用python3实现基于深度学习的AI人脸识别系统，脚本可以直接运行（包括源码文件、数据文件）

#!usr/bin/env python #encoding:utf-8 from __future__ import division ''' 功能： 构建人脸识别模型 ''' import os import cv2 import random import numpy as np from keras.utils import np_utils from keras.models import Sequential,load_model from sklearn.model_selection import train_test_split from keras.layers import Dense,Activation,Convolution2D,MaxPooling2D,Flatten,Dropout class DataSet(object): ''' 用于存储和格式化读取训练数据的类 ''' def __init__(self,path): ''' 初始化 ''' self.num_classes=None self.X_train=None self.X_test=None self.Y_train=None self.Y_test=None self.img_size=128 self.extract_data(path) def extract_data(self,path): ''' 抽取数据 ''' imgs,labels,counter=read_file(path) X_train,X_test,y_train,y_test=train_test_split(imgs,labels,test_size=0.2,random_state=random.randint(0, 100)) X_train=X_train.reshape(X_train.shape[0], 1, self.img_size, self.img_size)/255.0 X_test=X_test.reshape(X_test.shape[0], 1, self.img_size, self.img_size)/255.0 X_train=X_train.astype('float32') X_test=X_test.astype('float32') Y_train=np_utils.to_categorical(y_train, num_classes=counter) Y_test=np_utils.to_categorical(y_test, num_classes=counter) self.X_train=X_train self.X_test=X_test self.Y_train=Y_train self.Y_test=Y_test self.num_classes=counter def check(self): ''' 校验 ''' print('num of dim:', self.X_test.ndim) print('shape:', self.X_test.shape) print('size:', self.X_test.size) print('num of dim:', self.X_train.ndim) print('shape:', self.X_train.shape) print('size:', self.X_train.size) def endwith(s,*endstring): ''' 对字符串的后续和标签进行匹配 ''' resultArray = map(s.endswith,endstring) if True in resultArray: return True else: return False def read_file(path): ''' 图片读取 ''' img_list=[] label_list=[] dir_counter=0 IMG_SIZE=128 for child_dir in os.listdir(path): child_path=os.path.join(path, child_dir) for dir_image in os.listdir(child_path): if endwith(dir_image,'jpg'): img=cv2.imread(os.path.join(child_path, dir_image)) resized_img=cv2.resize(img, (IMG_SIZE, IMG_SIZE)) recolored_img=cv2.cvtColor(resized_img,cv2.COLOR_BGR2GRAY) img_list.append(recolored_img) label_list.append(dir_counter) dir_counter+=1 img_list=np.array(img_list) return img_list,label_list,dir_counter def read_name_list(path): ''' 读取训练数据集 ''' name_list=[] for child_dir in os.listdir(path): name_list.append(child_dir) return name_list class Model(object): ''' 人脸识别模型 ''' FILE_PATH="face.h5" IMAGE_SIZE=128 def __init__(self): self.model=None def read_trainData(self,dataset): self.dataset=dataset def build_model(self): self.model = Sequential() self.model.add( Convolution2D( filters=32, kernel_size=(5, 5), padding='same', dim_ordering='th', input_shape=self.dataset.X_train.shape[1:] ) ) self.model.add(Activation('relu')) self.model.add( MaxPooling2D( pool_size=(2, 2), strides=(2, 2), padding='same' ) ) self.model.add(Convolution2D(filters=64, kernel_size=(5,5), padding='same')) self.model.add(Activation('relu')) self.model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same')) self.model.add(Flatten()) self.model.add(Dense(1024)) self.model.add(Activation('relu')) self.model.add(Dense(self.dataset.num_classes)) self.model.add(Activation('softmax')) self.model.summary() def train_model(self): self.model.compile( optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) self.model.fit(self.dataset.X_train,self.dataset.Y_train,epochs=10,batch_size=10) def evaluate_model(self): print('\nTesting---------------') loss, accuracy = self.model.evaluate(self.dataset.X_test, self.dataset.Y_test) print('test loss;', loss) print('test accuracy:', accuracy) def save(self, file_path=FILE_PATH): print('Model Saved Finished!!!') self.model.save(file_path) def load(self, file_path=FILE_PATH): print('Model Loaded Successful!!!') self.model = load_model(file_path) def predict(self,img): img=img.reshape((1, 1, self.IMAGE_SIZE, self.IMAGE_SIZE)) img=img.astype('float32') img=img/255.0 result=self.model.predict_proba(img) max_index=np.argmax(result) return max_index,result[0][max_index] if __name__ == '__main__': dataset=DataSet('dataset/') model=Model() model.read_trainData(dataset) model.build_model() model.train_model() model.evaluate_model() model.save()