[configs] Documented all configuration files; Added script to...

[configs] Documented all configuration files; Added script to list/describe/copy configuration files; Re-structured user guide

bob/ip/binseg/configs/datasets/chasedb1.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""CHASE-DB1 (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]_
+* Original resolution (height x width): 960 x 999
+* Configuration resolution: 960 x 960 (after hand-specified crop)
+* Training samples: 20
+* Split reference: [CHASEDB1-2012]_
+"""
+
+
 from bob.db.chasedb1 import Database as CHASEDB1
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset
@@ -9,7 +32,7 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
 
 transforms = Compose(
     [
-        Crop(0, 18, 960, 960),
+        Crop(0, 18, 960, 960),  #(upper, left, height, width)
         RandomHFlip(),
         RandomVFlip(),
         RandomRotation(),

bob/ip/binseg/configs/datasets/chasedb1test.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""CHASE-DB1 (test 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]_
+* Original resolution (height x width): 960 x 999
+* Configuration resolution: 960 x 960 (after hand-specified crop)
+* Test samples: 8
+* Split reference: [CHASEDB1-2012]_
+"""
+
 from bob.db.chasedb1 import Database as CHASEDB1
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""DRIONS-DB (training set) for Optic Disc Segmentation
+
+The dataset originates from data collected from 55 patients with glaucoma
+(23.1%) and eye hypertension (76.9%), and random selected from an eye fundus
+image base belonging to the Ophthalmology Service at Miguel Servet Hospital,
+Saragossa (Spain).  It contains 110 eye fundus images with a resolution of 600
+x 400. Two sets of ground-truth optic disc annotations are available. The first
+set is commonly used for training and testing. The second set acts as a “human”
+baseline.
+
+* Reference: [DRIONSDB-2008]_
+* Original resolution (height x width): 400 x 600
+* Configuration resolution: 416 x 608 (after padding)
+* Training samples: 60
+* Split reference: [MANINIS-2016]_
+"""
+
+
 from bob.db.drionsdb import Database as DRIONS
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset
@@ -9,7 +27,7 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
 
 transforms = Compose(
     [
-        Pad((4, 8, 4, 8)),
+        Pad((4, 8, 4, 8)),  #(left, top, right, bottom)
         RandomHFlip(),
         RandomVFlip(),
         RandomRotation(),

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""DRIONS-DB (test set) for Optic Disc Segmentation
+
+The dataset originates from data collected from 55 patients with glaucoma
+(23.1%) and eye hypertension (76.9%), and random selected from an eye fundus
+image base belonging to the Ophthalmology Service at Miguel Servet Hospital,
+Saragossa (Spain).  It contains 110 eye fundus images with a resolution of 600
+x 400. Two sets of ground-truth optic disc annotations are available. The first
+set is commonly used for training and testing. The second set acts as a “human”
+baseline.
+
+* Reference: [DRIONSDB-2008]_
+* Original resolution (height x width): 400 x 600
+* Configuration resolution: 416 x 608 (after padding)
+* Training samples: 50
+* Split reference: [MANINIS-2016]_
+"""
+
 from bob.db.drionsdb import Database as DRIONS
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

bob/ip/binseg/configs/datasets/dristhigs1cup.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""DRISHTI-GS1 (training set) for Cup Segmentation
+
+Drishti-GS is a dataset meant for validation of segmenting OD, cup and
+detecting notching.  The images in the Drishti-GS dataset have been collected
+and annotated by Aravind Eye hospital, Madurai, India. This dataset is of a
+single population as all subjects whose eye images are part of this dataset are
+Indians.
+
+The dataset is divided into two: a training set and a testing set of images.
+Training images (50) are provided with groundtruths for OD and Cup segmentation
+and notching information.
+
+* Reference: [DRISHTIGS1-2014]_
+* Original resolution (height x width): varying (min: 1749 x 2045, max: 1845 x
+  2468)
+* Configuration resolution: 1760 x 2048 (after center cropping)
+* Training samples: 50
+* Split reference: [DRISHTIGS1-2014]_
+"""
+
 from bob.db.drishtigs1 import Database as DRISHTI
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

