def __call__(self, in_data):
img, mask, label = in_data
bbox = mask_to_bbox(mask)
_, orig_H, orig_W = img.shape
img = self.fcis.prepare(img)
_, H, W = img.shape
scale = H / orig_H
mask = transforms.resize(mask.astype(np.float32), (H, W))
bbox = transforms.resize_bbox(bbox, (orig_H, orig_W), (H, W))
img, params = transforms.random_flip(img,
x_random=True,
return_param=True)
mask = transforms.flip(mask, x_flip=params['x_flip'])
bbox = transforms.flip_bbox(bbox, (H, W), x_flip=params['x_flip'])
return img, mask, label, bbox, scale
def _get_overlayed_image(img, mask, instance_colors):
alpha = 0.5
bbox = mask_to_bbox(mask)
_, H, W = mask.shape
canvas_img = np.zeros((H, W, 3), dtype=np.uint8)
canvas_img[:] = img.transpose((1, 2, 0)).astype(np.uint8)
for i, (bb, msk) in enumerate(zip(bbox, mask)):
# The length of `colors` can be smaller than the number of instances
# if a non-default `colors` is used.
color = instance_colors[i % len(instance_colors)]
# rgba = np.append(color, alpha * 255)
bb = np.round(bb).astype(np.int32)
y_min, x_min, y_max, x_max = bb
if y_max > y_min and x_max > x_min:
canvas_img[msk] = color * alpha
return canvas_img
def vis_instance_segmentation(img,
mask,
label=None,
score=None,
label_names=None,
instance_colors=None,
alpha=0.7,
sort_by_score=True,
ax=None):
"""Visualize instance segmentation.
Example:
This example visualizes an image and an instance segmentation.
>>> from chainercv.datasets import SBDInstanceSegmentationDataset
>>> from chainercv.datasets \
... import sbd_instance_segmentation_label_names
>>> from chainercv.visualizations import vis_instance_segmentation
>>> import matplotlib.pyplot as plt
>>> dataset = SBDInstanceSegmentationDataset()
>>> img, mask, label = dataset[0]
>>> vis_instance_segmentation(
... img, mask, label,
... label_names=sbd_instance_segmentation_label_names)
>>> plt.show()
This example visualizes an image, an instance segmentation and
bounding boxes.
>>> from chainercv.datasets import SBDInstanceSegmentationDataset
>>> from chainercv.datasets \
... import sbd_instance_segmentation_label_names
>>> from chainercv.visualizations import vis_bbox
>>> from chainercv.visualizations import vis_instance_segmentation
>>> from chainercv.visualizations.colormap import voc_colormap
>>> from chainercv.utils import mask_to_bbox
>>> import matplotlib.pyplot as plt
>>> dataset = SBDInstanceSegmentationDataset()
>>> img, mask, label = dataset[0]
>>> bbox = mask_to_bbox(mask)
>>> colors = voc_colormap(list(range(1, len(mask) + 1)))
>>> ax = vis_bbox(img, bbox, label,
... label_names=sbd_instance_segmentation_label_names,
... instance_colors=colors, alpha=0.7, linewidth=0.5)
>>> vis_instance_segmentation(
... None, mask, instance_colors=colors, alpha=0.7, ax=ax)
>>> plt.show()
Args:
img (~numpy.ndarray): See the table below. If this is :obj:`None`,
no image is displayed.
mask (~numpy.ndarray): See the table below.
label (~numpy.ndarray): See the table below. This is optional.
score (~numpy.ndarray): See the table below. This is optional.
label_names (iterable of strings): Name of labels ordered according
to label ids.
instance_colors (iterable of tuple): List of colors.
Each color is RGB format and the range of its values is
:math:`[0, 255]`. The :obj:`i`-th element is the color used
to visualize the :obj:`i`-th instance.
If :obj:`instance_colors` is :obj:`None`, the default color map
is used.
alpha (float): The value which determines transparency of the figure.
The range of this value is :math:`[0, 1]`. If this
value is :obj:`0`, the figure will be completely transparent.
The default value is :obj:`0.7`. This option is useful for
overlaying the label on the source image.
sort_by_score (bool): When :obj:`True`, instances with high scores
are always visualized in front of instances with low scores.
ax (matplotlib.axes.Axis): The visualization is displayed on this
axis. If this is :obj:`None` (default), a new axis is created.
.. csv-table::
:header: name, shape, dtype, format
:obj:`img`, ":math:`(3, H, W)`", :obj:`float32`, \
"RGB, :math:`[0, 255]`"
:obj:`mask`, ":math:`(R, H, W)`", :obj:`bool`, --
:obj:`label`, ":math:`(R,)`", :obj:`int32`, \
":math:`[0, \#fg\_class - 1]`"
:obj:`score`, ":math:`(R,)`", :obj:`float32`, --
Returns:
matploblib.axes.Axes: Returns :obj:`ax`.
:obj:`ax` is an :class:`matploblib.axes.Axes` with the plot.
