face_recognition常用方法

face_recognition.load_image_file 加载图像

import face_recognition之后直接调用face_recognition.load_image_file()读入图像，参数给文件名字符串，注意：jpg文件要和程序放在同一个文件夹下。输出图像是rgb格式（opencv中是bgr格式）。

import face_recognition
#加载图像文件
image = face_recognition.load_image_file("your_file.jpg")

face_recognition.face_locations

Returns an array of bounding boxes of human faces in a image

Parameters:
img – An image (as a numpy array)
number_of_times_to_upsample – How many times to upsample the image looking for faces. Higher numbers find smaller faces.
model – Which face detection model to use. “hog” is less accurate but faster on CPUs. “cnn” is a more accurate deep-learning model which is GPU/CUDA accelerated (if available). The default is “hog”.
Returns:
A list of tuples of found face locations in css (top, right, bottom, left) order

加载图像文件后直接调用face_recognition.face_locations(image)，能定位所有图像中识别出的人脸位置信息，返回值是列表形式，列表中每一行是一张人脸的位置信息，包括[top, right, bottom, left]，也可理解为每个人脸是一个tuple存储，分别代表框住人脸的矩形中左上角和右下角的坐标（x1,y1,x2,y2）。可遍历列表打印出每张脸的位置信息，也可以通过位置信息截出识别出的人脸的图像显示出来，代码如下：

#定位所有找到的脸的位置
face_locations = face_recognition.face_locations(image)
# 循环找到的所有人脸
for face_location in face_locations:
        # 打印每张脸的位置信息
        top, right, bottom, left = face_location
        print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
        # 指定人脸的位置信息，然后显示人脸图片
        face_image = image[top:bottom, left:right]
        pil_image = Image.fromarray(face_image)
        pil_image.show()

face_recognition.face_landmarks() 识别人脸关键点

加载图像后，调用face_recognition.face_landmarks(image)可识别出人脸关键点信息，包括眼睛、鼻子、嘴巴和下巴等，参数仍是加载的图像image，返回值是包含面部特征字典的列表，列表中每一项对应一张人脸，包括nose_bridge、right_eyebrow、right_eye、chine、left_eyebrow、bottom_lip、nose_tip、top_lip、left_eye几个部分，每个部分包含若干个特征点(x,y)，总共有68个特征点。列表长度就是图中识别出的人脸数，可遍历此列表和字典的键值对，打印出所有面部特征点，也可在图像上画出来，代码如下：

from PIL import Image, ImageDraw
face_landmarks_list = face_recognition.face_landmarks(image)
pil_image = Image.fromarray(image)
d = ImageDraw.Draw(pil_image)
for face_landmarks in face_landmarks_list:
    #face_landmarks_list中每个元素都包含以下key的字典
    #打印此图像中每个面部特征的位置
    facial_features = [
        'chin',
        'left_eyebrow',
        'right_eyebrow',
        'nose_bridge',
        'nose_tip',
        'left_eye',
        'right_eye',
        'top_lip',
        'bottom_lip'
    ]
    for facial_feature in facial_features:
        print("The {} in this face has the following points: {}".format(facial_feature, face_landmarks[facial_feature]))
    #在图像中画出每个人脸特征！
    for facial_feature in facial_features:
        d.line(face_landmarks[facial_feature], width=5)
pil_image.show