Add eDN.

dockerfiles/Dockerfile.eDN

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+FROM ubuntu:16.04
+
+LABEL maintainer="Toni Kunic <[email protected]>"
+
+################################################################################
+### Apt and pip dependencies
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ build-essential \
+ python-pip \
+ python-setuptools \
+ python-matplotlib \
+ python-dev \
+ python-tk \
+ libxml2-dev \
+ libxslt-dev \
+ git && \
+ rm -rf /var/lib/apt/lists/*
+
+COPY ./smiler_tools /tmp/smiler_tools
+RUN pip install /tmp/smiler_tools
+
+################################################################################
+### Run command on container start.
+
+VOLUME ["/opt/model"]
+VOLUME ["/opt/input_vol"]
+VOLUME ["/opt/output_vol"]
+
+WORKDIR /opt/model
+
+CMD ["/bin/bash"]
+
+################################################################################
+### Set up liblinear and sthor.
+
+RUN pip install --upgrade pip setuptools
+RUN pip install setuptools cython numpy scipy numexpr scikit-image liblinear hyperopt
+
+RUN mkdir /opt/dependencies && \
+ cd /opt/dependencies && \
+ git clone https://github.com/tkunic/sthor.git && \
+ cd /opt/dependencies/sthor/sthor/operation && \
+ make && \
+ pip install /opt/dependencies/sthor
+
+RUN cd /opt/dependencies && \
+ git clone https://github.com/cjlin1/liblinear && \
+ cd liblinear && \
+ git checkout tags/v201 && \
+ make && \
+ cd python && \
+ make
+
+ENV PYTHONPATH "${PYTONPATH}:/opt/dependencies/liblinear/python:/opt/dependencies/sthor/:/opt/dependencies/sthor/build/lib.linux-x86_64-2.7/sthor/sthor/:/opt/dependencies/sthor/build/lib.linux-x86_64-2.7/sthor/sthor/resample/"

models/docker/eDN/model/.gitignore

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+*.py[cod]
+
+# C extensions
+*.so
+
+# Packages
+*.egg
+*.egg-info
+dist
+build
+eggs
+parts
+bin
+var
+sdist
+develop-eggs
+.installed.cfg
+lib
+lib64
+
+# Installer logs
+pip-log.txt
+
+# Unit test / coverage reports
+.coverage
+.tox
+nosetests.xml
+
+# Translations
+*.mo
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+
+# Model files
+svm-slm
+svm-slm-cntr
+whiten-slm
+whiten-slm-cntr

models/docker/eDN/model/LICENSE

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+The MIT License (MIT)
+
+Copyright (c) 2014 coxlab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

models/docker/eDN/model/README.md

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+eDN-saliency
+============
+This repository contains reference code for computing Ensembles of Deep Networks (eDN) saliency maps based on the CVPR'2014 paper "Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images".
+
+Usage
+-----
+
+```
+./eDNsaliency [--opts] <image> <output_saliency_map>
+
+Options:
+ -h, --help show this help message and exit
+ --descs DESCPATH path to eDN model(s) (default: ./slmBestDescrCombi.pkl)
+ --svm SVMPATH path to SVM file (default: ./svm-slm-cntr)
+ --white WHITEPATH path to whitening parameters (default: ./whiten-slm-cntr)
+ --fixMap FIXMAP fixation map / empirical saliency map, if available
+ --histeq histogram equalization with given empirical saliency map
+ (default: False); requires empirical saliency map
+ --auc computes AUC for given fixation map; requires fixation map
+ --no-blur disable the default smoothing of the final map
+
+```
+
+Input format:
+ + fixation map: black image with fixated pixels (one per fixation) set to 255 (see ./img_fixPts.jpg)
+ + empirical saliency map: superposition of Gaussians centered at fixations (see ./img_fixMap.jpg)
+
+These images should have the same size as the input image.
+
+Examples
+--------
+
+```
+./eDNsaliency img.jpg salMap.jpg 
+ Computes raw (non-histogram-equalized) saliency map (in salMap.jpg) for given image 
+
+./eDNsaliency --histeq --fixMap img_fixMap.jpg img.jpg salMap-histeq.jpg
+ Computes histogram-equalized saliency map with given empirical saliency map (img_fixMap.jpg)
+
+./eDNsaliency --auc --fixMap img_fixPts.jpg img.jpg salMap.jpg
+ Computes Area Under the Curve (AUC) for fixation map (img_fixPts.jpg)
+ 
+./eDNsaliency --svm ./svm-slm --white ./whiten-slm img.jpg salMap-noCntr.jpg
+ Computes non-centered saliency maps
+```
+
+
+Requirements
+------------
+
+```
+sthor
+liblinear
+```
+
+
+Installation
+------------
+
+(Tested under Ubuntu 14.04)
+
+1. Install dependencies 
+ ```
+ sudo apt-get install python-matplotlib python-setuptools curl python-dev libxml2-dev libxslt-dev
+ ```
+
+2. Install liblinear
+
+ Download toolbox from http:https://www.csie.ntu.edu.tw/~cjlin/liblinear/
+
+ ```
+ # extract the zip
+ make
+ cd python
+ make
+ ```
+
+3. Install sthor dependencies
+
+ ```
+ curl -O http:https://python-distribute.org/distribute_setup.py
+ sudo python distribute_setup.py
+ sudo easy_install pip
+ sudo easy_install -U scikit-image
+ sudo easy_install -U cython
+ sudo easy_install -U numexpr
+ sudo easy_install -U scipy
+ ```
+
+ For speedup, numpy and numexpr should be built against e.g. Intel MKL libraries.
+
+4. Install sthor
+
+ ```
+ git clone https://github.com/nsf-ri-ubicv/sthor.git
+ cd sthor/sthor/operation
+ sudo make
+ cd ../..
+ python setup.py install
+ ```
+ add the sthor directory and the liblinear/python directory to your PYTHONPATH
+
+5. Test sthor installation
+
+ ```
+ python
+ import sthor # should import without errors
+ ```
+
+Precomputed Saliency Maps
+-------------------------
+
+We provide precomputed saliency maps for three standard benchmarks:
+ + MIT data set: MIT1003_eDN.zip 
+ + Toronto data set: Toronto_eDN.zip 
+ + NUSEF data set: NUSEF_eDN.zip 
+
+
+Citing this Code
+----------------
+
+If you use this code in your own work, please cite the following paper:
+
+Eleonora Vig, Michael Dorr, David Cox, "Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images", IEEE Computer Vision and Pattern Recognition (CVPR), 2014. 
+
+Link to the paper: http:https://coxlab.org/pdfs/cvpr2014_vig_saliency.pdf 
+
+For questions and feedback please contact me at [email protected]

