Visualizations¶
vis_bbox¶
-
chainercv.visualizations.
vis_bbox
(img, bbox, label=None, score=None, label_names=None, instance_colors=None, alpha=1.0, linewidth=3.0, sort_by_score=True, ax=None)[source]¶ Visualize bounding boxes inside image.
Example
>>> from chainercv.datasets import VOCBboxDataset >>> from chainercv.datasets import voc_bbox_label_names >>> from chainercv.visualizations import vis_bbox >>> import matplotlib.pyplot as plt >>> dataset = VOCBboxDataset() >>> img, bbox, label = dataset[60] >>> vis_bbox(img, bbox, label, ... label_names=voc_bbox_label_names) >>> plt.show()
This example visualizes by displaying the same colors for bounding boxes assigned to the same labels.
>>> from chainercv.datasets import VOCBboxDataset >>> from chainercv.datasets import voc_bbox_label_names >>> from chainercv.visualizations import vis_bbox >>> from chainercv.visualizations.colormap import voc_colormap >>> import matplotlib.pyplot as plt >>> dataset = VOCBboxDataset() >>> img, bbox, label = dataset[61] >>> colors = voc_colormap(label + 1) >>> vis_bbox(img, bbox, label, ... label_names=voc_bbox_label_names, ... instance_colors=colors) >>> plt.show()
- Parameters
img (ndarray) – See the table below. If this is
None
, no image is displayed.bbox (ndarray) – See the table below.
label (ndarray) – See the table below. This is optional.
score (ndarray) – See the table below. This is optional.
label_names (iterable of strings) – Name of labels ordered according to label ids. If this is
None
, labels will be skipped.instance_colors (iterable of tuples) – List of colors. Each color is RGB format and the range of its values is \([0, 255]\). The
i
-th element is the color used to visualize thei
-th instance. Ifinstance_colors
isNone
, the red is used for all boxes.alpha (float) – The value which determines transparency of the bounding boxes. The range of this value is \([0, 1]\).
linewidth (float) – The thickness of the edges of the bounding boxes.
sort_by_score (bool) – When
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
None
(default), a new axis is created.
name
shape
dtype
format
img
\((3, H, W)\)
float32
RGB, \([0, 255]\)
bbox
\((R, 4)\)
float32
\((y_{min}, x_{min}, y_{max}, x_{max})\)
label
\((R,)\)
int32
\([0, \#fg\_class - 1]\)
score
\((R,)\)
float32
–
- Returns
Returns the Axes object with the plot for further tweaking.
- Return type
Axes
vis_image¶
vis_instance_segmentation¶
-
chainercv.visualizations.
vis_instance_segmentation
(img, mask, label=None, score=None, label_names=None, instance_colors=None, alpha=0.7, sort_by_score=True, ax=None)[source]¶ 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()
- Parameters
img (ndarray) – See the table below. If this is
None
, no image is displayed.mask (ndarray) – See the table below.
label (ndarray) – See the table below. This is optional.
score (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 \([0, 255]\). The
i
-th element is the color used to visualize thei
-th instance. Ifinstance_colors
isNone
, the default color map is used.alpha (float) – The value which determines transparency of the figure. The range of this value is \([0, 1]\). If this value is
0
, the figure will be completely transparent. The default value is0.7
. This option is useful for overlaying the label on the source image.sort_by_score (bool) – When
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
None
(default), a new axis is created.
name
shape
dtype
format
img
\((3, H, W)\)
float32
RGB, \([0, 255]\)
mask
\((R, H, W)\)
–
label
\((R,)\)
int32
\([0, \#fg\_class - 1]\)
score
\((R,)\)
float32
–
- Returns
Returns
ax
.ax
is anmatploblib.axes.Axes
with the plot.- Return type
matploblib.axes.Axes
vis_point¶
-
chainercv.visualizations.
vis_point
(img, point, visible=None, ax=None)[source]¶ Visualize points in an image.
Example
>>> import chainercv >>> import matplotlib.pyplot as plt >>> dataset = chainercv.datasets.CUBKeypointDataset() >>> img, point, visible = dataset[0] >>> chainercv.visualizations.vis_point(img, point, visible) >>> plt.show()
- Parameters
name
shape
dtype
format
img
\((3, H, W)\)
float32
RGB, \([0, 255]\)
point
\((R, K, 2)\) or \([(K, 2)]\)
float32
\((y, x)\)
visible
\((R, K)\) or \([(K,)]\)
–
- Returns
Returns the Axes object with the plot for further tweaking.
- Return type
Axes
vis_semantic_segmentation¶
-
chainercv.visualizations.
vis_semantic_segmentation
(img, label, label_names=None, label_colors=None, ignore_label_color=(0, 0, 0), alpha=1, all_label_names_in_legend=False, ax=None)[source]¶ Visualize a semantic segmentation.
Example
>>> from chainercv.datasets import VOCSemanticSegmentationDataset >>> from chainercv.datasets ... import voc_semantic_segmentation_label_colors >>> from chainercv.datasets ... import voc_semantic_segmentation_label_names >>> from chainercv.visualizations import vis_semantic_segmentation >>> import matplotlib.pyplot as plt >>> dataset = VOCSemanticSegmentationDataset() >>> img, label = dataset[60] >>> ax, legend_handles = vis_semantic_segmentation( ... img, label, ... label_names=voc_semantic_segmentation_label_names, ... label_colors=voc_semantic_segmentation_label_colors, ... alpha=0.9) >>> ax.legend(handles=legend_handles, bbox_to_anchor=(1, 1), loc=2) >>> plt.show()
- Parameters
img (ndarray) – See the table below. If this is
None
, no image is displayed.label (ndarray) – See the table below.
label_names (iterable of strings) – Name of labels ordered according to label ids.
label_colors – (iterable of tuple): An iterable of colors for regular labels. Each color is RGB format and the range of its values is \([0, 255]\). If
colors
isNone
, the default color map is used.ignore_label_color (tuple) – Color for ignored label. This is RGB format and the range of its values is \([0, 255]\). The default value is
(0, 0, 0)
.alpha (float) – The value which determines transparency of the figure. The range of this value is \([0, 1]\). If this value is
0
, the figure will be completely transparent. The default value is1
. This option is useful for overlaying the label on the source image.all_label_names_in_legend (bool) – Determines whether to include all label names in a legend. If this is
False
, the legend does not contain the names of unused labels. An unused label is defined as a label that does not appear inlabel
. The default value isFalse
.ax (matplotlib.axes.Axis) – The visualization is displayed on this axis. If this is
None
(default), a new axis is created.
name
shape
dtype
format
img
\((3, H, W)\)
float32
RGB, \([0, 255]\)
label
\((H, W)\)
int32
\([-1, \#class - 1]\)
- Returns
Returns
ax
andlegend_handles
.ax
is anmatploblib.axes.Axes
with the plot. It can be used for further tweaking.legend_handles
is a list of legends. It can be passedmatploblib.pyplot.legend()
to show a legend.- Return type
matploblib.axes.Axes and list of matplotlib.patches.Patch