diff --git a/bob/pad/face/preprocessor/FaceCropAlign.py b/bob/pad/face/preprocessor/FaceCropAlign.py
index deed493601b6a581ef60a947ed36d980622628f3..7efeb31ea6afd60e6d638881fde4df65ef895aca 100644
--- a/bob/pad/face/preprocessor/FaceCropAlign.py
+++ b/bob/pad/face/preprocessor/FaceCropAlign.py
@@ -19,6 +19,9 @@ import bob.ip.base
import importlib
+import bob.bio.face
+
+
# ==============================================================================
def auto_norm_image(data, annotations, n_sigma=3.0, norm_method='MAD'):
@@ -76,6 +79,31 @@ def auto_norm_image(data, annotations, n_sigma=3.0, norm_method='MAD'):
return data_n
+def get_mouth_center(lm):
+ """
+ This function returns the location of mouth center
+
+ **Parameters:**
+
+ ``lm`` : :py:class:`numpy.ndarray`
+ A numpy array containing the coordinates of facial landmarks, (68X2)
+
+ **Returns:**
+
+
+ ``mouth_center``
+ A tuple containing the location of mouth center
+
+ """
+
+ # Mean position of eye corners as eye centers , casted to int()
+
+ mouth_center_t = (lm[48, :] + lm[54, :]) / 2.0
+
+ mouth_center = (int(mouth_center_t[1]), int(mouth_center_t[0]))
+
+ return mouth_center
+
# ==============================================================================
def get_eye_pos(lm):
@@ -108,6 +136,32 @@ def get_eye_pos(lm):
return right_eye, left_eye
+def get_eye_center(lm):
+ """
+ This function returns the location of eye_center, midpoint of left and right eye
+
+ **Parameters:**
+
+ ``lm`` : :py:class:`numpy.ndarray`
+ A numpy array containing the coordinates of facial landmarks, (68X2)
+
+ **Returns:**
+
+ ``eye_center``
+ A tuple containing the location of eye_center
+
+ """
+
+ # 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
+
+ eye_center = (int((left_eye_t[1]+right_eye_t[1])/2.0), int((left_eye_t[0]+right_eye_t[0])/2.0))
+
+ return eye_center
+
+
# ==============================================================================
def detect_face_landmarks_in_image(image, method="dlib"):
"""
@@ -213,7 +267,7 @@ def detect_face_landmarks_in_image(image, method="dlib"):
# ==========================================================================
def normalize_image_size_in_grayscale(image, annotations,
- face_size, use_face_alignment):
+ face_size, use_face_alignment,alignment_type='default'):
"""
This function crops the face in the input Gray-scale image given annotations
defining the face bounding box, and eye positions.
@@ -250,16 +304,56 @@ def normalize_image_size_in_grayscale(image, annotations,
if use_face_alignment:
- face_eyes_norm = bob.ip.base.FaceEyesNorm(
- eyes_distance=((face_size + 1) / 2.),
- crop_size=(face_size, face_size),
- eyes_center=(face_size / 4., (face_size - 0.5) / 2.))
- right_eye, left_eye = annotations['reye'], annotations['leye']
+ if alignment_type=='default':
+
+ face_eyes_norm = bob.ip.base.FaceEyesNorm(
+ eyes_distance=((face_size + 1) / 2.),
+ crop_size=(face_size, face_size),
+ eyes_center=(face_size / 4., (face_size - 0.5) / 2.))
