diff --git a/bob/ip/binseg/script/analyze.py b/bob/ip/binseg/script/analyze.py
new file mode 100644
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+++ b/bob/ip/binseg/script/analyze.py
@@ -0,0 +1,275 @@
+#!/usr/bin/env python
+# coding=utf-8
+
+import os
+
+import click
+
+from bob.extension.scripts.click_helper import (
+ verbosity_option,
+ ConfigCommand,
+ ResourceOption,
+)
+
+from .binseg import save_sh_command
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@click.command(
+ entry_point_group="bob.ip.binseg.config",
+ cls=ConfigCommand,
+ epilog="""Examples:
+
+\b
+ 1. Re-evaluates a pre-trained M2U-Net model with DRIVE (vessel
+ segmentation), on the CPU, by running inference and evaluation on results
+ from its test set:
+
+ $ bob binseg analyze -vv m2unet drive --weight=model.path
+
+""",
+)
+@click.option(
+ "--output-folder",
+ "-o",
+ help="Path where to store experiment outputs (created if does not exist)",
+ required=True,
+ type=click.Path(),
+ default="results",
+ cls=ResourceOption,
+)
+@click.option(
+ "--model",
+ "-m",
+ help="A torch.nn.Module instance implementing the network to be trained, and then evaluated",
+ required=True,
+ cls=ResourceOption,
+)
+@click.option(
+ "--dataset",
+ "-d",
+ help="A dictionary mapping string keys to "
+ "bob.ip.binseg.data.utils.SampleList2TorchDataset's. At least one key "
+ "named 'train' must be available. This dataset will be used for training "
+ "the network model. All other datasets will be used for prediction and "
+ "evaluation. Dataset descriptions include all required pre-processing, "
+ "including eventual data augmentation, which may be eventually excluded "
+ "for prediction and evaluation purposes",
+ 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(
+ "--batch-size",
+ "-b",
+ help="Number of samples in every batch (this parameter affects "
+ "memory requirements for the network). If the number of samples in "
+ "the batch is larger than the total number of samples available for "
+ "training, this value is truncated. If this number is smaller, then "
+ "batches of the specified size are created and fed to the network "
+ "until there are no more new samples to feed (epoch is finished). "
+ "If the total number of training samples is not a multiple of the "
+ "batch-size, the last batch will be smaller than the first.",
+ required=True,
+ show_default=True,
+ default=1,
+ type=click.IntRange(min=1),
+ cls=ResourceOption,
+)
+@click.option(
+ "--device",
+ "-d",
+ help='A string indicating the device to use (e.g. "cpu" or "cuda:0")',
+ show_default=True,
+ required=True,
+ default="cpu",
+ cls=ResourceOption,
+)
+@click.option(
+ "--overlayed/--no-overlayed",
+ "-O",
+ help="Creates overlayed representations of the output probability maps, "
+ "similar to --overlayed in prediction-mode, except it includes "
+ "distinctive colours for true and false positives and false negatives. "
+ "If not set, or empty then do **NOT** output overlayed images.",
+ show_default=True,
+ default=False,
+ required=False,
+ cls=ResourceOption,
+)
+@click.option(
+ "--weight",
+ "-w",
+ help="Path or URL to pretrained model file (.pth extension)",
+ required=True,
+ cls=ResourceOption,
+)
+@verbosity_option(cls=ResourceOption)
+@click.pass_context
+def analyze(
+ ctx,
+ model,
+ output_folder,
+ batch_size,
+ dataset,
+ second_annotator,
+ device,
+ overlayed,
+ weight,
+ verbose,
+ **kwargs,
+):
+ """Runs a complete evaluation from prediction to comparison
+
+ This script is just a wrapper around the individual scripts for running
+ prediction and evaluating FCN models. It organises the output in a
+ preset way:
+
+ .. code-block:: text
+
+ └─ <output-folder>/
+ ├── predictions/ #the prediction outputs for the train/test set
+ ├── overlayed/ #the overlayed outputs for the train/test set
+ ├── predictions/ #predictions overlayed on the input images
+ ├── analysis/ #predictions overlayed on the input images
+ ├ #including analysis of false positives, negatives
+ ├ #and true positives
+ └── second-annotator/ #if set, store overlayed images for the
+ #second annotator here
+ └── analysis / #the outputs of the analysis of both train/test sets
+ #includes second-annotator "metrics" as well, if
+ # configured
+
+ N.B.: The tool is designed to prevent analysis bias and allows one to
+ provide separate subsets for training and evaluation. Instead of using
+ simple datasets, datasets for full experiment running should be
+ dictionaries with specific subset names:
+
+ * ``__train__``: dataset used for training, prioritarily. It is typically
+ the dataset containing data augmentation pipelines.
