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¶
- imgDonutnumpy.ndarray
Donut image.
- templateDonutNone 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)
- peakThresholdfloat, 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)
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¶
- imgBinarynumpy.ndarray
Binary image of donut.
- templateBinaryNone 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)
- peakThresholdfloat, 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)
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)
Returns¶
- list
X pixel coordinates for donut centroid.
- list
Y pixel coordinates for donut centroid.
- float
Effective weighting radius calculated using the template image.