samples/python/audio_spectrogram.py - 4.x - Alx89/OpenCV

samples/python/audio_spectrogram.py
804 строки33 KB
Sinitsina Maria
Merge pull request #20934 from SinM9:spectrogram_samples
14 дек 2021, 20:33
14 дек 2021, 20:33d8b1fc4
import numpy as np
import cv2 as cv
import math
import argparse

class AudioDrawing:
    '''
        Used for drawing audio graphics
    '''
    def __init__(self, args):

        self.inputType = args.inputType
        self.draw = args.draw
        self.graph = args.graph
        self.audio = cv.samples.findFile(args.audio)
        self.audioStream = args.audioStream

        self.windowType = args.windowType
        self.windLen = args.windLen
        self.overlap = args.overlap

        self.enableGrid = args.enableGrid

        self.rows = args.rows
        self.cols = args.cols

        self.xmarkup = args.xmarkup
        self.ymarkup = args.ymarkup
        self.zmarkup = args.zmarkup

        self.microTime = args.microTime
        self.frameSizeTime = args.frameSizeTime
        self.updateTime = args.updateTime
        self.waitTime = args.waitTime

        if self.initAndCheckArgs(args) is False:
            exit()


    def Draw(self):
        if self.draw == "static":

            if self.inputType == "file":
                samplingRate, inputAudio = self.readAudioFile(self.audio)

            elif self.inputType == "microphone":
                samplingRate, inputAudio = self.readAudioMicrophone()

            duration = len(inputAudio) // samplingRate

            # since the dimensional grid is counted in integer seconds,
            # if the input audio has an incomplete last second,
            # then it is filled with zeros to complete
            remainder = len(inputAudio) % samplingRate
            if remainder != 0:
                sizeToFullSec = samplingRate - remainder
                zeroArr = np.zeros(sizeToFullSec)
                inputAudio = np.concatenate((inputAudio, zeroArr), axis=0)
                duration += 1
                print("Update duration of audio to full second with ",
                    sizeToFullSec, " zero samples")
                print("New number of samples ", len(inputAudio))

            if duration <= self.xmarkup:
                self.xmarkup = duration + 1

            if self.graph == "ampl":
                imgAmplitude = self.drawAmplitude(inputAudio)
                imgAmplitude = self.drawAmplitudeScale(imgAmplitude, inputAudio, samplingRate)
                cv.imshow("Display window", imgAmplitude)
                cv.waitKey(0)

            elif self.graph == "spec":
                stft = self.STFT(inputAudio)
                imgSpec = self.drawSpectrogram(stft)
                imgSpec = self.drawSpectrogramColorbar(imgSpec, inputAudio, samplingRate, stft)
                cv.imshow("Display window", imgSpec)
                cv.waitKey(0)

            elif self.graph == "ampl_and_spec":
                imgAmplitude = self.drawAmplitude(inputAudio)
                imgAmplitude = self.drawAmplitudeScale(imgAmplitude, inputAudio, samplingRate)

                stft = self.STFT(inputAudio)
                imgSpec = self.drawSpectrogram(stft)
                imgSpec = self.drawSpectrogramColorbar(imgSpec, inputAudio, samplingRate, stft)

                imgTotal = self.concatenateImages(imgAmplitude, imgSpec)
                cv.imshow("Display window", imgTotal)
                cv.waitKey(0)

        elif self.draw == "dynamic":

            if self.inputType == "file":
                self.dynamicFile(self.audio)

            elif self.inputType == "microphone":
                self.dynamicMicrophone()


    def readAudioFile(self, file):
        cap = cv.VideoCapture(file)

        params = [cv.CAP_PROP_AUDIO_STREAM, self.audioStream,
                cv.CAP_PROP_VIDEO_STREAM, -1,
                cv.CAP_PROP_AUDIO_DATA_DEPTH, cv.CV_16S]
        params = np.asarray(params)

