Metadata-Version: 2.1
Name: imshowtools
Version: 0.6.2
Summary: imshowtools contains simplified imshow functions to show multiple images and with other options
Home-page: https://github.com/saravanabalagi/imshowtools
Author: Saravanabalagi Ramachandran
Author-email: saravanabalagi@hotmail.com
License: MIT
Description: # imshowtools
        
        ![](https://img.shields.io/pypi/v/imshowtools)
        ![](https://img.shields.io/pypi/wheel/imshowtools)
        ![](https://img.shields.io/pypi/l/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
        
        ```py
        pip install imshowtools
        ```
        
        ## Quick Plot
        
        Import `imshow` from `imshowtools` and use it:
         
        ```py
        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')
        ```
        
        You can use any matplotlib compatible `cmap`
        
        ![mnist](example/mnist_intro.png)
        
        Example [ipynb](example/example.ipynb) notebook and [Python](example/example.py) 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.
        
        ```py
        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:
        ```py
        (288, 432, 3)
        (288, 432, 4)
        (288, 432, 3)
        (288, 432)
        ```
        
        ## Rows and Columns
        
        ```py
        imshow(*x_train[:15], cmap='Purples', rows=1)
        imshow(*x_train[:24], cmap='Greens', columns=4)
        ```
        
        ![mnist](example/mnist_rc.png)
        
        ## Open CV Images
        
        ```py
        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](example/lenna_collage.png)
        
        ## 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
        
        ```py
        import imshowtools
        imshowtools.imshow(your_image)
        ```
        
        or if you like to use a custom namespace
        ```py
        import imshowtools as my_namespace
        my_namespace.imshow(your_image)
        ```
        
        ## Contributing
        
        Pull requests are very welcome.
        
        1. Fork the repo
        1. Create new branch with feature name as branch name
        1. Check if things work with a jupyter notebook
        1. Raise a pull request
        
        ## Licence
        
        Please see attached [Licence](LICENCE)
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Software Development :: Libraries
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
