Metadata-Version: 2.1
Name: chitra
Version: 0.0.16
Summary: Image utility library for Deep Learning
Home-page: https://github.com/aniketmaurya/chitra
Author: Aniket Maurya
Author-email: theaniketmaurya@gmail.com
License: Apache Software License 2.0
Description: # chitra
        
        
        
        <p align="center">
        <img src="nbs/../chitra_banner.png" alt="chitra">
        </p>
        
        ## What is chitra?
        
        **chitra** (**चित्र**) is an image utility library for Deep Learning tasks. *(It is not image-processing library)*
        
        chitra reduces image data loading boilerplates for classification and object-detection.
        
        It can also generate bounding-boxes from the annotated dataset.
        
        If you have more use cases please [**raise an issue**](https://github.com/aniketmaurya/chitra/issues/new/choose) with the feature you want.
        
        ## Installation
        
        ### Using pip (recommended)
        
        `pip install -U chitra`
        
        ### From source
        
        ```
        git clone https://github.com/aniketmaurya/chitra.git
        cd chitra
        pip install -e .
        ```
        
        ## Usage
        
        ### Loading data for image classification
        
        
        
        ```python
        import numpy as np
        import tensorflow as tf
        import chitra
        from chitra.dataloader import Clf, show_batch
        import matplotlib.pyplot as plt
        ```
        
        ```python
        path = '/Users/aniket/Pictures/data/train'
        
        clf_dl = Clf()
        data = clf_dl.from_folder(path, target_shape=(224, 224))
        
        clf_dl.show_batch(8, figsize=(8,8))
        ```
        
        ```python
        for e in data.take(1):
            image = e[0].numpy().astype('uint8')
            label = e[1].numpy()
        plt.imshow(image)
        plt.show()
        ```
        
        
        ![png](docs/images/output_6_0.png)
        
        
        ## Visualization
        
        ### Image annotation
        
        Thanks to [**fizyr**](https://github.com/fizyr/keras-retinanet) keras-retinanet.
        
        ```python
        from chitra.visualization import draw_annotations
        
        labels = np.array([label])
        bbox = np.array([[30, 50, 170, 190]])
        label_to_name = lambda x: 'Cat' if x==0 else 'Dog'
        ```
        
        ```python
        draw_annotations(image, ({'bboxes': bbox, 'labels':labels,}), label_to_name=label_to_name)
        plt.imshow(image)
        plt.show()
        ```
        
        
        ![png](docs/images/output_9_0.png)
        
        
        ## Image datagenerator
        Dataset class provides the flexibility to load image dataset by updating components of the class.
        
        Components of Dataset class are:
        - image file generator
        - resizer
        - label generator
        - image loader
        
        These components can be updated with custom function by the user according to their dataset structure. For example the Tiny Imagenet dataset is organized as-
        
        ```
        train_folder/
            folder1/
                       file.txt
                       folder2/
                             image1.jpg
                             image2.jpg
                             .
                             .
                             .
                             imageN.jpg
                            
                              
        ```
        
        The inbuilt file generator search for images on the `folder1`, now we can just update the `image file generator` and rest of the functionality will remain same.
        
        Dataset also support progressive resizing of images.
        
        ```python
        from chitra.datagenerator import Dataset
        from glob import glob
        ```
        
        ```python
        ds = Dataset('/data/aniket/tiny-imagenet/data/tiny-imagenet-200/train')
        # it will load the folders and NOT images
        ds.filenames[:3]
        ```
        
            No item present in the image size list
        
        
        
        
        
            ['/data/aniket/tiny-imagenet/data/tiny-imagenet-200/train/n03584254',
             '/data/aniket/tiny-imagenet/data/tiny-imagenet-200/train/n02403003',
             '/data/aniket/tiny-imagenet/data/tiny-imagenet-200/train/n02056570']
        
        
        
        ```python
        def new_image_fileloader(path): return glob(f'{path}/*/images/*')
        
        ds.update_component('get_filenames', new_image_fileloader)
        ds.filenames[:3]
        ```
        
            get_filenames updated with <function new_image_fileloader at 0x7fd1dc18fdd0>
        
        
        
        
        
            ['/data/aniket/tiny-imagenet/data/tiny-imagenet-200/train/n03584254/images/n03584254_251.JPEG',
             '/data/aniket/tiny-imagenet/data/tiny-imagenet-200/train/n03584254/images/n03584254_348.JPEG',
             '/data/aniket/tiny-imagenet/data/tiny-imagenet-200/train/n03584254/images/n03584254_465.JPEG']
        
        
        
        ### Progressive resizing
        
        ```python
        image_sz_list = [(28, 28), (32, 32), (64, 64)]
        
        ds = Dataset('/data/aniket/tiny-imagenet/data/tiny-imagenet-200/train', image_size=image_sz_list)
        ds.update_component('get_filenames', new_image_fileloader)
        
        # first call to generator
        for img, label in ds.generator():
            print('first call to generator:', img.shape)
            break
        
        # seconds call to generator
        for img, label in ds.generator():
            print('seconds call to generator:', img.shape)
            break
        
        # third call to generator
        for img, label in ds.generator():
            print('third call to generator:', img.shape)
            break
        
        ```
        
            get_filenames updated with <function new_image_fileloader at 0x7fd1dc18fdd0>
            first call to generator: (28, 28, 3)
            seconds call to generator: (32, 32, 3)
            third call to generator: (64, 64, 3)
        
        
        ## tf.data support
        Creating a `tf.data` dataloader was never as easy as this one liner. It converts the Python generator into `tf.data.Dataset` for a faster data loading, prefetching, caching and everything provided by tf.data.
        
        ```python
        image_sz_list = [(28, 28), (32, 32), (64, 64)]
        
        ds = Dataset('/data/aniket/tiny-imagenet/data/tiny-imagenet-200/train', image_size=image_sz_list)
        ds.update_component('get_filenames', new_image_fileloader)
        
        dl = ds.get_tf_dataset()
        
        for e in dl.take(1):
            print(e[0].shape)
        
        for e in dl.take(1):
            print(e[0].shape)
        
        for e in dl.take(1):
            print(e[0].shape)
        ```
        
            get_filenames updated with <function new_image_fileloader at 0x7fd1dc18fdd0>
            (32, 32, 3)
            (64, 64, 3)
            Returning the last set size which is: (64, 64)
            (64, 64, 3)
        
        
        ## Utils
        
        ```python
        from chitra.utils import limit_gpu
        
        # limit the amount of GPU required for your training
        limit_gpu(gpu_id=0, memory_limit=1024*2)
        ```
        
            1 Physical GPUs, 1 Logical GPUs
        
        
        ## Contributing
        
        Contributions of any kind are welcome. Please check the [**Contributing Guidelines**](https://github.com/aniketmaurya/chitra/blob/master/CONTRIBUTING.md) before contributing.
        
Keywords: Tensorflow,Input Pipeline,Deep Learning,visualization
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
