# Copyright 2015-2016 Carnegie Mellon University
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for dlib-based alignment."""
import cv2
import dlib
import numpy as np
TEMPLATE = np.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 = np.min(TEMPLATE, axis=0), np.max(TEMPLATE, axis=0)
MINMAX_TEMPLATE = (TEMPLATE - TPL_MIN) / (TPL_MAX - TPL_MIN)
[docs]class AlignDlib:
"""
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.
Normalized landmarks:
.. image:: ../images/dlib-landmark-mean.png
"""
#: Landmark indices.
INNER_EYES_AND_BOTTOM_LIP = [39, 42, 57]
OUTER_EYES_AND_NOSE = [36, 45, 33]
def __init__(self, facePredictor):
"""
Instantiate an 'AlignDlib' object.
:param facePredictor: The path to dlib's
:type facePredictor: str
"""
assert facePredictor is not None
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor(facePredictor)
[docs] def getAllFaceBoundingBoxes(self, rgbImg):
"""
Find all face bounding boxes in an image.
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:return: All face bounding boxes in an image.
:rtype: dlib.rectangles
"""
assert rgbImg is not None
try:
return self.detector(rgbImg, 1)
except Exception as e:
print("Warning: {}".format(e))
# In rare cases, exceptions are thrown.
return []
[docs] def getLargestFaceBoundingBox(self, rgbImg, skipMulti=False):
"""
Find the largest face bounding box in an image.
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:param skipMulti: Skip image if more than one face detected.
:type skipMulti: bool
:return: The largest face bounding box in an image, or None.
:rtype: dlib.rectangle
"""
assert rgbImg is not None
faces = self.getAllFaceBoundingBoxes(rgbImg)
if (not skipMulti and len(faces) > 0) or len(faces) == 1:
return max(faces, key=lambda rect: rect.width() * rect.height())
else:
return None
[docs] def findLandmarks(self, rgbImg, bb):
"""
Find the landmarks of a face.
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:param bb: Bounding box around the face to find landmarks for.
:type bb: dlib.rectangle
:return: Detected landmark locations.
:rtype: list of (x,y) tuples
"""
assert rgbImg is not None
assert bb is not None
points = self.predictor(rgbImg, bb)
return list(map(lambda p: (p.x, p.y), points.parts()))
[docs] def align(self, imgDim, rgbImg, bb=None,
landmarks=None, landmarkIndices=INNER_EYES_AND_BOTTOM_LIP,
skipMulti=False):
r"""align(imgDim, rgbImg, bb=None, landmarks=None, landmarkIndices=INNER_EYES_AND_BOTTOM_LIP)
Transform and align a face in an image.
:param imgDim: The edge length in pixels of the square the image is resized to.
:type imgDim: int
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:param bb: Bounding box around the face to align. \
Defaults to the largest face.
:type bb: dlib.rectangle
:param landmarks: Detected landmark locations. \
Landmarks found on `bb` if not provided.
:type landmarks: list of (x,y) tuples
:param landmarkIndices: The indices to transform to.
:type landmarkIndices: list of ints
:param skipMulti: Skip image if more than one face detected.
:type skipMulti: bool
:return: The aligned RGB image. Shape: (imgDim, imgDim, 3)
:rtype: numpy.ndarray
"""
assert imgDim is not None
assert rgbImg is not None
assert landmarkIndices is not None
if bb is None:
bb = self.getLargestFaceBoundingBox(rgbImg, skipMulti)
if bb is None:
return
if landmarks is None:
landmarks = self.findLandmarks(rgbImg, bb)
npLandmarks = np.float32(landmarks)
npLandmarkIndices = np.array(landmarkIndices)
H = cv2.getAffineTransform(npLandmarks[npLandmarkIndices],
imgDim * MINMAX_TEMPLATE[npLandmarkIndices])
thumbnail = cv2.warpAffine(rgbImg, H, (imgDim, imgDim))
return thumbnail