def singleFeatureTest(fu, typ, zDir): xname = 'feats_%s_%s_%i_xy.h5' % (name, typ, zDir) zname = 'feats_%s_%s_%i_z.h5' % (name, typ, zDir) xy_file = nh5.createFile(xname) z_file = nh5.createFile(zname) xy_shape = [ rag.totalNumberOfInSliceEdges if typ in ('xy', 'both') else 1, 9 if name == 'standard' else 9 * 12 ] xy_chunks = [min(2500, xy_shape[0]), xy_shape[1]] z_shape = [ rag.totalNumberOfInBetweenSliceEdges if typ in ('z', 'both') else 1, 9 if name == 'standard' else 9 * 12 ] z_chunks = [min(2500, z_shape[0]), z_shape[1]] xy_array = nh5.hdf5Array('float32', xy_file, 'data', xy_shape, xy_chunks) z_array = nh5.hdf5Array('float32', z_file, 'data', z_shape, z_chunks) fu(rag, self.dataArray, xy_array, z_array, zDirection=zDir) xfeats = xy_array.readSubarray([0, 0], xy_shape) zfeats = z_array.readSubarray([0, 0], z_shape) nh5.closeFile(xy_file) nh5.closeFile(z_file) os.remove(xname) os.remove(zname) return xname, zname, xfeats, zfeats
def singleFeatureTest(fu, typ, zDir): xname = 'feats_%s_%s_%i_xy.h5' % (name, typ, zDir) zname = 'feats_%s_%s_%i_z.h5' % (name, typ, zDir) xy_file = nh5.createFile(xname) z_file = nh5.createFile(zname) xy_shape = [ rag.totalNumberOfInSliceEdges if typ in ('xy', 'both') else 1, 9 if name == 'standard' else 9 * 12 ] xy_chunks = [min(2500, xy_shape[0]), xy_shape[1]] z_shape = [ rag.totalNumberOfInBetweenSliceEdges if typ in ('z', 'both') else 1, 9 if name == 'standard' else 9 * 12 ] z_chunks = [min(2500, z_shape[0]), z_shape[1]] xy_array = nh5.hdf5Array('float32', xy_file, 'data', xy_shape, xy_chunks) z_array = nh5.hdf5Array('float32', z_file, 'data', z_shape, z_chunks) fu(rag, self.dataArray, xy_array, z_array, zDirection=zDir) xfeats = xy_array.readSubarray([0, 0], xy_shape) zfeats = z_array.readSubarray([0, 0], z_shape) nh5.closeFile(xy_file) nh5.closeFile(z_file) os.remove(xname) os.remove(zname) return xname, zname, xfeats, zfeats
def test_stacked_rag_hdf5_serialize_deserialize(self): import nifty.hdf5 as nhdf5 hidT = nhdf5.createFile(self.path) chunkShape = [1, 2, 1] array = nhdf5.Hdf5ArrayUInt32(hidT, "data", self.bigShape, chunkShape) array[0:self.bigShape[0], 0:self.bigShape[1], 0:self.bigShape[2]] = self.bigLabels self.serialization_test(array, nrag.gridRagStacked2DHdf5) nhdf5.closeFile(hidT)
def test_grid_rag_hdf5_stacked2d(self): import nifty.hdf5 as nhdf5 hidT = nhdf5.createFile(self.path) chunkShape = [1, 2, 1] array = nhdf5.Hdf5ArrayUInt32(hidT, "data", self.shape, chunkShape) array[0:self.shape[0], 0:self.shape[1], 0:self.shape[2]] = self.labels self.small_array_test(array, nrag.gridRagStacked2DHdf5) nhdf5.closeFile(hidT)
def test_grid_rag_hdf5_stacked2d(self): import nifty.hdf5 as nhdf5 hidT = nhdf5.createFile(self.path) chunkShape = [1, 2, 1] array = nhdf5.Hdf5ArrayUInt32(hidT, "data", self.shape, chunkShape) array[0:self.shape[0], 0:self.shape[1], 0:self.shape[2]] = self.labels self.small_array_test(array, nrag.gridRagStacked2DHdf5) nhdf5.closeFile(hidT)
def test_stacked_rag_hdf5_serialize_deserialize(self): import nifty.hdf5 as nhdf5 hidT = nhdf5.createFile(self.path) chunkShape = [1, 2, 1] array = nhdf5.Hdf5ArrayUInt32(hidT, "data", self.bigShape, chunkShape) array[0:self.bigShape[0], 0:self.bigShape[1], 0:self.bigShape[2]] = self.bigLabels self.serialization_test(array, nrag.gridRagStacked2DHdf5) nhdf5.closeFile(hidT)
def test_hdf5_rag2d_large(self): import nifty.hdf5 as nhdf5 shape = [5, 6] blockShape = chunkShape = shape # FIXME these shapes cause incorrect edges !!! # chunkShape = [3, 2] # blockShape = [2, 3] hidT = nhdf5.createFile(self.path) array = nhdf5.Hdf5ArrayUInt32(hidT, "data", shape, chunkShape) self.assertEqual(array.shape[0], shape[0]) self.assertEqual(array.shape[1], shape[1]) labels = numpy.array([[0, 0, 0, 0, 1, 1], [0, 2, 2, 0, 1, 3], [0, 3, 3, 3, 3, 3], [0, 3, 4, 5, 5, 5], [0, 0, 4, 6, 6, 6]], dtype='uint32') self.assertEqual(labels.shape[0], shape[0]) self.assertEqual(labels.shape[1], shape[1]) array[0:shape[0], 0:shape[1]] = labels rag = nrag.gridRagHdf5(array, numberOfLabels=int(labels.max() + 1), blockShape=blockShape, numberOfThreads=1) shouldEdges = [(0, 1), (0, 2), (0, 3), (0, 4), (1, 3), (2, 3), (3, 4), (3, 5), (4, 5), (4, 6), (5, 6)] shouldNotEdges = [(0, 6), (0, 5), (1, 6), (1, 5)] self.generic_rag_test(rag=rag, numberOfNodes=labels.max() + 1, shouldEdges=shouldEdges, shouldNotEdges=shouldNotEdges) nhdf5.closeFile(hidT)