        cap.open(file, cv.CAP_ANY, params)
        if cap.isOpened() == False:
            print("Error : Can't read audio file: '", self.audio, "' with audioStream = ", self.audioStream)
            print("Error: problems with audio reading, check input arguments")
            exit()
        audioBaseIndex = int(cap.get(cv.CAP_PROP_AUDIO_BASE_INDEX))
        numberOfChannels = int(cap.get(cv.CAP_PROP_AUDIO_TOTAL_CHANNELS))

        print("CAP_PROP_AUDIO_DATA_DEPTH: ", str((int(cap.get(cv.CAP_PROP_AUDIO_DATA_DEPTH)))))
        print("CAP_PROP_AUDIO_SAMPLES_PER_SECOND: ", cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))
        print("CAP_PROP_AUDIO_TOTAL_CHANNELS: ", numberOfChannels)
        print("CAP_PROP_AUDIO_TOTAL_STREAMS: ", cap.get(cv.CAP_PROP_AUDIO_TOTAL_STREAMS))

        frame = []
        frame = np.asarray(frame)
        inputAudio = []

        while (1):
            if (cap.grab()):
                frame = []
                frame = np.asarray(frame)
                frame = cap.retrieve(frame, audioBaseIndex)
                for i in range(len(frame[1][0])):
                    inputAudio.append(frame[1][0][i])
            else:
                break

        inputAudio = np.asarray(inputAudio)
        print("Number of samples: ", len(inputAudio))
        samplingRate = int(cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))
        return samplingRate, inputAudio


    def readAudioMicrophone(self):
        cap = cv.VideoCapture()

        params = [cv.CAP_PROP_AUDIO_STREAM, 0, cv.CAP_PROP_VIDEO_STREAM, -1]
        params = np.asarray(params)

        cap.open(0, cv.CAP_ANY, params)
        if cap.isOpened() == False:
            print("Error: Can't open microphone")
            print("Error: problems with audio reading, check input arguments")
            exit()
        audioBaseIndex = int(cap.get(cv.CAP_PROP_AUDIO_BASE_INDEX))
        numberOfChannels = int(cap.get(cv.CAP_PROP_AUDIO_TOTAL_CHANNELS))

        print("CAP_PROP_AUDIO_DATA_DEPTH: ", str((int(cap.get(cv.CAP_PROP_AUDIO_DATA_DEPTH)))))
        print("CAP_PROP_AUDIO_SAMPLES_PER_SECOND: ", cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))
        print("CAP_PROP_AUDIO_TOTAL_CHANNELS: ", numberOfChannels)
        print("CAP_PROP_AUDIO_TOTAL_STREAMS: ", cap.get(cv.CAP_PROP_AUDIO_TOTAL_STREAMS))

        cvTickFreq = cv.getTickFrequency()
        sysTimeCurr = cv.getTickCount()
        sysTimePrev = sysTimeCurr

        frame = []
        frame = np.asarray(frame)
        inputAudio = []

        while ((sysTimeCurr - sysTimePrev) / cvTickFreq < self.microTime):
            if (cap.grab()):
                frame = []
                frame = np.asarray(frame)
                frame = cap.retrieve(frame, audioBaseIndex)
                for i in range(len(frame[1][0])):
                    inputAudio.append(frame[1][0][i])
                sysTimeCurr = cv.getTickCount()
            else:
                print("Error: Grab error")
                break

        inputAudio = np.asarray(inputAudio)
        print("Number of samples: ", len(inputAudio))
        samplingRate = int(cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))

        return samplingRate, inputAudio


    def drawAmplitude(self, inputAudio):
        color = (247, 111, 87)
        thickness = 5
        frameVectorRows = 500
        middle = frameVectorRows // 2

        # usually the input data is too big, so it is necessary
        # to reduce size using interpolation of data
        frameVectorCols = 40000
        if len(inputAudio) < frameVectorCols:
            frameVectorCols = len(inputAudio)

        img = np.zeros((frameVectorRows, frameVectorCols, 3), np.uint8)
        img += 255  # white background

        audio = np.array(0)
        audio = cv.resize(inputAudio, (1, frameVectorCols), interpolation=cv.INTER_LINEAR)
        reshapeAudio = np.reshape(audio, (-1))

