Question

我是python的学习者，开发了一个与图像分析相关的小项目，学习了我试图理解各种python代码的概念，但这次我很糟糕，可以解释一下这段代码吗？特别是FFT部分？

class HeartMonitor(object):

    def __init__(self, window_duration, fps = 30, min_bpm = 50, max_bpm = 200):
        """
        Class which detects heart-beats in a sequence of image colour samples.
        @param window_duration The number of seconds of samples to use
        @param fps             The nominal sample rate
        @param min_bpm         Minimum cut-off for possible heartrates
        @param max_bpm         Maximum cut-off for possible heartrates
        """

        self.min_bpm = min_bpm
        self.max_bpm = max_bpm

        # The maximum number of samples to buffer
        self.buf_size = int(window_duration*fps)

        # Buffer of (timestamp, value) tuples
        self.buf = []


    @property
    def fps(self):
        """
        The average framerate/samplerate of the buffer
        """
        return float(len(self.buf)) / (self.buf[-1][0] - self.buf[0][0])


    def get_fft(self):
        """
        Perform an Fast-Fourier-Transform on the buffer and return (magnitude,
        phase) tuples for each of the bins.
        """
        # Get the "ideal" evenly spaced times
        even_times = numpy.linspace(self.buf[0][0], self.buf[-1][0], len(self.buf))

        # Interpolate the data to generate evenly temporally spaced samples
        interpolated = numpy.interp(even_times, *zip(*self.buf))

        # Perform the FFT
        fft = numpy.fft.rfft(interpolated)
        return zip(numpy.abs(fft), numpy.angle(fft))

Answer 1

numpy.fft.rfft是一个库函数，用于计算真实数据的fft

样本需要在时域中均匀分布。

由于某些样本在buf中可能不均匀，因此使用numpy.interp进行插值

self.buf[0]是buf的第一项 self.buf[-1]是buf的最后一项 len(self.buf)是buf

中的项目数

因此，您最终得到相同数量的样本，但沿时间轴移动，使它们均匀分布（存储在变量interpolated中）。

现在interpolated可以传递给numpy.fft.rfft

如何fft（快速傅立叶变换）工作

1 个答案: