diff --git a/bob/ip/binseg/configs/datasets/chasedb1/first_annotator.py b/bob/ip/binseg/configs/datasets/chasedb1/first_annotator.py
index 06b01170e936dc5ea8f67f5faaa5de395fe9f200..07c5684212db6a510a32b246c73a0d7bbe0f8c70 100644
--- a/bob/ip/binseg/configs/datasets/chasedb1/first_annotator.py
+++ b/bob/ip/binseg/configs/datasets/chasedb1/first_annotator.py
@@ -6,7 +6,9 @@
* Split reference: [CHASEDB1-2012]_
* Configuration resolution: 960 x 960 (after hand-specified crop)
* See :py:mod:`bob.ip.binseg.data.chasedb1` for dataset details
+* This dataset offers a second-annotator comparison
"""
from bob.ip.binseg.configs.datasets.chasedb1 import _maker
dataset = _maker("first-annotator")
+second_annotator = _maker("second-annotator")
diff --git a/bob/ip/binseg/configs/datasets/chasedb1/second_annotator.py b/bob/ip/binseg/configs/datasets/chasedb1/second_annotator.py
index 04b5d5a0733d61f9304879753e03b5e9cf5846a3..87a98310bbbe2c6784a0d4d525d58a6126543e2c 100644
--- a/bob/ip/binseg/configs/datasets/chasedb1/second_annotator.py
+++ b/bob/ip/binseg/configs/datasets/chasedb1/second_annotator.py
@@ -6,7 +6,9 @@
* Split reference: [CHASEDB1-2012]_
* Configuration resolution: 960 x 960 (after hand-specified crop)
* See :py:mod:`bob.ip.binseg.data.chasedb1` for dataset details
+* This dataset offers a second-annotator comparison (using "first-annotator")
"""
from bob.ip.binseg.configs.datasets.chasedb1 import _maker
dataset = _maker("second-annotator")
+second_annotator = _maker("first-annotator")
diff --git a/bob/ip/binseg/configs/datasets/drive/default.py b/bob/ip/binseg/configs/datasets/drive/default.py
index 29f255d1f247b25563911d0fe3b3dc143871275a..2331734969206068bdfb32926c495662cb5d08e7 100644
--- a/bob/ip/binseg/configs/datasets/drive/default.py
+++ b/bob/ip/binseg/configs/datasets/drive/default.py
@@ -6,7 +6,9 @@
* Split reference: [DRIVE-2004]_
* This configuration resolution: 544 x 544 (center-crop)
* See :py:mod:`bob.ip.binseg.data.drive` for dataset details
+* This dataset offers a second-annotator comparison for the test set only
"""
from bob.ip.binseg.configs.datasets.drive import _maker
dataset = _maker("default")
+second_annotator = _maker("second-annotator")
diff --git a/bob/ip/binseg/configs/datasets/stare/ah.py b/bob/ip/binseg/configs/datasets/stare/ah.py
index d6c4147ca3a581f3c02f07dd38bd77cb8fd45294..6b7cec73b37fb19f15f3ea6b94dc61a1a58b06fb 100644
--- a/bob/ip/binseg/configs/datasets/stare/ah.py
+++ b/bob/ip/binseg/configs/datasets/stare/ah.py
@@ -6,7 +6,9 @@
* Configuration resolution: 704 x 608 (after padding)
* Split reference: [MANINIS-2016]_
* See :py:mod:`bob.ip.binseg.data.stare` for dataset details
+* This dataset offers a second-annotator comparison (using protocol "vk")
"""
from bob.ip.binseg.configs.datasets.stare import _maker
dataset = _maker("ah")
+second_annotator = _maker("vk")
diff --git a/bob/ip/binseg/configs/datasets/stare/vk.py b/bob/ip/binseg/configs/datasets/stare/vk.py
index b0468f672d204533f383131ffe664eed3c609c16..0ae41c87553b013e0de8ad4d8601c80a80be96e2 100644
--- a/bob/ip/binseg/configs/datasets/stare/vk.py
+++ b/bob/ip/binseg/configs/datasets/stare/vk.py
@@ -6,7 +6,9 @@
* Configuration resolution: 704 x 608 (after padding)
* Split reference: [MANINIS-2016]_
* See :py:mod:`bob.ip.binseg.data.stare` for dataset details
+* This dataset offers a second-annotator comparison (using protocol "ah")
"""
from bob.ip.binseg.configs.datasets.stare import _maker
dataset = _maker("vk")
+second_annotator = _maker("ah")
diff --git a/bob/ip/binseg/engine/evaluator.py b/bob/ip/binseg/engine/evaluator.py
index d927c84cf758034eab3f20740208af77a1ed72b9..96870093a79892e8dfe69fc35cfc022e905c7eb7 100644
--- a/bob/ip/binseg/engine/evaluator.py
+++ b/bob/ip/binseg/engine/evaluator.py
@@ -186,7 +186,7 @@ def _sample_analysis(
return tp_pil_colored
-def run(data_loader, predictions_folder, output_folder, overlayed_folder=None,
+def run(dataset, predictions_folder, output_folder, overlayed_folder=None,
overlay_threshold=None):
"""
Runs inference and calculates metrics
@@ -195,8 +195,8 @@ def run(data_loader, predictions_folder, output_folder, overlayed_folder=None,
Parameters
---------
- data_loader : py:class:`torch.torch.utils.data.DataLoader`
- an iterable over the transformed input dataset, containing ground-truth
+ dataset : py:class:`torch.utils.data.Dataset`
+ a dataset to iterate on
predictions_folder : str
folder where predictions for the dataset images has been previously
@@ -216,6 +216,13 @@ def run(data_loader, predictions_folder, output_folder, overlayed_folder=None,
the training set or a separate validation set. Using a test set value
may bias your analysis.
+
+ Returns
+ -------
+
+ threshold : float
+ Threshold to achieve the highest possible F1-score for this dataset
+
"""
logger.info(f"Output folder: {output_folder}")
@@ -227,11 +234,10 @@ def run(data_loader, predictions_folder, output_folder, overlayed_folder=None,
# Collect overall metrics
data = {}
- for sample in tqdm(data_loader):
- name = sample[0]
- stem = os.path.splitext(name)[0]
- image = sample[1].to("cpu")
- gt = sample[2].to("cpu")
+ for sample in tqdm(dataset):
+ stem = sample[0]
+ image = sample[1]
+ gt = sample[2]
pred_fullpath = os.path.join(predictions_folder, stem + ".hdf5")
with h5py.File(pred_fullpath, "r") as f:
pred = f["array"][:]
@@ -299,3 +305,99 @@ def run(data_loader, predictions_folder, output_folder, overlayed_folder=None,
["data"],
)
fig.savefig(figure_path)
+
+ return optimal_f1_threshold
+
+
+def compare_annotators(baseline, other, output_folder, overlayed_folder=None):
+ """
+ Compares annotations on the **same** dataset
+
+
+ Parameters
+ ---------
+
+ baseline : py:class:`torch.utils.data.Dataset`
+ a dataset to iterate on, containing the baseline annotations
+
+ other : py:class:`torch.utils.data.Dataset`
+ a second dataset, with the same samples as ``baseline``, but annotated
+ by a different annotator than in the first dataset.
+
+ output_folder : str
+ folder where to store results
+
+ overlayed_folder : :py:class:`str`, Optional
+ if not ``None``, then it should be the name of a folder where to store
+ overlayed versions of the images and ground-truths
+
+ overlay_threshold : :py:class:`float`, Optional
+ if ``overlayed_folder``, then this should be threshold (floating point)
+ to apply to prediction maps to decide on positives and negatives for
+ overlaying analysis (graphical output). This number should come from
+ the training set or a separate validation set. Using a test set value
+ may bias your analysis.
+
+ """
+
+ logger.info(f"Output folder: {output_folder}")
+
+ if not os.path.exists(output_folder):
+ logger.info(f"Creating {output_folder}...")
