Final project for Math 111A, asking the question: can we use the similarity structure of our written language to model the spatial relationships of everyday objects in our environment?
preprocess_data.ipynb: reads in the .mat file from the NYU Depth Dataset V2, and processes the data into relevant .npy and .csv files. Creates the following files within data/:
depth_arr.npy: A (N, W, H) numpy array of depth clouds for each image in the sample.image_arr.npy: A (N, W, H, 3) numpy array representing the RGB values of each image.label_arr.npy: A (N, W, H) numpy array of object category labels, for each WxH pixel on the screen.instance_arr.npy: A (N, W, H) numpy array of instance labels, for each WxH pixel on the screen.metadata.csv: contains metadata for each image, including the image's scene ID, scene type, unique semantic labels, and unique instances.