/A-Geometry-Sensitive-Approach-for-Photographic-Style-Classification

IMVIP 2018

Primary LanguagePython

A Geometry-Sensitive Approach for Photographic Style Classification

Koustav Ghosal, Mukta Prasad, Aljosa Smolic

IMVIP 2018

Prerequisites

Ubuntu (14 or higher)
Cuda 9
python 2.7

Installing python libraries

pip install -r requirements.txt

Data and Pretrained models

For faster training, we have extracted the saliency maps using [1] and appended them with the RGB images as RGBA PILs.

  • RGB
  • RGB + Saliency
  • Pretrained Models

Download Link

Running the scripts

Features

  • One can train both single and double column architectures using this code.
  • Testing is optional. One can try only training for different data augmentation and network architectures.
  • A web based GUI is provided which can be used to observe the performance for a single image

Only Training

Training a double column CNN

python -W ignore CNN_Master.py --datapath /root/directory/containing/train/val/subdirectories/ --model DenseNet_Saliency_Direct --save /path/for/saving/models/ --pooling --pretrained --aug_train SAL_ICC --batch_size 8

Training a single column CNN

python -W ignore CNN_Master.py --datapath /root/directory/containing/train/val/subdirectories/ --model DenseNet_161 --save /path/for/saving/models/ --pooling --pretrained --aug_train ICC --batch_size 8

Check CNN_Master.py for the full list of command line options, models, augmentation strategies

Both training and testing

For double column

python -W ignore CNN_Master.py --datapath /root/directory/containing/train/val/subdirectories/ --model DenseNet_Saliency_Direct --save /path/for/saving/models/ --pooling --pretrained --aug_train SAL_ICC --batch_size 8 --withTesting --aug_test SAL_ICC --testDataPath /path/to/test/data --testLabels /path/to/test/data/test.multilab --testIds /path/to/test/data/test.jpgl

For single column

python -W ignore CNN_Master.py --datapath /root/directory/containing/train/val/subdirectories/ --model DenseNet_161 --save /path/for/saving/models/ --pooling --pretrained --aug_train ICC --batch_size 8 --withTesting --aug_test ICC --testDataPath /path/to/test/data --testLabels /path/to/test/data/test.multilab --testIds /path/to/test/data/test.jpgl

Important

Make sure you use a SAL_ prefix for augmentation for the double column model. For a single column remove the SAL_ prefix.

GUI

This GUI can be used to observe predictions on single image from the web or local directories.

Testing RGB images with single column methods

cd GUI
python Master.py /path/to/model/xyz.model ICC

Testing RGB-Saliency images with double column methods

cd GUI
python Master.py /path/to/model/xyz.model SAL_ICC

Final Step

Open browser and go to 0.0.0.0:3134.

For images from the web just paste the raw link of the image. For example,

https://pbs.twimg.com/profile_images/969919433820041216/lx_WkhmQ_200x200.jpg

For local images, paste the location followed by file://. For example,

file:///home/path/to/image/XYZ.jpg

Press submit. If everything works one should observe something as follows.

Single Column Output

Double Column Output

Important

Make sure you use a SAL_ prefix for augmentation and use a 4-channel image for the double column model. For a single column remove the SAL_ prefix and use it on a RGB image.

Contact

ghosalk@tcd.ie

Referneces

  1. Cornia, M., Baraldi, L., Serra, G., and Cucchiara, R. (2016). Predicting human eye fixations via an lstm-based saliency attentive model. arXiv preprint arXiv:1611.09571