+ os.makedirs(output_folder, exist_ok=True)
+
+ # Collect overall metrics
+ data = {}
+
+ for baseline_sample, other_sample in tqdm(zip(baseline, other)):
+ stem = baseline_sample[0]
+ image = baseline_sample[1]
+ gt = baseline_sample[2]
+ pred = other_sample[2] #works as a prediction
+ if stem in data:
+ raise RuntimeError(f"{stem} entry already exists in data. "
+ f"Cannot overwrite.")
+ data[stem] = _sample_metrics(pred, gt)
+
+ if overlayed_folder is not None:
+ overlay_image = _sample_analysis(image, pred, gt, threshold=0.5,
+ overlay=True)
+ fullpath = os.path.join(overlayed_folder, f"{stem}.png")
+ tqdm.write(f"Saving {fullpath}...")
+ fulldir = os.path.dirname(fullpath)
+ if not os.path.exists(fulldir):
+ tqdm.write(f"Creating directory {fulldir}...")
+ os.makedirs(fulldir, exist_ok=True)
+ overlay_image.save(fullpath)
+
+ # Merges all dataframes together
+ df_metrics = pandas.concat(data.values())
+
+ # Report and Averages
+ avg_metrics = df_metrics.groupby("threshold").mean()
+ std_metrics = df_metrics.groupby("threshold").std()
+
+ # Uncomment below for F1-score calculation based on average precision and
+ # metrics instead of F1-scores of individual images. This method is in line
+ # with Maninis et. al. (2016)
+ #
+ # avg_metrics["f1_score"] = \
+ # (2* avg_metrics["precision"]*avg_metrics["recall"])/ \
+ # (avg_metrics["precision"]+avg_metrics["recall"])
+
+ avg_metrics["std_pr"] = std_metrics["precision"]
+ avg_metrics["pr_upper"] = avg_metrics["precision"] + avg_metrics["std_pr"]
+ avg_metrics["pr_lower"] = avg_metrics["precision"] - avg_metrics["std_pr"]
+ avg_metrics["std_re"] = std_metrics["recall"]
+ avg_metrics["re_upper"] = avg_metrics["recall"] + avg_metrics["std_re"]
+ avg_metrics["re_lower"] = avg_metrics["recall"] - avg_metrics["std_re"]
+ avg_metrics["std_f1"] = std_metrics["f1_score"]
+
+ metrics_path = os.path.join(output_folder, "metrics.csv")
+ logger.info(f"Saving averages over all input images at {metrics_path}...")
+ avg_metrics.to_csv(metrics_path)
+
+ maxf1 = avg_metrics["f1_score"].max()
+ optimal_f1_threshold = avg_metrics["f1_score"].idxmax()
+
+ logger.info(f"Highest F1-score of {maxf1:.5f} (second annotator)")
diff --git a/bob/ip/binseg/engine/predictor.py b/bob/ip/binseg/engine/predictor.py
index 704899e6666718cd963ec15b2623e44c31530690..a7ab078db93964e664360128d9437f6cb90aa944 100644
--- a/bob/ip/binseg/engine/predictor.py
+++ b/bob/ip/binseg/engine/predictor.py
@@ -168,8 +168,7 @@ def run(model, data_loader, device, output_folder, overlayed_folder):
times.append(batch_time)
len_samples.append(len(images))
- for name, img, prob in zip(names, images, predictions):
- stem = os.path.splitext(name)[0]
+ for stem, img, prob in zip(names, images, predictions):
_save_hdf5(stem, prob, output_folder)
if overlayed_folder is not None:
_save_overlayed_png(stem, img, prob, overlayed_folder)
diff --git a/bob/ip/binseg/script/evaluate.py b/bob/ip/binseg/script/evaluate.py
index f4a3f9fc522991b34f6cc25c26ac061e1d3afa9f..8c8e575fcb65e85ab588c7a622bf7f03d13ec360 100644
--- a/bob/ip/binseg/script/evaluate.py
+++ b/bob/ip/binseg/script/evaluate.py
@@ -3,7 +3,6 @@
import os
import click
-from torch.utils.data import DataLoader
from bob.extension.scripts.click_helper import (
verbosity_option,
@@ -11,9 +10,10 @@ from bob.extension.scripts.click_helper import (
ResourceOption,
)
-from ..engine.evaluator import run
+from ..engine.evaluator import run, compare_annotators
import logging
