SampleLoaders as transformers

9a38b350 · Tiago de Freitas Pereira · e2459dc5 · 9a38b350 · 9a38b350 · 9a38b350
Commit 9a38b350 authored 4 years ago by Tiago de Freitas Pereira
--- a/bob/pipelines/datasets/__init__.py
+++ b/bob/pipelines/datasets/__init__.py
+from .sample_loaders import CSVToSampleLoader, AnnotationsLoader
--- a/bob/pipelines/datasets/sample_loaders.py
+++ b/bob/pipelines/datasets/sample_loaders.py
@@ -9,10 +9,14 @@ 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

+from sklearn.base import TransformerMixin, BaseEstimator
+import csv
+import functools
+import bob.db.base

-class CSVBaseSampleLoader(metaclass=ABCMeta):
+
+class CSVToSampleLoader(TransformerMixin, BaseEstimator):
    """    
    Base class that converts the lines of a CSV file, like the one below to
    :any:`bob.pipelines.DelayedSample` or :any:`bob.pipelines.SampleSet`
@@ -36,9 +40,6 @@ class CSVBaseSampleLoader(metaclass=ABCMeta):
            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
        
@@ -48,47 +49,133 @@ class CSVBaseSampleLoader(metaclass=ABCMeta):
    """

    def __init__(
-        self,
-        data_loader,
-        metadata_loader=None,
-        dataset_original_directory="",
-        extension="",
+        self, data_loader, 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
+    def fit(self, X, y=None):
+        return self
+
+    def _more_tags(self):
+        return {
+            "stateless": True,
+            "requires_fit": False,
+        }
+
+    def transform(self, X):
+        """
+        Transform one CVS line to ONE :any:`bob.pipelines.DelayedSample`
+
+        Parameters
+        ----------
+        X:
+            CSV File Object (open file)
+
+        """
+        X.seek(0)
+        reader = csv.reader(X)
+        header = next(reader)
+
+        self.check_header(header)
+        return [self.convert_row_to_sample(row, header) for row in reader]
+
+    def check_header(self, header):
+        """
+        A header should have at least "reference_id" AND "PATH"
+        """
+        header = [h.lower() for h in header]
+        if not "reference_id" in header:
+            raise ValueError(
+                "The field `reference_id` is not available in your dataset."
+            )
+
+        if not "path" in header:
+            raise ValueError("The field `path` is not available in your dataset.")
+
+    def __call__(self, f):
+        f.seek(0)
+        reader = csv.reader(f)
+        header = next(reader)
+
+        self.check_header(header)
+        return [self.convert_row_to_sample(row, header) for row in reader]

-    @abstractmethod
    def convert_row_to_sample(self, row, header):
-        pass
+        path = row[0]
+        reference_id = row[1]
+
+        kwargs = dict([[str(h).lower(), r] for h, r in zip(header[2:], row[2:])])
+
+        return DelayedSample(
+            functools.partial(
+                self.data_loader,
+                os.path.join(self.dataset_original_directory, path + self.extension),
+            ),
+            key=path,
+            reference_id=reference_id,
+            **kwargs
+        )
+
+
+class AnnotationsLoader(TransformerMixin, BaseEstimator):
+    """
+    Metadata loader that loads annotations in the Idiap format using the function
+    :any:`bob.db.base.read_annotation_file`

-    def convert_samples_to_samplesets(
-        self, samples, group_by_reference_id=True, references=None
+    Parameters
+    ----------
+
+    csv_to_sample_loader: :any:`CSVToSampleLoader`
+        Mechanisms that knows how to convert one line to one sample
+
+    annotation_directory: str
+        Path where the annotations are store
+
+    annotation_extension: str
+        Extension of the annotations
+    
+    annotation_type: str
+        Annotations type
+
+    """
+
+    def __init__(
+        self,
+        csv_to_sample_loader,
+        annotation_directory=None,
+        annotation_extension=".json",
+        annotation_type="json",
    ):
-        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]
+        self.annotation_directory = annotation_directory
+        self.annotation_extension = annotation_extension
+        self.annotation_type = annotation_type
+        self.csv_to_sample_loader = csv_to_sample_loader
+
+    def transform(self, X):
+        if self.annotation_directory is None:
+            return None
+
+        samples = self.csv_to_sample_loader.transform(X)
+        annotated_samples = []
+        for x in samples:
+
+            # since the file id is equal to the file name, we can simply use it
+            annotation_file = os.path.join(
+                self.annotation_directory, x.key + self.annotation_extension
+            )
+
+            annotated_samples.append(
+                DelayedSample(
+                    x._load,
+                    parent=x,
+                    delayed_attributes=dict(
+                        annotations=lambda: bob.db.base.read_annotation_file(
+                            annotation_file, self.annotation_type
+                        )
+                    ),
+                )
            )

+        return annotated_samples
--- a/bob/pipelines/tests/data/samples/1.pgm
+++ b/bob/pipelines/tests/data/samples/1.pgm
+P5
+92 112
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\ No newline at end of file
--- a/bob/pipelines/tests/data/samples/1.pos
+++ b/bob/pipelines/tests/data/samples/1.pos
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\ No newline at end of file
--- a/bob/pipelines/tests/data/samples/2.pgm
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\ No newline at end of file
--- a/bob/pipelines/tests/data/samples/2.pos
+++ b/bob/pipelines/tests/data/samples/2.pos
+101 121 101 1201
\ No newline at end of file
--- a/bob/pipelines/tests/data/samples/samples.csv
+++ b/bob/pipelines/tests/data/samples/samples.csv
+PATH,REFERENCE_ID,GENDER,AGE
+1,001,M,104
+2,001,M,110
\ No newline at end of file
--- a/bob/pipelines/tests/test_sample_loaders.py
+++ b/bob/pipelines/tests/test_sample_loaders.py
+from bob.pipelines.datasets import CSVToSampleLoader, AnnotationsLoader
+import pkg_resources
+import bob.io.base
+import bob.io.image
+import os
+import numpy as np
+
+
+def test_sample_loader():
+    path = pkg_resources.resource_filename(__name__, os.path.join("data", "samples"))
+
+    sample_loader = CSVToSampleLoader(
+        data_loader=bob.io.base.load, dataset_original_directory=path, extension=".pgm"
+    )
+
+    f = open(os.path.join(path, "samples.csv"))
+
+    samples = sample_loader.transform(f)
+    assert len(samples) == 2
+    assert np.alltrue([s.data.shape == (112, 92) for s in samples])
+
+
+def test_annotations_loader():
+    path = pkg_resources.resource_filename(__name__, os.path.join("data", "samples"))
+
+    sample_loader = CSVToSampleLoader(
+        data_loader=bob.io.base.load, dataset_original_directory=path, extension=".pgm"
+    )
+    annotation_loader = AnnotationsLoader(
+        sample_loader,
+        annotation_directory=path,
+        annotation_extension=".pos",
+        annotation_type="eyecenter",
+    )
+
+    f = open(os.path.join(path, "samples.csv"))
+
+    samples = annotation_loader.transform(f)
+    assert len(samples) == 2
+    assert np.alltrue([s.data.shape == (112, 92) for s in samples])
+    assert np.alltrue([isinstance(s.annotations, dict) for s in samples])