def __init__(self,
box_bounds=None,
box_pos=None,
box_size=None,
box_var=None,
keep_aspect_ratio=True):
super(Boxed, self).__init__(box_var=box_var)
self._key_pressed = None
self.keep_aspect_ratio = keep_aspect_ratio
# Find the box bounds if only the box positions are passed.
if box_bounds is None:
assert box_pos is not None
# This will find a good box size automatically if it is not
# specified.
box_bounds = _get_boxes(box_pos,
size=box_size,
keep_aspect_ratio=self.keep_aspect_ratio,
margin=self.margin)
self._box_bounds = np.atleast_2d(box_bounds)
assert self._box_bounds.shape[1] == 4
self.transforms = TransformChain()
self.transforms.add_on_gpu(
[Scale('u_box_scaling'),
Range(NDC, 'box_bounds')], origin=self)
def update_boxes(self, box_pos, box_size):
"""Set the box bounds from specified box positions and sizes."""
assert box_pos.shape == (self.n_boxes, 2)
assert len(box_size) == 2
self.box_bounds = _get_boxes(box_pos,
size=box_size,
margin=self.margin,
keep_aspect_ratio=self.keep_aspect_ratio)
def _get_box_bounds(bunchs, channel_ids):
cp = {}
for d in bunchs:
cp.update(
{cid: pos
for cid, pos in zip(d.channel_ids, d.channel_positions)})
box_pos = np.stack([cp[cid] for cid in channel_ids])
bounds = _get_boxes(box_pos, margin=Boxed.margin)
return bounds
def _iter_channel(positions):
size = 100
margin = 5
boxes = _get_boxes(positions, keep_aspect_ratio=False)
xmin, ymin = boxes[:, :2].min(axis=0)
xmax, ymax = boxes[:, 2:].max(axis=0)
x = boxes[:, [0, 2]].mean(axis=1)
y = -boxes[:, [1, 3]].mean(axis=1)
positions = np.c_[x, y]
tr = [margin, margin, size - margin, size - margin]
positions_tr = range_transform([-1, -1, 1, 1], tr, positions)
for x, y in positions_tr:
yield x, y
def test_get_boxes():
positions = [[-1, 0], [1, 0]]
boxes = _get_boxes(positions)
ac(boxes, [[-1, -.25, 0, .25],
[+0, -.25, 1, .25]], atol=1e-4)
positions = [[-1, 0], [1, 0]]
boxes = _get_boxes(positions, keep_aspect_ratio=False)
ac(boxes, [[-1, -1, 0, 1],
[0, -1, 1, 1]], atol=1e-4)
positions = linear_positions(4)
boxes = _get_boxes(positions)
ac(boxes, [[-0.5, -1.0, +0.5, -0.5],
[-0.5, -0.5, +0.5, +0.0],
[-0.5, +0.0, +0.5, +0.5],
[-0.5, +0.5, +0.5, +1.0],
], atol=1e-4)
positions = staggered_positions(8)
boxes = _get_boxes(positions)
ac(boxes[:, 1], np.arange(.75, -1.1, -.25), atol=1e-6)
ac(boxes[:, 3], np.arange(1, -.76, -.25), atol=1e-7)
def _get_pos_data_bounds(positions):
boxes = _get_boxes(positions, keep_aspect_ratio=False)
xmin, ymin = boxes[:, :2].min(axis=0)
xmax, ymax = boxes[:, 2:].max(axis=0)
x = boxes[:, [0, 2]].mean(axis=1)
y = boxes[:, [1, 3]].mean(axis=1)
positions = np.c_[x, y]
# x, y = positions.T
# xmin, ymin, xmax, ymax = x.min(), y.min(), x.max(), y.max()
w = xmax - xmin
h = ymax - ymin
k = .05
data_bounds = (xmin - w * k, ymin - h * k, xmax + w * k, ymax + h * k)
return positions, data_bounds
def box_size(self, val):
assert len(val) == 2
self.box_bounds = _get_boxes(self.box_pos,
size=val,
margin=self.margin,
keep_aspect_ratio=self.keep_aspect_ratio)
def box_pos(self, val):
self.box_bounds = _get_boxes(val,
size=self.box_size,
margin=self.margin,
keep_aspect_ratio=self.keep_aspect_ratio)