+
+ right_eye, left_eye = annotations['reye'], annotations['leye']
+
+ normalized_image = face_eyes_norm( image, right_eye = right_eye, left_eye = left_eye )
+
+ normbbx=normalized_image.astype('uint8')
+
+ elif alignment_type=='lightcnn': # This option overrides the facesize argument
+
+ # This is the size of the image that this model expects
+
+ CROPPED_IMAGE_HEIGHT = 128
+ CROPPED_IMAGE_WIDTH = 128
+
+ # eye positions for frontal images
+ RIGHT_EYE_POS = (32, 44)
+ LEFT_EYE_POS = (32, 84)
+
+ EYE_CENTER_POS = (40, 64)
+ MOUTH_CENTER_POS = (88, 64)
+
- normalized_image = face_eyes_norm( image, right_eye = right_eye, left_eye = left_eye )
+ lm=np.array(annotations['landmarks'])
- normbbx=normalized_image.astype('uint8')
+ mouth_center=get_mouth_center(lm)
+
+ eye_center=get_eye_center(lm)
+
+ annotations['eye_center'] =eye_center
+
+ annotations['mouth_center']=mouth_center
+
+ light_cnn_face_cropper=bob.bio.face.preprocessor.FaceCrop(
+ cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
+ cropped_positions={'eye_center': EYE_CENTER_POS, 'mouth_center': MOUTH_CENTER_POS})
+
+
+ normalized_image = light_cnn_face_cropper( image, annotations=annotations)
+
+ normbbx=normalized_image.astype('uint8')
+
+ else:
+ print('The specified alignment method {} is not implemented!'.format(alignment_type))
else:
@@ -278,7 +372,7 @@ def normalize_image_size_in_grayscale(image, annotations,
# ==========================================================================
def normalize_image_size(image, annotations, face_size,
- rgb_output_flag, use_face_alignment):
+ rgb_output_flag, use_face_alignment,alignment_type='default'):
"""
This function crops the face in the input image given annotations defining
the face bounding box. The size of the face is also normalized to the
@@ -334,7 +428,7 @@ def normalize_image_size(image, annotations, face_size,
for image_channel in image: # for all color channels in the input image
cropped_face = normalize_image_size_in_grayscale(
- image_channel, annotations, face_size, use_face_alignment)
+ image_channel, annotations, face_size, use_face_alignment,alignment_type=alignment_type)
result.append(cropped_face)
@@ -388,6 +482,15 @@ class FaceCropAlign(Preprocessor):
using the facial landmarks detected locally.
Works only when ``face_detection_method is not None``.
+ ``alignment_type`` : :py:class:`str`
+ Specifies the alignment type to use if ``use_face_alignment`` is set to ``True``
+ Two methods are currently implemented:
+ ``default`` which would do alignment by making eyes
+ horizontally
+ ``lightcnn`` which aligns the face such that eye center are mouth centers are aligned to
+ predefined positions. This option overrides the face size option as the output required
+ is always 128x128. This is suitable for use with LightCNN model.
+
``max_image_size`` : :py:class:`int`
The maximum size of the image to be processed.
``max_image_size`` is only supported when
@@ -420,6 +523,7 @@ class FaceCropAlign(Preprocessor):
def __init__(self, face_size,
rgb_output_flag,
use_face_alignment,
+ alignment_type='default',
max_image_size=None,
face_detection_method=None,
min_face_size=None,
@@ -429,6 +533,7 @@ class FaceCropAlign(Preprocessor):
Preprocessor.__init__(self, face_size=face_size,
rgb_output_flag=rgb_output_flag,
use_face_alignment=use_face_alignment,
+ alignment_type=alignment_type,
max_image_size=max_image_size,
face_detection_method=face_detection_method,
min_face_size=min_face_size,
@@ -438,6 +543,7 @@ class FaceCropAlign(Preprocessor):
self.face_size = face_size
self.rgb_output_flag = rgb_output_flag
self.use_face_alignment = use_face_alignment
+ self.alignment_type=alignment_type
self.max_image_size = max_image_size
self.face_detection_method = face_detection_method
@@ -447,6 +553,17 @@ class FaceCropAlign(Preprocessor):
self.supported_face_detection_method = ["dlib", "mtcnn"]
+
+ self.supported_alignment_method = ["default", "lightcnn"]
+
+ if use_face_alignment:
+
+ if self.alignment_type not in self.supported_alignment_method:
+
+ raise ValueError('The alignment type {} is not supported'.format(self.alignment_type))
+
+
+
if self.face_detection_method is not None:
if self.face_detection_method not in self.supported_face_detection_method:
@@ -546,6 +663,7 @@ class FaceCropAlign(Preprocessor):
annotations=annotations,
face_size=self.face_size,
rgb_output_flag=self.rgb_output_flag,
- use_face_alignment=self.use_face_alignment)
+ use_face_alignment=self.use_face_alignment,
+ alignment_type=self.alignment_type)
return norm_face_image