#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
# @date: Fri 17 Jun 2016 10:41:36 CEST
import os
import bob.core
logger = bob.core.log.setup("bob.ip.dlib")
bob.core.log.set_verbosity_level(logger, 3)
import dlib
from .utils import bounding_box_2_rectangle, bob_to_dlib_image_convertion
import cv2
import numpy
from .DlibLandmarkExtraction import DlibLandmarkExtraction
from .utils import bob_to_dlib_image_convertion, dlib_to_bob_image_convertion
from .FaceDetector import FaceDetector
TEMPLATE = numpy.float32([
(0.0792396913815, 0.339223741112), (0.0829219487236, 0.456955367943),
(0.0967927109165, 0.575648016728), (0.122141515615, 0.691921601066),
(0.168687863544, 0.800341263616), (0.239789390707, 0.895732504778),
(0.325662452515, 0.977068762493), (0.422318282013, 1.04329000149),
(0.531777802068, 1.06080371126), (0.641296298053, 1.03981924107),
(0.738105872266, 0.972268833998), (0.824444363295, 0.889624082279),
(0.894792677532, 0.792494155836), (0.939395486253, 0.681546643421),
(0.96111933829, 0.562238253072), (0.970579841181, 0.441758925744),
(0.971193274221, 0.322118743967), (0.163846223133, 0.249151738053),
(0.21780354657, 0.204255863861), (0.291299351124, 0.192367318323),
(0.367460241458, 0.203582210627), (0.4392945113, 0.233135599851),
(0.586445962425, 0.228141644834), (0.660152671635, 0.195923841854),
(0.737466449096, 0.182360984545), (0.813236546239, 0.192828009114),
(0.8707571886, 0.235293377042), (0.51534533827, 0.31863546193),
(0.516221448289, 0.396200446263), (0.517118861835, 0.473797687758),
(0.51816430343, 0.553157797772), (0.433701156035, 0.604054457668),
(0.475501237769, 0.62076344024), (0.520712933176, 0.634268222208),
(0.565874114041, 0.618796581487), (0.607054002672, 0.60157671656),
(0.252418718401, 0.331052263829), (0.298663015648, 0.302646354002),
(0.355749724218, 0.303020650651), (0.403718978315, 0.33867711083),
(0.352507175597, 0.349987615384), (0.296791759886, 0.350478978225),
(0.631326076346, 0.334136672344), (0.679073381078, 0.29645404267),
(0.73597236153, 0.294721285802), (0.782865376271, 0.321305281656),
(0.740312274764, 0.341849376713), (0.68499850091, 0.343734332172),
(0.353167761422, 0.746189164237), (0.414587777921, 0.719053835073),
(0.477677654595, 0.706835892494), (0.522732900812, 0.717092275768),
(0.569832064287, 0.705414478982), (0.635195811927, 0.71565572516),
(0.69951672331, 0.739419187253), (0.639447159575, 0.805236879972),
(0.576410514055, 0.835436670169), (0.525398405766, 0.841706377792),
(0.47641545769, 0.837505914975), (0.41379548902, 0.810045601727),
(0.380084785646, 0.749979603086), (0.477955996282, 0.74513234612),
(0.523389793327, 0.748924302636), (0.571057789237, 0.74332894691),
(0.672409137852, 0.744177032192), (0.572539621444, 0.776609286626),
(0.5240106503, 0.783370783245), (0.477561227414, 0.778476346951)])
TPL_MIN, TPL_MAX = numpy.min(TEMPLATE, axis=0), numpy.max(TEMPLATE, axis=0)
MINMAX_TEMPLATE = (TEMPLATE - TPL_MIN) / (TPL_MAX - TPL_MIN)
[docs]class AlignDLib(object):
"""
Use `dlib's landmark estimation <http://blog.dlib.net/2014/08/real-time-face-pose-estimation.html>`_ to align faces.
The alignment preprocess faces for input into a neural network.
Faces are resized to the same size (such as 96x96) and transformed
to make landmarks (such as the eyes and nose) appear at the same
location on every image.
Code copied from here
https://raw.githubusercontent.com/cmusatyalab/openface/master/openface/align_dlib.py
"""
def __init__(self):
"""
"""
self.inner_eyes_and_bottom_lip = [39, 42, 57]
self.outer_eyes_and_nose = [36, 45, 33]
self.landmark_extractor = DlibLandmarkExtraction()
self.face_detector = FaceDetector()
def __call__(self, image, bb=None, landmark_indices=[39, 42, 57], image_size=(224, 224)):
# Detecting the face if the bounding box is not passed
if bb is None:
bb = self.face_detector.detect_single_face(image)[0]
if bb is None:
return None
landmarks = numpy.float32(self.landmark_extractor(image, bb=bb, xy_output=True))
landmark_indices = numpy.array(landmark_indices)
H = cv2.getAffineTransform(landmarks[landmark_indices],
image_size[0] * MINMAX_TEMPLATE[landmark_indices])
return dlib_to_bob_image_convertion(cv2.warpAffine(bob_to_dlib_image_convertion(image), H, image_size))