imgviz

Image Visualization Tools


Installation

pip install imgviz

# there are optional dependencies like skimage, below installs all.
pip install imgviz[all]

Dependencies

Getting Started

# getting_started.py

import numpy as np

import imgviz

# sample data of rgb, depth, class label and instance masks
data = imgviz.data.arc2017()

rgb = data["rgb"]
gray = imgviz.rgb2gray(rgb)

# colorize depth image with viridis colormap
depth = data["depth"]
depthviz = imgviz.colorize(depth, vmin=0.3, vmax=1)

# colorize label image
class_label = data["class_label"]
labelviz = imgviz.label2rgb(
    class_label, image=gray, label_names=data["class_names"], font_size=20
)

# instance bboxes
bboxes = data["bboxes"].astype(int)
labels = data["labels"]
masks = data["masks"] == 1
captions = [data["class_names"][l] for l in labels]
maskviz = imgviz.instances2rgb(gray, masks=masks, labels=labels, captions=captions)

# per-instance flags as pie glyphs
centers = np.array([np.argwhere(m).mean(axis=0) for m in masks])
flags = np.column_stack(
    (masks.sum(axis=(1, 2)) < 7000, centers[:, 1] < rgb.shape[1] / 2)
)
flagviz = imgviz.flags2rgb(
    rgb, flags=flags, centers=centers, flag_names=["small", "left"], wedges="all"
)

# tile instance masks
insviz = [
    (rgb * m[:, :, None])[b[0] : b[2], b[1] : b[3]] for b, m in zip(bboxes, masks)
]
insviz = imgviz.tile(images=insviz, border=(255, 255, 255))
insviz = imgviz.resize(insviz, height=rgb.shape[0])

# tile visualization
tiled = imgviz.tile(
    [rgb, depthviz, labelviz, maskviz, flagviz, insviz],
    row=2,
    col=3,
    border=(255, 255, 255),
    border_width=5,
)

Examples

examples/blur_pixelate.py
 
examples/colorize.py
 
examples/diff.py
 
examples/draw.py
 
examples/flags2rgb.py
 
examples/flow2rgb.py
 
examples/instances2rgb.py
 
examples/label2rgb.py
 
examples/letterbox.py
 
examples/mask2rgb.py
 
examples/nchannel2rgb.py
 
examples/pie.py
 
examples/resize.py
 
examples/tile.py

Project links

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  • Released:
Latest release

1 maintainer

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Avatar for wkentaro from gravatar.com wkentaro

Credits

Author: Kentaro Wada

GitHub Statistics

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License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

all

Classifiers

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