Moved the CSVBaseSampleLoader from bob.bio.base to bob.pipelines. This is a general function

bob/pipelines/datasets/sample_loaders.py

+#!/usr/bin/env python
+# coding=utf-8
+
+
+"""
+Base mechanism that converts CSV lines to Samples
+"""
+
+from bob.extension.download import find_element_in_tarball
+from bob.pipelines import DelayedSample, Sample, SampleSet
+import os
+from abc import ABCMeta, abstractmethod
+
+
+class CSVBaseSampleLoader(metaclass=ABCMeta):
+    """    
+    Base class that converts the lines of a CSV file, like the one below to
+    :any:`bob.pipelines.DelayedSample` or :any:`bob.pipelines.SampleSet`
+
+    .. code-block:: text
+
+       PATH,REFERENCE_ID
+       path_1,reference_id_1
+       path_2,reference_id_2
+       path_i,reference_id_j
+       ...
+
+    .. note::
+       This class should be extended, because the meaning of the lines depends on
+       the final application where thoses CSV files are used.
+
+    Parameters
+    ----------
+
+        data_loader:
+            A python function that can be called parameterlessly, to load the
+            sample in question from whatever medium
+
+        metadata_loader:
+            A python function that transforms parts of a `row` in a more complex object (e.g. convert eyes annotations embedded in the CSV file to a python dict)
+
+        dataset_original_directory: str
+            Path of where data is stored
+        
+        extension: str
+            Default file extension
+
+    """
+
+    def __init__(
+        self,
+        data_loader,
+        metadata_loader=None,
+        dataset_original_directory="",
+        extension="",
+    ):
+        self.data_loader = data_loader
+        self.extension = extension
+        self.dataset_original_directory = dataset_original_directory
+        self.metadata_loader = metadata_loader
+
+    @abstractmethod
+    def __call__(self, filename):
+        pass
+
+    @abstractmethod
+    def convert_row_to_sample(self, row, header):
+        pass
+
+    def convert_samples_to_samplesets(
+        self, samples, group_by_reference_id=True, references=None
+    ):
+        if group_by_reference_id:
+
+            # Grouping sample sets
+            sample_sets = dict()
+            for s in samples:
+                if s.reference_id not in sample_sets:
+                    sample_sets[s.reference_id] = (
+                        SampleSet([s], parent=s)
+                        if references is None
+                        else SampleSet([s], parent=s, references=references)
+                    )
+                else:
+                    sample_sets[s.reference_id].append(s)
+            return list(sample_sets.values())
+
+        else:
+            return (
+                [SampleSet([s], parent=s) for s in samples]
+                if references is None
+                else [SampleSet([s], parent=s, references=references) for s in samples]
+            )
+