示例#1
0
 def init_candidates(self, qaid2_qres):
     self.qaid2_qres = qaid2_qres
     from ibeis.gui import inspect_gui
     self.cand_match_list = inspect_gui.get_automatch_candidates(
         self.qaid2_qres, ranks_lt=self.ranks_lt, directed=False)
     (qaids, aids, scores, ranks) = self.cand_match_list
     self.qaids = qaids
     self.aids = aids
     self.nCands = len(self.qaids)
     self.nPages = vt.iceil(self.nCands / self.nPerPage)
示例#2
0
 def init_candidates(self, qaid2_qres):
     self.qaid2_qres = qaid2_qres
     from ibeis.gui import inspect_gui
     self.cand_match_list = inspect_gui.get_automatch_candidates(self.qaid2_qres,
                                                                 ranks_lt=self.ranks_lt,
                                                                 directed=False)
     (qaids, aids, scores, ranks) = self.cand_match_list
     self.qaids = qaids
     self.aids = aids
     self.nCands = len(self.qaids)
     self.nPages = vt.iceil(self.nCands / self.nPerPage)
    def __init__(
        self,
        gpath_list,
        nPerPage=4,
        bboxes_list=None,
        thetas_list=None,
        verts_list=None,
        gid_list=None,
        nImgs=None,
        fnum=None,
        context_option_funcs=None,
        xlabel_list=None,
        vizkw=None,
        **kwargs,
    ):
        # TODO: overlay function or draw function using a metadata object
        print('Creating multi-image interaction')
        # def __init__(self, img_list, nImgs=None, gid_list=None, aids_list=None,
        # bboxes_list=None, nPerPage=10,fnum=None):
        print('[pt] maX ', nPerPage)
        self.context_option_funcs = context_option_funcs
        if nImgs is None:
            nImgs = len(gpath_list)
        if bboxes_list is None:
            bboxes_list = [[]] * nImgs
        if thetas_list is None:
            thetas_list = [[0] * len(bboxes) for bboxes in bboxes_list]
        # How many images we are showing and per page
        self.thetas_list = thetas_list
        self.bboxes_list = bboxes_list
        self.xlabel_list = xlabel_list
        if gid_list is None:
            self.gid_list = None
        else:
            self.gid_list = gid_list

        self.vizkw = vizkw

        self.nImgs = nImgs
        self.nPerPage = min(nPerPage, nImgs)
        self.current_index = 0
        self.page_number = -1
        # Initialize iterator over the image paths
        self.gpath_list = gpath_list
        # Display the first page
        self.first_load = True
        self.scope = []
        self.current_pagenum = 0
        self.nPages = vt.iceil(self.nImgs / nPerPage)

        # self.show_page()
        super(MultiImageInteraction, self).__init__(fnum=fnum, **kwargs)
示例#4
0
def testdata_coverage(fname=None):
    """ testing function """
    import vtool as vt
    # build test data
    kpts, vecs = vt.dummy.get_testdata_kpts(fname, with_vecs=True)
    # HACK IN DISTINCTIVENESS
    if fname is not None:
        from ibeis.algo.hots import distinctiveness_normalizer
        cachedir = ut.get_app_resource_dir('ibeis', 'distinctiveness_model')
        species = 'zebra_plains'
        dstcnvs_normer = distinctiveness_normalizer.DistinctivnessNormalizer(species, cachedir=cachedir)
        dstcnvs_normer.load(cachedir)
        weights = dstcnvs_normer.get_distinctiveness(vecs)
    else:
        kpts = np.vstack((kpts, [0, 0, 1, 1, 1, 0]))
        kpts = np.vstack((kpts, [0.01, 10, 1, 1, 1, 0]))
        kpts = np.vstack((kpts, [0.94, 11.5, 1, 1, 1, 0]))
        weights = np.ones(len(kpts))
    chipsize = tuple(vt.iceil(vt.get_kpts_image_extent(kpts)).tolist())
    return kpts, chipsize, weights
示例#5
0
    def __init__(self, gpath_list, nPerPage=4, bboxes_list=None,
                 thetas_list=None, verts_list=None, gid_list=None, nImgs=None,
                 fnum=None, context_option_funcs=None, xlabel_list=None,
                 vizkw=None, **kwargs):
        # TODO: overlay function or draw function using a metadata object
        print('Creating multi-image interaction')
        #def __init__(self, img_list, nImgs=None, gid_list=None, aids_list=None,
        #bboxes_list=None, nPerPage=10,fnum=None):
        print('[pt] maX ', nPerPage)
        self.context_option_funcs = context_option_funcs
        if nImgs is None:
            nImgs = len(gpath_list)
        if bboxes_list is None:
            bboxes_list = [[]] * nImgs
        if thetas_list is None:
            thetas_list = [[0] * len(bboxes) for bboxes in bboxes_list]
        # How many images we are showing and per page
        self.thetas_list = thetas_list
        self.bboxes_list = bboxes_list
        self.xlabel_list = xlabel_list
        if gid_list is None:
            self.gid_list = None
        else:
            self.gid_list = gid_list

        self.vizkw = vizkw

        self.nImgs = nImgs
        self.nPerPage = min(nPerPage, nImgs)
        self.current_index = 0
        self.page_number = -1
        # Initialize iterator over the image paths
        self.gpath_list = gpath_list
        # Display the first page
        self.first_load = True
        self.scope = []
        self.current_pagenum = 0
        self.nPages = vt.iceil(self.nImgs / nPerPage)

        #self.show_page()
        super(MultiImageInteraction, self).__init__(fnum=fnum, **kwargs)
示例#6
0
def testdata_coverage(fname=None):
    """ testing function """
    import vtool as vt
    # build test data
    kpts, vecs = vt.dummy.get_testdata_kpts(fname, with_vecs=True)
    # HACK IN DISTINCTIVENESS
    if fname is not None:
        from ibeis.algo.hots import distinctiveness_normalizer
        cachedir = ut.get_app_resource_dir('ibeis', 'distinctiveness_model')
        species = 'zebra_plains'
        dstcnvs_normer = distinctiveness_normalizer.DistinctivnessNormalizer(
            species, cachedir=cachedir)
        dstcnvs_normer.load(cachedir)
        weights = dstcnvs_normer.get_distinctiveness(vecs)
    else:
        kpts = np.vstack((kpts, [0, 0, 1, 1, 1, 0]))
        kpts = np.vstack((kpts, [0.01, 10, 1, 1, 1, 0]))
        kpts = np.vstack((kpts, [0.94, 11.5, 1, 1, 1, 0]))
        weights = np.ones(len(kpts))
    chipsize = tuple(vt.iceil(vt.get_kpts_image_extent(kpts)).tolist())
    return kpts, chipsize, weights