diff --git a/bob/ip/facelandmarks/__init__.py b/bob/ip/facelandmarks/__init__.py
index 0aa15339d9ca4f4b213ca102fc46c215499de646..380abf60442fbec74dd4bcfa80a737c937a4cfe1 100644
--- a/bob/ip/facelandmarks/__init__.py
+++ b/bob/ip/facelandmarks/__init__.py
@@ -2,7 +2,7 @@
# vim: set fileencoding=utf-8 :
from .utils import *
-
+from .utils import _Result
# gets sphinx autodoc done right - don't remove it
def __appropriate__(*args):
@@ -18,6 +18,7 @@ def __appropriate__(*args):
__appropriate__(
+ _Result,
Result,
detect_landmarks_on_boundingbox,
detect_landmarks,
diff --git a/bob/ip/facelandmarks/utils.py b/bob/ip/facelandmarks/utils.py
index 9c7ccb98c076f768c541dbaf282643d7004c1877..0a230e73c7cc6249343fe24d970d88e46c4135e0 100644
--- a/bob/ip/facelandmarks/utils.py
+++ b/bob/ip/facelandmarks/utils.py
@@ -132,11 +132,10 @@ class Result(_Result):
'''A :py:class:`collections.namedtuple` with landmark information
Attributes:
-
bounding_box (:py:class:`bob.ip.facedetect.BoundingBox`): A bounding box
extracted with :py:mod:`bob.ip.facedetect`.
- quality (float): A floating-point number expressing the quality of the
+ quality (:py:class:`float`): the quality of the
extracted bounding-box, as returned by :py:mod:`bob.ip.facedetect`'s
Boosted classifier
@@ -173,11 +172,11 @@ def _detect_multiple_landmarks_on_gray_image(data, top=0, min_quality=0.):
data (:py:class:`numpy.ndarray`): An ``uint8`` array with 2 dimensions,
corresponding to a gray-scale image loaded with Bob (y, x) ordering.
- top (int): An integer which indicates if we should only consider the first
+ top (:py:class:`int`): An integer which indicates if we should only consider the first
N detections or all of them. A value of zero means the selector ignores
this field.
- min_quality (float): A float that also trims the face detector output list
+ min_quality (:py:class:`float`): also trims the face detector output list
by considering a minimum quality for the detection. A value of zero (0.0)
means "any quality will do". Good detections have a typical value which
is greater than 30. Use this parameter with care. If this and ``top`` are
@@ -265,11 +264,11 @@ def _detect_multiple_landmarks_on_color_image(data, top=0, min_quality=0.):
data (:py:class:`numpy.ndarray`): An ``uint8`` array with 3 dimensions,
corresponding to a color image loaded with Bob (planes, y, x) ordering.
- top (int): An integer which indicates if we should only consider the first
+ top (:py:class:`int`): An integer which indicates if we should only consider the first
N detections or all of them. A value of zero means the selector ignores
this field.
- min_quality (float): A float that also trims the face detector output list
+ min_quality (:py:class:`float`): also trims the face detector output list
by considering a minimum quality for the detection. A value of zero (0.0)
means "any quality will do". Good detections have a typical value which
is greater than 30. Use this parameter with care. If this and ``top`` are
@@ -300,11 +299,11 @@ def detect_landmarks(data, top=0, min_quality=0.):
dimensions, corresponding to a either a gray-scale or color image loaded
with Bob.
- top (int): An integer which indicates if we should only consider the first
+ top (:py:class:`int`): An integer which indicates if we should only consider the first
N detections or all of them. A value of zero means the selector ignores
this field.
- min_quality (float): A float that also trims the face detector output list
+ min_quality (:py:class:`float`): trims the face detector output list
by considering a minimum quality for the detection. A value of zero (0.0)
means "any quality will do". Good detections have a typical value which
is greater than 30. Use this parameter with care. If this and ``top`` are
@@ -399,7 +398,7 @@ def save_landmarks(results, fname):
:py:class:`.utils.Result`, each containing the result of face detection
and landmarks extracted from the input image.
- fname (str): A path with the output filename
+ fname (:py:class:`str`): A path with the output filename
'''
diff --git a/doc/guide.rst b/doc/guide.rst
index f1aac12c4a6b58e7b5d38d6f16d5b320158e67b5..c2e2fb90a34bc6767476af8ec6774decb1af8f1c 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -55,7 +55,7 @@ This package also provides a handy function, ``draw_landmarks()``, for plotting
.. doctest::
- >>> bob.ip.facelandmarks.utils.draw_landmarks(gray_image, key_points)
+ >>> bob.ip.facelandmarks.draw_landmarks(gray_image, key_points)
The result is shown in the image below.
@@ -113,7 +113,7 @@ Note that the return-value of ``detect_landmarks_on_boundingbox()`` is a 2D nump
>>> gray_image = bob.ip.color.rgb_to_gray(face_image)
>>> my_bounding_box, _ = bob.ip.facedetect.detect_single_face(gray_image)
- >>> my_key_points = bob.ip.facelandmarks.utils.detect_landmarks_on_boundingbox(gray_image, my_bounding_box)
+ >>> my_key_points = bob.ip.facelandmarks.detect_landmarks_on_boundingbox(gray_image, my_bounding_box)
>>> print(my_key_points.shape)
(68, 2)
@@ -129,7 +129,7 @@ In the following example, the input image contains several faces, out of which,
>>> multi_image = bob.io.base.load('multiple-faces.jpg') # doctest: +SKIP
>>> gray_image = bob.ip.color.rgb_to_gray(multi_image)
- >>> key_points = bob.ip.facelandmarks.utils.detect_landmarks(gray_image, top=5)
+ >>> key_points = bob.ip.facelandmarks.detect_landmarks(gray_image, top=5)
>>> for i in range(5):
... print(key_points[i].bounding_box.topleft)
(136, 2243)
diff --git a/doc/py_api.rst b/doc/py_api.rst
index 2abe2eda451d17efb6a6517e875317ec71c622bd..a46ce76cba8ec8471a7859ff9c7435b4ae9500b1 100644
--- a/doc/py_api.rst
+++ b/doc/py_api.rst
@@ -7,4 +7,4 @@
Detailed Information
--------------------
-.. automodule:: bob.ip.facelandmarks.utils
+.. automodule:: bob.ip.facelandmarks