Generic Foreground Segmentation in Images

Related tags

Deep Learning computer-vision deep-learning pretrained-models image-segmentation pixel-objectness
Overview

Pixel Objectness

The following repository contains pretrained model for pixel objectness.

Please visit our project page for the paper and visual results.

If you use this in your research, please cite the following paper:

@article{pixelobjectness,
  Author = {Jain, Suyog and Xiong, Bo and Grauman, Kristen},
  Journal = {arXiv preprint arXiv:1701.05349},
  Title = {Pixel Objectness},
  Year = {2017}
}

These models are freely available for research and academic purposes. However it's patent pending, so please contact us for any commercial use.

Using the pretrained models:

This model is trained using Deeplab-v1 caffe library. Please cite [1] and [2] if you use the code.

  • Setup: Download and install Deeplab-v1 from here

  • Refer to demo.py for step-by-step instruction on how to run the code.

  • Store the images that you want to process in the images folder.

  • Update the caffe binary path and image extension variable in demo.py

  • Running demo.py will produce three files 1) image_list.txt : contains list of of input images, 2) output_list.txt: contains names to be used to store the output of pixel objectness 3) test.protoxt: prototxt file required for loading the pretrained model.

  • Please resize your images so that the maximum side is < 513, otherwise update the crop_size value in test_template.prototxt. Bigger crop sizes require larger gpu memory.

Visualizing the results:

After execution demo.py will store pixel objectness results as matlab files.

Please refer to show_results.m to see how to visualize and extract foreground masks.

Please cite these too if you use the code:

[1] Caffe:

@article{jia2014caffe,
Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
Journal = {arXiv preprint arXiv:1408.5093},
Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
Year = {2014}
}

[2] Deeplab-v1:

@inproceedings{chen14semantic,
title={Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs},
author={Liang-Chieh Chen and George Papandreou and Iasonas Kokkinos and Kevin Murphy and Alan L Yuille},
booktitle={ICLR},
url={http://arxiv.org/abs/1412.7062},
year={2015}
}
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Comments
  • how can I run network in python?

    how can I run network in python?

    I tried to run pixelobjectness network in python. following code is the code.

    The output of following code is not the expected result.

    I want to know what is the wrong code.

    ` import numpy as np from PIL import Image as PILImage import caffe _FOLDER = '/opt/pixelobjectness/' MODEL_FILE = _FOLDER+'test.prototxt' PRETRAINED = _FOLDER+'pixel_objectness.caffemodel' net = caffe.Net(MODEL_FILE, PRETRAINED)

    net.set_mode_gpu() net.set_device(0) net.set_phase_test()

    mean_vec = np.array([104.008, 116.669, 122.675], dtype=np.float32) reshaped_mean_vec = mean_vec.reshape(1, 1, 3);

    net.set_raw_scale('data',255) net.set_mean('data', reshaped_mean_vec) net.set_channel_swap('data', (2, 1, 0))

    IMAGE_FILE = '/opt/pixelobjectness/images/n01736375_3996.JPEG' im = caffe.io.load_image(IMAGE_FILE) im2 = net.preprocess('data',im)

    net.blobs['data'].data[...] = im2

    output = net.forward() d = output['fc8_interp'].reshape(513,513,2,1)

    import cPickle as pickle with file("/root/workspace/images/a2.mat","w") as fout: pickle.dump(output['fc8_interp'], fout)

    `

    opened by justinbuzzni 1
  • Trouble replicating the results

    Trouble replicating the results

    Hi, I was trying to replicate the results presented in your paper and ran into some problems. As far as I understand, you more or less follow the training procedure for DeepLab_LargeFOV network outlined here but with 2 classes instead of the original 21 and without the final CRF refinement layer (correct me if this is wrong). I am training on the 10,582 images from the augmented PASCAL dataset, initialize the weights with VGG trained on ImageNet and use learning parameters as specified in the paper, however the results are not nearly as good. I haven't run the full set of tests yet, but on the example images results produced by my network are far worse than the published pixelobjectness model. Also suspiciously, the loss fluctuates significantly and training for 10000 iterations takes about 3 hours on NVIDIA Titan X instead of 8 hours stated in the paper. Below is the solver I've been using, could you please let me know if I'm missing something?

    `lr_policy: "step" gamma: 0.1 stepsize: 2000 base_lr: 0.001

    display: 10 max_iter: 10000 momentum: 0.9 weight_decay: 0.0005`

    opened by ykotseruba 0
  • After run demo.py, there are 4-dimensional .mat files generated?

    After run demo.py, there are 4-dimensional .mat files generated?

    Hi, I use this codes and run demo.py, there are 4-dimensional .mat files generated. But show_result.m　just process 3-dimensional .mat files, what is wrong with me??

    thanks!!

    When I run show_result.m file, there is a error:

    Error using permute ORDER must have at least N elements for an N-D array.

    Error in show_results (line 23) raw_result = permute(raw_result, [2 1 3]);

    opened by luluerry 5
Owner
Suyog Jain
PhD in Computer Vision, Research Scientist at PathAI
Suyog Jain
GitHub http://vision.cs.utexas.edu/projects/pixelobjectness/
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