python代码实现将真实人脸和AI人脸进行分类并计算精确度
时间: 2024-03-25 09:39:54 浏览: 85
python实现人脸识别
要实现将真实人脸和AI人脸进行分类并计算精确度,需要准备一个带有标签的数据集,其中包含真实人脸和AI人脸的图像。
以下是一个基于Python的示例代码:
```python
import os
import cv2
import dlib
import numpy as np
import tensorflow as tf
# 加载深度学习模型
model = tf.keras.models.load_model('path/to/model')
# 加载人脸检测器
detector = dlib.get_frontal_face_detector()
# 加载人脸关键点检测器
predictor = dlib.shape_predictor('path/to/shape_predictor_68_face_landmarks.dat')
# 定义函数,用于提取人脸特征
def extract_features(image):
face_rects = detector(image, 0)
if len(face_rects) == 0:
return None
shape = predictor(image, face_rects[0])
face_descriptor = np.array([model.predict(np.expand_dims(image, axis=0))[0] for i in range(10)])
face_descriptor = np.mean(face_descriptor, axis=0)
return face_descriptor
# 定义函数,用于加载数据集
def load_dataset(path):
X = []
y = []
for folder in os.listdir(path):
if folder.startswith('.'):
continue
folder_path = os.path.join(path, folder)
for file_name in os.listdir(folder_path):
if file_name.startswith('.'):
continue
file_path = os.path.join(folder_path, file_name)
image = cv2.imread(file_path)
feature = extract_features(image)
if feature is not None:
X.append(feature)
y.append(int(folder))
return np.array(X), np.array(y)
# 加载数据集
X, y = load_dataset('path/to/dataset')
# 划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练分类器
from sklearn.svm import SVC
clf = SVC(kernel='linear', probability=True)
clf.fit(X_train, y_train)
# 在测试集上进行预测
y_pred = clf.predict(X_test)
y_prob = clf.predict_proba(X_test)
# 计算精确度
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy: {:.2f}%'.format(accuracy * 100))
```
该代码将图像中的人脸特征提取出来,并使用支持向量机(SVM)分类器进行分类训练和预测。通过计算测试集上的精确度,可以评估分类器的性能。需要注意的是,该模型的准确率取决于训练数据的质量和数量,可能存在误判的情况。
阅读全文