def __init__(self, image_reader, name='image', output_path=os.path.join('.', 'output'), window_border=(), interp_order=0): ImageWindowsAggregator.__init__(self, image_reader=image_reader) self.name = name self.output_path = os.path.abspath(output_path) self.window_border = window_border self.output_interp_order = interp_order
def __init__(self, image_reader, name='image', output_path=os.path.join('.', 'output'), window_border=(), interp_order=0): ImageWindowsAggregator.__init__(self, image_reader=image_reader) self.name = name self.image_out = None self.output_path = os.path.abspath(output_path) self.window_border = window_border self.output_interp_order = interp_order
def __init__(self, image_reader=None, output_path=os.path.join('.', 'output'), prefix='_niftynet_generated'): ImageWindowsAggregator.__init__( self, image_reader=image_reader, output_path=output_path) self.output_path = os.path.abspath(output_path) self.inferred_csv = os.path.join(self.output_path, 'inferred.csv') self.output_id = {'base_name': None, 'relative_id': 0} self.prefix = prefix if os.path.exists(self.inferred_csv): os.remove(self.inferred_csv)
def __init__(self, image_reader, name='image', output_path=os.path.join('.', 'output'), postfix='_niftynet_out'): ImageWindowsAggregator.__init__( self, image_reader=image_reader, output_path=output_path) self.name = name self.output_interp_order = 0 self.postfix = postfix self.csv_path = os.path.join(self.output_path, self.postfix+'.csv') if os.path.exists(self.csv_path): os.remove(self.csv_path)
def __init__(self, image_reader, name='image', output_path=os.path.join('.', 'output'), window_border=(), interp_order=0, prefix='_niftynet_out'): ImageWindowsAggregator.__init__( self, image_reader=image_reader, output_path=output_path) self.name = name self.window_border = window_border self.output_interp_order = interp_order self.prefix = prefix
def __init__(self, image_reader=None, output_path=os.path.join('.', 'output'), prefix='_niftynet_generated'): ImageWindowsAggregator.__init__(self, image_reader=image_reader, output_path=output_path) self.output_path = os.path.abspath(output_path) self.inferred_csv = os.path.join(self.output_path, 'inferred.csv') self.output_id = {'base_name': None, 'relative_id': 0} self.prefix = prefix if os.path.exists(self.inferred_csv): os.remove(self.inferred_csv)
def __init__(self, image_reader, name='image', output_path=os.path.join('.', 'output'), prefix='_niftynet_out'): ImageWindowsAggregator.__init__( self, image_reader=image_reader, output_path=output_path) self.name = name self.output_interp_order = 0 self.prefix = prefix self.csv_path = os.path.join(self.output_path, self.prefix+'.csv') if os.path.exists(self.csv_path): os.remove(self.csv_path)
def __init__(self, image_reader, name='image', output_path=os.path.join('.', 'output'), window_border=(), interp_order=0, postfix='_niftynet_out'): ImageWindowsAggregator.__init__(self, image_reader=image_reader, output_path=output_path) self.name = name self.window_border = window_border self.output_interp_order = interp_order self.postfix = postfix
def __init__(self, image_reader, name='image', output_path=os.path.join('.', 'output'), window_border=(), interp_order=0, postfix='niftynet_out', fill_constant=0.0): ImageWindowsAggregator.__init__(self, image_reader=image_reader, output_path=output_path) self.name = name self.image_out = None self.csv_out = None self.window_border = window_border self.output_interp_order = interp_order self.postfix = postfix self.fill_constant = fill_constant
def __init__(self, image_reader=None, output_path=os.path.join('.', 'output')): ImageWindowsAggregator.__init__(self, image_reader=image_reader) self.output_path = os.path.abspath(output_path) self.output_id = {'base_name': None, 'relative_id': 0}
coords = [] with tf.Session() as sess: for _ in range(20): uniform_windows = sess.run(next_window) coords.append(uniform_windows['MR_location']) coords = np.concatenate(coords, axis=0) vis_coordinates(image_2d, coords, 'output/uniform.png') ### # create & show all grid samples ### grid_sampler = GridSampler(reader, spatial_window_size, window_border=border) next_grid = grid_sampler.pop_batch_op() coords = [] with tf.Session() as sess: while True: window = sess.run(next_grid) if window['MR_location'][0, 0] == -1: break coords.append(window['MR_location']) coords = np.concatenate(coords, axis=0) vis_coordinates(image_2d, coords, 'output/grid.png') ### # create & show cropped grid samples (in aggregator) ### n_window = coords.shape[0] dummy_window = np.zeros((n_window, 800, 800, 1, 1)) _, coords = IA.crop_batch(dummy_window, coords, border=border) vis_coordinates(image_2d, coords, 'output/grid_cropped.png')