CentroidConvolveTemplate#
- class lsst.ts.wep.centroid.CentroidConvolveTemplate#
Bases:
CentroidDefaultCentroidDefault child class to get the centroid of donut by convolution with a template donut image.
Methods Summary
getCenterAndR(imgDonut[, templateDonut, ...])Get the centroid data and effective weighting radius.
getCenterAndRfromImgBinary(imgBinary[, ...])Get the centroid data and effective weighting radius.
getCenterAndRfromTemplateConv(imageBinary[, ...])Get the centers of the donuts by convolving a binary template image with the binary image of the donut or donuts.
getImgBinary(imgDonut)Get the binary image.
Methods Documentation
- getCenterAndR(imgDonut, templateDonut=None, peakThreshold=0.95)#
Get the centroid data and effective weighting radius.
- Parameters:
imgDonut (numpy.ndarray) – Donut image.
templateDonut (None or numpy.ndarray, optional) – Template image for a single donut. If set to None then the image will be convolved with itself. (The Default is None)
peakThreshold (float, optional) – This value is a specifies a number between 0 and 1 that is the fraction of the highest pixel value in the convolved image. The code then sets all pixels with a value below this to 0 before running the K-means algorithm to find peaks that represent possible donut locations. (The default is 0.95)
- Return type:
- Returns:
float – Centroid x.
float – Centroid y.
float – Effective weighting radius.
- getCenterAndRfromImgBinary(imgBinary, templateBinary=None, peakThreshold=0.95)#
Get the centroid data and effective weighting radius.
- Parameters:
imgBinary (numpy.ndarray) – Binary image of donut.
templateBinary (None or numpy.ndarray, optional) – Binary image of template for a single donut. If set to None then the image will be convolved with itself. (The Default is None)
peakThreshold (float, optional) – This value is a specifies a number between 0 and 1 that is the fraction of the highest pixel value in the convolved image. The code then sets all pixels with a value below this to 0 before running the K-means algorithm to find peaks that represent possible donut locations. (The default is 0.95)
- Return type:
- Returns:
float – Centroid x.
float – Centroid y.
float – Effective weighting radius.
- getCenterAndRfromTemplateConv(imageBinary, templateImgBinary=None, nDonuts=1, peakThreshold=0.95, dbscanEps=5.0)#
Get the centers of the donuts by convolving a binary template image with the binary image of the donut or donuts.
Peaks will appear as bright spots in the convolved image. Since we use binary images the brightness of the stars does not matter and the peaks of any stars in the image should have about the same brightness if the template is correct.
- Parameters:
imageBinary (numpy.ndarray) – Binary image of postage stamp.
templateImgBinary (None or numpy.ndarray, optional) – Binary image of template donut. If set to None then the image is convolved with itself. (The default is None)
nDonuts (int, optional) – Number of donuts there should be in the binary image. If the number is >= 1 then K-Means clustering will be used to return the specified number of donut centers. However, this can also be set to -1 if the number of donuts is unknown and it will perform DBSCAN clustering to find and return a set of donut centers. (The default is 1)
peakThreshold (float, optional) – This value is a specifies a number between 0 and 1 that is the fraction of the highest pixel value in the convolved image. The code then sets all pixels with a value below this to 0 before running the K-means algorithm to find peaks that represent possible donut locations. (The default is 0.95)
dbscanEps (float, optional) – Maximum distance scikit-learn DBSCAN algorithm allows “between two samples for one to considered in the neighborhood of the other”. (The default is 5.0)
- Return type:
- Returns:
list – X pixel coordinates for donut centroid.
list – Y pixel coordinates for donut centroid.
float – Effective weighting radius calculated using the template image.