        # normalization data by maximum element
        minCv, maxCv, _, _ = cv.minMaxLoc(reshapeAudio)
        maxElem = int(max(abs(minCv), abs(maxCv)))

        # if all data values are zero (silence)
        if maxElem == 0:
            maxElem = 1
        for i in range(len(reshapeAudio)):
            reshapeAudio[i] = middle - reshapeAudio[i] * middle // maxElem

        for i in range(1, frameVectorCols, 1):
            cv.line(img, (i - 1, int(reshapeAudio[i - 1])), (i, int(reshapeAudio[i])), color, thickness)

        img = cv.resize(img, (900, 400), interpolation=cv.INTER_AREA)
        return img


    def drawAmplitudeScale(self, inputImg, inputAudio, samplingRate, xmin=None, xmax=None):
        # function of layout drawing for graph of volume amplitudes
        # x axis for time
        # y axis for amplitudes

        # parameters for the new image size
        preCol = 100
        aftCol = 100
        preLine = 40
        aftLine = 50

        frameVectorRows = inputImg.shape[0]
        frameVectorCols = inputImg.shape[1]

        totalRows = preLine + frameVectorRows + aftLine
        totalCols = preCol + frameVectorCols + aftCol

        imgTotal = np.zeros((totalRows, totalCols, 3), np.uint8)
        imgTotal += 255  # white background
        imgTotal[preLine: preLine + frameVectorRows, preCol: preCol + frameVectorCols] = inputImg

        # calculating values on x axis
        if xmin is None:
            xmin = 0
        if xmax is None:
            xmax = len(inputAudio) / samplingRate

        if xmax > self.xmarkup:
            xList = np.linspace(xmin, xmax, self.xmarkup).astype(int)
        else:
            # this case is used to display a dynamic update
            tmp = np.arange(xmin, xmax, 1).astype(int) + 1
            xList = np.concatenate((np.zeros(self.xmarkup - len(tmp)), tmp[:]), axis=None)

        # calculating values on y axis
        ymin = np.min(inputAudio)
        ymax = np.max(inputAudio)
        yList = np.linspace(ymin, ymax, self.ymarkup)

        # parameters for layout drawing
        textThickness = 1
        gridThickness = 1
        gridColor = (0, 0, 0)
        textColor = (0, 0, 0)
        font = cv.FONT_HERSHEY_SIMPLEX
        fontScale = 0.5

        # horizontal axis under the graph
        cv.line(imgTotal, (preCol, totalRows - aftLine),
                (preCol + frameVectorCols, totalRows - aftLine),
                gridColor, gridThickness)
        # vertical axis for amplitude
        cv.line(imgTotal, (preCol, preLine), (preCol, preLine + frameVectorRows),
                gridColor, gridThickness)

        # parameters for layout calculation
        serifSize = 10
        indentDownX = serifSize * 2
        indentDownY = serifSize // 2
        indentLeftX = serifSize
        indentLeftY = 2 * preCol // 3

        # drawing layout for x axis
        numX = frameVectorCols // (self.xmarkup - 1)
        for i in range(len(xList)):
            a1 = preCol + i * numX
            a2 = frameVectorRows + preLine
            b1 = a1
            b2 = a2 + serifSize
            if self.enableGrid is True:
                d1 = a1
                d2 = preLine
                cv.line(imgTotal, (a1, a2), (d1, d2), gridColor, gridThickness)
            cv.line(imgTotal, (a1, a2), (b1, b2), gridColor, gridThickness)
            cv.putText(imgTotal, str(int(xList[i])), (b1 - indentLeftX, b2 + indentDownX),
                    font, fontScale, textColor, textThickness)

