diff --git a/bob/bio/face/preprocessor/FaceCrop.py b/bob/bio/face/preprocessor/FaceCrop.py
index 9b04b9decc79e3099b987b1e67c2843e34a096e0..23cfc2494eff78c86703cc6b9f2ff098a73a43d1 100644
--- a/bob/bio/face/preprocessor/FaceCrop.py
+++ b/bob/bio/face/preprocessor/FaceCrop.py
@@ -373,12 +373,26 @@ class FaceCrop(Base):
self._init_non_pickables()
-class MultiFaceCrop(TransformerMixin, BaseEstimator):
+class MultiFaceCrop(Base):
+ """ Wraps around FaceCrop to enable a dynamical cropper that can handle several annotation types.
+ Initialization and usage is similar to the FaceCrop, but the main difference here is that one specifies
+ a *list* of cropped_positions, and optionally a *list* of associated fixed positions.
+
+ For each set of cropped_positions in the list, a new FaceCrop will be instanciated that handles this
+ exact set of annotations.
+ When calling the *transform* method, the MultiFaceCrop matches each sample to its associated cropper
+ based on the received annotation, then performs the cropping of each subset, and finally gathers the results.
+
+ In case of ambiguity (when no cropper is a match for the received annotations, or when several croppers
+ match the received annotations), raises a ValueError.
+
+ """
+
def __init__(
self,
cropped_image_size,
cropped_positions_list,
- fixed_positions=None,
+ fixed_positions_list=None,
mask_sigma=None,
mask_neighbors=5,
mask_seed=None,
@@ -388,9 +402,14 @@ class MultiFaceCrop(TransformerMixin, BaseEstimator):
):
assert isinstance(cropped_positions_list, list)
+ if fixed_positions_list is None:
+ fixed_positions_list = [None] * len(cropped_positions_list)
+ assert isinstance(fixed_positions_list, list)
self.croppers = {}
- for cropped_positions in cropped_positions_list:
+ for cropped_positions, fixed_positions in zip(
+ cropped_positions_list, fixed_positions_list
+ ):
assert len(cropped_positions) == 2
self.croppers[tuple(cropped_positions)] = FaceCrop(
cropped_image_size,
@@ -408,26 +427,38 @@ class MultiFaceCrop(TransformerMixin, BaseEstimator):
subsets = {k: {"X": [], "annotations": []} for k in self.croppers.keys()}
def assign(X_elem, annotations_elem):
+ # Assign a single sample to its matching cropper
+
+ # Compare the received annotations keys to the cropped_positions keys of each cropper
valid_keys = [
k
for k in self.croppers.keys()
if set(k).issubset(set(annotations_elem.keys()))
]
- assert (
- len(valid_keys) == 1
- ), "Cropper selection from the annotations is ambiguous ({} valid croppers)".format(
- len(valid_keys)
- )
- subsets[valid_keys[0]]["X"].append(X_elem)
- subsets[valid_keys[0]]["annotations"].append(annotations_elem)
+ # Ensure exactly one cropper is a match
+ if len(valid_keys) != 1:
+ raise ValueError(
+ "Cropper selection from the annotations is ambiguous ({} valid croppers)".format(
+ len(valid_keys)
+ )
+ )
+ else:
+ # Assign the sample to this particuler cropper
+ cropper_key = valid_keys[0]
+ subsets[cropper_key]["X"].append(X_elem)
+ subsets[cropper_key]["annotations"].append(annotations_elem)
+
+ # Assign each sample to its matching cropper
for X_elem, annotations_elem in zip(X, annotations):
assign(X_elem, annotations_elem)
+ # Call each FaceCrop on its sample subset
transformed_subsets = {
k: self.croppers[k].transform(**subsets[k]) for k in subsets.keys()
