dlib实现人脸对齐方法

操作步骤

1. 人脸检测
2. 人脸关键点检测
3. 人脸对齐

关键类与接口方法定义

类

1. 人脸检测类 : dlib.fhog_object_detector和 dlib.cnn_face_detection_model_v1,前者基于HOG模型，后者基于CNN模型，前者检测方法调用为__call(img)__ ->dlib.rectangles和run(img,upsample_num,threshold)->(dlib.rectangles,List[scores:int],List[int]),后者检测方法调用为__call(img)__->dlib.mmod_rectangles
2. 关键点检测类: dlib.shape_predictor,预测调用方法__call__(self,image, box: dlib.rectangle)->dlib.full_object_detection
3. 检测结果类 : dlib.full_object_detection， 常用方法 part(self, idx: int)->dlib.point 单个关键点信息 、parts(self)->dlib.points 所有的关键点信息
4. 关键点类：dlib.point关键点，成员包含x，y，dlib.points关键点列表

方法

1. 获取单个人脸对齐结果

   padding参数可调整图像返回值的尺寸大小

get_face_chip(img: numpy.ndarray[(rows,cols,3),uint8], face: _dlib_pybind11.full_object_detection, size: int=150, padding: float=0.25) -> numpy.ndarray[(rows,cols,3),uint8]

Takes an image and a full_object_detection that references a face in that image and returns the face as a Numpy array representing the image.  The face will be rotated upright and scaled to 150x150 pixels or with the optional specified size and padding.

2. 获取多个人脸对齐结果

get_face_chips(img: numpy.ndarray[(rows,cols,3),uint8], faces: _dlib_pybind11.full_object_detections, size: int=150, padding: float=0.25)->list
  
  Takes an image and a full_object_detections object that reference faces in that image and returns the faces as a list of Numpy arrays representing the image.  The faces will be rotated upright and scaled to 150x150 pixels or with the optional specified size and padding.
  

示例代码

import dlib
import numpy as np

from cv2 import cv2

# step 1. create face detect and shape predict model
face_detector_model_path = '../models/mmod_human_face_detector.dat'
face_detector = dlib.cnn_face_detection_model_v1(face_detector_model_path)  # dlib.cnn_face_detection_model_v1
shape_model_path = r'../models/shape_predictor_68_face_landmarks.dat'
face_shape_predictor = dlib.shape_predictor(shape_model_path)  # dlib.shape_predictor

# step 2. get all face detections
image_path = "sample.jpg"
img = dlib.load_rgb_image(image_path)
detections = face_detector(img, 1)  # dlib.mmod_rectangles

# step 3. fetch one of all face detections, and if it's mmod_rectangle , convert to rectabgle
detection = detections[0]  # dlib.mmod_rectangle
# the mmod_rectangle contains two parts : confidence and rect

# step 4. get one face shape and return a single object name full_object_detection
shape = face_shape_predictor(img, detection.rect)  # dlib.full_object_detection

# step 5. get one aligned face 
image = dlib.get_face_chip(img, shape, size=150, padding=0.45) # numpy.ndarray

# Optionally: get all the aligned faces that was detected in the first place
faces = [face.rect for face in detections]
# Get the aligned face images
# faces = dlib.full_object_detections()
# faces.append(shape)
# Optionally:
images = dlib.get_face_chips(img, faces, size=160, padding=0.25)