chitra 0.0.18

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

clf_dl = Clf()
data = clf_dl.from_folder(cat_dog_path, target_shape=(224, 224))

clf_dl.show_batch(8, figsize=(8,8))

CLASSES ENCODED: {'cat': 0, 'dog': 1}

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

# data_path = '/data/aniket/tiny-imagenet/data/tiny-imagenet-200/train'
ds = Dataset(data_path)
# 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 load_files(path):
    return glob(f'{path}/*/images/*')

def get_label(path):
    return path.split('/')[-3]

ds.update_component('get_filenames', load_files)
ds.filenames[:3]

get_filenames updated with <function load_files at 0x7fe0a5aa9560>
No item present in the image size list





['/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_path, image_size=image_sz_list)
ds.update_component('get_filenames', load_files)
ds.update_component('get_label', get_label)


print()
# 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 load_files at 0x7fe0a5aa9560>
get_label updated with <function get_label at 0x7fe14838ab00>

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_path, image_size=image_sz_list)
ds.update_component('get_filenames', load_files)
ds.update_component('get_label', get_label)

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 load_files at 0x7fe0a5aa9560>
get_label updated with <function get_label at 0x7fe14838ab00>
(28, 28, 3)
(32, 32, 3)
(64, 64, 3)

Learner

The Learner class inherits from tf.keras.Model, it contains everything that is required for training.

from chitra.learner import Learner
from chitra.datagenerator import Dataset
from PIL import Image

ds = Dataset(cat_dog_path, image_size=(224,224))

learner = Learner(ds, tf.keras.applications.MobileNetV2)

WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.

learner.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
mobilenetv2_1.00_224 (Functi (None, None, None, 1280)  2257984   
_________________________________________________________________
global_average_pooling2d_1 ( (None, 1280)              0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 1280)              0         
_________________________________________________________________
output (Dense)               (None, 1)                 1281      
=================================================================
Total params: 2,259,265
Trainable params: 2,225,153
Non-trainable params: 34,112
_________________________________________________________________

learner.compile(loss=tf.keras.losses.BinaryCrossentropy(), metrics=['binary_accuracy'])

learner.cyclic_fit(epochs=10,
                   batch_size=1,
                   lr_range=(0.01, 0.01)
                  )

Returning the last set size which is: (224, 224)
Epoch 1/10
8/8 [==============================] - 1s 109ms/step - loss: 7.7125 - binary_accuracy: 0.5000
Epoch 2/10
Returning the last set size which is: (224, 224)
8/8 [==============================] - 1s 109ms/step - loss: 7.7125 - binary_accuracy: 0.5000
Epoch 3/10
Returning the last set size which is: (224, 224)
8/8 [==============================] - 1s 112ms/step - loss: 7.7125 - binary_accuracy: 0.5000
Epoch 4/10
Returning the last set size which is: (224, 224)
8/8 [==============================] - 1s 111ms/step - loss: 7.7125 - binary_accuracy: 0.5000
Epoch 5/10
Returning the last set size which is: (224, 224)
8/8 [==============================] - 1s 108ms/step - loss: 7.7125 - binary_accuracy: 0.5000
Epoch 6/10
Returning the last set size which is: (224, 224)
8/8 [==============================] - 1s 108ms/step - loss: 7.7125 - binary_accuracy: 0.5000
Epoch 7/10
Returning the last set size which is: (224, 224)
8/8 [==============================] - 1s 109ms/step - loss: 7.7125 - binary_accuracy: 0.5000
Epoch 8/10
Returning the last set size which is: (224, 224)
8/8 [==============================] - 1s 115ms/step - loss: 7.7125 - binary_accuracy: 0.5000
Epoch 9/10
Returning the last set size which is: (224, 224)
8/8 [==============================] - 1s 116ms/step - loss: 7.7125 - binary_accuracy: 0.5000
Epoch 10/10
Returning the last set size which is: (224, 224)
8/8 [==============================] - 1s 117ms/step - loss: 7.7125 - binary_accuracy: 0.5000





<tensorflow.python.keras.callbacks.History at 0x7fb21487d2d0>

Model Visualization

from chitra.learner import InterpretModel

learner = Learner(ds, tf.keras.applications.Xception, include_top=True)
model_interpret = InterpretModel(False, learner)

image = ds[1][0].numpy().astype('uint8')
image = Image.fromarray(image)
model_interpret(image)

Returning the last set size which is: (224, 224)
index: 285

IMAGENET_LABELS[285]

'Egyptian Mau'

image = ds[3][0].numpy().astype('uint8')
image = Image.fromarray(image)
print(IMAGENET_LABELS[208])
model_interpret(image)

Labrador Retriever
Returning the last set size which is: (224, 224)
index: 208

Utils

Limit GPU memory or enable dynamic GPU memory growth for Tensorflow

from chitra.utils import limit_gpu, gpu_dynamic_mem_growth

# limit the amount of GPU required for your training
limit_gpu(gpu_id=0, memory_limit=1024*2)

No GPU:0 found in your system!

gpu_dynamic_mem_growth()

No GPU found on the machine!

Contributing

Contributions of any kind are welcome. Please check the Contributing Guidelines before contributing.