This logic can be used to perform database related operations on header keys of one or two stacks of images to filter them for certain criteria.
In order to filter a stack of images SQL syntax can be applied. SQL is a powerful language used to filter large datasets. To learn more about SQL please see https://www.sqlite.org/docs.html for detailed information.
SELECT *, FROM t0
SELECT *,classID as clusterMember FROM t0
SELECT *, (referenceData -120) as referenceData FROM t0
SELECT DISTINCT t0.*, COUNT(t1.referenceData) as counter FROM t0, t1 WHERE t1.referenceData == t0.imageID GROUP BY t1.referenceData
SELECT * FROM t0 where img%2=0
SELECT sum(imageID)/count(imageID) as sum FROM t0 where imageID >10 and imageID < 20
SELECT *, COUNT(croppedFromFile) FROM t0 GROUP BY croppedFromFile
SELECT * FROM t0 WHERE eulerBeta = 130 AND eulerGamma BETWEEN 190 AND 195
SELECT *, (180-eulerBeta) AS eulerBeta,
CASE
WHEN eulerGamma >= 180 THEN eulerGamma-180
ELSE eulerGamma+180
END
AS eulerGamma FROM t0
| Parameters | Description |
|---|---|
| Statement | This is the actual SQL statement used to filter the input(s) |
| Write 1D images | Writes a 1D image for every header key. Every value in the output image is the header value of one particular image of the input dataset. |
| Input | Description |
|---|---|
| Input table t0 | Stack of input images |
| Input table t1 | Stack of input images |
| Output | Description |
|---|---|
| Output result | Stack of output images |
| New/Changed Header Values | Description |
|---|
New or changed header values depend on how this logic is used, since header values can be added or modified by this logic.
This module creates a database which is basically the union of all input header values. Then the given SQL statement is applied onto this database and the resulting header keys (with the corresponding images) are written to the output.