[config] Streamline more configuration files

bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608.py

-from bob.ip.binseg.configs.datasets.drive608 import dataset as drive
-from bob.ip.binseg.configs.datasets.chasedb1608 import dataset as chase
-from bob.ip.binseg.configs.datasets.iostarvessel608 import dataset as iostar
-from bob.ip.binseg.configs.datasets.hrf608 import dataset as hrf
-import torch
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
 
-#### Config ####
+"""COVD-STARE (training set) for Vessel Segmentation
 
-# PyTorch dataset
-dataset = torch.utils.data.ConcatDataset([drive, chase, iostar, hrf])
+* Configuration resolution: 704 x 608 (after padding)
+
+The dataset available in this file is composed of DRIVE, CHASE-DB1, IOSTAR
+vessel and HRF (with annotated samples).
+"""
+
+from bob.ip.binseg.configs.datasets.drive608 import dataset as _drive
+from bob.ip.binseg.configs.datasets.chasedb1608 import dataset as _chase
+from bob.ip.binseg.configs.datasets.iostarvessel608 import dataset as _iostar
+from bob.ip.binseg.configs.datasets.hrf608 import dataset as _hrf
+
+import torch.utils.data
+dataset = torch.utils.data.ConcatDataset([_drive, _chase, _iostar, _hrf])

bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608sslstare.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-"""STARE (SSL training set) for Vessel Segmentation
+"""COVD-STARE + SSL (training set) for Vessel Segmentation
 
-A subset of the original STARE dataset contains 20 annotated eye fundus images
-with a resolution of 605 x 700 (height x width). Two sets of ground-truth
-vessel annotations are available. The first set by Adam Hoover is commonly used
-for training and testing. The second set by Valentina Kouznetsova acts as a
-“human” baseline.
-
-* Reference: [STARE-2000]_
-* Configuration resolution: 704 x 608 (after padding)
+* Configuration resolution: 704 x 608
 
 The dataset available in this file is composed of DRIVE, CHASE-DB1, IOSTAR
 vessel and HRF (with annotated samples) and STARE without labels.
 """
 
-# Labelled bits
-import torch.utils.data
-
-from bob.ip.binseg.configs.datasets.drive608 import dataset as _drive
-from bob.ip.binseg.configs.datasets.chasedb1608 import dataset as _chase
-from bob.ip.binseg.configs.datasets.iostarvessel608 import dataset as _iostar
-from bob.ip.binseg.configs.datasets.hrf608 import dataset as _hrf
-_labelled = torch.utils.data.ConcatDataset([_drive, _chase, _iostar, _hrf])
-
-# Use STARE without labels in this setup
+from bob.ip.binseg.configs.datasets.drivechasedb1iostarhrf608 import dataset as _labelled
 from bob.ip.binseg.configs.datasets.stare import dataset as _unlabelled
-
 from bob.ip.binseg.data.utils import SSLDataset
 dataset = SSLDataset(_labelled, _unlabelled)

bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168sslhrf.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-"""HRF (SSL training set) for Vessel Segmentation
+"""COVD-HRF + SSL (training set) for Vessel Segmentation
 
-The database includes 15 images of each healthy, diabetic retinopathy (DR), and
-glaucomatous eyes.  It contains 45 eye fundus images with a resolution of 3504
-x 2336. One set of ground-truth vessel annotations is available.
-
-* Reference: [HRF-2013]_
 * Configuration resolution: 1168 x 1648
 
 The dataset available in this file is composed of STARE, CHASE-DB1, IOSTAR

bob/ip/binseg/configs/datasets/drivestareiostarhrf960sslchase.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-"""CHASE-DB1 (SSL training set) for Vessel Segmentation
+"""COVD-CHASE-DB1 + SSL (training set) for Vessel Segmentation
 
-The CHASE_DB1 is a retinal vessel reference dataset acquired from multiethnic
-school children. This database is a part of the Child Heart and Health Study in
-England (CHASE), a cardiovascular health survey in 200 primary schools in
-London, Birmingham, and Leicester. The ocular imaging was carried out in
-46 schools and demonstrated associations between retinal vessel tortuosity and
-early risk factors for cardiovascular disease in over 1000 British primary
-school children of different ethnic origin. The retinal images of both of the
-eyes of each child were recorded with a hand-held Nidek NM-200-D fundus camera.
-The images were captured at 30 degrees FOV camera. The dataset of images are
-characterized by having nonuniform back-ground illumination, poor contrast of
-blood vessels as compared with the background and wider arteriolars that have a
-bright strip running down the centre known as the central vessel reflex.
-
-* Reference: [CHASEDB1-2012]_
 * Configuration resolution (height x width): 960 x 960
 
 The dataset available in this file is composed of STARE, CHASE-DB1, IOSTAR

bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544ssldrive.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-"""DRIVE (SSL training set) for Vessel Segmentation
+"""COVD-DRIVE + SSL (training set) for Vessel Segmentation
 
-The DRIVE database has been established to enable comparative studies on
-segmentation of blood vessels in retinal images.
-
-* Reference: [DRIVE-2004]_
 * Configuration resolution: 544 x 544
 
 The dataset available in this file is composed of STARE, CHASE-DB1, IOSTAR
 vessel and HRF (with annotated samples) and DRIVE without labels.
 """
 
-# Labelled bits
-import torch.utils.data
-from bob.ip.binseg.configs.datasets.stare544 import dataset as _stare
-from bob.ip.binseg.configs.datasets.chasedb1544 import dataset as _chase
-from bob.ip.binseg.configs.datasets.iostarvessel544 import dataset as _iostar
-from bob.ip.binseg.configs.datasets.hrf544 import dataset as _hrf
-_labelled = torch.utils.data.ConcatDataset([_stare, _chase, _iostar, _hrf])
-
-# Use DRIVE without labels in this setup
+from bob.ip.binseg.configs.datasets.starechasedb1iostarhrf544 import dataset as _unlabelled
 from bob.ip.binseg.configs.datasets.drive import dataset as _unlabelled
-
 from bob.ip.binseg.data.utils import SSLDataset
 dataset = SSLDataset(_labelled, _unlabelled)