}
+ # Gather the results
return [item for sublist in transformed_subsets.values() for item in sublist]
def fit(self, X, y=None):
diff --git a/bob/bio/face/preprocessor/__init__.py b/bob/bio/face/preprocessor/__init__.py
index 61ac1346cc73dd165e717f91595f08969f300d09..1a0c9b64a3e248993333f3161503a1d3b2eae1af 100644
--- a/bob/bio/face/preprocessor/__init__.py
+++ b/bob/bio/face/preprocessor/__init__.py
@@ -1,5 +1,5 @@
from .Base import Base
-from .FaceCrop import FaceCrop
+from .FaceCrop import FaceCrop, MultiFaceCrop
from .TanTriggs import TanTriggs
from .INormLBP import INormLBP
diff --git a/bob/bio/face/test/data/cropped_bbox.hdf5 b/bob/bio/face/test/data/cropped_bbox.hdf5
new file mode 100644
index 0000000000000000000000000000000000000000..a0048061a5901e7af7d5278cd0430f79294254bd
Binary files /dev/null and b/bob/bio/face/test/data/cropped_bbox.hdf5 differ
diff --git a/bob/bio/face/test/data/testimage_bbox.pos b/bob/bio/face/test/data/testimage_bbox.pos
new file mode 100644
index 0000000000000000000000000000000000000000..933a7652a24eb5a8b8a0663100a3c729c3ff1fbd
--- /dev/null
+++ b/bob/bio/face/test/data/testimage_bbox.pos
@@ -0,0 +1,2 @@
+topleft 85 130
+bottomright 270 330
diff --git a/bob/bio/face/test/test_preprocessors.py b/bob/bio/face/test/test_preprocessors.py
index 4b02fc8c46a8095a5f2a742ced9213f4199294d6..51e6675f5c50efd009f04508800577f347a0671d 100644
--- a/bob/bio/face/test/test_preprocessors.py
+++ b/bob/bio/face/test/test_preprocessors.py
@@ -151,6 +151,56 @@ def test_face_crop():
# reset the configuration, so that later tests don't get screwed.
cropper.color_channel = "gray"
+def test_multi_face_crop():
+ # read input
+ image = _image()
+ eye_annotation, bbox_annotation = [
+ bob.db.base.read_annotation_file(
+ pkg_resources.resource_filename("bob.bio.face.test", "data/" + filename + ".pos"),
+ "named"
+ )
+ for filename in ["testimage", "testimage_bbox"]
+ ]
+
+ # define the preprocessor
+ cropper = bob.bio.face.preprocessor.MultiFaceCrop(
+ cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
+ cropped_positions_list=[
+ {'leye': LEFT_EYE_POS, 'reye': RIGHT_EYE_POS},
+ {'topleft': (0, 0), 'bottomright': (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)}
+ ]
+ )
+
+ # execute face cropper
+ eye_reference, bbox_reference = [
+ pkg_resources.resource_filename(
+ "bob.bio.face.test", "data/" + filename + ".hdf5"
+ )
+ for filename in ["cropped", "cropped_bbox"]
+ ]
+
+ eye_cropped, bbox_cropped = cropper.transform([image, image], [eye_annotation, bbox_annotation])
+
+ # Compare the cropped results to the reference
+ _compare(eye_cropped, eye_reference)
+ _compare(bbox_cropped, bbox_reference)
+
+ # test a ValueError is raised if the annotations don't match any cropper
+ try:
+ annot = dict(landmark_A=(60, 60), landmark_B=(120, 120))
+ cropper.transform([image], [annot])
+ assert 0, "MultiFaceCrop did not raise a ValueError for annotations matching no cropper"
+ except ValueError:
+ pass
+
+ # test a ValueError is raised if the annotations match several croppers
+ try:
+ annot = {**eye_annotation, **bbox_annotation}
+ cropper.transform([image], [annot])
+ assert 0, "MultiFaceCrop did not raise a ValueError for annotations matching several croppers"
+ except ValueError:
+ pass
+
def test_tan_triggs():
# read input