1dlib获取关键点

https://www.jianshu.com/p/848014d8dea9

https://www.pyimagesearch.com/2017/05/01/install-dlib-raspberry-pi/

库下载

表情识别教程

https://www.cnblogs.com/qsyll0916/p/8893790.html

42 在Raspberry Pi上安装dlib表情识别
二人脸表情识别系统（含UI界面，python实现
dilb程序实现
三、安装依赖库
四、用pip3安装其他dlib运行依赖的库
五、正式安装
六、验证
七、把虚拟内存和GPU使用内存改回原始值

识别代码

https://gitee.com/Andrew_Qian/face/blob/master/from_video.py

依赖权重

https://github.com/AKSHAYUBHAT/TensorFace/blob/master/openface/models/dlib/shape_predictor_68_face_landmarks.dat

https://blog.csdn.net/qq_32892383/article/details/91347164

程序实现

面部表情跟踪的原理就是检测人脸特征点，根据特定的特征点可以对应到特定的器官，比如眼睛、鼻子、嘴巴、耳朵等等，以此来跟踪各个面部器官的动作。

https://blog.csdn.net/hongbin_xu/article/details/79926839

三、安装依赖库

dlib需要以下依赖：

Boost
Boost.Python
CMake
X11
安装方法：

$ sudo apt-get update
$ sudo apt-get install build-essential cmake libgtk-3-dev libboost-all-dev -y

四、用pip3安装其他dlib运行依赖的库

$ pip3 install numpy
$ pip3 install scipy
$ pip3 install scikit-image

五、正式安装

解压下载好的dlib，进入dlib目录后

$ sudo python3 setup.py install

这一步耗时是最长的了，耐心等待。

六、验证

$ python3
Python 3.4.2 (default, Oct 19 2014, 13:31:11) 
[GCC 4.9.1] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import dlib
>>>

七、把虚拟内存和GPU使用内存改回原始值

修改方法见“二、修改前的准备工作”

收工。

python代码

#!Anaconda/anaconda/python
#coding: utf-8

"""
从视屏中识别人脸，并实时标出面部特征点
"""

import dlib                     #人脸识别的库dlib
import numpy as np              #数据处理的库numpy
import cv2                      #图像处理的库OpenCv


class face_emotion():

    def __init__(self):
        # 使用特征提取器get_frontal_face_detector
        self.detector = dlib.get_frontal_face_detector()
        # dlib的68点模型，使用作者训练好的特征预测器
        self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

        #建cv2摄像头对象，这里使用电脑自带摄像头，如果接了外部摄像头，则自动切换到外部摄像头
        self.cap = cv2.VideoCapture(0)
        # 设置视频参数，propId设置的视频参数，value设置的参数值
        self.cap.set(3, 480)
        # 截图screenshoot的计数器
        self.cnt = 0


    def learning_face(self):

        # 眉毛直线拟合数据缓冲
        line_brow_x = []
        line_brow_y = []

        # cap.isOpened（） 返回true/false 检查初始化是否成功
        while(self.cap.isOpened()):

            # cap.read()
            # 返回两个值：
            #    一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾
            #    图像对象，图像的三维矩阵
            flag, im_rd = self.cap.read()

            # 每帧数据延时1ms，延时为0读取的是静态帧
            k = cv2.waitKey(1)

            # 取灰度
            img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)

            # 使用人脸检测器检测每一帧图像中的人脸。并返回人脸数rects
            faces = self.detector(img_gray, 0)

            # 待会要显示在屏幕上的字体
            font = cv2.FONT_HERSHEY_SIMPLEX

            # 如果检测到人脸
            if(len(faces)!=0):

                # 对每个人脸都标出68个特征点
                for i in range(len(faces)):
                    # enumerate方法同时返回数据对象的索引和数据，k为索引，d为faces中的对象
                    for k, d in enumerate(faces):
                        # 用红色矩形框出人脸
                        cv2.rectangle(im_rd, (d.left(), d.top()), (d.right(), d.bottom()), (0, 0, 255))
                        # 计算人脸热别框边长
                        self.face_width = d.right() - d.left()

