keras-人脸关键点检测

淡淡的烟草味﹌ 2021-11-17 08:48 440阅读 0赞

# -*- coding: utf-8 -*-
    """
    Created on 2019/5/21 10:46
    @Author: Johnson
    @Email:593956670@qq.com
    @File: keras_人脸关键点检测.py
    """
    from tensorflow.contrib.keras.api.keras.preprocessing.image import ImageDataGenerator,img_to_array
    from keras.models import Sequential
    from keras.layers.core import Dense, Dropout, Activation, Flatten
    from keras.layers.advanced_activations import PReLU
    from keras.layers.convolutional import Conv2D, MaxPooling2D,ZeroPadding2D
    from keras.preprocessing.image import load_img, img_to_array
    from keras.optimizers import  SGD
    import numpy as np
    import cv2
    from keras.callbacks import *
    import keras
    
    file_path = 'cnn_model_final.h5'
    train_path = 'D:/BaiduNetdiskDownload/new_data_50000/50000train/'
    test_path = 'D:/BaiduNetdiskDownload/new_data_50000/50000test/'
    
    imgsize = 178
    train_samples = 40000
    test_samples = 200
    batch_size = 32
    
    def data_label(path):
        f = open(path+"lable-40.txt","r")
        j = 0
        i = -1
        datalist = []
        labellist = []
        while True:
            for line in f.readlines():
                i+=1
                j+=1
                a = line.replace("\n","")
                b = a.split(",")
                label = b[1:]
                #对标签进行归一化处理(不归一化也行)
                for num in b[1:]:
                    lab = int(num)/255.0
                    labellist.append(lab)
                lab = labellist[i*10:j*10]
                imgname = path+b[0]
                images = load_img(imgname)
                images = img_to_array(images).astype("float32")
                #对图片进行归一化
                images/=255.0
                image = np.expand_dims(images,axis=0) #
                labels = np.array(label)
    
                # label = keras.utils.np_utils.to_categorical(label)
                # label = np.expand_dims(label,axis=0)
    
                label = labels.reshape(1,10)
    
                yield (image,label)
    
    ######
    #开始建立CNN模型
    ######
    class Model():
    
        def __CNN__(self):
            model = Sequential() #218*178*3
            model.add(Conv2D(32,(3,3),input_shape=(imgsize,imgsize,3)))
            model.add(Activation('relu'))
            model.add(MaxPooling2D(pool_size=(2,2)))
    
            model.add(Conv2D(32, (3, 3)))
            model.add(Activation('relu'))
            model.add(MaxPooling2D(pool_size=(2, 2)))
    
            model.add(Conv2D(64, (3, 3)))
            model.add(Activation('relu'))
            model.add(MaxPooling2D(pool_size=(2, 2)))
    
            model.add(Flatten())
            model.add(Dense(64))
            model.add(Activation('relu'))
            model.add(Dropout(0.5))
            model.add(Dense(10))
            model.summary()
            return model
    
        def train(self,model):
            model = Sequential()  # 218*178*3
            model.add(Conv2D(32, (3, 3), input_shape=(imgsize, imgsize, 3)))
            model.add(Activation('relu'))
            model.add(MaxPooling2D(pool_size=(2, 2)))
    
            model.add(Conv2D(32, (3, 3)))
            model.add(Activation('relu'))
            model.add(MaxPooling2D(pool_size=(2, 2)))
    
            model.add(Conv2D(64, (3, 3)))
            model.add(Activation('relu'))
            model.add(MaxPooling2D(pool_size=(2, 2)))
    
            model.add(Flatten())
            model.add(Dense(64))
            model.add(Activation('relu'))
            model.add(Dropout(0.5))
            model.add(Dense(10))
            model.summary()
            # optimizer = SGD(lr=0.03,momentum=0.9,nesterov=True)
            model.compile(loss="mse",optimizer="adam",metrics=["accuracy"])
            epoch_num = 10
            learning_rate = np.linspace(0.03,0.01,epoch_num)
            change_lr = LearningRateScheduler(lambda epoch:float(learning_rate[epoch]))
            early_stop = EarlyStopping(monitor="val_liss",patience=20,verbose=1,mode='auto')
            check_point = ModelCheckpoint('CNN_model_final.h5', monitor='val_loss', verbose=0, save_best_only=True,
                                       save_weights_only=False, mode='auto', period=1)
    
            model.fit_generator(data_label(train_path), callbacks=[check_point, early_stop, change_lr],
                                samples_per_epoch=int(train_samples // batch_size),
            epochs = epoch_num, validation_steps = int(test_samples // batch_size), validation_data = data_label(
                test_path))
            # model.fit(traindata,trainlabel,batch_size=32,epoch=50,validation_data=(testdata,testlabel))
            model.evaluate_generator(data_label(test_path))
    
    
        def save(self,model,file_path=file_path):
            print("Model saved...")
            model.save_weights(file_path)
    
        def load(self,model,file_path=file_path):
            print("Model loaded...")
            model.load_weigths(file_path)
    
        def predict(self,model,image):
            #预测样本分类
            print(image.shape)
            image = cv2.resize(image,(imgsize,imgsize))
            image.astype('float32')
            image = np.expand_dims(image,axis=0)
    
            #归一化
            result = model.predict(image)
    
            print(result)
            return result
    
    
    #模型测试代码
    t = Model()
    model = t.__CNN__()
    t.train(model)