This code was written fairly early into my work on unsupervised scientific image analysis, and is written for an old version of Keras and assumed the use of Theano. An update of this code will be made in the near future, but until then, be warned that it might require quite a bit of work to get this code to work
A tool to create maps of image similarities from VGG16 Features, using tSNE for the reduction of features into a 2-Dimensional space representative of how similar the image features are.
As the bare minimum, this tool requires the installation of the following packages:
You also need to specify the path to the weights for the VGG16 model. This setting is controlled by the option "-v" in the interface, which is by default set to point to "weights/vgg16_weights.h5". These weights are available here.
If you want to enable clustering, which displays images only if they are a part of clusters in the feature-space, you must have HDBScan installed.
Currently, this tool is available as a python package using Click for the command line interface. The directory of images must be provided as an argument, in this fashion:
python image_map.py [OPTIONS] DIRECTORYOptions include the usage of GPU, cnmem, clustering, if you are using color images, etc. For example, this turns on GPU usage with CNMeM:
python image_map.py --gpu True --cnmem True DIRECTORYTo view the available options, use:
python image_map.py -hor
python image_map.py --helpThis tool was originally used to explore datasets of microstructure microscopy images. Traditional feature extraction methods (such as SIFT) were considerably less successful in grouping semantically similar images together (in terms of their microstructural constituents), and thus this tool was created as an alternative method.
This tool was made in collaboration with Brian DeCost of Carnegie Mellon University.