                        # 使用预测器得到68点数据的坐标
                        shape = self.predictor(im_rd, d)
                        # 圆圈显示每个特征点
                        for i in range(68):
                            cv2.circle(im_rd, (shape.part(i).x, shape.part(i).y), 2, (0, 255, 0), -1, 8)
                            #cv2.putText(im_rd, str(i), (shape.part(i).x, shape.part(i).y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                            #            (255, 255, 255))

                        # 分析任意n点的位置关系来作为表情识别的依据
                        mouth_width = (shape.part(54).x - shape.part(48).x) / self.face_width  # 嘴巴咧开程度
                        mouth_higth = (shape.part(66).y - shape.part(62).y) / self.face_width  # 嘴巴张开程度
                        # print("嘴巴宽度与识别框宽度之比：",mouth_width_arv)
                        # print("嘴巴高度与识别框高度之比：",mouth_higth_arv)

                        # 通过两个眉毛上的10个特征点，分析挑眉程度和皱眉程度
                        brow_sum = 0  # 高度之和
                        frown_sum = 0  # 两边眉毛距离之和
                        for j in range(17, 21):
                            brow_sum += (shape.part(j).y - d.top()) + (shape.part(j + 5).y - d.top())
                            frown_sum += shape.part(j + 5).x - shape.part(j).x
                            line_brow_x.append(shape.part(j).x)
                            line_brow_y.append(shape.part(j).y)

                        # self.brow_k, self.brow_d = self.fit_slr(line_brow_x, line_brow_y)  # 计算眉毛的倾斜程度
                        tempx = np.array(line_brow_x)
                        tempy = np.array(line_brow_y)
                        z1 = np.polyfit(tempx, tempy, 1)  # 拟合成一次直线
                        self.brow_k = -round(z1[0], 3)  # 拟合出曲线的斜率和实际眉毛的倾斜方向是相反的

                        brow_hight = (brow_sum / 10) / self.face_width  # 眉毛高度占比
                        brow_width = (frown_sum / 5) / self.face_width  # 眉毛距离占比
                        # print("眉毛高度与识别框高度之比：",round(brow_arv/self.face_width,3))
                        # print("眉毛间距与识别框高度之比：",round(frown_arv/self.face_width,3))

                        # 眼睛睁开程度
                        eye_sum = (shape.part(41).y - shape.part(37).y + shape.part(40).y - shape.part(38).y +
                                   shape.part(47).y - shape.part(43).y + shape.part(46).y - shape.part(44).y)
                        eye_hight = (eye_sum / 4) / self.face_width
                        # print("眼睛睁开距离与识别框高度之比：",round(eye_open/self.face_width,3))

                        # 分情况讨论
                        # 张嘴，可能是开心或者惊讶
                        if round(mouth_higth >= 0.03):
                            if eye_hight >= 0.056:
                                cv2.putText(im_rd, "amazing", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
                                            (0, 0, 255), 2, 4)
                            else:
                                cv2.putText(im_rd, "happy", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
                                            (0, 0, 255), 2, 4)

                        # 没有张嘴，可能是正常和生气
                        else:
                            if self.brow_k <= -0.3:
                                cv2.putText(im_rd, "angry", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
                                            (0, 0, 255), 2, 4)
                            else:
                                cv2.putText(im_rd, "nature", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
                                            (0, 0, 255), 2, 4)

                # 标出人脸数
                cv2.putText(im_rd, "Faces: "+str(len(faces)), (20,50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
            else:
                # 没有检测到人脸
                cv2.putText(im_rd, "No Face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)

            # 添加说明
            im_rd = cv2.putText(im_rd, "S: screenshot", (20, 400), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
            im_rd = cv2.putText(im_rd, "Q: quit", (20, 450), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)

