def run(method_readFaceRS='lbp'): # 读取经过上一步特征处理处理的四个输出(lbp/lbp_pca) X_train, X_test, y_train, y_test = readFaceRS(method_readFaceRS) # 取age y_train_age = y_train[['age']] y_test_age = y_test[['age']] predict(X_train, X_test, y_train_age, y_test_age)
def run(method_readFaceRS='hog'): # 读取经过上一步特征处理处理的四个输出(hog/hog_pca) X_train, X_test, y_train, y_test = readFaceRS(method_readFaceRS) # 取age y_train_age = y_train[['age']] y_test_age = y_test[['age']] method_name = method_readFaceRS + '_svm' predict(X_train, X_test, y_train_age, y_test_age, method_name)
def run(method_readFaceRS='resnet'): # 读取经过上一步特征处理处理的四个输出(ResNet/ResNet_kpca) X_train, X_test, y_train, y_test = readFaceRS(method_readFaceRS) # 取age y_train_age = y_train[['age']] y_test_age = y_test[['age']] method_name = method_readFaceRS + '_xgb' predict(X_train, X_test, y_train_age, y_test_age, method_name)
def run(method_readFaceRS='lbp_pca'): # 读取经过上一步特征处理处理的四个输出(lbp/lbp_pca) X_train, X_test, y_train, y_test = readFaceRS(method_readFaceRS) # 取age y_train_age = y_train[['age']] y_test_age = y_test[['age']] method_name = method_readFaceRS + '_adaboost' predict(X_train, X_test, y_train_age, y_test_age, method_name)
def run(method_readFaceRS='sift', k=70): X_train, X_test, y_train, y_test = readFaceRS(method_readFaceRS) # age y_train_age = y_train[['age']] y_test_age = y_test[['age']] method_name = method_readFaceRS + '_knn' predict(X_train[:, 1:], X_test[:, 1:], y_train_age, y_test_age, k, method_name)
def run(method_readFaceRS='densenet', method_generateUpdateFaceRS='densenet_kpca'): ''' @param:进行特征提取+降维,第一个参数为特征提取方法,第二个参数为特征提取+降维方法 ''' # 读取已经经过特征处理过的四个文件作为输入 X_train, X_test, y_train, y_test = readFaceRS(method_readFaceRS) # 特征提取+降维 X_pca_train, X_pca_test = lower_dimen(X_train, X_test) # 把特征提取+降维和标签输出 generateUpdateFaceRS(X_pca_train, X_pca_test, y_train, y_test, method_generateUpdateFaceRS)
def run(method_readFaceRS='densenet', method_generateUpdateFaceRS='densenet_kpca', n_components=99): ''' @param:进行densenet+kpca特征处理,第一个参数为densenet,第二个参数为densenet_kpca ''' # 读取已经经过HOG特征处理过的四个文件作为输入 X_train, X_test, y_train, y_test = readFaceRS(method_readFaceRS) # HOG+PCA的输出 X_pca_train, X_pca_test = lower_dimen(X_train, X_test, n_components) # 把HOG+PCA的输出和标签输出到hog_pca目录 generateUpdateFaceRS(X_pca_train, X_pca_test, y_train, y_test, method_generateUpdateFaceRS)
def run(method_readFaceRS='densetnet'): X_train, X_test, y_train, y_test = readFaceRS(method_readFaceRS) # 在标签中取出age列 y_train_age = y_train[['age']] y_test_age = y_test[['age']] # 得到one-hot编码 y_train_age_one = pd.get_dummies(y_train_age) y_test_age_one = pd.get_dummies(y_test_age) input_size = CNNInputSize(method=method_readFaceRS) dataset = DataSet(X_train, X_test, y_train_age_one, y_test_age_one, input_size) model = build_model(dataset) train_model(model, dataset) evaluate_model(model, dataset)