K-Means image clustering that just works.

1. Initialize and update the Git submodules: git submodule init && git submodule update.
2. Compile with make. Can also compile for ARM architecture with make TARGET=arm.

Compile the project with make. There are 4 modes: collect, train, predict, and live train.

• Mode 0 – train now: train with existing images in given directory without persisting the training data in a file. Optionally enable copying the input image files into
• Mode 1 – collect: save training data in a CSV file in case image files are transient. This file can be read later to build the clusters when enough training data has been collected.
• Mode 2 – train: read training_data CSV file and build clusters. Write centroids in CSV file at the given file path.
• Mode 3 – predict: calculate distances from each cluster centroid and return the nearest cluster id for the image input.
• Mode 4 – batch predict: calculate distances from each cluster centroid for each image in a given directory and moves the images into their respective cluster/label directory.

A total of 4 or 5 arguments are expected:

• Mode id i.e., the "train now" mode in this case.
• K number of clusters.
• Output CSV file where the cluster centroids will be written to.
• Image directory where the images to be clustered are located.
• (Optional) Cluster directory where the images will be copied to.

Example:

./K_Means 0 4 kmeans/centroids_earth.csv examples/earth/

Also try:

./K_Means 0 4 kmeans/centroids_earth.csv examples/earth/ kmeans/clustered/earth/

Other example image folders to try:

• examples/edge/
• examples/bad/

A total of 3 arguments are expected:

• Mode id i.e., the "collect" mode in this case.
• Image directory where the images to be clustered are located.
• CSV file path where training data will be written to.

Example:

./K_Means 1 examples/earth/ kmeans/training_data_earth.csv

A total of 4 arguments are expected:

• Mode id i.e., the "train" mode in this case.
• K number of clusters.
• CSV file path of the training data.
• Output CSV file where the cluster centroids will be written to.

Example:

./K_Means 2 4 kmeans/training_data_earth.csv kmeans/centroids_earth.csv

A total of 3 arguments are expected:

• Mode id i.e., the "predict" mode in this case.
• File path of the image to label.
• CSV file path of the centroid file used to determine which label to apply to the given image.

Example:

./K_Means 3 examples/earth/img_msec_1606835961336_2_thumbnail.jpeg kmeans/centroids_earth.csv
./K_Means 3 examples/earth/img_msec_1609324358567_2_thumbnail.jpeg kmeans/centroids_earth.csv
./K_Means 3 examples/earth/img_msec_1606876023471_2_thumbnail.jpeg kmeans/centroids_earth.csv
./K_Means 3 examples/earth/img_msec_1609362399310_2_thumbnail.jpeg kmeans/centroids_earth.csv

A total of 4 arguments are expected:

• Mode id i.e., the "batch predict" mode in this case.
• Directory path of images to label.
• Directory path to move the labeled imaged to.
• CSV file of the centroid file used to determine the labels to apply to the given images.

Example:

./K_Means 4 examples/earth/ kmeans/clustered/earth/ kmeans/centroids_earth.csv