            # 按下s键截图保存
            if (k == ord('s')):
                self.cnt+=1
                cv2.imwrite("screenshoot"+str(self.cnt)+".jpg", im_rd)

            # 按下q键退出
            if(k == ord('q')):
                break

            # 窗口显示
            cv2.imshow("camera", im_rd)

        # 释放摄像头
        self.cap.release()

        # 删除建立的窗口
        cv2.destroyAllWindows()


if __name__ == "__main__":
    my_face = face_emotion()
    my_face.learning_face()

# *_*coding:utf-8 *_*
# author: 许鸿斌

import sys
import cv2
import dlib
import os
import logging
import datetime
import numpy as np

def cal_face_boundary(img, shape):
    for index_, pt in enumerate(shape.parts()):
        if index_ == 0:
            x_min = pt.x
            x_max = pt.x
            y_min = pt.y
            y_max = pt.y
        else:
            if pt.x < x_min:
                x_min = pt.x

            if pt.x > x_max:
                x_max = pt.x

            if pt.y < y_min:
                y_min = pt.y

            if pt.y > y_max:
                y_max = pt.y

    # print('x_min:{}'.format(x_min))
    # print('x_max:{}'.format(x_max))
    # print('y_min:{}'.format(y_min))
    # print('y_max:{}'.format(y_max))

    # 如果出现负值，即人脸位于图像框之外的情况，应当忽视图像外的部分，将负值置为0
    if x_min < 0:
        x_min = 0

    if y_min < 0:
        y_min = 0

    if x_min == x_max or y_min == y_max:
        return None
    else:
        return img[y_min:y_max, x_min:x_max]

def draw_left_eyebrow(img, shape):
    # 17 - 21
    pt_pos = []
    for index, pt in enumerate(shape.parts()[17:21 + 1]):
        pt_pos.append((pt.x, pt.y))

    for num in range(len(pt_pos)-1):
        cv2.line(img, pt_pos[num], pt_pos[num+1], 255, 2)


def draw_right_eyebrow(img, shape):
    # 22 - 26
    pt_pos = []
    for index, pt in enumerate(shape.parts()[22:26 + 1]):
        pt_pos.append((pt.x, pt.y))

    for num in range(len(pt_pos) - 1):
        cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)

def draw_left_eye(img, shape):
    # 36 - 41
    pt_pos = []
    for index, pt in enumerate(shape.parts()[36:41 + 1]):
        pt_pos.append((pt.x, pt.y))

    for num in range(len(pt_pos) - 1):
        cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)

    cv2.line(img, pt_pos[0], pt_pos[-1], 255, 2)

def draw_right_eye(img, shape):
    # 42 - 47
    pt_pos = []
    for index, pt in enumerate(shape.parts()[42:47 + 1]):
        pt_pos.append((pt.x, pt.y))

    for num in range(len(pt_pos) - 1):
        cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)

    cv2.line(img, pt_pos[0], pt_pos[-1], 255, 2)

def draw_nose(img, shape):
    # 27 - 35
    pt_pos = []
    for index, pt in enumerate(shape.parts()[27:35 + 1]):
        pt_pos.append((pt.x, pt.y))

    for num in range(len(pt_pos) - 1):
        cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)

    cv2.line(img, pt_pos[0], pt_pos[4], 255, 2)
    cv2.line(img, pt_pos[0], pt_pos[-1], 255, 2)
    cv2.line(img, pt_pos[3], pt_pos[-1], 255, 2)

def draw_mouth(img, shape):
    # 48 - 59
    pt_pos = []
    for index, pt in enumerate(shape.parts()[48:59 + 1]):
        pt_pos.append((pt.x, pt.y))

    for num in range(len(pt_pos) - 1):
        cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)

    cv2.line(img, pt_pos[0], pt_pos[-1], 255, 2)