+
logger = logging.getLogger(__name__)
@@ -25,7 +25,7 @@ logger = logging.getLogger(__name__)
\b
1. Runs evaluation on an existing dataset configuration:
\b
- $ bob binseg evaluate -vv m2unet drive-test --predictions-folder=path/to/predictions --output-folder=path/to/results
+ $ bob binseg evaluate -vv m2unet drive --predictions-folder=path/to/predictions --output-folder=path/to/results
\b
2. To run evaluation on a folder with your own images and annotations, you
must first specify resizing, cropping, etc, so that the image can be
@@ -69,6 +69,27 @@ logger = logging.getLogger(__name__)
required=True,
cls=ResourceOption,
)
+@click.option(
+ "--second-annotator",
+ "-S",
+ help="A dataset or dictionary, like in --dataset, with the same "
+ "sample keys, but with annotations from a different annotator that is "
+ "going to be compared to the one in --dataset",
+ required=False,
+ default=None,
+ cls=ResourceOption,
+ show_default=True,
+)
+@click.option(
+ "--second-annotator-folder",
+ "-O",
+ help="Path where to store the analysis result for second annotator "
+ "comparisons (only used if --second-annotator is also passed)",
+ required=True,
+ default="second-annotator",
+ type=click.Path(),
+ cls=ResourceOption,
+)
@click.option(
"--overlayed",
"-O",
@@ -99,15 +120,51 @@ logger = logging.getLogger(__name__)
cls=ResourceOption,
)
@verbosity_option(cls=ResourceOption)
-def evaluate(output_folder, predictions_folder, dataset, overlayed,
- overlay_threshold, **kwargs):
+def evaluate(
+ output_folder,
+ predictions_folder,
+ dataset,
+ second_annotator,
+ second_annotator_folder,
+ overlayed,
+ overlay_threshold,
+ **kwargs,
+):
"""Evaluates an FCN on a binary segmentation task.
"""
- if isinstance(dataset, dict):
- for k,v in dataset.items():
- analysis_folder = os.path.join(output_folder, k)
- with v.not_augmented() as d:
- data_loader = DataLoader(dataset=d, batch_size=1,
- shuffle=False, pin_memory=False)
- run(d, predictions_folder, analysis_folder, overlayed,
- overlay_threshold)
+
+ # if we work with dictionaries of datasets, then output evaluation
+ # information into sub-directories of the output_folder
+ config = {}
+ if not isinstance(dataset, dict):
+ config["test"] = {
+ "dataset": dataset,
+ "output_folder": output_folder,
+ "second_annotator": second_annotator,
+ "second_annotator_folder": second_annotator_folder,
+ }
+ else:
+ for k, v in dataset.items():
+ config[k] = {
+ "dataset": v,
+ "output_folder": os.path.join(output_folder, k),
+ "second_annotator": second_annotator.get(k),
+ "second_annotator_folder": os.path.join(
+ second_annotator_folder, k
+ ),
+ }
+
+ for k, v in config.items():
+ with v["dataset"].not_augmented() as d:
+ run(
+ d,
+ predictions_folder,
+ v["output_folder"],
+ overlayed,
+ overlay_threshold,
+ )
+ if v["second_annotator"] is not None:
+ with v["second_annotator"].not_augmented() as d2:
+ compare_annotators(
+ d, d2, v["second_annotator_folder"], overlayed
+ )
diff --git a/bob/ip/binseg/script/experiment.py b/bob/ip/binseg/script/experiment.py
index e4027229a7dd057065379dc534f6b17ae20083c9..6882d5b931bf69399d48bc83956ce78b459c4de0 100644
--- a/bob/ip/binseg/script/experiment.py
+++ b/bob/ip/binseg/script/experiment.py
@@ -59,6 +59,17 @@ logger = logging.getLogger(__name__)
required=True,
cls=ResourceOption,
)
+@click.option(
+ "--second-annotator",
+ "-S",
+ help="A dataset or dictionary, like in --dataset, with the same "
+ "sample keys, but with annotations from a different annotator that is "
