人脸表情识别

• 一、图片预处理
• 二、数据集划分
• 三、识别笑脸
• 四、Dlib提取人脸特征识别笑脸和非笑脸
• 参考

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环境搭建可查看Python人脸识别微笑检测

数据集可在https://inc.ucsd.edu/mplab/wordpress/index.html%3Fp=398.html获取

数据如下：

一、图片预处理

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 import os# dlib预测器 detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')# 读取图像的路径 path_read = ".\ImageFiles\\files" num=0 for file_name in os.listdir(path_read):#aa是图片的全路径aa=(path_read +"/"+file_name)#读入的图片的路径中含非英文img=cv2.imdecode(np.fromfile(aa, dtype=np.uint8), cv2.IMREAD_UNCHANGED)#获取图片的宽高img_shape=img.shapeimg_height=img_shape[0]img_width=img_shape[1]# 用来存储生成的单张人脸的路径path_save=".\ImageFiles\\files1" # dlib检测dets = detector(img,1)print("人脸数：", len(dets))for k, d in enumerate(dets):if len(dets)>1:continuenum=num+1# 计算矩形大小# (x,y), (宽度width, 高度height)pos_start = tuple([d.left(), d.top()])pos_end = tuple([d.right(), d.bottom()])# 计算矩形框大小height = d.bottom()-d.top()width = d.right()-d.left()# 根据人脸大小生成空的图像img_blank = np.zeros((height, width, 3), np.uint8)for i in range(height):if d.top()+i>=img_height:# 防止越界continuefor j in range(width):if d.left()+j>=img_width:# 防止越界continueimg_blank[i][j] = img[d.top()+i][d.left()+j]img_blank = cv2.resize(img_blank, (200, 200), interpolation=cv2.INTER_CUBIC)cv2.imencode('.jpg', img_blank)[1].tofile(path_save+"\\"+"file"+str(num)+".jpg") # 正确方法

运行结果：

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二、数据集划分

import os, shutil # 原始数据集路径 original_dataset_dir = '.\ImageFiles\\files1'# 新的数据集 base_dir = '.\ImageFiles\\files2' os.mkdir(base_dir)# 训练图像、验证图像、测试图像的目录 train_dir = os.path.join(base_dir, 'train') os.mkdir(train_dir) validation_dir = os.path.join(base_dir, 'validation') os.mkdir(validation_dir) test_dir = os.path.join(base_dir, 'test') os.mkdir(test_dir)train_cats_dir = os.path.join(train_dir, 'smile') os.mkdir(train_cats_dir)train_dogs_dir = os.path.join(train_dir, 'unsmile') os.mkdir(train_dogs_dir)validation_cats_dir = os.path.join(validation_dir, 'smile') os.mkdir(validation_cats_dir)validation_dogs_dir = os.path.join(validation_dir, 'unsmile') os.mkdir(validation_dogs_dir)test_cats_dir = os.path.join(test_dir, 'smile') os.mkdir(test_cats_dir)test_dogs_dir = os.path.join(test_dir, 'unsmile') os.mkdir(test_dogs_dir)# 复制1000张笑脸图片到train_c_dir fnames = ['file{}.jpg'.format(i) for i in range(1,900)] for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(train_cats_dir, fname)shutil.copyfile(src, dst)fnames = ['file{}.jpg'.format(i) for i in range(900, 1350)] for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(validation_cats_dir, fname)shutil.copyfile(src, dst)# Copy next 500 cat images to test_cats_dir fnames = ['file{}.jpg'.format(i) for i in range(1350, 1800)] for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(test_cats_dir, fname)shutil.copyfile(src, dst)fnames = ['file{}.jpg'.format(i) for i in range(2127,3000)] for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(train_dogs_dir, fname)shutil.copyfile(src, dst)# Copy next 500 dog images to validation_dogs_dir fnames = ['file{}.jpg'.format(i) for i in range(3000,3304)] for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(validation_dogs_dir, fname)shutil.copyfile(src, dst)# # Copy next 500 dog images to test_dogs_dir # fnames = ['file{}.jpg'.format(i) for i in range(3000,3878)] # for fname in fnames: # src = os.path.join(original_dataset_dir, fname) # dst = os.path.join(test_dogs_dir, fname) # shutil.copyfile(src, dst)

