy. Hmm, this looks reasonable.<http://scipy.org/Getting_Started>. > Somehow I missed it and instead landed on > <http://www.scipy.org/wikis/topical_software/Tutorial>, which looks > fairly imposing. Those are tar and gzip files. In any case, the whole > page looks quite imposing. Where did you find the simple one below? > =============================== > > > On 5/28/2010 8:31 PM, Anne Archibald wrote: > >> On 28 May 2010 23:59, Wayne Watson<sierra_mtnv...@sbcglobal.net> wrote: >> >> >>> That opened a few avenues. After reading this, I went on a merry search with >>> Google. I hit upon one interesting book, Handbook of CCD astronomy (Steve B. >>> Howell), that discusses PSFs. A Amazon Look Inside suggests this is mostly >>> about h/w. I tried to figure out how to reach the scipy mail list, but, as >>> once a year ago, couldn't figure out the newsgroup GMANE connection. This >>> search recalled to mind my Handbook of Astro Image Processing by Berry and >>> Burnell. It has a few pages on the PSF. In the ref section for that >>> material(PSFs) there's another ref to Steve Howell that may be of use: Astro >>> CCD Observing and Reduction Techniques, ASP, Pacific Conf. Series, vol. 23, >>> 1992. There are further Berry and Burnell refs that may be applicable. >>> >>> >> Ah, sorry, I've been at an astro conference all week, I should have >> expanded that acronym. PSF is short for "Point Spread Function"; the >> idea is that with an optically good telescope, a point source anywhere >> in the field of view produces a blob of characteristic shape (often >> roughly a two-dimensional Gaussian) in your detector. The shape and >> size of this blob is set by your optics (including diffraction) and >> the atmospheric seeing. A star, being intrinsically a point source, >> produces a brighter or less bright version of this blob centered on >> the star's true position. To accurately measure the star's position >> (and often brightness) one usually fits a model blob to the noisy blob >> coming from the star of interest. >> >> I should note that this requires you to have more pixels than you >> "need", so that even a point source is spread over many pixels; >> without this it's impossible to get subpixel positioning (among other >> things). Older consumer digital cameras often lacked this, since it >> was difficult to put enough pixels on a CCD, but fortunately megapixel >> mania has helpfully ensured that no matter how sharp the focus, every >> feature in your image is smeared over many pixels. >> >> >> >>> I probed IRAF, SciPy, and Python, but it looks like a steep learning curve. >>> The SciPy tutorial page looks like overkill. They have what looks like very >>> large tutorials. Perhaps daunting. I did a quick shot at pyraf, a tutorial >>> page, but note it has a prereq of IRAF. Another daunting path. >>> >>> >> Wait, you think SciPy has too many tutorials? Or that they're too >> detailed? Just pick a short, easy, or sketchy one then. Here's one >> that's all three: >> >> >> >>>>> import scipy.stats >>>>> scipy.stats.norm.cdf(3) >>>>> >>>>> >> 0.9986501019683699 >> >> That's the value of the CDF of a standard normal at three sigma, i.e., >> one minus the false positive probability for a one-sided three sigma >> detection. >> >> >> >>> Well, maybe a DIY approach will do the trick for me. >>> >>> >> I haven't used IRAF yet (though I have data sets waiting), and I do >> understand the urge to write your own code rather than understanding >> someone else's, but let me point out that reliably extracting source >> parameters from astronomical images is *hard* and requires cleverness, >> attention to countless special cases, troubleshooting, and experience. >> But it's an old problem, and astronomers have taken all of the needed >> things listed above and built them into IRAF. Do consider using it. >> >> Anne >> >> >> >>> On 5/28/2010 5:41 PM, Anne Archibald wrote: >>> >>> On 28 May 2010 21:09, Charles R Harris<charlesr.har...@gmail.com> wrote: >>> >>> >>> On Fri, May 28, 2010 at 5:45 PM, Wayne Watson<sierra_mtnv...@sbcglobal.net> >>> wrote: >>> >>> >>> Suppose I have a 640x480 pixel video chip and would like to find star >>> images on it, possible planets and the moon. A possibility of noise >>> exits, or bright pixels. Is there a known method for finding the >>> centroids of these astro objects? >>> >>> >>> >>> You can threshold the image and then cluster the pixels in objects. I've >>> done this on occasion using my own software, but I think there might be >>> something in scipy/ndimage that does the same. Someone here will know. >>> >>> >>> There are sort of two passes here - the first is to find all the >>> stars, and the second is to fine down their positions, ideally to less >>> than a pixel. For the former, thresholding and clumping is probably >>> the way to go. >>> >>> For the latter I think a standard approach is PSF fitting - that is, >>> you fit (say) a two-dimensional Gaussian to the pixels near your star. >>> You'll fit for at least central (subpixel) position, probably radius, >>> and maybe eccentricity and orientation. You might even fit for a more >>> sophisticated PSF (doughnuts are natural for Schmidt-Cassegrain >>> telescopes, or the diffraction pattern of your spider). Any spot whose >>> best-fit PSF is just one pixel wide is noise or a cosmic ray hit or a >>> hotpixel; any spot whose best-fit PSF is huge is a detector splodge or >>> a planet or galaxy. >>> >>> All this assumes that your CCD has more resolution than your optics; >>> if this is not the case you're more or less stuck, since a star is >>> then just a bright pixel. In this case your problem is one of >>> combining multiple offset images, dark skies, and dome flats to try to >>> distinguish detector crud and cosmic ray hits from actual stars. It >>> can be done, but it will be a colossal pain if your pointing accuracy >>> is not subpixel (which it probably won't be). >>> >>> In any case, my optical friends tell me that the Right Way to do all >>> this is to use all the code built into IRAF (or its python wrapper, >>> pyraf) that does all this difficult work for you. >>> >>> Anne >>> P.S. if your images have been fed through JPEG or some other lossy >>> compression the process will become an utter nightmare. -A >>> >>> >>> >>> Chuck >>> >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> >>> >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> >>> >>> -- >>> Wayne Watson (Watson Adventures, Prop., Nevada City, CA) >>> >>> (121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time) >>> Obz Site: 39° 15' 7" N, 121° 2' 32" W, 2700 feet >>> >>> There are no statues or memorials dedicated to >>> Thomas Paine for his substantial part in the >>> American Revolution. >>> >>> -- An observation in The Science of Liberty >>> by Timothy Ferris >>> >>> >>> Web Page:<www.speckledwithstars.net/> >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> >>> >>> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
