From 6ae969d4f7f6c0acb95ad24d109f897cd5ac5c3a Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Tue, 31 Mar 2020 17:16:28 +0200
Subject: [PATCH] Check if there are samples available for the function
train_background_model
---
.../pipelines/vanilla_biometrics/pipeline.py | 29 +++++++++++++++++--
1 file changed, 26 insertions(+), 3 deletions(-)
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/pipeline.py b/bob/bio/base/pipelines/vanilla_biometrics/pipeline.py
index 9ac00cbb..4df6aa89 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/pipeline.py
+++ b/bob/bio/base/pipelines/vanilla_biometrics/pipeline.py
@@ -9,6 +9,7 @@ for bob.bio experiments
"""
import logging
+import numpy
logger = logging.getLogger(__name__)
@@ -19,6 +20,7 @@ def biometric_pipeline(
probe_samples,
transformer,
biometric_algorithm,
+ allow_scoring_with_all_biometric_references=False,
):
logger.info(
f" >> Vanilla Biometrics: Training background model with pipeline {transformer}"
@@ -43,12 +45,25 @@ def biometric_pipeline(
# Scores all probes
return compute_scores(
- probe_samples, biometric_references, transformer, biometric_algorithm
+ probe_samples,
+ biometric_references,
+ transformer,
+ biometric_algorithm,
+ allow_scoring_with_all_biometric_references,
)
def train_background_model(background_model_samples, transformer):
# background_model_samples is a list of Samples
+
+ # We might have algorithms that has no data for training
+ if len(background_model_samples) <= 0:
+ logger.warning(
+ "There's no data to train background model."
+ "For the rest of the execution it will be assumed that the pipeline is stateless."
+ )
+ return transformer
+
transformer = transformer.fit(background_model_samples)
return transformer
@@ -67,13 +82,21 @@ def create_biometric_reference(
def compute_scores(
- probe_samples, biometric_references, transformer, biometric_algorithm
+ probe_samples,
+ biometric_references,
+ transformer,
+ biometric_algorithm,
+ allow_scoring_with_all_biometric_references=False,
):
# probes is a list of SampleSets
probe_features = transformer.transform(probe_samples)
- scores = biometric_algorithm.score_samples(probe_features, biometric_references)
+ scores = biometric_algorithm.score_samples(
+ probe_features,
+ biometric_references,
+ allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references,
+ )
# scores is a list of Samples
return scores
--
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