bob/ip/binseg/configs/datasets/dristhigs1cuptest.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
+
+"""DRISHTI-GS1 (test set) for Cup Segmentation
+
+Drishti-GS is a dataset meant for validation of segmenting OD, cup and
+detecting notching.  The images in the Drishti-GS dataset have been collected
+and annotated by Aravind Eye hospital, Madurai, India. This dataset is of a
+single population as all subjects whose eye images are part of this dataset are
+Indians.
+
+The dataset is divided into two: a training set and a testing set of images.
+Training images (50) are provided with groundtruths for OD and Cup segmentation
+and notching information.
+
+* Reference: [DRISHTIGS1-2014]_
+* Original resolution (height x width): varying (min: 1749 x 2045, max: 1845 x
+  2468)
+* Configuration resolution: 1760 x 2048 (after center cropping)
+* Test samples: 51
+* Split reference: [DRISHTIGS1-2014]_
+"""
+
 from bob.db.drishtigs1 import Database as DRISHTI
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

bob/ip/binseg/configs/datasets/dristhigs1od.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""DRISHTI-GS1 (training set) for Optic Disc Segmentation
+
+Drishti-GS is a dataset meant for validation of segmenting OD, cup and
+detecting notching.  The images in the Drishti-GS dataset have been collected
+and annotated by Aravind Eye hospital, Madurai, India. This dataset is of a
+single population as all subjects whose eye images are part of this dataset are
+Indians.
+
+The dataset is divided into two: a training set and a testing set of images.
+Training images (50) are provided with groundtruths for OD and Cup segmentation
+and notching information.
+
+* Reference: [DRISHTIGS1-2014]_
+* Original resolution (height x width): varying (min: 1749 x 2045, max: 1845 x
+  2468)
+* Configuration resolution: 1760 x 2048 (after center cropping)
+* Training samples: 50
+* Split reference: [DRISHTIGS1-2014]_
+"""
+
 from bob.db.drishtigs1 import Database as DRISHTI
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

bob/ip/binseg/configs/datasets/dristhigs1odtest.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""DRISHTI-GS1 (test set) for Optic Disc Segmentation
+
+Drishti-GS is a dataset meant for validation of segmenting OD, cup and
+detecting notching.  The images in the Drishti-GS dataset have been collected
+and annotated by Aravind Eye hospital, Madurai, India. This dataset is of a
+single population as all subjects whose eye images are part of this dataset are
+Indians.
+
+The dataset is divided into two: a training set and a testing set of images.
+Training images (50) are provided with groundtruths for OD and Cup segmentation
+and notching information.
+
+* Reference: [DRISHTIGS1-2014]_
+* Original resolution (height x width): varying (min: 1749 x 2045, max: 1845 x
+  2468)
+* Configuration resolution: 1760 x 2048 (after center cropping)
+* Test samples: 51
+* Split reference: [DRISHTIGS1-2014]_
+"""
+
 from bob.db.drishtigs1 import Database as DRISHTI
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""DRIVE (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]_
+* Original resolution (height x width): 584 x 565
+* This configuration resolution: 544 x 544 (center-crop)
+* Training samples: 20
+* Split reference: [DRIVE-2004]_
+"""
+
 from bob.db.drive import Database as DRIVE
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""DRIVE (test 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]_
+* Original resolution (height x width): 584 x 565
+* Configuration resolution: 544 x 544 (after center-crop)
+* Test samples: 20
+* Split reference: [DRIVE-2004]_
+"""
+
 from bob.db.drive import Database as DRIVE
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

