Python人臉識別之微笑檢測

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一.實驗準備

環境搭建

pip install tensorflow==1.2.0
pip install keras==2.0.6
pip install dlib==19.6.1
pip install h5py==2.10

如果是新建虛擬環境，還需安裝以下包

pip install opencv_python==4.1.2.30
pip install pillow
pip install matplotlib
pip install h5py

使用genki-4k數據集

可從此處下載

二.圖片預處理

打開數據集

我們需要將人臉檢測出來並對圖片進行裁剪

代碼如下：

import dlib         # 人臉識別的庫dlib
import numpy as np  # 數據處理的庫numpy
import cv2          # 圖像處理的庫OpenCv
import os
 
# dlib預測器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('D:\\shape_predictor_68_face_landmarks.dat')
 
# 讀取圖像的路徑
path_read = "C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files"
num=0
for file_name in os.listdir(path_read):
	#aa是圖片的全路徑
    aa=(path_read +"/"+file_name)
    #讀入的圖片的路徑中含非英文
    img=cv2.imdecode(np.fromfile(aa, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
    #獲取圖片的寬高
    img_shape=img.shape
    img_height=img_shape[0]
    img_width=img_shape[1]
   
    # 用來存儲生成的單張人臉的路徑
    path_save="C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files1" 
    # dlib檢測
    dets = detector(img,1)
    print("人臉數：", len(dets))
    for k, d in enumerate(dets):
        if len(dets)>1:
            continue
        num=num+1
        # 計算矩形大小
        # (x,y), (寬度width, 高度height)
        pos_start = tuple([d.left(), d.top()])
        pos_end = tuple([d.right(), d.bottom()])
 
        # 計算矩形框大小
        height = d.bottom()-d.top()
        width = d.right()-d.left()
 
        # 根據人臉大小生成空的圖像
        img_blank = np.zeros((height, width, 3), np.uint8)
        for i in range(height):
            if d.top()+i>=img_height:# 防止越界
                continue
            for j in range(width):
                if d.left()+j>=img_width:# 防止越界
                    continue
                img_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") # 正確方法

運行效果如下：

共識別出3878張圖片。

某些圖片沒有識別出人臉，所以沒有裁剪保存，可以自行添加圖片補充。

三.劃分數據集

代碼：

import os, shutil
# 原始數據集路徑
original_dataset_dir = 'C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files1'

# 新的數據集
base_dir = 'C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files2'
os.mkdir(base_dir)

# 訓練圖像、驗證圖像、測試圖像的目錄
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)

# 復制1000張笑臉圖片到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(3000,3878)]
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)

運行效果如下：

四.CNN提取人臉識別笑臉和非笑臉

1.創建模型

代碼：

#創建模型
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

運行效果：

2.歸一化處理

代碼：

#歸一化
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(
        # 目標文件目錄
        train_dir,
        #所有圖片的size必須是150x150
        target_size=(150, 150),
        batch_size=20,
        # Since we use binary_crossentropy loss, we need binary labels
        class_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

3.數據增強

代碼：

#數據增強
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')
#數據增強後圖片變化
import matplotlib.pyplot as plt
# This is module with image preprocessing utilities
from keras.preprocessing import image
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 += 1
    if i % 4 == 0:
        break
plt.show()

運行效果：

4.創建網絡

代碼：

#創建網絡
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'])
#歸一化處理
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 directory
        train_dir,
        # All images will be resized to 150x150
        target_size=(150, 150),
        batch_size=32,
        # Since we use binary_crossentropy loss, we need binary labels
        class_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')

#數據增強過後的訓練集與驗證集的精確度與損失度的圖形
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()

運行結果：

速度較慢，要等很久

5.單張圖片測試

代碼：

# 單張圖片進行判斷  是笑臉還是非笑臉
import cv2
from keras.preprocessing import image
from keras.models import load_model
import numpy as np
#加載模型
model = load_model('smileAndUnsmile1.h5')
#本地圖片路徑
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='非笑臉'
else:
    result='笑臉'
print(result)

運行結果：

6.攝像頭實時測試

代碼：

#檢測視頻或者攝像頭中的人臉
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:
        break
    rec(img_rd)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video.release()
cv2.destroyAllWindows()

運行結果：

五.Dlib提取人臉特征識別笑臉和非笑臉

代碼：

import cv2                     #  圖像處理的庫 OpenCv
import dlib                    # 人臉識別的庫 dlib
import numpy as np             # 數據處理的庫 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
     
            # 如果檢測到人臉
            if(len(faces) != 0):
                
                # 對每個人臉都標出68個特征點
                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_width
                        brow_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).x
                            line_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  # 眉毛距離占比

                        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_width
                        if 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 += 1
                cv2.imwrite("screenshoot" + str(self.cnt) + ".jpg", im_rd)
            # 按下 q 鍵退出
            if (cv2.waitKey(1)) == ord('q'):
                break
            # 窗口顯示
            cv2.imshow("Face Recognition", im_rd)
        self.cap.release()
        cv2.destroyAllWindows()
if __name__ == "__main__":
    my_face = face_emotion()
    my_face.learning_face()

運行結果：

 

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