def test_hdf5_rag_3d(self): import nifty.hdf5 as nhdf5 shape = [3, 2, 2] chunkShape = [1, 2, 1] blockShape = [1, 2, 3] hidT = nhdf5.createFile(self.path) array = nhdf5.Hdf5ArrayUInt32(hidT, "data", shape, chunkShape) self.assertEqual(array.shape[0], shape[0]) self.assertEqual(array.shape[1], shape[1]) self.assertEqual(array.shape[2], shape[2]) labels = [[[0, 1], [0, 0]], [[1, 1], [2, 2]], [[3, 3], [3, 3]]] labels = numpy.array(labels, dtype='uint32') self.assertEqual(labels.shape[0], shape[0]) self.assertEqual(labels.shape[1], shape[1]) self.assertEqual(labels.shape[2], shape[2]) array[0:shape[0], 0:shape[1], 0:shape[2]] = labels rag = nrag.gridRagHdf5(array, numberOfLabels=labels.max() + 1, blockShape=blockShape, numberOfThreads=-1) shouldEdges = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)] shouldNotEdges = [(0, 3)] self.generic_rag_test(rag=rag, numberOfNodes=labels.max() + 1, shouldEdges=shouldEdges, shouldNotEdges=shouldNotEdges) nhdf5.closeFile(hidT)
def run(self): # read stuff from the sub solver sub_solver = self.input()['sub_solver'] sub_results = sub_solver.read('sub_results') block_begins = sub_solver.read('block_begins') block_ends = sub_solver.read('block_ends') sub_nodes = sub_solver.read('sub_nodes') has_defects = False if PipelineParameter().defectPipeline: defect_slices_path = self.input()['defect_slices'].path defect_slices = vigra.readHDF5(defect_slices_path, 'defect_slices') if defect_slices.size: has_defects = True # get the rag rag = self.input()['rag'].read() out_path = self.output().path if not os.path.exists(out_path): os.mkdir(out_path) # iterate over the blocks and serialize the sub-block result # for block_id in range(1): for block_id in range(len(sub_results)): sub_result = { sub_nodes[block_id][i]: sub_results[block_id][i] for i in range(len(sub_nodes[block_id])) } print("Saving Block-Result for block %i / %i" % (block_id, len(sub_results))) block_begin = block_begins[block_id] block_end = block_ends[block_id] # save the begin and end coordinates of this block for later use block_path = os.path.join(out_path, 'block%i_coordinates.h5' % block_id) vigra.writeHDF5(block_begin, block_path, 'block_begin') vigra.writeHDF5(block_end, block_path, 'block_end') # determine the shape of this subblock block_shape = block_end - block_begin chunk_shape = [ 1, min(512, block_shape[1]), min(512, block_shape[2]) ] # save the segmentation for this subblock res_path = os.path.join(out_path, 'block%i_segmentation.h5' % block_id) res_file = nh5.createFile(res_path) out_array = nh5.Hdf5ArrayUInt32( res_file, 'data', block_shape.tolist(), chunk_shape, compression=PipelineParameter().compressionLevel) nrag.projectScalarNodeDataInSubBlock(rag, sub_result, out_array, block_begins[block_id], block_ends[block_id]) # if we have defected slices in this sub-block, replace them by an adjacent slice if has_defects: # project the defected slicces in global coordinates to the subblock coordinates this_defect_slices = defect_slices - block_begin[0] this_defect_slices = this_defect_slices[np.logical_and( this_defect_slices > 0, this_defect_slices < block_shape[0])] # only replace slices if there are any in the subblock if this_defect_slices.size: replace_slice = get_replace_slices(this_defect_slices, block_shape) for z in this_defect_slices: replace_z = replace_slice[z] workflow_logger.debug( "SubblockSegmentationWorkflow: block %i replacing defected slice %i by %i" % (block_id, z, replace_z)) out_array.writeSubarray( [z, 0, 0], out_array.readSubarray([replace_z, 0, 0], [ replace_z + 1, block_shape[1], block_shape[2] ])) nh5.closeFile(res_file)
def tearDownClass(self): nh5.closeFile(self.segFile) nh5.closeFile(self.dataFile) rmtree('./data') rmtree('./features')
def tearDownClass(self): nh5.closeFile(self.segFile) nh5.closeFile(self.dataFile) rmtree('./data') rmtree('./features')