diff --git a/bob/ip/facelandmarks/__init__.py b/bob/ip/facelandmarks/__init__.py
index d5d61f05021867a5aac356dc8a567c2a4a8d4347..0aa15339d9ca4f4b213ca102fc46c215499de646 100644
--- a/bob/ip/facelandmarks/__init__.py
+++ b/bob/ip/facelandmarks/__init__.py
@@ -1,6 +1,29 @@
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
+from .utils import *
+
+
+# gets sphinx autodoc done right - don't remove it
+def __appropriate__(*args):
+ """Says object was actually declared here, an not on the import module.
+
+ Parameters: *args: An iterable of objects to modify
+
+ Resolves `Sphinx referencing issues <https://github.com/sphinx-
+ doc/sphinx/issues/3048>` """
+
+ for obj in args:
+ obj.__module__ = __name__
+
+
+__appropriate__(
+ Result,
+ detect_landmarks_on_boundingbox,
+ detect_landmarks,
+ draw_landmarks,
+ save_landmarks,
+)
def get_config():
@@ -12,7 +35,5 @@ def get_config():
return bob.extension.get_config(__name__)
-
# gets sphinx autodoc done right - don't remove it
__all__ = [_ for _ in dir() if not _.startswith('_')]
-
diff --git a/doc/guide.rst b/doc/guide.rst
index 7a69e87ef76fd094ff391d6e3dc727eef3dbd1fe..f1aac12c4a6b58e7b5d38d6f16d5b320158e67b5 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -11,7 +11,6 @@
import bob.ip.color
import bob.ip.facedetect
import bob.ip.facelandmarks
- import bob.ip.facelandmarks.utils
import pkg_resources
#lena_file = '/idiap/user/sbhatta/work/git/bob.ip.facelandmarks/data/lena.jpg'
@@ -27,8 +26,8 @@
This Bob package allows you to use the [Menpofit_] package to detect facial landmarks.
Given a gray-level image depicting a human face, this package can be used to extract a specific set of 68 landmarks,
-as defined in Menpofit. Please refer to the original Menpofit [documentation_] for implementation details.
-Here, we show some examples of how to use the ``bob.ip.facelandmarks`` package.
+as defined in Menpofit. Please refer to the original Menpofit [documentation_] for implementation details.
+Here, we show some examples of how to use the ``bob.ip.facelandmarks`` package.
Landmark Detection on a Single Face
@@ -42,7 +41,7 @@ The following code-example shows how to extract facial keypoints for a single fa
>>> face_image = bob.io.base.load('lena.jpg') # doctest: +SKIP
>>> gray_image = bob.ip.color.rgb_to_gray(face_image)
- >>> key_points = bob.ip.facelandmarks.utils.detect_landmarks(gray_image, 1)
+ >>> key_points = bob.ip.facelandmarks.detect_landmarks(gray_image, 1)
>>> print(key_points[0].landmarks.shape)
(68, 2)
@@ -69,23 +68,23 @@ The ``detect_landmarks()`` function has the following signature: `detect_landmar
* ``gray_image``: a numpy-array containing the gray-level input image, and,
* ``top``: positive integer (default=0), specifying the number of faces to be detected in this image.
- * ``min_quality``: positive floating-point number (default=0), specifying the minimum acceptable quality for the result of face-detection.
+ * ``min_quality``: positive floating-point number (default=0), specifying the minimum acceptable quality for the result of face-detection.
The first parameter is obligatory, and should be a valid 2D numpy-array representing a gray-level image.
The remaining two parameters are optional.
In the example above, ``top`` is specified as 1, hence, landmarks for only one face are extracted.
-The function ``detect_landmarks()`` first detects faces in the input image, using ``bob.ip.facedetect``, and then uses the result of the face-detection-step for detecting facial-landmarks.
+The function ``detect_landmarks()`` first detects faces in the input image, using ``bob.ip.facedetect``, and then uses the result of the face-detection-step for detecting facial-landmarks.
If the ``min_quality`` parameter is specified, then bounding-boxes having a quality-value lower than the specified value are ignored.
-The return value of ``detect_landmarks()`` is a list.
+The return value of ``detect_landmarks()`` is a list.
When only one face is expected in the input, this list will contain only one element.
Each element in the list is an object with three members, named as follows:
* ``bounding_box``: an object with two elements (topright, and bottomleft), each of which is a tuple (row,col) giving the coordinates of the top-left and bottom-right corners of the detected face-bounding-box.
- * ``quality``: a floating-point number between 0 and 100.0, giving a quality-estimate for the result of the face-detection step.
+ * ``quality``: a floating-point number between 0 and 100.0, giving a quality-estimate for the result of the face-detection step.
* ``landmarks``: a numpy-array of shape (68, 2).
The first two members, ``bounding_box`` and ``quality``, come from ``bob.ip.facedetect``.
@@ -108,10 +107,10 @@ As described in the Menpofit documentation, The facial-landmarks are listed in a
If the bounding-box of the desired face is already available (via a preceding call to the function ``face.ip.facedetect.detect_single_face()``), the function ``detect_landmarks_on_boundingbox(gray_image, bounding_box)`` may be used to determine the facial-landmarks within this bounding-box.
-Note that the return-value of ``detect_landmarks_on_boundingbox()`` is a 2D numpy-array representing the coordinates of the 68 landmarks (and not an object as in the case of ``detect_landmarks()``).
+Note that the return-value of ``detect_landmarks_on_boundingbox()`` is a 2D numpy-array representing the coordinates of the 68 landmarks (and not an object as in the case of ``detect_landmarks()``).
.. doctest::
-
+
>>> 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)
@@ -130,9 +129,9 @@ 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.utils.detect_landmarks(gray_image, top=5)
>>> for i in range(5):
- ... print(key_points[i].bounding_box.topleft)
+ ... print(key_points[i].bounding_box.topleft)
(136, 2243)
(1480, 2226)
(1574, 2959)
@@ -140,6 +139,6 @@ In the following example, the input image contains several faces, out of which,
(107, 3016)
-.. _Menpofit: http://www.menpo.org/menpofit/
+.. _Menpofit: http://www.menpo.org/menpofit/
.. _documentation: https://menpofit.readthedocs.io/en/stable/