运行结果：

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三、识别笑脸

• 模式构建：

#创建模型 from keras import layers from keras import models model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Flatten()) model.add(layers.Dense(512, activation='relu')) model.add(layers.Dense(1, activation='sigmoid')) model.summary()#查看

• 进行归一化

#归一化 from keras import optimizers model.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc']) from keras.preprocessing.image import ImageDataGenerator train_datagen = ImageDataGenerator(rescale=1./255) validation_datagen=ImageDataGenerator(rescale=1./255) test_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow_from_directory(# 目标文件目录train_dir,#所有图片的size必须是150x150target_size=(150, 150),batch_size=20,# Since we use binary_crossentropy loss, we need binary labelsclass_mode='binary') validation_generator = test_datagen.flow_from_directory(validation_dir,target_size=(150, 150),batch_size=20,class_mode='binary') test_generator = test_datagen.flow_from_directory(test_dir,target_size=(150, 150),batch_size=20,class_mode='binary') for data_batch, labels_batch in train_generator:print('data batch shape:', data_batch.shape)print('labels batch shape:', labels_batch)break #'smile': 0, 'unsmile': 1

• 增强数据

#数据增强 datagen = ImageDataGenerator(rotation_range=40,width_shift_range=0.2,height_shift_range=0.2,shear_range=0.2,zoom_range=0.2,horizontal_flip=True,fill_mode='nearest') #数据增强后图片变化 import matplotlib.pyplot as plt # This is module with image preprocessing utilities from keras.preprocessing import image train_smile_dir = './ImageFiles//files2//train//smile/' fnames = [os.path.join(train_smile_dir, fname) for fname in os.listdir(train_smile_dir)] img_path = fnames[3] img = image.load_img(img_path, target_size=(150, 150)) x = image.img_to_array(img) x = x.reshape((1,) + x.shape) i = 0 for batch in datagen.flow(x, batch_size=1):plt.figure(i)imgplot = plt.imshow(image.array_to_img(batch[0]))i += 1if i % 4 == 0:break plt.show()

• 创建网络：

#创建网络 model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Flatten()) model.add(layers.Dropout(0.5)) model.add(layers.Dense(512, activation='relu')) model.add(layers.Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc']) #归一化处理 train_datagen = ImageDataGenerator(rescale=1./255,rotation_range=40,width_shift_range=0.2,height_shift_range=0.2,shear_range=0.2,zoom_range=0.2,horizontal_flip=True,)test_datagen = ImageDataGenerator(rescale=1./255)train_generator = train_datagen.flow_from_directory(# This is the target directorytrain_dir,# All images will be resized to 150x150target_size=(150, 150),batch_size=32,# Since we use binary_crossentropy loss, we need binary labelsclass_mode='binary')validation_generator = test_datagen.flow_from_directory(validation_dir,target_size=(150, 150),batch_size=32,class_mode='binary')history = model.fit_generator(train_generator,steps_per_epoch=100,epochs=60, validation_data=validation_generator,validation_steps=50) model.save('smileAndUnsmile1.h5')#数据增强过后的训练集与验证集的精确度与损失度的图形 acc = history.history['acc'] val_acc = history.history['val_acc'] loss = history.history['loss'] val_loss = history.history['val_loss']epochs = range(len(acc))plt.plot(epochs, acc, 'bo', label='Training acc') plt.plot(epochs, val_acc, 'b', label='Validation acc') plt.title('Training and validation accuracy') plt.legend() plt.figure()plt.plot(epochs, loss, 'bo', label='Training loss') plt.plot(epochs, val_loss, 'b', label='Validation loss') plt.title('Training and validation loss') plt.legend() plt.show()

• 单张图片测试：

# 单张图片进行判断 是笑脸还是非笑脸 import cv2 from keras.preprocessing import image from keras.models import load_model import numpy as np #加载模型 model = load_model('smileAndUnsmile1.h5') #本地图片路径 img_path='test.jpg' img = image.load_img(img_path, target_size=(150, 150))img_tensor = image.img_to_array(img)/255.0 img_tensor = np.expand_dims(img_tensor, axis=0) prediction =model.predict(img_tensor) print(prediction) if prediction[0][0]>0.5:result='非笑脸' else:result='笑脸' print(result)

