npm install
Since flickrnode module can not be installed by npm, you shoule git clone it into node_modules directory.
cd node_modules
git clone git://github.com/ciaranj/flickrnode.git
coffee query.coffee -t <query term> -i <image download path> -f <feature storing path> -m <image processing path> -l <geolocation> -c <database collection> -n <from datetime> -a <to datetime> -p <starting page> -o <total pages> -s <sorting option> -u <flickr user NSID>
For example:
coffee query.coffee -t paris -i ./test -f ./feature -m ./tmp -l 'Paris, France' -c paris -n "2012/4/1" -a "2012/4/5" -p 1 -o 2 -s interestingness-desc
sample.coffee -c <database collection> -f <feature storing path> -d <output file> -s <sample number>
For example:
sample.coffee -c paris -f ./features -d test.clu -s 1000
python cluster.py -f <sampled photo features> -o <output file> -k <number of clusters>
In addition to "output file", another file "output file" + ".meta" will be created that contains mean and std feature vector of sampled features.
For example:
python cluster.py -f test.clu -o sample.kmean -k 64
python vlad_encoder.py -c <codebook filename> -n <codebook metadata filename> -f <raw feature filename> -o <output filename>
For example:
python vlad_encoder.py -c flickr_sample_500.k_16 -n flickr_sample_500.k_16.meta -f features.hes -o features.vlad
coffee vlad.coffee -c <database collection> -f <raw feature path> -v <vlad path> -b <codebook filename> -m <codebook metadata filename>
For example:
coffee vlad.coffee -c paris -f ./features -v ./vlad -b flickr_sample_500.k_16 -m flickr_sample_500.k_16.meta
coffee vlad_from_filelist.coffee -l <image list file> -f <raw feature path> -v <vlad storing path> -b <codebook> -m <codebook metadata filename>
For example:
coffee vlad_from_filelist.coffee -l image_list.txt -f ./features -v ./vlad -b flickr_sample_500.k_16 -m flickr_sample_500.k_16.meta
python vlad_validate_distance.py -d <vlad feature path> -q <query list file> -g <groundtruth filename>
For example:
python vlad_validate_distance.py -d ./vlad -q query.txt -g ground_truth.txt
python vlad_pairwise_distance.py -d <vlad feature path> -o <output filename>
For example:
python vlad_pairwise_distance.py -d ./vlad -o paris_7910.distance
python vlad_data_matrix.py -d <vlad feature path> -o <outout filename> -s <optional sample number> -f <optional output format> -l <optional data label>
The output format could be 'libsvm'. If not given, the output format is simply the arraies of vlad features. The sample number could be set to generate specified numbers of parts of the data. The data label could be set to generate '1' or '-1' label in output file of libsvm format.
For example:
python vlad_data_matrix.py -d ./vlad -o paris_7910.data -f libsvm -l positive
# Running R interaction envrionment
> source("apc_cluster.R")
> data_cluster("paris_7910.data") # given the vlad feature matrix
# Or under command line
R --slave --args paris_7910 < apc_cluster.R
4 files will be generated under current path. Use "paris_7910.data" as example:
- paris_7910.apc: Binary stored APResult object of APCluster package.
- paris_7910.apc.clusters: Text-format cluster list.
- paris_7910.apc.exemplars: Text-format exemplar list.
- paris_7910.apc.similarity: Negative squared distances (Euclidean) matrix.
coffee app.coffee -i <image base path> -a <apc cluster list filename> -c <image set sub-path under image base path>
For example:
coffee app.coffee -i /images -a ./paris_7910.apc.clusters -c paris
A node.js application will be running at port 3000. Open browser to see the clustering results at a URL such as http://localhost:3000/
coffee apc_mapreduce.coffee -i <image base path> -a <apc cluster result pathname> -c <image set sub-path under image base path> -s <the threshold of cluster size>
For example:
coffee apc_mapreduce.coffee -i /images -a ./output -c paris -s 20
python vlad_data_matrix_for_clusters.py -d <vlad feature path> -c <apc cluster list filename> -o <output path and filename prefix> -t <threshold for cluster size>
For example:
python vlad_data_matrix_for_clusters.py -d ./vlad -c paris_7910.apc.clusters -o ./clusters_vlad/cluster_data -t 5
The files ./clusters_vlad/cluster_data.cluster.[0 ~ (clusters_number -1)] will be generated. Each file contains vlad features for photos in the corresponding APC cluster.
./train_model.sh <training data path> <model output path>
./test_model.sh <model path> <test data> <classification result output path>
./create_dataset.sh <base path> <dataset name>
coffee svm_state.coffee -r <svm testing result path> -g <calculating statistics for positive or negative>
For example:
coffee svm_state.coffee -r ./classification -g positive
coffee image.coffee -i <image base path under public> -c <image relative path under image base path>
For example:
coffee image.coffee -i images -c test
python crowdsourcing.py -d <image path> -o <output file> -n <number of sampled images> -u <image prefix> -m <file output mode {w|a}>
For example:
python crowdsourcing.py -d ./images -o crowd.txt -n 10 -u "http://testurl/images/" -m w
coffee pinterest.coffee -i <image download path> -f <feature storing path> -m <image processing path> -c <database collection> -p <starting page> -o <total pages>
For example:
coffee pinterest.coffee -i ./images -f ./features -m ./tmp -c pinterest -p 1 -o 2