museotoolbox.ai.SuperLearner.predict_image¶
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SuperLearner.
predict_image
(in_image, out_image, confidence_per_class=False, higher_confidence=False, in_image_mask=False, out_nodata=0, compress=True)[source]¶ Predict label from raster using previous learned model. This function will call self.predictArray(X).
- Parameters
in_image (str.) – A filename or path of a raster file. It could be any file that GDAL can open.
out_image (str.) – A geotiff extension filename corresponding to a raster image to create.
confidence_per_class (str or bool, optional (default=False)) – A path to a geotiff extension filename to store each confidence per class (one band = one label).
higher_confidence (str or bool, optional (default=False)) – A path to a geotiff extension filename to store the max confidence from all classes.
in_image_mask (str or False, optional (default=False)) – Path of the raster where 0 is mask and value above are no mask.
outNumpyDT (numpy datatype, default will get the datatype according to your maximum class value.) – Get numpy datatype throught : convert_dt(get_gdt_from_minmax_values(maximumClassValue)))
out_nodata (int, optional (default=0)) – Value of no data only for the out_image.