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_path
def other_face():
path = os.listdir("./other_faces")
image_path = [os.path.join("./other_faces/",img) for img in path]
return image_path
image_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 = ad_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,y
data = tf.data.Dataset.from_tensor_slices((image_path,label))
data_loader = peat().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.maxpool1 = layers.MaxPool2D(2,strides=2)
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 = v1(x)
x = self.maxpool1(x)
x = v2(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 x
model = 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)
duce_mean(loss)
def accuracy(y_pred,y_true):
correct_pred = tf.equal(tf.argmax(y_pred,1),tf.cast(y_true,tf.int64))
duce_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 = ainable_variables
gradient = g.gradient(loss,training_variabel)
optimizer.apply_gradients(zip(gradient,training_variabel))
model.save_weights("face_weight") #保存模型
最后跑的准确率还是挺⾼的。
七.openCV登场
最后利⽤OpenCV的⼈脸检测模块，将检测到的⼈脸送⼊到我们训练好了的模型中进⾏预测根据预测的结果进⾏标识。
cap = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier('C:\\Users\Wuhuipeng\AppData\Local\Programs\Python\Python36\Lib\site-packages\cv2\data/haarcascade_l') while True:
ret,frame = ad()
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray,scaleFactor=1.2,minNeighbors=5,minSize=(5,5))
for (x,y,z,t) in faces:
img = frame[x:x+z,y:y+t]
try:
img = size(img,(64,64))
img = tf.cast(img,tf.float32) / 255.0
img = tf.reshape(img,[-1,64,64,3])
pred = model(img)
pred = tf.argmax(pred,axis=1).numpy()
except:
rectangle函数opencvpass
if(pred[0]==1):
cv2.putText(frame,"wuhuipeng",(x-10,y-10),cv2.FONT_HERSHEY_SIMPLEX,1.2,(255,255,0),2)    angle(frame,(x,y),(x+z,y+t),(0,255,0),2)
cv2.imshow('find faces',frame)
if cv2.waitKey(1)&0xff ==ord('q'):
break
cv2.destroyAllWindows()
完整代码地址.
以上就是本⽂的全部内容，希望对⼤家的学习有所帮助，也希望⼤家多多⽀持。

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