• 摄像头测试：

#检测视频或者摄像头中的人脸 import cv2 from keras.preprocessing import image from keras.models import load_model import numpy as np import dlib from PIL import Image model = load_model('smileAndUnsmile1.h5') detector = dlib.get_frontal_face_detector() video=cv2.VideoCapture(0) font = cv2.FONT_HERSHEY_SIMPLEX def rec(img):gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)dets=detector(gray,1)if dets is not None:for face in dets:left=face.left()top=face.top()right=face.right()bottom=face.bottom()cv2.rectangle(img,(left,top),(right,bottom),(0,255,0),2)img1=cv2.resize(img[top:bottom,left:right],dsize=(150,150))img1=cv2.cvtColor(img1,cv2.COLOR_BGR2RGB)img1 = np.array(img1)/255.img_tensor = img1.reshape(-1,150,150,3)prediction =model.predict(img_tensor) if prediction[0][0]>0.5:result='unsmile'else:result='smile'cv2.putText(img, result, (left,top), font, 2, (0, 255, 0), 2, cv2.LINE_AA)cv2.imshow('Video', img) while video.isOpened():res, img_rd = video.read()if not res:breakrec(img_rd)if cv2.waitKey(1) & 0xFF == ord('q'):break video.release() cv2.destroyAllWindows()

运行结果：

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四、Dlib提取人脸特征识别笑脸和非笑脸

import cv2 # 图像处理的库 OpenCv import dlib # 人脸识别的库 dlib import numpy as np # 数据处理的库 numpy class face_emotion():def __init__(self):self.detector = dlib.get_frontal_face_detector()self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")self.cap = cv2.VideoCapture(0)self.cap.set(3, 480)self.cnt = 0 def learning_face(self):line_brow_x = []line_brow_y = []while(self.cap.isOpened()):flag, im_rd = self.cap.read()k = cv2.waitKey(1)# 取灰度img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY) faces = self.detector(img_gray, 0)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)):for k, d in enumerate(faces):cv2.rectangle(im_rd, (d.left(), d.top()), (d.right(), d.bottom()), (0,0,255))self.face_width = d.right() - d.left()shape = self.predictor(im_rd, d)mouth_width = (shape.part(54).x - shape.part(48).x) / self.face_width mouth_height = (shape.part(66).y - shape.part(62).y) / self.face_widthbrow_sum = 0 frown_sum = 0 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).xline_brow_x.append(shape.part(j).x)line_brow_y.append(shape.part(j).y)tempx = np.array(line_brow_x)tempy = np.array(line_brow_y)z1 = np.polyfit(tempx, tempy, 1) self.brow_k = -round(z1[0], 3) brow_height = (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_widthif round(mouth_height >= 0.03) and eye_hight<0.56:cv2.putText(im_rd, "smile", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 2,(0,255,0), 2, 4)if round(mouth_height<0.03) and self.brow_k>-0.3:cv2.putText(im_rd, "unsmile", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 2,(0,255,0), 2, 4)cv2.putText(im_rd, "Face-" + str(len(faces)), (20,50), font, 0.6, (0,0,255), 1, cv2.LINE_AA)else:cv2.putText(im_rd, "No Face", (20,50), font, 0.6, (0,0,255), 1, cv2.LINE_AA)im_rd = cv2.putText(im_rd, "S: screenshot", (20,450), font, 0.6, (255,0,255), 1, cv2.LINE_AA)im_rd = cv2.putText(im_rd, "Q: quit", (20,470), font, 0.6, (255,0,255), 1, cv2.LINE_AA)if (cv2.waitKey(1) & 0xFF) == ord('s'):self.cnt += 1cv2.imwrite("screenshoot" + str(self.cnt) + ".jpg", im_rd)# 按下 q 键退出if (cv2.waitKey(1)) == ord('q'):break# 窗口显示cv2.imshow("Face Recognition", im_rd)self.cap.release()cv2.destroyAllWindows() if __name__ == "__main__":my_face = face_emotion()my_face.learning_face()

运行结果：

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参考

Python人脸识别微笑检测

Python-人脸识别并判断表情 笑脸或非笑脸 使用笑脸数据集genki4k

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