请使用python写一个能对指定目录内图片进行基于KNN和PCA人脸识别并统计正确率的程序
时间: 2023-08-31 21:18:45 浏览: 74
### 回答1:
```python
import os
import numpy as np
from sklearn import neighbors, decomposition
from PIL import Image
# 读取图片并返回灰度值矩阵
def read_image(file_path):
img = Image.open(file_path).convert('L')
return np.array(img)
# 计算PCA特征
def get_pca_feature(data):
pca = decomposition.PCA(n_components=100) # 取100个主成分
pca.fit(data)
return pca.transform(data)
# 读取人脸数据
def read_faces(path):
X, y = [], []
for dir_name, subdir_list, file_list in os.walk(path):
for subdir in subdir_list:
label = int(subdir) # 文件夹名即为人脸标签
for file_name in os.listdir(os.path.join(dir_name, subdir)):
file_path = os.path.join(dir_name, subdir, file_name)
img = read_image(file_path)
X.append(img.ravel()) # 将图像展平
y.append(label)
return np.array(X), np.array(y)
# KNN人脸识别
def face_recognition(train_data, train_label, test_data):
knn = neighbors.KNeighborsClassifier(n_neighbors=3)
knn.fit(train_data, train_label)
return knn.predict(test_data)
# 计算正确率
def accuracy(y_true, y_pred):
return np.mean(y_true == y_pred)
if __name__ == '__main__':
train_path = 'path/to/train' # 训练数据目录
test_path = 'path/to/test' # 测试数据目录
train_data, train_label = read_faces(train_path)
test_data, test_label = read_faces(test_path)
train_data = get_pca_feature(train_data) # PCA降维
test_data = get_pca_feature(test_data) # PCA降维
y_pred = face_recognition(train_data, train_label, test_data)
acc = accuracy(test_label, y_pred)
print('Accuracy: {:.2f}%'.format(acc*100))
```
这是一个使用 Python 和 sklearn 库写成的人脸识别程序,该程
### 回答2:
import os
import cv2
import numpy as np
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsClassifier
# 定义数据路径和类别
data_path = '指定目录路径'
categories = os.listdir(data_path)
categories.sort()
# 读取图片并提取特征
def extract_features(image_path):
img = cv2.imread(image_path, 0)
resized_img = cv2.resize(img, (50, 50))
flattened_img = resized_img.flatten()
return flattened_img
# 初始化训练数据和标签
train_data = []
train_labels = []
# 读取训练图片
for category in categories:
image_folder = os.path.join(data_path, category)
images = os.listdir(image_folder)
for image in images:
image_path = os.path.join(image_folder, image)
features = extract_features(image_path)
train_data.append(features)
train_labels.append(category)
# 使用PCA进行特征降维
pca = PCA(n_components=100)
train_data = pca.fit_transform(train_data)
# 初始化KNN分类器
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(train_data, train_labels)
# 初始化测试数据和正确预测计数器
test_data = []
test_labels = []
correct_count = 0
# 读取测试图片进行预测
for category in categories:
image_folder = os.path.join(data_path, category)
images = os.listdir(image_folder)
for image in images:
image_path = os.path.join(image_folder, image)
features = extract_features(image_path)
test_data.append(features)
test_labels.append(category)
test_data_pca = pca.transform(test_data)
predicted_label = knn.predict(test_data_pca)[-1]
if predicted_label == category:
correct_count += 1
test_data.pop()
test_labels.pop()
# 计算正确率
accuracy = correct_count / len(test_labels) * 100
print("正确率:", accuracy)
### 回答3:
以下是一个使用Python编写的能够对指定目录内图片进行基于KNN和PCA人脸识别并统计正确率的程序:
```python
import os
import cv2
import numpy as np
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsClassifier
def load_images(folder):
images = []
labels = []
for filename in os.listdir(folder):
img = cv2.imread(os.path.join(folder, filename), 0)
if img is not None:
images.append(img.flatten())
labels.append(filename.split('.')[0])
return images, labels
def knn_pca_face_recognition(train_folder, test_folder):
train_images, train_labels = load_images(train_folder)
test_images, test_labels = load_images(test_folder)
# 使用PCA进行降维
pca = PCA(n_components=0.95)
train_images = pca.fit_transform(train_images)
test_images = pca.transform(test_images)
# 使用KNN分类器进行训练和预测
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(train_images, train_labels)
predicted_labels = knn.predict(test_images)
# 统计正确率
num_correct = np.sum(predicted_labels == test_labels)
accuracy = num_correct / len(test_labels)
return accuracy
train_folder = '/path/to/train/folder'
test_folder = '/path/to/test/folder'
accuracy = knn_pca_face_recognition(train_folder, test_folder)
print("Accuracy: %.2f" % (accuracy * 100))
```
请确保将`/path/to/train/folder`和`/path/to/test/folder`替换为实际的训练和测试目录路径。该程序首先加载训练和测试图像,然后使用PCA进行降维处理,接着使用KNN分类器对降维后的训练图像进行训练,并对测试图像进行预测,最后统计预测结果的正确率并输出。
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