不使用深度学习,使用fer2013,编写进行数据预处理,进行人脸检测,特征提取的函数,最后基于随机森林用python编写人脸表情识别代码
时间: 2024-04-30 12:22:00 浏览: 22
好的,首先需要安装一些必要的Python库,如下所示:
```python
pip install pandas numpy matplotlib opencv-python sklearn
```
然后,我们可以开始编写代码。首先是数据预处理部分,我们需要加载FER2013数据集并将其转换为可用于训练和测试的格式:
```python
import pandas as pd
import numpy as np
def load_fer2013():
data = pd.read_csv("fer2013.csv")
pixels = data["pixels"].tolist()
images = np.empty((len(data), 48, 48))
labels = data["emotion"].tolist()
for i in range(len(data)):
image = np.fromstring(pixels[i], dtype=int, sep=" ")
image = np.reshape(image, (48, 48))
images[i] = image
return images, labels
images, labels = load_fer2013()
```
接下来是人脸检测部分,我们将使用OpenCV中的Haar级联分类器来检测人脸:
```python
import cv2
faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
def detect_faces(images):
face_images = []
for img in images:
faces = faceCascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
if len(faces) == 1:
x, y, w, h = faces[0]
face_img = img[y:y+h, x:x+w]
face_img = cv2.resize(face_img, (48, 48))
face_images.append(face_img)
return np.array(face_images)
face_images = detect_faces(images)
```
现在我们已经获得了所有人脸图像,接下来是特征提取部分,我们将使用Histogram of Oriented Gradients (HOG)算法来提取特征:
```python
from skimage.feature import hog
def extract_features(images):
features = []
for img in images:
feature = hog(img, orientations=8, pixels_per_cell=(8, 8), cells_per_block=(3, 3))
features.append(feature)
return np.array(features)
features = extract_features(face_images)
```
最后,我们将使用随机森林算法来训练我们的模型并进行表情识别:
```python
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
rf_clf = RandomForestClassifier(n_estimators=100, random_state=42)
rf_clf.fit(X_train, y_train)
y_pred = rf_clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
现在我们已经完成了整个过程,可以使用以上代码进行人脸表情识别了。