请问你可以提供一份Openpose识别KTH源代码吗
时间: 2023-07-16 20:12:31 浏览: 55
当然可以,我可以提供以下步骤:
1. 下载Openpose:首先,你需要从[Openpose官网](https://github.com/CMU-Perceptual-Computing-Lab/openpose)下载并安装Openpose。安装完成后,你需要将视频文件放置在Openpose的example/media目录下。
2. 运行Openpose:打开命令提示符或终端窗口,进入Openpose的主目录,并使用以下命令运行Openpose:
```
bin\OpenPoseDemo.exe --video examples\media\<filename>.avi
```
其中,`<filename>`是你要处理的视频文件的名称。
3. 生成关键点数据:运行上述命令后,Openpose将为视频中的每个帧生成一个JSON文件,其中包含每个人的关键点数据。这些文件将保存在Openpose的output目录下。
4. 解析JSON数据:最后,你需要编写一个解析JSON文件的脚本,以从中提取关键点数据并将其转换为可用于训练模型的格式。你可以使用Python的json库来处理JSON文件,具体实现可以参考[Openpose官方文档](https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/output.md)。
以上就是利用Openpose识别KTH数据集并生成关键点数据的基本步骤,希望能对你有所帮助!
相关问题
请问你可以提供一份CNN识别KTH源代码吗
当然可以,下面是使用Python和TensorFlow框架实现的CNN识别KTH数据集的源代码:
``` python
import tensorflow as tf
import numpy as np
import os
import cv2
# 数据路径
data_dir = './data/KTH_Action_Dataset/'
train_dir = os.path.join(data_dir, 'train/')
test_dir = os.path.join(data_dir, 'test/')
# 训练集和测试集的大小
train_size = 2400
test_size = 600
# 定义标签
label_names = ['boxing', 'handclapping', 'handwaving', 'jogging', 'running', 'walking']
num_classes = len(label_names)
label_to_index = dict(zip(label_names, range(num_classes)))
# 加载数据函数
def load_data(data_dir, num_samples):
images = []
labels = []
for label_name in label_names:
dir_name = os.path.join(data_dir, label_name)
class_index = label_to_index[label_name]
for i, file_name in enumerate(os.listdir(dir_name)):
if i >= num_samples // num_classes:
break
# 加载图像并将其调整为统一大小
image = cv2.imread(os.path.join(dir_name, file_name))
image = cv2.resize(image, (80, 80))
images.append(image)
labels.append(class_index)
return np.array(images), np.array(labels)
# 加载训练集和测试集
train_images, train_labels = load_data(train_dir, train_size)
test_images, test_labels = load_data(test_dir, test_size)
# 数据预处理
train_images = train_images.astype('float32') / 255
test_images = test_images.astype('float32') / 255
# 将标签转换为独热编码
train_labels = tf.keras.utils.to_categorical(train_labels, num_classes)
test_labels = tf.keras.utils.to_categorical(test_labels, num_classes)
# 定义模型
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(80, 80, 3)),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(num_classes, activation='softmax')
])
# 编译模型
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# 训练模型
model.fit(train_images, train_labels, epochs=10, batch_size=32)
# 评估模型
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('Test accuracy:', test_acc)
```
在这个示例代码中,我们使用了KTH数据集作为示例数据,通过加载数据、预处理数据、定义模型、编译模型、训练模型和评估模型等步骤,实现了一个简单的CNN模型来识别KTH数据集中的动作类别。你可以根据自己的需求修改代码并应用到自己的项目中。
写一个可以直接运行的java代码获取wav文件的频谱
以下是一个可以获取wav文件频谱的Java代码示例:
```
import javax.sound.sampled.*;
import java.io.*;
public class WavSpectrumAnalyzer {
public static void main(String[] args) {
String wavFileName = "test.wav"; // 替换为你的wav文件名
try {
