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()

调用face_recognition.face_landmarks()方法即可得到人脸特征点, 返回一个字典,包括chin(下巴), left_eye(左眼)等.

face_encodings 获取图像文件中所有面部编码

face_recognition.face_encodings(image)方法可获取每个图像文件中每个面部的面部编码,参数仍是加载的图像image，由于每个图像中可能有多个脸，所以返回的是一个编码列表，后续访问时注意加上索引号或者依次遍历。
由打印可看出每张人脸是一个128维的向量。

face_encodings = face_recognition.face_encodings(image)
for face_encoding in face_encodings:
    print("face_encoding len = {} \nencoding:{}\n\n".format(len(face_encoding),face_encoding))

Image.fromarray

array转换成image
简而言之，就是实现array到image的转换

compare_faces 由面部编码匹配脸

用face_recognition.compare_faces()方法可匹配两个面部特征编码，利用两个向量的内积来衡量相似度，根据阈值确认是否是同一人脸。
位置参数：第一个参数给出一个面部编码列表（很多张脸），第二个参数给出单个面部编码（一张脸），compare_faces方法会将第二个参数的编码与第一个参数中的编码依次匹配，返回值是一个布尔值列表，匹配成功的脸返回True，匹配失败的返回False，顺序与第一个参数中脸的顺序一致。
默认参数：tolerance=0.6,可根据自己需求更改，tolerance越小匹配越严格，例如
matches ==face_recognition.compare_faces(known_face_encodings,face_encoding,tolerance=0.39)，
将阈值改为了0.39,阈值太低容易造成无法成功识别人脸，太高容易造成人脸识别混淆，这个是根据我自己测试后慢慢试出来的一个值。

matche_result = face_recognition.compare_faces(known_faces, face_encoding[0],tolerance=0.39)[0]

实践:
demo1：识别人脸特征并打印出来

from PIL import Image, ImageDraw
import face_recognition

# 将jpg文件加载到numpy 数组中
image = face_recognition.load_image_file("view_Mr_G.jpg")

#查找图像中所有面部的所有面部特征
face_landmarks_list = face_recognition.face_landmarks(image)

print("I found {} face(s) in this photograph.".format(len(face_landmarks_list)))
pil_image = Image.fromarray(image)
d = ImageDraw.Draw(pil_image)
for face_landmarks in face_landmarks_list:
    #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()

demo2 : 识别图片中的所有人脸并显示

from PIL import Image
import face_recognition

# 将jpg文件加载到numpy 数组中
image = face_recognition.load_image_file("view_Mr_G.jpg")

# 使用默认的给予HOG模型查找图像中所有人脸
# 这个方法已经相当准确了，但还是不如CNN模型那么准确，因为没有使用GPU加速
# 另请参见: find_faces_in_picture_cnn.py
face_locations = face_recognition.face_locations(image)