models/docker/eDN/model/eDNSalModel.py

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+import numpy as np
+import scipy as sp
+from scipy import misc
+import time
+import logging
+
+from liblinearutil import predict
+
+from features import eDN_features, dist_to_cntr_features, whiten_features
+
+EDN_INSIZE = (512, 384)
+
+
+class EDNSaliencyModel(object):
+ def __init__(self, descriptions, svmModel, biasToCntr=False):
+ self.descriptions = descriptions
+ self.svm = svmModel['svm']
+ self.whitenParams = svmModel['whitenParams']
+ self.biasToCntr = biasToCntr
+ nFeatures = np.sum([
+ d['desc'][-1][0][1]['initialize']['n_filters']
+ for d in self.descriptions if d != None
+ ])
+ diff = self.svm.get_nr_feature() - nFeatures
+ if diff != 0:
+ if diff == 1 and not biasToCntr:
+ raise ValueError("The number of features in eDN and svm "
+ "models does not match! Is the center bias "
+ "correctly set?")
+
+ def saliency(self, img, normalize=True):
+ """Computes eDN saliency map for single image or image sequence"""
+
+ descs = self.descriptions
+
+ # rescale image to typical input size
+ imgSize = img.shape[:2]
+ rescale_factor = 0.5*EDN_INSIZE[0]/max(imgSize) + \
+ 0.5*EDN_INSIZE[1]/min(imgSize)
+ # single image:
+ # img = misc.imresize(image, rescale_factor, 'bicubic')
+
+ # image sequence (or single image)
+ scaledImg = np.zeros([a * rescale_factor
+ for a in imgSize] + [img.shape[2]])
+ for j in xrange(img.shape[2] / 3):
+ scaledImg[:, :, j * 3:j * 3 + 3] = misc.imresize(
+ img[:, :, j * 3:j * 3 + 3], rescale_factor, 'bicubic')
+ img = scaledImg
+ # compute eDN features for description(s)
+ t1 = time.time()
+ fMapEDN, fMapSize = eDN_features(img, descs)
+ t2 = time.time()
+ logging.info("Feature computation took %0.3fs" % (t2 - t1))
+ print('fMapSize', fMapSize)
+ if self.biasToCntr:
+ fMapCntr = dist_to_cntr_features(img, fMapSize)
+ fMap = np.hstack((fMapCntr, fMapEDN))
+ else:
+ fMap = fMapEDN
+
+ fMapW, fwParams = whiten_features(fMap, self.whitenParams)
+
+ # SVM prediction
+ t1 = time.time()
+ bs, pAcc, pred = predict([], fMapW.tolist(), self.svm, options="-q")
+ t2 = time.time()
+ logging.info("Prediction took %0.3fs" % (t2 - t1))
+ pred = np.array(pred)
+
+ # reshaping and upscaling
+ pred = pred.reshape(fMapSize, order='F')
+ predLarge = sp.ndimage.interpolation.zoom(
+ pred, (imgSize[0] / float(pred.shape[0]),
+ imgSize[1] / float(pred.shape[1])))
+
+ # normalization
+ if normalize:
+ rescaled = (255.0 / (predLarge.max() - predLarge.min()) *
+ (predLarge - predLarge.min())).astype(np.uint8)
+ return rescaled
+ else:
+ return predLarge