+ "going to be compared to the one in --dataset",
+ required=False,
+ default=None,
+ cls=ResourceOption,
+ show_default=True,
+)
@click.option(
"--optimizer",
help="A torch.optim.Optimizer that will be used to train the network",
@@ -205,6 +216,7 @@ def experiment(
drop_incomplete_batch,
criterion,
dataset,
+ second_annotator,
checkpoint_period,
device,
seed,
@@ -301,11 +313,14 @@ def experiment(
)
analysis_folder = os.path.join(output_folder, "analysis")
+ second_annotator_folder = os.path.join(analysis_folder, "second-annotator")
ctx.invoke(
evaluate,
output_folder=analysis_folder,
predictions_folder=predictions_folder,
dataset=dataset,
+ second_annotator=second_annotator,
+ second_annotator_folder=second_annotator_folder,
overlayed=overlayed_folder,
overlay_threshold=0.5,
verbose=verbose,
@@ -321,7 +336,11 @@ def experiment(
systems = []
for k, v in dataset.items():
- systems += [k, os.path.join(output_folder, "analysis", k, "metrics.csv")]
+ systems += [k, os.path.join(analysis_folder, k, "metrics.csv")]
+ if second_annotator is not None:
+ for k, v in second_annotator.items():
+ systems += [f"{k} (2nd. annot.)",
+ os.path.join(second_annotator_folder, k, "metrics.csv")]
output_pdf = os.path.join(output_folder, "comparison.pdf")
ctx.invoke(compare, label_path=systems, output=output_pdf, verbose=verbose)
diff --git a/bob/ip/binseg/script/predict.py b/bob/ip/binseg/script/predict.py
index d8af8c011c9b5ea2ede8d1056a5c0a9ee1d19bc4..41419ece1c3a32f41b46310e65d31b3f58ad8f5e 100644
--- a/bob/ip/binseg/script/predict.py
+++ b/bob/ip/binseg/script/predict.py
@@ -28,7 +28,7 @@ logger = logging.getLogger(__name__)
\b
1. Runs prediction on an existing dataset configuration:
\b
- $ bob binseg predict -vv m2unet drive-test --weight=path/to/model_final.pth --output-folder=path/to/predictions
+ $ bob binseg predict -vv m2unet drive --weight=path/to/model_final.pth --output-folder=path/to/predictions
\b
2. To run prediction on a folder with your own images, you must first
specify resizing, cropping, etc, so that the image can be correctly
diff --git a/bob/ip/binseg/test/test_cli.py b/bob/ip/binseg/test/test_cli.py
index b244bf8b8e78d3fedac17d2897a09aea21646e9d..cbaeea2849a67dcf7160d071b0d965c2e2da1155 100644
--- a/bob/ip/binseg/test/test_cli.py
+++ b/bob/ip/binseg/test/test_cli.py
@@ -79,6 +79,7 @@ def test_experiment_stare():
"from bob.ip.binseg.configs.datasets.stare import _maker\n"
)
config.write("dataset = _maker('ah', _raw)\n")
+ config.write("second_annotator = _maker('vk', _raw)\n")
config.flush()
result = runner.invoke(
@@ -97,12 +98,12 @@ def test_experiment_stare():
# "Saving results/overlayed/probabilities": 1, #tqdm.write
"Ended prediction": 1, # logging
"Started evaluation": 1, # logging
- "Highest F1-score of": 2, # logging
+ "Highest F1-score of": 4, # logging
"Saving overall precision-recall plot": 2, # logging
# "Saving results/overlayed/analysis": 1, #tqdm.write
"Ended evaluation": 1, # logging
"Started comparison": 1, # logging
- "Loading metrics from results/analysis": 2, # logging
+ "Loading metrics from results/analysis": 4, # logging
"Ended comparison": 1, # logging
}
buf.seek(0)
@@ -114,7 +115,7 @@ def test_experiment_stare():
# f"instead of the expected {v}")
assert _str_counter(k, logging_output) == v, (
f"Count for string '{k}' appeared "
- f"({_str_counter(k, result.output)}) "
+ f"({_str_counter(k, logging_output)}) "
f"instead of the expected {v}"
)