# 使用CNN模型
# face_locations = face_recognition.face_locations(image, number_of_times_to_upsample=0, model="cnn")

# 打印：我从图片中找到了 多少 张人脸
print("I found {} face(s) in this photograph.".format(len(face_locations)))

# 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()

demo3 : 显示未知图片中已知人物的脸

import face_recognition
from PIL import Image, ImageDraw
import cv2
PATH = "/Users/tanyashi/Python/python project/face_recognition/一起同过窗"
VIEW_PIC_NAME="view2.jpg"
UNKNOWN_IMAGE="view_Mr_G.jpg"
#将jpg文件加载到numpy数组中
view_image = face_recognition.load_image_file(VIEW_PIC_NAME)
#要识别的图片
unknown_image = face_recognition.load_image_file(UNKNOWN_IMAGE)
results = []
#获取每个图像文件中每个面部的面部编码
#由于每个图像中可能有多个面，所以返回一个编码列表。
#但是由于我知道每个图像只有一个脸，我只关心每个图像中的第一个编码，所以我取索引0。
view_face_encoding = face_recognition.face_encodings(view_image)[0]
print("view_face_encoding:{}\n\n".format(view_face_encoding))
known_faces = [view_face_encoding]
face_locations = face_recognition.face_locations(unknown_image)
print('got {} face(s) in {}:'.format(len(face_locations), UNKNOWN_IMAGE))
for face_location in face_locations:
    top, right, bottom, left = face_location
    #print(top, right, bottom, left)
    face_image = unknown_image[top:bottom, left:right]
    face_encoding = face_recognition.face_encodings(face_image)
    if face_encoding:
        result = {}
        result['face_encoding'] = face_encoding
        result['is_view'] = face_recognition.compare_faces(known_faces, face_encoding[0])[0]
        result['location'] = face_location
        result['face_id'] = face_locations.index(face_location)
        results.append(result)
        if result['is_view']:
            print('face {} is view!!'.format(result['face_id']+1))
            #print(top, right, bottom, left)
#print("results :{}".format([i['is_view'] for i in results]))
print("please find out view in this image:")
view_face_location = [i['location'] for i in results if i['is_view']]
if view_face_location:
    top, right, bottom, left = view_face_location[0]
    #print(top, right, bottom, left)
    face_image = unknown_image[top:bottom, left:right]
    pil_image = Image.fromarray(face_image)
    pil_image.show()
else:
    print('view is not in this pic!')

demo4 : 摄像头头像识别

import face_recognition
import cv2

video_capture = cv2.VideoCapture(0)

# 本地图像
chenduling_image = face_recognition.load_image_file("chenduling.jpg")
chenduling_face_encoding = face_recognition.face_encodings(chenduling_image)[0]

# 本地图像二
sunyizheng_image = face_recognition.load_image_file("sunyizheng.jpg")
sunyizheng_face_encoding = face_recognition.face_encodings(sunyizheng_image)[0]

# 本地图片三
zhangzetian_image = face_recognition.load_image_file("zhangzetian.jpg")
zhangzetian_face_encoding = face_recognition.face_encodings(zhangzetian_image)[0]

# Create arrays of known face encodings and their names
# 脸部特征数据的集合
known_face_encodings = [
    chenduling_face_encoding,
    sunyizheng_face_encoding,
    zhangzetian_face_encoding
]

# 人物名称的集合
known_face_names = [
    "michong",
    "sunyizheng",
    "chenduling"
]

face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

while True:
    # 读取摄像头画面
    ret, frame = video_capture.read()

    # 改变摄像头图像的大小，图像小，所做的计算就少
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    # opencv的图像是BGR格式的，而我们需要是的RGB格式的，因此需要进行一个转换。
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:
        # 根据encoding来判断是不是同一个人，是就输出true，不是为flase
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        face_names = []
        for face_encoding in face_encodings:
            # 默认为unknown
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "Unknown"

            # if match[0]:
            #     name = "michong"
            # If a match was found in known_face_encodings, just use the first one.
            if True in matches:
                first_match_index = matches.index(True)
                name = known_face_names[first_match_index]
            face_names.append(name)

    process_this_frame = not process_this_frame

    # 将捕捉到的人脸显示出来
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # 矩形框
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        #加上标签
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    # Display
    cv2.imshow('monitor', frame)

    # 按Q退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

video_capture.release()
cv2.destroyAllWindows()

PIL image转换成array

img = np.asarray(image)
需要注意的是，如果出现read-only错误，并不是转换的错误，一般是你读取的图片的时候，默认选择的是"r","rb"模式有关。

修正的办法:　手动修改图片的读取状态

img.flags.writeable = True # 将数组改为读写模式

ImageDraw

模块提供了图像对象的简单2D绘制。用户可以使用这个模块创建新的图像，注释或润饰已存在图像，为web应用实时产生各种图形。

1、Draw

定义：Draw(image) ⇒ Draw instance

含义：创建一个可以在给定图像上绘图的对象。

（IronPIL）用户可以使用ImageWin模块的HWND或者HDC对象来代替图像。这个允许用户直接在屏幕上绘图。

注意：图像内容将会被修改。