AudioInputStream audioInputStream = AudioSystem.getAudioInputStream(new File(wavFileName));
byte[] audioBytes = new byte[(int) (audioInputStream.getFrameLength() * audioInputStream.getFormat().getFrameSize())];
audioInputStream.read(audioBytes);
int numChannels = audioInputStream.getFormat().getChannels();
int sampleSize = audioInputStream.getFormat().getSampleSizeInBits();
double sampleRate = audioInputStream.getFormat().getSampleRate();
// 将字节数组转换为double数组
double[] audioData = new double[audioBytes.length / (sampleSize / 8)];
int index = 0;
for (int i = 0; i < audioBytes.length; i += (sampleSize / 8)) {
double sample = 0.0;
if (sampleSize == 8) {
sample = audioBytes[i];
} else {
sample = ((audioBytes[i + 1] & 0xff) << 8) | (audioBytes[i] & 0xff);
}
if (numChannels == 2) {
i++;
}
audioData[index++] = sample / 32768.0;
}
// 计算FFT
int fftSize = 1024;
double[] fftData = new double[fftSize];
Complex[] fftComplex = new Complex[fftSize];
for (int i = 0; i < fftSize; i++) {
fftData[i] = audioData[i];
fftComplex[i] = new Complex(fftData[i], 0.0);
}
fftComplex = FFT.fft(fftComplex);
// 绘制频谱
int width = 800;
int height = 600;
StdDraw.setCanvasSize(width, height);
StdDraw.setXscale(0, sampleRate / 2);
StdDraw.setYscale(-100, 100);
StdDraw.setPenRadius(0.005);
for (int i = 0; i <= fftSize / 2; i++) {
double freq = i * sampleRate / fftSize;
double magnitude = 10 * Math.log10(fftComplex[i].abs() * fftComplex[i].abs() / (fftSize * fftSize));
StdDraw.point(freq, magnitude);
}
} catch (Exception ex) {
ex.printStackTrace();
}
}
}
class Complex {
public double re, im;
public Complex(double real, double imag) {
re = real;
im = imag;
}
public Complex plus(Complex b) {
double real = re + b.re;
double imag = im + b.im;
return new Complex(real, imag);
}
public Complex minus(Complex b) {
double real = re - b.re;
double imag = im - b.im;
return new Complex(real, imag);
}
public Complex times(Complex b) {
double real = re * b.re - im * b.im;
double imag = re * b.im + im * b.re;
return new Complex(real, imag);
}
public double abs() {
return Math.sqrt(re * re + im * im);
}
}
class FFT {
public static Complex[] fft(Complex[] x) {
int N = x.length;
// 偶数项和奇数项分别进行FFT
Complex[] even = new Complex[N / 2];
Complex[] odd = new Complex[N / 2];
for (int k = 0; k < N / 2; k++) {
even[k] = x[2 * k];
odd[k] = x[2 * k + 1];
}
Complex[] q = fft(even);
Complex[] r = fft(odd);
// 合并两个FFT结果
Complex[] y = new Complex[N];
for (int k = 0; k < N / 2; k++) {
double kth = -2 * k * Math.PI / N;
Complex wk = new Complex(Math.cos(kth), Math.sin(kth));
y[k] = q[k].plus(wk.times(r[k]));
y[k + N / 2] = q[k].minus(wk.times(r[k]));
}
return y;
}
}
```
注意,这个代码使用了一个名为StdDraw的类,用于绘制频谱图。如果你没有这个类,可以到官网下载:https://introcs.cs.princeton.edu/java/stdlib/StdDraw.java.html
此外,代码中还使用了一个名为FFT的类,用于实现快速傅里叶变换。如果你对FFT算法不熟悉,可以参考这篇文章:https://www.jianshu.com/p/5a5e5a8c5e5b
最后,如果你想要更好的频谱分析效果,可以尝试使用更大的FFT窗口大小,更高的采样率,以及其他的信号处理技术。