        # drawing layout for y axis
        numY = frameVectorRows // (self.ymarkup - 1)
        for i in range(len(yList)):
            a1 = preCol
            a2 = totalRows - aftLine - i * numY
            b1 = preCol - serifSize
            b2 = a2
            if self.enableGrid is True:
                d1 = preCol + frameVectorCols
                d2 = a2
                cv.line(imgTotal, (a1, a2), (d1, d2), gridColor, gridThickness)
            cv.line(imgTotal, (a1, a2), (b1, b2), gridColor, gridThickness)
            cv.putText(imgTotal, str(int(yList[i])), (b1 - indentLeftY, b2 + indentDownY),
                    font, fontScale, textColor, textThickness)
        imgTotal = cv.resize(imgTotal, (self.cols, self.rows), interpolation=cv.INTER_AREA)
        return imgTotal


    def STFT(self, inputAudio):
        """
        The Short-time Fourier transform (STFT), is a Fourier-related transform used to determine
        the sinusoidal frequency and phase content of local sections of a signal as it changes over
        time.
        In practice, the procedure for computing STFTs is to divide a longer time signal into
        shorter segments of equal length and then compute the Fourier transform separately on each
        shorter segment. This reveals the Fourier spectrum on each shorter segment. One then usually
        plots the changing spectra as a function of time, known as a spectrogram or waterfall plot.

        https://en.wikipedia.org/wiki/Short-time_Fourier_transform
        """

        time_step = self.windLen - self.overlap
        stft = []

        if self.windowType == "Hann":
            # https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
            Hann_wind = []
            for i in range (1 - self.windLen, self.windLen, 2):
                Hann_wind.append(i * (0.5 + 0.5 * math.cos(math.pi * i / (self.windLen - 1))))
            Hann_wind = np.asarray(Hann_wind)

        elif self.windowType == "Hamming":
            # https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
            Hamming_wind = []
            for i in range (1 - self.windLen, self.windLen, 2):
                Hamming_wind.append(i * (0.53836 - 0.46164 * (math.cos(2 * math.pi * i / (self.windLen - 1)))))
            Hamming_wind = np.asarray(Hamming_wind)

        for index in np.arange(0, len(inputAudio), time_step).astype(int):

            section = inputAudio[index:index + self.windLen]
            zeroArray = np.zeros(self.windLen - len(section))
            section = np.concatenate((section, zeroArray), axis=None)

            if self.windowType == "Hann":
                section *= Hann_wind
            elif self.windowType == "Hamming":
                section *= Hamming_wind

            dst = np.empty(0)
            dst = cv.dft(section, dst, flags=cv.DFT_COMPLEX_OUTPUT)
            reshape_dst = np.reshape(dst, (-1))
            # we need only the first part of the spectrum, the second part is symmetrical
            complexArr = np.zeros(len(dst) // 4, dtype=complex)
            for i in range(len(dst) // 4):
                complexArr[i] = complex(reshape_dst[2 * i], reshape_dst[2 * i + 1])
            stft.append(np.abs(complexArr))

        stft = np.array(stft).transpose()
        # convert elements to the decibel scale
        np.log10(stft, out=stft, where=(stft != 0.))
        return 10 * stft


    def drawSpectrogram(self, stft):

        frameVectorRows = stft.shape[0]
        frameVectorCols = stft.shape[1]

        # Normalization of image values from 0 to 255 to get more contrast image
        # and this normalization will be taken into account in the scale drawing
        colormapImageRows = 255

        imgSpec = np.zeros((frameVectorRows, frameVectorCols, 3), np.uint8)
        stftMat = np.zeros((frameVectorRows, frameVectorCols), np.float64)
        cv.normalize(stft, stftMat, 1.0, 0.0, cv.NORM_INF)

        for i in range(frameVectorRows):
            for j in range(frameVectorCols):
                imgSpec[frameVectorRows - i - 1, j] = int(stftMat[i][j] * colormapImageRows)

        imgSpec = cv.applyColorMap(imgSpec, cv.COLORMAP_INFERNO)
        imgSpec = cv.resize(imgSpec, (900, 400), interpolation=cv.INTER_LINEAR)
        return imgSpec


    def drawSpectrogramColorbar(self, inputImg, inputAudio, samplingRate, stft, xmin=None, xmax=None):
        # function of layout drawing for the three-dimensional graph of the spectrogram
        # x axis for time
        # y axis for frequencies
        # z axis for magnitudes of frequencies shown by color scale

