我正在尝试创建一个应用程序，可以使用计算机网络摄像头检测心跳。我已经在编写代码两周了，开发了以下代码：
1. 使用opencv检测脸部 2. 获取额头图像 3. 应用滤镜将其转换为灰度图像[您可以跳过此步骤] 4. 每帧查找绿色像素的平均强度 5. 将平均值保存到数组中 6. 应用FFT（我使用了minim库）从FFT频谱中提取心跳（在这里，我需要一些帮助）
在这里，我需要帮助从FFT频谱中提取心跳。有人能帮我吗？这里是使用Python开发的类似应用程序，但我无法理解这段代码，所以我正在处理相同的问题。有人能帮我理解这个Python代码的部分，它正在提取心跳吗？
1. 使用opencv检测脸部 2. 获取额头图像 3. 应用滤镜将其转换为灰度图像[您可以跳过此步骤] 4. 每帧查找绿色像素的平均强度 5. 将平均值保存到数组中 6. 应用FFT（我使用了minim库）从FFT频谱中提取心跳（在这里，我需要一些帮助）
在这里，我需要帮助从FFT频谱中提取心跳。有人能帮我吗？这里是使用Python开发的类似应用程序，但我无法理解这段代码，所以我正在处理相同的问题。有人能帮我理解这个Python代码的部分，它正在提取心跳吗？
//---------import required ilbrary -----------
import gab.opencv.*;
import processing.video.*;
import java.awt.*;
import java.util.*;
import ddf.minim.analysis.*;
import ddf.minim.*;
//----------create objects---------------------------------
Capture video; // camera object
OpenCV opencv; // opencv object
Minim minim;
FFT fft;
//IIRFilter filt;
//--------- Create ArrayList--------------------------------
ArrayList<Float> poop = new ArrayList();
float[] sample;
int bufferSize = 128;
int sampleRate = 512;
int bandWidth = 20;
int centerFreq = 80;
//---------------------------------------------------
void setup() {
size(640, 480); // size of the window
minim = new Minim(this);
fft = new FFT( bufferSize, sampleRate);
video = new Capture(this, 640/2, 480/2); // initializing video object
opencv = new OpenCV(this, 640/2, 480/2); // initializing opencv object
opencv.loadCascade(OpenCV.CASCADE_FRONTALFACE); // loading haar cscade file for face detection
video.start(); // start video
}
void draw() {
background(0);
// image(video, 0, 0 ); // show video in the background
opencv.loadImage(video);
Rectangle[] faces = opencv.detect();
video.loadPixels();
//------------ Finding faces in the video -----------
float gavg = 0;
for (int i = 0; i < faces.length; i++) {
noFill();
stroke(#FFB700); // yellow rectangle
rect(faces[i].x, faces[i].y, faces[i].width, faces[i].height); // creating rectangle around the face (YELLOW)
stroke(#0070FF); //blue rectangle
rect(faces[i].x, faces[i].y, faces[i].width, faces[i].height-2*faces[i].height/3); // creating a blue rectangle around the forehead
//-------------------- storing forehead white rectangle part into an image -------------------
stroke(0, 255, 255);
rect(faces[i].x+faces[i].width/2-15, faces[i].y+15, 30, 15);
PImage img = video.get(faces[i].x+faces[i].width/2-15, faces[i].y+15, 30, 15); // storing the forehead aera into a image
img.loadPixels();
img.filter(GRAY); // converting capture image rgb to gray
img.updatePixels();
int numPixels = img.width*img.height;
for (int px = 0; px < numPixels; px++) { // For each pixel in the video frame...
final color c = img.pixels[px];
final color luminG = c>>010 & 0xFF;
final float luminRangeG = luminG/255.0;
gavg = gavg + luminRangeG;
}
//--------------------------------------------------------
gavg = gavg/numPixels;
if (poop.size()< bufferSize) {
poop.add(gavg);
}
else poop.remove(0);
}
sample = new float[poop.size()];
for (int i=0;i<poop.size();i++) {
Float f = (float) poop.get(i);
sample[i] = f;
}
if (sample.length>=bufferSize) {
//fft.window(FFT.NONE);
fft.forward(sample, 0);
// bpf = new BandPass(centerFreq, bandwidth, sampleRate);
// in.addEffect(bpf);
float bw = fft.getBandWidth(); // returns the width of each frequency band in the spectrum (in Hz).
println(bw); // returns 21.5332031 Hz for spectrum [0] & [512]
for (int i = 0; i < fft.specSize(); i++)
{
// println( " Freq" + max(sample));
stroke(0, 255, 0);
float x = map(i, 0, fft.specSize(), 0, width);
line( x, height, x, height - fft.getBand(i)*100);
// text("FFT FREQ " + fft.getFreq(i), width/2-100, 10*(i+1));
// text("FFT BAND " + fft.getBand(i), width/2+100, 10*(i+1));
}
}
else {
println(sample.length + " " + poop.size());
}
}
void captureEvent(Capture c) {
c.read();
}