bob/ip/binseg/configs/datasets/hrf1168.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""HRF (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 3304
+x 2336. One set of ground-truth vessel annotations is available.
+
+* Reference: [HRF-2013]_
+* Original resolution (height x width): 2336 x 3504
+* Configuration resolution: 1168 x 1648 (after specific cropping and rescaling)
+* Training samples: 15
+* Split reference: [ORLANDO-2017]_
+"""
+
 from bob.db.hrf import Database as HRF
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset
@@ -9,8 +22,8 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
 
 transforms = Compose(
     [
-        Crop(0, 108, 2336, 3296),
-        Resize((1168)),
+        Crop(0, 108, 2336, 3296),  #(upper, left, height, width)
+        Resize((1168)),  # applies to the smaller edge
         RandomHFlip(),
         RandomVFlip(),
         RandomRotation(),

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""HRF (test 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 3304
+x 2336. One set of ground-truth vessel annotations is available.
+
+* Reference: [HRF-2013]_
+* Original resolution (height x width): 2336 x 3504
+* Configuration resolution: 1168 x 1648 (after specific cropping and rescaling)
+* Test samples: 30
+* Split reference: [ORLANDO-2017]_
+"""
+
 from bob.db.hrf import Database as HRF
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""IOSTAR (training set) for Optic Disc Segmentation
+
+The IOSTAR vessel segmentation dataset includes 30 images with a resolution of
+1024 × 1024 pixels. All the vessels in this dataset are annotated by a group of
+experts working in the field of retinal image analysis. Additionally the
+dataset includes annotations for the optic disc and the artery/vein ratio.
+
+* Reference: [IOSTAR-2016]_
+* Original resolution (height x width): 1024 x 1024
+* Configuration resolution: 1024 x 1024
+* Training samples: 20
+* Split reference: [MEYER-2017]_
+"""
+
 from bob.db.iostar import Database as IOSTAR
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""IOSTAR (test set) for Optic Disc Segmentation
+
+The IOSTAR vessel segmentation dataset includes 30 images with a resolution of
+1024 × 1024 pixels. All the vessels in this dataset are annotated by a group of
+experts working in the field of retinal image analysis. Additionally the
+dataset includes annotations for the optic disc and the artery/vein ratio.
+
+* Reference: [IOSTAR-2016]_
+* Original resolution (height x width): 1024 x 1024
+* Configuration resolution: 1024 x 1024
+* Test samples: 10
+* Split reference: [MEYER-2017]_
+"""
+
 from bob.db.iostar import Database as IOSTAR
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""IOSTAR (training set) for Vessel Segmentation
+
+The IOSTAR vessel segmentation dataset includes 30 images with a resolution of
+1024 × 1024 pixels. All the vessels in this dataset are annotated by a group of
+experts working in the field of retinal image analysis. Additionally the
+dataset includes annotations for the optic disc and the artery/vein ratio.
+
+* Reference: [IOSTAR-2016]_
+* Original resolution (height x width): 1024 x 1024
+* Configuration resolution: 1024 x 1024
+* Training samples: 20
+* Split reference: [MEYER-2017]_
+"""
+
 from bob.db.iostar import Database as IOSTAR
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""IOSTAR (test set) for Vessel Segmentation
+
+The IOSTAR vessel segmentation dataset includes 30 images with a resolution of
+1024 × 1024 pixels. All the vessels in this dataset are annotated by a group of
+experts working in the field of retinal image analysis. Additionally the
+dataset includes annotations for the optic disc and the artery/vein ratio.
+
+* Reference: [IOSTAR-2016]_
+* Original resolution (height x width): 1024 x 1024
+* Configuration resolution: 1024 x 1024
+* Training samples: 10
+* Split reference: [MEYER-2017]_
+"""
+
 from bob.db.iostar import Database as IOSTAR
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""REFUGE (training set) for Cup Segmentation
+
+The dataset consists of 1200 color fundus photographs, created for a MICCAI
+challenge. The goal of the challenge is to evaluate and compare automated
+algorithms for glaucoma detection and optic disc/cup segmentation on a common
+dataset of retinal fundus images.
+
+* Reference: [REFUGE-2018]_
+* Original resolution (height x width): 2056 x 2124
+* Configuration resolution: 1632 x 1632 (after center cropping)
+* Training samples: 400
+* Split reference: [REFUGE-2018]_
+
+.. warning:
+
+   Notice that the data cropping/resizing algorithm applied on training and
+   "validation" data are slightly different and need to be cross-checked.
+
+"""
+
 from bob.db.refuge import Database as REFUGE
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset
@@ -10,7 +30,7 @@ from bob.ip.binseg.data.binsegdataset import BinSegDataset
 transforms = Compose(
     [
         Resize((1539)),
-        Pad((21, 46, 22, 47)),
+        Pad((21, 46, 22, 47)),  #(left, top, right, bottom)
         RandomHFlip(),
         RandomVFlip(),
         RandomRotation(),

