CutoutViewer

class ibb.widgets.CutoutViewer(**kwargs: Any)

This widget can visualize a brambox dataset as bounding boxes drawn on top of the images.
Its arguments work a lot like brambox.util.BoxDrawer. The main difference with BramboxViewer is that each polygon is shown as a separate cutout.

Parameters

• images (callable or dict-like object) – A way to get the image or path to the image from the image labels in the dataframe

• boxes (pandas.DataFrame) – Bounding boxes to draw

• pad (int, float or tuple of 2 int/float) – Padding to add around the width and height (see Note); Default 10

• label (pandas.Series) – Label to write above the boxes; Default class_label (confidence)

• color (pandas.Series) – Color to use for drawing; Default every class_label will get its own color, up to 10 labels

• size (pandas.Series) – Thickness of the border of the bounding boxes; Default 3

• alpha (pandas.Series) – Alpha fill value of the bounding boxes; Default 00

• **kwargs (dict) – Extra keyword arguments that will be passed to Viewer

Note

If the images argument is callable, the image or path to the image will be retrieved in the following way: >>> image = images(image_label)

Otherwise the image or path is retrieved as: >>> image = images[image_label]

Note

The padding property can be one of 4 possible types: - int : A fixed pixel padding is added to each side. - float : A fixed pixel padding is added to each side. The padding is computed as pad*box_width. - (int, int) : Separate pixel padding for the width and height. - (float, float) : Separate pixel padding for the width and height. The padding is computed as (pad[0]*box_width, pad[1]*box_height).

Note that the padding is clipped inside of the image boundaries.

Note

The label, color, size and alpha arguments can also be tacked on to the boxes dataframe as columns. They can also be a single value, which will then be used for each bounding box.
Basically, as long as you can assign the value as a new column to the dataframe, it will work.

Examples

Warning

Note that the widgets are not responding as we do not have a python backend running!

import brambox as bb
import ibb

# Load annotations
anno = bb.io.load('pandas', 'annotations.h5')
anno

	image	class_label	id	x_top_left	y_top_left	width	height	occluded	truncated	lost	difficult	ignore
0	000001	dog	NaN	48.0	240.0	147.0	131.0	0.0	1.0	False	False	False
1	000001	person	NaN	8.0	12.0	344.0	486.0	0.0	1.0	False	False	False
2	000002	train	NaN	139.0	200.0	68.0	101.0	0.0	0.0	False	False	False
3	000003	sofa	NaN	123.0	155.0	92.0	40.0	0.0	0.0	False	False	False
4	000003	chair	NaN	239.0	156.0	68.0	49.0	0.0	0.0	False	False	False
5	000004	car	NaN	13.0	311.0	71.0	51.0	0.0	0.0	False	False	False
6	000004	car	NaN	362.0	330.0	138.0	59.0	0.0	1.0	False	False	False
7	000004	car	NaN	235.0	328.0	99.0	47.0	0.0	0.0	False	False	False
8	000004	car	NaN	175.0	327.0	77.0	37.0	0.0	0.0	False	False	False
9	000004	car	NaN	139.0	320.0	50.0	39.0	0.0	0.0	False	False	False
10	000004	car	NaN	108.0	325.0	42.0	28.0	0.0	0.0	False	False	False
11	000004	car	NaN	84.0	323.0	37.0	27.0	0.0	0.0	False	False	False

ibb.CutoutViewer(
    # Dictionary or Function to map image column to image paths
    lambda name: f'images/{name}.jpg',
    anno,
)