结合OpenCV与TensorFlow进行人脸识别的实现

作为新手来说，这是一个最简单的人脸识别模型，难度不大，代码量也不算多，下面就逐一来讲解，数据集的准备就不多说了，因人而异。

一. 获取数据集的所有路径

利用os模块来生成一个包含所有数据路径的list

def my_face():  path = os.listdir("./my_faces")  image_path = [os.path.join("./my_faces/",img) for img in path]  return image_pathdef other_face():  path = os.listdir("./other_faces")  image_path = [os.path.join("./other_faces/",img) for img in path]  return image_pathimage_path = my_face().__add__(other_face())  #将两个list合并成为一个list

二. 构造标签

标签的构造较为简单，1表示本人，0表示其他人。

label_my= [1 for i in my_face()] label_other = [0 for i in other_face()] label = label_my.__add__(label_other)       #合并两个list

三.构造数据集

利用tf.data.Dataset.from_tensor_slices()构造数据集，

def preprocess(x,y):  x = tf.io.read_file(x)  #读取数据  x = tf.image.decode_jpeg(x,channels=3) #解码成jpg格式的数据  x = tf.cast(x,tf.float32) / 255.0   #归一化  y = tf.convert_to_tensor(y)				#转成tensor  return x,ydata = tf.data.Dataset.from_tensor_slices((image_path,label))data_loader = data.repeat().shuffle(5000).map(preprocess).batch(128).prefetch(1)

四.构造模型

class CNN_WORK(Model):  def __init__(self):    super(CNN_WORK,self).__init__()    self.conv1 = layers.Conv2D(32,kernel_size=5,activation=tf.nn.relu)    self.maxpool1 = layers.MaxPool2D(2,strides=2)        self.conv2 = layers.Conv2D(64,kernel_size=3,activation=tf.nn.relu)    self.maxpool2 = layers.MaxPool2D(2,strides=2)        self.flatten = layers.Flatten()    self.fc1 = layers.Dense(1024)    self.dropout = layers.Dropout(rate=0.5)    self.out = layers.Dense(2)    def call(self,x,is_training=False):    x = self.conv1(x)    x = self.maxpool1(x)    x = self.conv2(x)    x = self.maxpool2(x)        x = self.flatten(x)    x = self.fc1(x)    x = self.dropout(x,training=is_training)    x = self.out(x)          if not is_training:      x = tf.nn.softmax(x)    return xmodel = CNN_WORK()

五.定义损失函数，精度函数，优化函数

def cross_entropy_loss(x,y):  y = tf.cast(y,tf.int64)  loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,logits=x)  return tf.reduce_mean(loss)def accuracy(y_pred,y_true):  correct_pred = tf.equal(tf.argmax(y_pred,1),tf.cast(y_true,tf.int64))  return tf.reduce_mean(tf.cast(correct_pred,tf.float32),axis=-1)optimizer = tf.optimizers.SGD(0.002)  

六.开始跑步我们的模型

def run_optimizer(x,y):  with tf.GradientTape() as g:    pred = model(x,is_training=True)    loss = cross_entropy_loss(pred,y)  training_variabel = model.trainable_variables  gradient = g.gradient(loss,training_variabel)  optimizer.apply_gradients(zip(gradient,training_variabel))model.save_weights("face_weight") #保存模型