keras-人脸关键点检测-蒲公英云

keras-人脸关键点检测

# -*- 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)