        # parameters for the new image size
        preCol = 100
        aftCol = 100
        preLine = 40
        aftLine = 50
        colColor = 20
        ind_col = 20

        frameVectorRows = inputImg.shape[0]
        frameVectorCols = inputImg.shape[1]

        totalRows = preLine + frameVectorRows + aftLine
        totalCols = preCol + frameVectorCols + aftCol + colColor

        imgTotal = np.zeros((totalRows, totalCols, 3), np.uint8)
        imgTotal += 255  # white background
        imgTotal[preLine: preLine + frameVectorRows, preCol: preCol + frameVectorCols] = inputImg

        # colorbar image due to drawSpectrogram(..) picture has been normalised from 255 to 0,
        # so here colorbar has values from 255 to 0
        colorArrSize = 256
        imgColorBar = np.zeros((colorArrSize, colColor, 1), np.uint8)

        for i in range(colorArrSize):
            imgColorBar[i] += colorArrSize - 1 - i

        imgColorBar = cv.applyColorMap(imgColorBar, cv.COLORMAP_INFERNO)
        imgColorBar = cv.resize(imgColorBar, (colColor, frameVectorRows), interpolation=cv.INTER_AREA)  #

        imgTotal[preLine: preLine + frameVectorRows,
        preCol + frameVectorCols + ind_col:
        preCol + frameVectorCols + ind_col + colColor] = imgColorBar

        # calculating values on x axis
        if xmin is None:
            xmin = 0
        if xmax is None:
            xmax = len(inputAudio) / samplingRate
        if xmax > self.xmarkup:
            xList = np.linspace(xmin, xmax, self.xmarkup).astype(int)
        else:
            # this case is used to display a dynamic update
            tmpXList = np.arange(xmin, xmax, 1).astype(int) + 1
            xList = np.concatenate((np.zeros(self.xmarkup - len(tmpXList)), tmpXList[:]), axis=None)

        # calculating values on y axis
        # according to the Nyquist sampling theorem,
        # signal should posses frequencies equal to half of sampling rate
        ymin = 0
        ymax = int(samplingRate / 2.)
        yList = np.linspace(ymin, ymax, self.ymarkup).astype(int)

        # calculating values on z axis
        zList = np.linspace(np.min(stft), np.max(stft), self.zmarkup)

        # parameters for layout drawing
        textThickness = 1
        textColor = (0, 0, 0)
        gridThickness = 1
        gridColor = (0, 0, 0)
        font = cv.FONT_HERSHEY_SIMPLEX
        fontScale = 0.5

        serifSize = 10
        indentDownX = serifSize * 2
        indentDownY = serifSize // 2
        indentLeftX = serifSize
        indentLeftY = 2 * preCol // 3

        # horizontal axis
        cv.line(imgTotal, (preCol, totalRows - aftLine), (preCol + frameVectorCols, totalRows - aftLine),
                gridColor, gridThickness)
        # vertical axis
        cv.line(imgTotal, (preCol, preLine), (preCol, preLine + frameVectorRows),
                gridColor, gridThickness)

        # drawing layout for x axis
        numX = frameVectorCols // (self.xmarkup - 1)
        for i in range(len(xList)):
            a1 = preCol + i * numX
            a2 = frameVectorRows + preLine
            b1 = a1
            b2 = a2 + serifSize
            cv.line(imgTotal, (a1, a2), (b1, b2), gridColor, gridThickness)
            cv.putText(imgTotal, str(int(xList[i])), (b1 - indentLeftX, b2 + indentDownX),
                    font, fontScale, textColor, textThickness)

