chitra 0.0.17

Image utility library for Deep Learning

chitra

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 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

import numpy as np
import tensorflow as tf
import chitra
from chitra.dataloader import Clf, show_batch
import matplotlib.pyplot as plt

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))

for e in data.take(1):
    image = e[0].numpy().astype('uint8')
    label = e[1].numpy()
plt.imshow(image)
plt.show()

Visualization

Image annotation

Thanks to fizyr keras-retinanet.

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'

draw_annotations(image, ({'bboxes': bbox, 'labels':labels,}), label_to_name=label_to_name)
plt.imshow(image)
plt.show()

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.

from chitra.datagenerator import Dataset
from glob import glob

Updating component

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']

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

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.

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

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 before contributing.