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imshowtools contains simplified imshow functions to show multiple images and with other options

Project description

imshowtools

This library lets you view images in Jupyter notebooks in a much simpler and intuitive way. Ships with a better 'imshow' function with Multi Images, Smart Wrap and BGR support!.

Installation

To install imshowtools, simply do

pip install imshowtools

Quick Plot

Import imshow from imshowtools and use it:

from imshowtools import imshow
import tensorflow as tf
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

imshow(x_train[0])
imshow(x_train[0], x_train[1], x_train[2])
imshow(*x_train[:20], cmap='binary')
imshow(*x_train[:100], cmap='binary', size=(10, 10))

You can use any matplotlib compatible cmap

mnist

Example ipynb notebook and Python along with test images provided in the example folder.

Get Numpy Image

You can use obtain numpy image in any of ['RGB', 'RGBA', 'ARGB', 'BW', 'L', "BGR", "BGRA", "ABGR"] colorspaces.

image = imshow(*x_train[:100], return_image=True)
image = imshow(*x_train[:100], return_image="RGBA")
image = imshow(*x_train[:100], return_image="RGB")
image = imshow(*x_train[:100], return_image="BW")
print(image.shape)

# cv2.imwrite("saved_sample.png", image)
# do stuff with 'image' or even
# imshow(image)

Output:

(288, 432, 3)
(288, 432, 4)
(288, 432, 3)
(288, 432)

Rows and Columns

imshow(*x_train[:15], cmap='Purples', rows=1)
imshow(*x_train[:24], cmap='Greens', columns=4)

mnist

Open CV Images

lenna = cv2.imread('example/lenna.png')
imshow(lenna)
cvshow(lenna)
imshow(lenna, mode='BGR')

image = imshow(*[lenna for _ in range(12)], return_image="BW")
print(image.shape)
imshow(image)

lenna

Namespaces

If you do not want to use imshow directly in your app (maybe you have another function named imshow), you shall use it like

import imshowtools
imshowtools.imshow(your_image)

or if you like to use a custom namespace

import imshowtools as my_namespace
my_namespace.imshow(your_image)

Contributing

Pull requests are very welcome.

  1. Fork the repo
  2. Create new branch with feature name as branch name
  3. Check if things work with a jupyter notebook
  4. Raise a pull request

Licence

Please see attached Licence

Project details


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