diff --git a/bob/pad/face/test/test.py b/bob/pad/face/test/test.py
index ac1078e42c5fb5509af2a2ea0d7a124bb6a3afeb..799d8efb2b6a0506776dfd07e4b10516b6fd8c83 100644
--- a/bob/pad/face/test/test.py
+++ b/bob/pad/face/test/test.py
@@ -35,8 +35,40 @@ from ..algorithm import VideoSvmPadAlgorithm
from ..algorithm import VideoGmmPadAlgorithm
+from ..utils import face_detection_utils
+
import random
+
+
+def test_detect_face_landmarks_in_image():
+
+ img = load(datafile('testimage.jpg', 'bob.bio.face.test'))
+ assert len(img) == 3
+ annotations = face_detection_utils.detect_face_landmarks_in_image(img)
+ assert len(annotations['landmarks']) == 68
+ assert len(annotations['left_eye']) == 2
+ assert len(annotations['right_eye']) == 2
+ assert len(annotations['topleft']) == 2
+ assert len(annotations['bottomright']) == 2
+
+ #assert len(annotations['left_eye']) == (176, 220)
+
+
+
+def test_detect_face_landmarks_in_video():
+
+ img = load(datafile('testimage.jpg', 'bob.bio.face.test'))
+ assert len(img) == 3
+ frame_container= bob.bio.video.FrameContainer()
+ frame_container.add(1,img)
+ frame_container.add(2,img)
+
+ annotations = face_detection_utils.detect_face_landmarks_in_video(frame_container)
+ assert len(annotations) == 2
+ assert len(annotations['1']['landmarks']) == 68
+
+
#==============================================================================
def test_lbp_histogram():
lbp = LBPHistogram()
diff --git a/bob/pad/face/utils/face_detection_utils.py b/bob/pad/face/utils/face_detection_utils.py
index 66b16de7dbdf996f5b26a6addb3da08667894f44..0ba1f2308344618aaba65a889722d90acc2731ed 100644
--- a/bob/pad/face/utils/face_detection_utils.py
+++ b/bob/pad/face/utils/face_detection_utils.py
@@ -8,6 +8,7 @@ This file contains face detection utils.
# Import here:
import importlib
+import numpy as np
#==============================================================================
@@ -96,8 +97,139 @@ def detect_faces_in_video(frame_container, method = "dlib"):
annotations[str(idx)] = frame_annotations
return annotations
+
+def getEyePos(lm):
+ """
+ This function returns the locations of left and right eyes
+
+ **Parameters:**
+
+ ``lm`` : :py:class:`array`
+ A numpy array containing the coordinates of facial landmarks, (68X2)
+
+
+ **Returns:**
+
+ ``right_eye``
+ A tuple containing the location of right eye,
+
+ ``left_eye``
+ A tuple containing the location of left eye
+
+ """
+
+ # Mean position of eye corners as eye centers , casted to int()
+
+ left_eye_t = (lm[36,:] + lm[39,:])/2.0
+ right_eye_t = (lm[42,:] + lm[45,:])/2.0
+
+ right_eye = (int(left_eye_t[1]),int(left_eye_t[0]))
+ left_eye = (int(right_eye_t[1]),int(right_eye_t[0]))
+
+ return right_eye,left_eye
+
+
+
+def detect_face_landmarks_in_image(image, method = "dlib"):
+ """
+ This function detects a face and facial landmarks in the input image.
+
+ **Parameters:**
+
+ ``image`` : 3D :py:class:`numpy.ndarray`
+ A color image to detect the face in.
+
+ ``method`` : :py:class:`str`
+ A package to be used for face detection. Options supported by this
+ package: "dlib" (dlib is a dependency of this package).
+
+ **Returns:**
+
+ ``annotations`` : :py:class:`dict`
+ A dictionary containing the annotations for an image.
+ Dictionary must be as follows ``{'topleft': (row, col), 'bottomright': (row, col), 'left_eye': (row, col), 'right_eye': (row, col), 'landmarks': [(row1,col1), (row2,col2), ...]}``
+ Where (rowK,colK) is the location of Kth facial landmark (K=0,...,67).
+ If no annotations found an empty dictionary is returned.
+ """
+
+ try:
+ face_landmark_detection_module = importlib.import_module("bob.ip.facelandmarks");
+ except ImportError:
+ raise ImportError("No module named bob.ip.facelandmarks")
+
+ if not hasattr(face_landmark_detection_module, 'detect_landmarks'):
+ raise AttributeError("bob.ip.facelandmarks module has no attribute detect_landmarks")
+
+ key_points = face_landmark_detection_module.detect_landmarks(image, 1);
+
+ annotations = {}
+
+ try:
+ kp = key_points[0]
+ except:
+ kp = None
+
+ if kp is not None:
+
+ lm = np.vstack((kp.landmarks[:,1],kp.landmarks[:,0])).T
+
+ right_eye,left_eye = getEyePos(lm)
+
+ points = []
+
+ for i in range(lm.shape[0]):
+ points.append((int(lm[i,0]),int(lm[i,1])))
+
+ annotations['topleft'] = kp.bounding_box.topleft
+ annotations['bottomright'] = kp.bounding_box.bottomright
+ annotations['landmarks'] = points # list of landmarks
+ annotations['left_eye'] = left_eye
+ annotations['right_eye'] = right_eye
+
+
+ return annotations
+
+
+
+def detect_face_landmarks_in_video(frame_container, method = "dlib"):
+ """
+ This function detects a face and face landmarks in each farme of the input video.
+
+ **Parameters:**
+
+ ``frame_container`` : FrameContainer
+ FrameContainer containing the frames data.
+
+ ``method`` : :py:class:`str`
+ A package to be used for face detection. Options supported by this
+ package: "dlib" (dlib is a dependency of this package).
+
+ **Returns:**
+
+ ``annotations`` : :py:class:`dict`
+ A dictionary containing the annotations for each frame in the video.
+ Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
+ Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col), 'left_eye': (row, col), 'right_eye': (row, col), 'landmarks': [(row1,col1), (row2,col2), ...]}``
+ is the dictionary defining the coordinates of the face bounding box in frame N.
+ Where (rowK,colK) is the location of Kth facial landmark (K=0,...,67).
+ If no annotations found an empty dictionary is returned.
+ """
+
+ annotations = {}
+
+ for idx, frame in enumerate(frame_container):
+
+ image = frame[1]
+
+ frame_annotations = detect_face_landmarks_in_image(image, method);
+
+ if frame_annotations:
+
+ annotations[str(idx)] = frame_annotations
+
+ return annotations
diff --git a/requirements.txt b/requirements.txt
index 6692bce0eb1efa0062578f7755eea27928de0e94..9518917ce953e255d508bf20116ce3eac9e5f92c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,6 +13,7 @@ bob.io.image
bob.ip.color
bob.ip.qualitymeasure
bob.ip.dlib
+bob.ip.facelandmarks
bob.learn.libsvm
bob.learn.linear
scikit-learn