+ * ``train`` (optional): a copy of the ``__train__`` dataset, without data
+ augmentation, that will be evaluated alongside other sets available
+ * ``*``: any other name, not starting with an underscore character (``_``),
+ will be considered a test set for evaluation.
+
+ N.B.2: The threshold used for calculating the F1-score on the test set, or
+ overlay analysis (false positives, negatives and true positives overprinted
+ on the original image) also follows the logic above.
+ """
+
+ command_sh = os.path.join(output_folder, "command.sh")
+ if not os.path.exists(command_sh):
+ # only save if experiment has not saved yet something similar
+ save_sh_command(command_sh)
+
+
+
+ ## Prediction
+ logger.info("Started prediction")
+
+ from .predict import predict
+
+ predictions_folder = os.path.join(output_folder, "predictions")
+ overlayed_folder = (
+ os.path.join(output_folder, "overlayed", "predictions")
+ if overlayed
+ else None
+ )
+
+ ctx.invoke(
+ predict,
+ output_folder=predictions_folder,
+ model=model,
+ dataset=dataset,
+ batch_size=batch_size,
+ device=device,
+ weight=weight,
+ overlayed=overlayed_folder,
+ verbose=verbose,
+ )
+ logger.info("Ended prediction")
+
+ ## Evaluation
+ logger.info("Started evaluation")
+
+ from .evaluate import evaluate
+
+ overlayed_folder = (
+ os.path.join(output_folder, "overlayed", "analysis")
+ if overlayed
+ else None
+ )
+
+ # choosing the overlayed_threshold
+ if "validation" in dataset:
+ threshold = "validation"
+ elif "train" in dataset:
+ threshold = "train"
+ else:
+ threshold = 0.5
+ logger.info(f"Setting --threshold={threshold}...")
+
+ analysis_folder = os.path.join(output_folder, "analysis")
+ ctx.invoke(
+ evaluate,
+ output_folder=analysis_folder,
+ predictions_folder=predictions_folder,
+ dataset=dataset,
+ second_annotator=second_annotator,
+ overlayed=overlayed_folder,
+ threshold=threshold,
+ verbose=verbose,
+ )
+
+ logger.info("Ended evaluation")
+
+ ## Comparison
+ logger.info("Started comparison")
+
+ # compare performances on the various sets
+ from .compare import compare
+
+ systems = []
+ for k, v in dataset.items():
+ if k.startswith("_"):
+ logger.info(f"Skipping dataset '{k}' (not to be compared)")
+ continue
+ systems += [k, os.path.join(analysis_folder, f"{k}.csv")]
+ if second_annotator is not None:
+ for k, v in second_annotator.items():
+ if k.startswith("_"):
+ logger.info(f"Skipping dataset '{k}' (not to be compared)")
+ continue
+ systems += [
+ f"{k} (2nd. annot.)",
+ os.path.join(
+ analysis_folder, "second-annotator", f"{k}.csv"
+ ),
+ ]
+
+ output_figure = os.path.join(output_folder, "comparison.pdf")
+ output_table = os.path.join(output_folder, "comparison.rst")
+
+ ctx.invoke(
+ compare,
+ label_path=systems,
+ output_figure=output_figure,
+ output_table=output_table,
+ threshold=threshold,
+ verbose=verbose,
+ )
+
+ logger.info("Ended comparison")