"""
# Returns newly instantiated matplotlib.axes.Axes object if ax is None
ax = vis_image(img, ax=ax)
if score is not None and len(mask) != len(score):
raise ValueError('The length of score must be same as that of mask')
if label is not None and len(mask) != len(label):
raise ValueError('The length of label must be same as that of mask')
if sort_by_score and score is not None:
order = np.argsort(score)
mask = mask[order]
score = score[order]
if label is not None:
label = label[order]
bbox = mask_to_bbox(mask)
n_inst = len(bbox)
if instance_colors is None:
instance_colors = voc_colormap(list(range(1, n_inst + 1)))
instance_colors = np.array(instance_colors)
_, H, W = mask.shape
canvas_img = np.zeros((H, W, 4), dtype=np.uint8)
for i, (bb, msk) in enumerate(zip(bbox, mask)):
# The length of `colors` can be smaller than the number of instances
# if a non-default `colors` is used.
color = instance_colors[i % len(instance_colors)]
rgba = np.append(color, alpha * 255)
bb = np.round(bb).astype(np.int32)
y_min, x_min, y_max, x_max = bb
if y_max > y_min and x_max > x_min:
canvas_img[msk] = rgba
caption = []
if label is not None and label_names is not None:
lb = label[i]
if not (0 <= lb < len(label_names)):
raise ValueError('No corresponding name is given')
caption.append(label_names[lb])
if score is not None:
sc = score[i]
caption.append('{:.2f}'.format(sc))
if len(caption) > 0:
ax.text((x_max + x_min) / 2,
y_min,
': '.join(caption),
style='italic',
bbox={
'facecolor': color / 255,
'alpha': alpha
},
fontsize=8,
color='white')
ax.imshow(canvas_img)
return ax
def vis_instance_segmentation(img,
mask,
label=None,
score=None,
label_names=None,
colors=None,
alpha=0.7,
ax=None):
"""Visualize instance segmentation.
Example:
>>> from chainercv.datasets import SBDInstanceSegmentationDataset
>>> from chainercv.datasets \
... import sbd_instance_segmentation_label_names
>>> from chainercv.visualizations import vis_instance_segmentation
>>> import matplotlib.pyplot as plot
>>> dataset = SBDInstanceSegmentationDataset()
>>> img, mask, label = dataset[0]
>>> vis_instance_segmentation(
... img, mask, label,
... label_names=sbd_instance_segmentation_label_names)
>>> plot.show()
Args:
img (~numpy.ndarray): An array of shape :math:`(3, H, W)`.
This is in RGB format and the range of its value is
:math:`[0, 255]`. If this is :obj:`None`, no image is displayed.
mask (~numpy.ndarray): A bool array of shape
:math`(R, H, W)`.
If there is an object, the value of the pixel is :obj:`True`,
and otherwise, it is :obj:`False`.
label (~numpy.ndarray): An integer array of shape :math:`(R, )`.
The values correspond to id for label names stored in
:obj:`label_names`.
score (~numpy.ndarray): A float array of shape :math:`(R,)`.
Each value indicates how confident the prediction is.
This is optional.
label_names (iterable of strings): Name of labels ordered according
to label ids.
colors: (iterable of tuple): List of colors.
Each color is RGB format and the range of its values is
:math:`[0, 255]`. The :obj:`i`-th element is the color used
to visualize the :obj:`i`-th instance.
If :obj:`colors` is :obj:`None`, the default color map is used.
alpha (float): The value which determines transparency of the figure.
The range of this value is :math:`[0, 1]`. If this
value is :obj:`0`, the figure will be completely transparent.
The default value is :obj:`0.7`. This option is useful for
overlaying the label on the source image.
ax (matplotlib.axes.Axis): The visualization is displayed on this
axis. If this is :obj:`None` (default), a new axis is created.
Returns:
matploblib.axes.Axes: Returns :obj:`ax`.
:obj:`ax` is an :class:`matploblib.axes.Axes` with the plot.
"""
# Returns newly instantiated matplotlib.axes.Axes object if ax is None
ax = vis_image(img, ax=ax)
bbox = mask_to_bbox(mask)
if len(bbox) != len(mask):
raise ValueError('The length of mask must be same as that of bbox')
if label is not None and len(bbox) != len(label):
raise ValueError('The length of label must be same as that of bbox')
if score is not None and len(bbox) != len(score):
raise ValueError('The length of score must be same as that of bbox')
n_inst = len(bbox)
if colors is None:
colors = [voc_colormap(l) for l in range(1, n_inst + 1)]
colors = np.array(colors)
_, H, W = mask.shape
canvas_img = np.zeros((H, W, 4), dtype=np.uint8)
for i, (bb, msk) in enumerate(zip(bbox, mask)):
# The length of `colors` can be smaller than the number of instances
# if a non-default `colors` is used.
color = colors[i % len(colors)]
rgba = np.append(color, alpha * 255)
bb = np.round(bb).astype(np.int32)
y_min, x_min, y_max, x_max = bb
if y_max > y_min and x_max > x_min:
canvas_img[msk] = rgba
caption = []
if label is not None and label_names is not None:
lb = label[i]
if not (0 <= lb < len(label_names)):
raise ValueError('No corresponding name is given')
caption.append(label_names[lb])
if score is not None:
sc = score[i]
caption.append('{:.2f}'.format(sc))
if len(caption) > 0:
ax.text((x_max + x_min) / 2,
y_min,
': '.join(caption),
style='italic',
bbox={
'facecolor': color / 255,
'alpha': alpha
},
fontsize=8,
color='white')
ax.imshow(canvas_img)
return ax