Added face detection option to VideoFaceCrop preprocessor + unit tests for IQM-GMM algo.

bob/pad/face/config/preprocessor/video_face_crop.py

+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+
+from bob.pad.face.preprocessor import VideoFaceCrop
+
+#=======================================================================================
+# Define instances here:
+
+CROPPED_IMAGE_SIZE = (64, 64) # The size of the resulting face
+CROPPED_POSITIONS = {'topleft' : (0,0) , 'bottomright' : CROPPED_IMAGE_SIZE}
+FIXED_POSITIONS = None
+MASK_SIGMA = None             # The sigma for random values areas outside image
+MASK_NEIGHBORS = 5            # The number of neighbors to consider while extrapolating
+MASK_SEED = None              # The seed for generating random values during extrapolation
+CHECK_FACE_SIZE_FLAG = True   # Check the size of the face
+MIN_FACE_SIZE = 50
+USE_LOCAL_CROPPER_FLAG = True # Use the local face cropping class (identical to Ivana's paper)
+RGB_OUTPUT_FLAG = True        # Return RGB cropped face using local cropper
+DETECT_FACES_FLAG = True      # find annotations locally replacing the database annotations
+
+preprocessor_rgb_face_detect = VideoFaceCrop(cropped_image_size = CROPPED_IMAGE_SIZE,
+                                             cropped_positions = CROPPED_POSITIONS,
+                                             fixed_positions = FIXED_POSITIONS,
+                                             mask_sigma = MASK_SIGMA,
+                                             mask_neighbors = MASK_NEIGHBORS,
+                                             mask_seed = None,
+                                             check_face_size_flag = CHECK_FACE_SIZE_FLAG,
+                                             min_face_size = MIN_FACE_SIZE,
+                                             use_local_cropper_flag = USE_LOCAL_CROPPER_FLAG,
+                                             rgb_output_flag = RGB_OUTPUT_FLAG,
+                                             detect_faces_flag = DETECT_FACES_FLAG)