        # drawing layout for y axis
        numY = frameVectorRows // (self.ymarkup - 1)
        for i in range(len(yList)):
            a1 = preCol
            a2 = totalRows - aftLine - i * numY
            b1 = preCol - serifSize
            b2 = a2
            cv.line(imgTotal, (a1, a2), (b1, b2), gridColor, gridThickness)
            cv.putText(imgTotal, str(int(yList[i])), (b1 - indentLeftY, b2 + indentDownY),
                    font, fontScale, textColor, textThickness)

        # drawing layout for z axis
        numZ = frameVectorRows // (self.zmarkup - 1)
        for i in range(len(zList)):
            a1 = preCol + frameVectorCols + ind_col + colColor
            a2 = totalRows - aftLine - i * numZ
            b1 = a1 + serifSize
            b2 = a2
            cv.line(imgTotal, (a1, a2), (b1, b2), gridColor, gridThickness)
            cv.putText(imgTotal, str(int(zList[i])), (b1 + 10, b2 + indentDownY),
                    font, fontScale, textColor, textThickness)
        imgTotal = cv.resize(imgTotal, (self.cols, self.rows), interpolation=cv.INTER_AREA)
        return imgTotal


    def concatenateImages(self, img1, img2):
        # first image will be under the second image
        totalRows = img1.shape[0] + img2.shape[0]
        totalCols = max(img1.shape[1], img2.shape[1])

        # if images columns do not match, the difference is filled in white
        imgTotal = np.zeros((totalRows, totalCols, 3), np.uint8)
        imgTotal += 255

        imgTotal[:img1.shape[0], :img1.shape[1]] = img1
        imgTotal[img2.shape[0]:, :img2.shape[1]] = img2

        return imgTotal


    def dynamicFile(self, file):
        cap = cv.VideoCapture(file)
        params = [cv.CAP_PROP_AUDIO_STREAM, self.audioStream,
                cv.CAP_PROP_VIDEO_STREAM, -1,
                cv.CAP_PROP_AUDIO_DATA_DEPTH, cv.CV_16S]
        params = np.asarray(params)

        cap.open(file, cv.CAP_ANY, params)
        if cap.isOpened() == False:
            print("ERROR! Can't to open file")
            return

        audioBaseIndex = int(cap.get(cv.CAP_PROP_AUDIO_BASE_INDEX))
        numberOfChannels = int(cap.get(cv.CAP_PROP_AUDIO_TOTAL_CHANNELS))
        samplingRate = int(cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))

        print("CAP_PROP_AUDIO_DATA_DEPTH: ", str((int(cap.get(cv.CAP_PROP_AUDIO_DATA_DEPTH)))))
        print("CAP_PROP_AUDIO_SAMPLES_PER_SECOND: ", cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))
        print("CAP_PROP_AUDIO_TOTAL_CHANNELS: ", numberOfChannels)
        print("CAP_PROP_AUDIO_TOTAL_STREAMS: ", cap.get(cv.CAP_PROP_AUDIO_TOTAL_STREAMS))

        step = int(self.updateTime * samplingRate)
        frameSize = int(self.frameSizeTime * samplingRate)
        # since the dimensional grid is counted in integer seconds,
        # if duration of audio frame is less than xmarkup, to avoid an incorrect display,
        # xmarkup will be taken equal to duration
        if self.frameSizeTime <= self.xmarkup:
            self.xmarkup = self.frameSizeTime

        buffer = []
        section = np.zeros(frameSize, dtype=np.int16)
        currentSamples = 0

        while (1):
            if (cap.grab()):
                frame = []
                frame = np.asarray(frame)
                frame = cap.retrieve(frame, audioBaseIndex)

                for i in range(len(frame[1][0])):
                    buffer.append(frame[1][0][i])

                buffer_size = len(buffer)
                if (buffer_size >= step):

                    section = list(section)
                    currentSamples += step

                    del section[0:step]
                    section.extend(buffer[0:step])
                    del buffer[0:step]

                    section = np.asarray(section)

