diff --git a/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608.py b/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608.py index a3410d465b1585c72d7085717edb73296baa6d60..8cbe06ee2fecb869ac7cfdb9c14f01a84cb50533 100644 --- a/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608.py +++ b/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608.py @@ -1,10 +1,18 @@ -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]) diff --git a/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608sslstare.py b/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608sslstare.py index e09f9b033536d776a1d045c68d1abb58a796fd91..767c0e189e33bc9db2989f0a3f18f30d86c36804 100644 --- a/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608sslstare.py +++ b/bob/ip/binseg/configs/datasets/drivechasedb1iostarhrf608sslstare.py @@ -1,32 +1,15 @@ #!/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) diff --git a/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168sslhrf.py b/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168sslhrf.py index 9cf7b838c43042962f7fa7dda2abccc845032778..243651968ebedcc2458c488eaae2bccc0b253276 100644 --- a/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168sslhrf.py +++ b/bob/ip/binseg/configs/datasets/drivestarechasedb1iostar1168sslhrf.py @@ -1,13 +1,8 @@ #!/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 diff --git a/bob/ip/binseg/configs/datasets/drivestareiostarhrf960sslchase.py b/bob/ip/binseg/configs/datasets/drivestareiostarhrf960sslchase.py index 1306a2f41ace4223cb924b60434c010b92c7e3a8..2e96cecfaf2b08fa0ea4f7219640501942c82170 100644 --- a/bob/ip/binseg/configs/datasets/drivestareiostarhrf960sslchase.py +++ b/bob/ip/binseg/configs/datasets/drivestareiostarhrf960sslchase.py @@ -1,22 +1,8 @@ #!/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 diff --git a/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544ssldrive.py b/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544ssldrive.py index 63385586ee2a731d1b2e06264eccbcf4411a657a..2605dc8183660171ae4d14555f0e129f3a9e8658 100644 --- a/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544ssldrive.py +++ b/bob/ip/binseg/configs/datasets/starechasedb1iostarhrf544ssldrive.py @@ -1,28 +1,15 @@ #!/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)