PixelLib

Pixellib is a library for performing segmentation of objects in images and videos. It supports the two major types of image segmentation:

1.Semantic segmentation

2.Instance segmentation

Install PixelLib and its dependencies

Install Tensorflow:

Install latest version of tensorflow(Tensorflow 2.0+) with:

pip3 install tensorflow

If you have have a pc enabled GPU, Install tensorflow--gpu's version that is compatible with the cuda's version on your pc:

pip3 install tensorflow--gpu

Install Pixellib with: pip3 install pixellib

Visit PixelLib's official documentation on readthedocs

You can implement both semantic and instance segmentation with few lines of code.

There are two types of Deeplabv3+ models available for performing semantic segmentation with PixelLib:

1. Deeplabv3+ model with xception as network backbone trained on Ade20k dataset, a dataset with 150 classes of objects.
2. Deeplabv3+ model with xception as network backbone trained on Pascalvoc dataset, a dataset with 20 classes of objects.

Instance segmentation is implemented with PixelLib by using Mask R-CNN model trained on coco dataset.

The latest version of PixelLib supports custom training of object segmentation models using pretrained coco model.

• Instance Segmentation of Images With PixelLib Using Mask-RCNN COCO Model

• Instance Segmentation of Videos With PixelLib Using Mask-RCNN COCO Model

• Custom Instance Segmentation Training

• Instance Segmentation of objects in images and videos With Custom Model

• Semantic Segmentation of Images With PixelLib Using Ade20k model

• Semantic Segmentation of Videos With PixelLib Using Ade20k model

• Semantic Segmentation of Images With PixelLib Using Pascalvoc model

• Semantic Segmentation of Videos With PixelLib Using Pascalvoc model

Note Deeplab and mask r-ccn models are available in the release of this repository.

Instance Segmentation of Images With PixelLib Using Mask-RCNN COCO Model

Learn how to implement state of the art instance segmentation of objects with Mask-RCNN with PixelLib using 5 Lines of Code.

Instance Segmentation of Videos With PixelLib Using Mask-RCNN COCO Model

Implement state of the art instance segmentation of objects in video's feeds with Mask-RCNN model using 5 Lines of Code.

Custom Instance Segmentation Training

Perform instance segmentation on objects with your custom model with 7 Lines of Code.

Instance Segmentation of objects in images and videos With A Custom Model

Perform inference on objects with your custom model.

Semantic Segmentation of Images With PixelLib Using Ade20k model

Learn how to perform state of the art semantic segmentation of 150 classes of objects with Ade20k model using 5 Lines of Code. Perform indoor and outdoor segmentation of scenes with PixelLib by using Ade20k model.

Semantic Segmentation of Videos With PixelLib Using Ade20k model

Implement state of the art semantic segmentation of 150 classes objects in video's feeds using Ade20k model with PixelLib using 5 Lines of Code.

Semantic Segmentation of Images With PixelLib Using Pascalvoc model

Learn how to perform state of the art semantic segmentation of 20 common objects with Pascalvoc model using 5 Lines of Code. Perform segmentation of unique objects with PixelLib by using Pascalvoc model.

Semantic Segmentation of Videos With PixelLib Using Pascalvoc model

Implement state of the art semantic segmentation of 20 unique objects in video's feeds using Pascalvoc model with PixelLib using 5 Lines of Code.

Projects Using PixelLib

1. A segmentation api integrated with PixelLib to perform Semantic and Instance Segmentation of images on ios https://github.com/omarmhaimdat/segmentation_api

2. PixelLib is integerated in drone's cameras to perform instance segmentation of live video's feeds https://elbruno.com/2020/05/21/coding4fun-how-to-control-your-drone-with-20-lines-of-code-20-n/?utm_source=twitter&utm_medium=social&utm_campaign=tweepsmap-Default

References

1. Bonlime, Keras implementation of Deeplab v3+ with pretrained weights https://github.com/bonlime/keras-deeplab-v3-plus

2. Liang-Chieh Chen. et al, Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation https://arxiv.org/abs/1802.02611

3. Matterport, Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow https://github.com/matterport/Mask_RCNN

4. Mask R-CNN code made compatible with tensorflow 2.0, https://github.com/tomgross/Mask_RCNN/tree/tensorflow-2.0

5. Kaiming He et al, Mask R-CNN https://arxiv.org/abs/1703.06870

6. TensorFlow DeepLab Model Zoo https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md

7. Pascalvoc and Ade20k datasets' colormaps https://github.com/tensorflow/models/blob/master/research/deeplab/utils/get_dataset_colormap.py

Back To Top