Alignment added,WIP

bob/pad/face/preprocessor/ImageFaceCrop.py

@@ -42,16 +42,17 @@ class ImageFaceCrop(Preprocessor):
     """
 
     #==========================================================================
-    def __init__(self, face_size, rgb_output_flag=False):
+    def __init__(self, face_size, rgb_output_flag=False,use_face_alignment=False):
 
         Preprocessor.__init__(
-            self, face_size=face_size, rgb_output_flag=rgb_output_flag)
+            self, face_size=face_size, rgb_output_flag=rgb_output_flag,use_face_alignment=use_face_alignment)
 
         self.face_size = face_size
         self.rgb_output_flag = rgb_output_flag
+        self.use_face_alignment=use_face_alignment
 
     #==========================================================================
-    def normalize_image_size_in_grayscale(self, image, annotations, face_size):
+    def normalize_image_size_in_grayscale(self, image, annotations, face_size,use_face_alignment):
         """
         This function crops the face in the input Gray-scale image given annotations
         defining the face bounding box. The size of the face is also normalized to the
@@ -78,21 +79,33 @@ class ImageFaceCrop(Preprocessor):
             An image of the cropped face of the size (self.face_size, self.face_size).
         """
 
-        cutframe = image[annotations['topleft'][0]:annotations['bottomright'][
-            0], annotations['topleft'][1]:annotations['bottomright'][1]]
 
-        tempbbx = np.ndarray((face_size, face_size), 'float64')
-        normbbx = np.ndarray((face_size, face_size), 'uint8')
-        bob.ip.base.scale(cutframe, tempbbx)  # normalization
-        tempbbx_ = tempbbx + 0.5
-        tempbbx_ = np.floor(tempbbx_)
-        normbbx = np.cast['uint8'](tempbbx_)
+        if not use_face_alignment:
+
+            cutframe = image[annotations['topleft'][0]:annotations['bottomright'][
+                0], annotations['topleft'][1]:annotations['bottomright'][1]]
+
+            tempbbx = np.ndarray((face_size, face_size), 'float64')
+            normbbx = np.ndarray((face_size, face_size), 'uint8')
+            bob.ip.base.scale(cutframe, tempbbx)  # normalization
+            tempbbx_ = tempbbx + 0.5
+            tempbbx_ = np.floor(tempbbx_)
+            normbbx = np.cast['uint8'](tempbbx_)
+        else:
+            face_eyes_norm = bob.ip.base.FaceEyesNorm(eyes_distance = 32.5, crop_size = (face_size, face_size), eyes_center = (16, 31.75)) # Add more params, 
+            right_eye,left_eye=annotations['right_eye'],annotations['left_eye']
+
+
+            normalized_image = face_eyes_norm( image, right_eye = right_eye, left_eye = left_eye )
+            
+            normbbx=normalized_image.astype('uint8')
+
 
         return normbbx
 
     #==========================================================================
     def normalize_image_size(self, image, annotations, face_size,
-                             rgb_output_flag):
+                             rgb_output_flag,use_face_alignment):
         """
         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
@@ -140,7 +153,7 @@ class ImageFaceCrop(Preprocessor):
         for image_channel in image:  # for all color channels in the input image
 
             cropped_face = self.normalize_image_size_in_grayscale(
-                image_channel, annotations, face_size)
+                image_channel, annotations, face_size,use_face_alignment)
 
             result.append(cropped_face)
 
@@ -172,6 +185,6 @@ class ImageFaceCrop(Preprocessor):
         """
 
         norm_face_image = self.normalize_image_size(
-            image, annotations, self.face_size, self.rgb_output_flag)
+            image, annotations, self.face_size, self.rgb_output_flag,self.use_face_alignment)
 
         return norm_face_image

bob/pad/face/preprocessor/VideoFaceCrop.py

 #!/usr/bin/env python2
 # -*- coding: utf-8 -*-
 """
-Created on Fri May 12 14:14:23 2017
+Created on Thu Jan 18 12:03:12 2018
 
-@author: Olegs Nikisins
+@author: Anjith George
 """
 #==============================================================================
 # Import what is needed here:
@@ -18,7 +18,9 @@ import numpy as np
 
 from bob.pad.face.preprocessor.ImageFaceCrop import ImageFaceCrop
 
-from ..utils.face_detection_utils import detect_faces_in_video
+
+#TODO: Only one is needed
+from ..utils.face_detection_utils import detect_faces_in_video, detect_face_landmarks_in_video
 
 #==============================================================================
 # Main body:
@@ -91,6 +93,11 @@ class VideoFaceCrop(Preprocessor, object):
         is installed in your system you can use it as-well (bob.ip.mtcnn is NOT
         a dependency of this package).
 
+    ``use_face_alignment`` : :py:class:`bool`
+        If set to ``True`` the face will be aligned aligned using the facial landmarks 
+        detected locally. Works only when 'detect_faces_flag==True' and use_local_cropper_flag==True
+        Default: ``False``.
+
     ``kwargs``
         Remaining keyword parameters passed to the Base constructor, such as ``color_channel`` or ``dtype``.
     """
@@ -105,10 +112,11 @@ class VideoFaceCrop(Preprocessor, object):
                  mask_seed=None,
                  check_face_size_flag=False,
                  min_face_size=50,
-                 use_local_cropper_flag=False,
-                 rgb_output_flag=False,
-                 detect_faces_flag=False,
+                 use_local_cropper_flag=False,#TODO
+                 rgb_output_flag=False, #TODO: Needs RGB output->config
+                 detect_faces_flag=False, #TODO
                  face_detection_method="dlib",
+                 use_face_alignment=False, #TODO: uses aligncrop if true
                  **kwargs):
 
         super(VideoFaceCrop, self).__init__(
@@ -124,6 +132,7 @@ class VideoFaceCrop(Preprocessor, object):
             rgb_output_flag=rgb_output_flag,
             detect_faces_flag=detect_faces_flag,
             face_detection_method=face_detection_method,
+            use_face_alignment=use_face_alignment,
             **kwargs)
 
         self.cropped_image_size = cropped_image_size
@@ -138,6 +147,7 @@ class VideoFaceCrop(Preprocessor, object):
         self.rgb_output_flag = rgb_output_flag
         self.detect_faces_flag = detect_faces_flag
         self.face_detection_method = face_detection_method
+        self.use_face_alignment=use_face_alignment
 
         # Save also the data stored in the kwargs:
         for (k, v) in kwargs.items():
@@ -145,9 +155,10 @@ class VideoFaceCrop(Preprocessor, object):
 
         if self.use_local_cropper_flag:
 
+
             preprocessor = ImageFaceCrop(
-                face_size=self.cropped_image_size[0],
-                rgb_output_flag=self.rgb_output_flag)
+                face_size=self.cropped_image_size[0],rgb_output_flag=self.rgb_output_flag, use_face_alignment=self.use_face_alignment)
+            
 
         else:
 
@@ -303,8 +314,8 @@ class VideoFaceCrop(Preprocessor, object):
 
         if self.detect_faces_flag:
 
-            annotations = detect_faces_in_video(frames,
-                                                self.face_detection_method)
+            annotations = detect_face_landmarks_in_video(frames,
+                                                self.face_detection_method) #TODO: new dicts
 
         if len(frames) != len(annotations):  # if some annotations are missing