[cv001] python+opencv情绪识别

Let’s code to identify your emotions.
Coding识别你的喜怒哀乐。
                  ---《python情绪识别》.BY Andy

Emotions.png

一、原图

二、情绪识别结果

三、代码实现

'''
@filename:faceEmotions_image.py
'''
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
from skimage import io          #>pip install scikit-image,scipy

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")
    def learning_face(self):
        # 眉毛直线拟合数据缓冲
        line_brow_x = []
        line_brow_y = []
        im_rd = cv2.imread("p4.jpg")
        img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)
        faces = self.detector(img_gray, 0) # 检测到的人脸数
        # 待会要显示在屏幕上的字体
        font = cv2.FONT_HERSHEY_SIMPLEX
        if(len(faces)!=0):
            # 对每个人脸都标出68个特征点
            for k, d in enumerate(faces):
                cv2.rectangle(im_rd, (d.left(), d.top()),
                              (d.right(), d.bottom()), (0, 0, 255))
                cv2.rectangle(im_rd, (d.left()-10, d.top()-10),
                              (d.right()+10, d.bottom()+10), (0, 255, 0))
                # 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, 0.3,(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:
                        print(f'amazing-->[mouth_higth:{mouth_higth},eye_hight:{eye_hight},self.brow_k:{self.brow_k}]')
                        cv2.putText(im_rd, "amazing", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
                                    (0, 0, 255), 2, 4)
                    else:
                        print(f'happy-->[mouth_higth:{mouth_higth},eye_hight:{eye_hight},self.brow_k:{self.brow_k}]')
                        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 <= -0.2:# modify 0.3 as 0.2 by Andy
                        print(f'angry-->[mouth_higth:{mouth_higth},eye_hight:{eye_hight},self.brow_k:{self.brow_k}]')
                        cv2.putText(im_rd, "angry", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,(0, 0, 255), 2, 4)
                    else:
                        print(f'nature-->[mouth_higth:{mouth_higth},eye_hight:{eye_hight},self.brow_k:{self.brow_k}]')
                        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)
        # 窗口显示
        cv2.imshow("camera", im_rd)
        cv2.imwrite("Andy0"+".jpg", im_rd)
if __name__ == "__main__":
    my_face = face_emotion()
    my_face.learning_face()

#coding: utf-8
'''
@filename:faceEmotions_video.py
'''
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
from skimage import io          #>pip install scikit-image,scipy

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, 0.3,
                                        (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)
                        print("self:"+str(self))
                        # 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()

四、闲聊

[1].代码截止2019-04-21调试无误。
[2].需要全部代码及相关文件，留言邮箱。

[cv001] python+opencv情绪识别

一、原图

二、情绪识别结果

三、代码实现

四、闲聊

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