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""REFUGE (validation set) for Cup Segmentation
+
+The dataset consists of 1200 color fundus photographs, created for a MICCAI
+challenge. The goal of the challenge is to evaluate and compare automated
+algorithms for glaucoma detection and optic disc/cup segmentation on a common
+dataset of retinal fundus images.
+
+* Reference: [REFUGE-2018]_
+* Original resolution (height x width): 2056 x 2124
+* Configuration resolution: 1632 x 1632 (after center cropping)
+* Validation samples: 400
+* Split reference: [REFUGE-2018]_
+
+.. warning:
+
+   Notice 2 aspects before using these configurations:
+
+   1. The data cropping/resizing algorithm applied on training and "validation"
+      data are slightly different and need to be cross-checked.
+   2. This is the **validation** set!  The real **test** set is still not
+      integrated to the originating bob.db.refuge package: See
+      https://gitlab.idiap.ch/bob/bob.db.refuge/issues/1
+
+"""
+
 from bob.db.refuge import Database as REFUGE
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""REFUGE (training set) for Optic Disc Segmentation
+
+The dataset consists of 1200 color fundus photographs, created for a MICCAI
+challenge. The goal of the challenge is to evaluate and compare automated
+algorithms for glaucoma detection and optic disc/cup segmentation on a common
+dataset of retinal fundus images.
+
+* Reference: [REFUGE-2018]_
+* Original resolution (height x width): 2056 x 2124
+* Configuration resolution: 1632 x 1632 (after center cropping)
+* Training samples: 400
+* Split reference: [REFUGE-2018]_
+
+.. warning:
+
+   Notice that the data cropping/resizing algorithm applied on training and
+   "validation" data are slightly different and need to be cross-checked.
+
+"""
+
 from bob.db.refuge import Database as REFUGE
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset

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

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+"""REFUGE (validation set) for Optic Disc Segmentation
+
+The dataset consists of 1200 color fundus photographs, created for a MICCAI
+challenge. The goal of the challenge is to evaluate and compare automated
+algorithms for glaucoma detection and optic disc/cup segmentation on a common
+dataset of retinal fundus images.
+
+* Reference: [REFUGE-2018]_
+* Original resolution (height x width): 2056 x 2124
+* Configuration resolution: 1632 x 1632 (after center cropping)
+* Validation samples: 400
+* Split reference: [REFUGE-2018]_
+
+.. warning:
+
+   Notice 2 aspects before using these configurations:
+
+   1. The data cropping/resizing algorithm applied on training and "validation"
+      data are slightly different and need to be cross-checked.
+   2. This is the **validation** set!  The real **test** set is still not
+      integrated to the originating bob.db.refuge package: See
+      https://gitlab.idiap.ch/bob/bob.db.refuge/issues/1
+
+"""
+
 from bob.db.refuge import Database as REFUGE
 from bob.ip.binseg.data.transforms import *
 from bob.ip.binseg.data.binsegdataset import BinSegDataset