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!.
To install imshowtools, simply do
pip install imshowtoolsImport 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
Example ipynb notebook and Python along with test images provided in the example folder.
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)imshow(*x_train[:15], cmap='Purples', rows=1)
imshow(*x_train[:24], cmap='Greens', columns=4)
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)
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)Pull requests are very welcome.
- Fork the repo
- Create new branch with feature name as branch name
- Check if things work with a jupyter notebook
- Raise a pull request
Please see attached Licence
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