Example #1
0
    def test_segmap_from_classified_fails():
        """Test the segmap_from_classified method."""

        data = dh.get_expected_morpheus_output()
        h, _, _, _ = example.get_sample()
        mask = np.zeros_like(h, dtype=np.int)
        mask[5:-5, 5:-5] = 1

        with pytest.raises(ValueError):
            Classifier.segmap_from_classified(data, h, mask=mask, bkg_src_threshold=1.0)
Example #2
0
    def test_segmap_from_classified():
        """Test the segmap_from_classified method."""

        data = dh.get_expected_morpheus_output()
        h, _, _, _ = example.get_sample()
        mask = np.zeros_like(h, dtype=np.int)
        mask[5:-5, 5:-5] = 1

        expected_segmap = dh.get_expected_segmap()["segmap"]

        actual_segmap = Classifier.segmap_from_classified(data, h, mask=mask)

        np.testing.assert_array_equal(expected_segmap, actual_segmap)
Example #3
0
def main():
    args = _parse_args(sys.argv[1:])

    if args.action == "None":
        Classifier.classify(
            h=args.h,
            j=args.j,
            v=args.v,
            z=args.z,
            batch_size=args.batch_size,
            out_dir=args.out_dir,
            gpus=args.gpus,
            cpus=args.cpus,
        )
    elif args.action == "catalog":
        classified = Classifier.classify(
            h=args.h,
            j=args.j,
            v=args.v,
            z=args.z,
            batch_size=args.batch_size,
            out_dir=args.out_dir,
            gpus=args.gpus,
            cpus=args.cpus,
        )

        segmap = Classifier.segmap_from_classified(classified,
                                                   args.h,
                                                   out_dir=args.out_dir)

        Classifier.catalog_from_classified(
            classified,
            args.h,
            segmap,
            out_file=os.path.join(args.out_dir, "colorized.png"),
        )
    elif args.action == "segmap":
        classified = Classifier.classify(
            h=args.h,
            j=args.j,
            v=args.v,
            z=args.z,
            batch_size=args.batch_size,
            out_dir=args.out_dir,
            gpus=args.gpus,
            cpus=args.cpus,
        )

        Classifier.segmap_from_classified(classified,
                                          args.h,
                                          out_dir=args.out_dir)
    elif args.action == "colorize":
        classified = Classifier.classify(
            h=args.h,
            j=args.j,
            v=args.v,
            z=args.z,
            batch_size=args.batch_size,
            out_dir=args.out_dir,
            gpus=args.gpus,
            cpus=args.cpus,
        )

        Classifier.colorize_classified(classified, out_dir=args.out_dir)
    elif args.action == "all":
        classified = Classifier.classify(
            h=args.h,
            j=args.j,
            v=args.v,
            z=args.z,
            batch_size=args.batch_size,
            out_dir=args.out_dir,
            gpus=args.gpus,
            cpus=args.cpus,
        )

        segmap = Classifier.segmap_from_classified(classified,
                                                   args.h,
                                                   out_dir=args.out_dir)

        Classifier.catalog_from_classified(
            classified,
            args.h,
            segmap,
            out_file=os.path.join(args.out_dir, "colorized.png"),
        )

        Classifier.colorize_classified(classified, out_dir=args.out_dir)