    # 60 - 67
    pt_pos = []
    for index, pt in enumerate(shape.parts()[60:]):
        pt_pos.append((pt.x, pt.y))

    for num in range(len(pt_pos) - 1):
        cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)

    cv2.line(img, pt_pos[0], pt_pos[-1], 255, 2)



def draw_jaw(img, shape):
    # 0 - 16
    pt_pos = []
    for index, pt in enumerate(shape.parts()[0:16 + 1]):
        pt_pos.append((pt.x, pt.y))

    for num in range(len(pt_pos) - 1):
        cv2.line(img, pt_pos[num], pt_pos[num + 1], 255, 2)

# 获取logger实例，如果参数为空则返回root logger
logger = logging.getLogger("PedestranDetect")
# 指定logger输出格式
formatter = logging.Formatter('%(asctime)s %(levelname)-8s: %(message)s')
# 文件日志
# file_handler = logging.FileHandler("test.log")
# file_handler.setFormatter(formatter)  # 可以通过setFormatter指定输出格式
# 控制台日志
console_handler = logging.StreamHandler(sys.stdout)
console_handler.formatter = formatter  # 也可以直接给formatter赋值
# 为logger添加的日志处理器
# logger.addHandler(file_handler)
logger.addHandler(console_handler)
# 指定日志的最低输出级别，默认为WARN级别
logger.setLevel(logging.INFO)


pwd = os.getcwd()
predictor_path = os.path.join(pwd, 'shape_predictor_68_face_landmarks.dat')

logger.info(u'导入人脸检测器')
detector = dlib.get_frontal_face_detector()
logger.info(u'导入人脸特征点检测器')
predictor = dlib.shape_predictor(predictor_path)

cap = cv2.VideoCapture(0)
cnt = 0
total_time = 0
start_time = 0
while(1):

    ret, frame = cap.read()
    # cv2.imshow("window", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

    img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    dets = detector(img, 1)
    if dets:
        logger.info('Face detected')
    else:
        logger.info('No face detected')
    for index, face in enumerate(dets):
        # print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(),
        #                                                              face.bottom()))
        shape = predictor(img, face)

        # for index_, pt in enumerate(shape.parts()):
        #     pt_pos = (pt.x, pt.y)
        #     cv2.circle(frame, pt_pos, 2, (255, 0, 0), 1)

        features = np.zeros(img.shape[0:-1], dtype=np.uint8)
        for index_, pt in enumerate(shape.parts()):
            pt_pos = (pt.x, pt.y)
            cv2.circle(features, pt_pos, 2, 255, 1)

        draw_left_eyebrow(features, shape)
        draw_right_eyebrow(features, shape)
        draw_left_eye(features, shape)
        draw_right_eye(features, shape)
        draw_nose(features, shape)
        draw_mouth(features, shape)
        draw_jaw(features, shape)

        logger.info('face shape: {} {}'.format(face.right()-face.left(), face.bottom()-face.top()))
        faceROI = cal_face_boundary(features, shape)
        logger.info('ROI shape: {}'.format(faceROI.shape))
        # faceROI = features[face.top():face.bottom(), face.left():face.right()]
        faceROI = cv2.resize(faceROI, (500, 500), interpolation=cv2.INTER_LINEAR)
        # logger.info('face {}'.format(index))
        cv2.imshow('face {}'.format(index), faceROI)

    if cnt == 0:
        start_time = datetime.datetime.now()
        cnt += 1
    elif cnt == 100:
        end_time = datetime.datetime.now()
        frame_rate = float(100) / (end_time-start_time).seconds
        # logger.info(start_time)
        # logger.info(end_time)
        logger.info(u'帧率：{:.2f}fps'.format(frame_rate))
        cnt = 0
    else:
        cnt += 1

    # logger.info(cnt)

42 在Raspberry Pi上安装dlib表情识别 二人脸表情识别系统（含UI界面，python实现 dilb程序实现 三、安装依赖库 四、用pip3安装其他dlib运行依赖的库 五、正式安装 六、验证 七、把虚拟内存和GPU使用内存改回原始值