a.class_names.py

#!/usr/bin/env python

import gdown
import numpy as np
import scipy.io

import corvus_segmentation_models


mat_file = gdown.cached_download(
    "https://github.com/ankurhanda/nyuv2-meta-data/raw/master/classMapping40.mat",
    quiet=True,
)
mat_40 = scipy.io.loadmat(
    mat_file, squeeze_me=True, chars_as_strings=True, struct_as_record=True
)
mat_file = gdown.cached_download(
    "https://github.com/ankurhanda/nyuv2-meta-data/raw/master/class13Mapping.mat",
    quiet=True,
)
mat_13 = scipy.io.loadmat(
    mat_file, squeeze_me=True, chars_as_strings=True, struct_as_record=True
)

class_mapping_40 = mat_40["mapClass"]
class_mapping_13 = []
for cls_id_40 in class_mapping_40:
    cls_id_13 = mat_13["classMapping13"]["labels13"].item()[cls_id_40 - 1]
    class_mapping_13.append(cls_id_13)
class_mapping_13 = np.r_[[0], np.array(class_mapping_13)]
class_names_13 = np.r_[
    ["__background__"], mat_13["classMapping13"]["classNames"].item()
]

dataset = corvus_segmentation_models.datasets.NyuDepthV2Dataset("all")
for cls_id, cls_name in enumerate(dataset.class_names):
    cls_id_13 = class_mapping_13[cls_id]
    cls_name_13 = class_names_13[cls_id_13]
    print(f"{cls_id:03d}: {cls_name:s} -> {cls_id_13:02d}: {cls_name_13:s}")