                    if currentSamples < frameSize:
                        xmin = 0
                        xmax = (currentSamples) / samplingRate
                    else:
                        xmin = (currentSamples - frameSize) / samplingRate + 1
                        xmax = (currentSamples) / samplingRate

                    if self.graph == "ampl":
                        imgAmplitude = self.drawAmplitude(section)
                        imgAmplitude = self.drawAmplitudeScale(imgAmplitude, section, samplingRate, xmin, xmax)
                        cv.imshow("Display amplitude graph", imgAmplitude)
                        cv.waitKey(self.waitTime)

                    elif self.graph == "spec":
                        stft = self.STFT(section)
                        imgSpec = self.drawSpectrogram(stft)
                        imgSpec = self.drawSpectrogramColorbar(imgSpec, section, samplingRate, stft, xmin, xmax)
                        cv.imshow("Display spectrogram", imgSpec)
                        cv.waitKey(self.waitTime)

                    elif self.graph == "ampl_and_spec":

                        imgAmplitude = self.drawAmplitude(section)
                        stft = self.STFT(section)
                        imgSpec = self.drawSpectrogram(stft)

                        imgAmplitude = self.drawAmplitudeScale(imgAmplitude, section, samplingRate, xmin, xmax)
                        imgSpec = self.drawSpectrogramColorbar(imgSpec, section, samplingRate, stft, xmin, xmax)

                        imgTotal = self.concatenateImages(imgAmplitude, imgSpec)
                        cv.imshow("Display amplitude graph and spectrogram", imgTotal)
                        cv.waitKey(self.waitTime)
            else:
                break


    def dynamicMicrophone(self):
        cap = cv.VideoCapture()
        params = [cv.CAP_PROP_AUDIO_STREAM, 0, cv.CAP_PROP_VIDEO_STREAM, -1]
        params = np.asarray(params)

        cap.open(0, cv.CAP_ANY, params)
        if cap.isOpened() == False:
            print("ERROR! Can't to open file")
            return
        audioBaseIndex = int(cap.get(cv.CAP_PROP_AUDIO_BASE_INDEX))
        numberOfChannels = int(cap.get(cv.CAP_PROP_AUDIO_TOTAL_CHANNELS))

        print("CAP_PROP_AUDIO_DATA_DEPTH: ", str((int(cap.get(cv.CAP_PROP_AUDIO_DATA_DEPTH)))))
        print("CAP_PROP_AUDIO_SAMPLES_PER_SECOND: ", cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))
        print("CAP_PROP_AUDIO_TOTAL_CHANNELS: ", numberOfChannels)
        print("CAP_PROP_AUDIO_TOTAL_STREAMS: ", cap.get(cv.CAP_PROP_AUDIO_TOTAL_STREAMS))

        frame = []
        frame = np.asarray(frame)
        samplingRate = int(cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))

        step = int(self.updateTime * samplingRate)
        frameSize = int(self.frameSizeTime * samplingRate)
        self.xmarkup = self.frameSizeTime

        currentSamples = 0

        buffer = []
        section = np.zeros(frameSize, dtype=np.int16)

        cvTickFreq = cv.getTickFrequency()
        sysTimeCurr = cv.getTickCount()
        sysTimePrev = sysTimeCurr
        self.waitTime = self.updateTime * 1000
        while ((sysTimeCurr - sysTimePrev) / cvTickFreq < self.microTime):
            if (cap.grab()):
                frame = []
                frame = np.asarray(frame)
                frame = cap.retrieve(frame, audioBaseIndex)

                for i in range(len(frame[1][0])):
                    buffer.append(frame[1][0][i])

                sysTimeCurr = cv.getTickCount()
                buffer_size = len(buffer)
                if (buffer_size >= step):

                    section = list(section)
                    currentSamples += step

                    del section[0:step]
                    section.extend(buffer[0:step])
                    del buffer[0:step]

                    section = np.asarray(section)

                    if currentSamples < frameSize:
                        xmin = 0
                        xmax = (currentSamples) / samplingRate
                    else:
                        xmin = (currentSamples - frameSize) / samplingRate + 1
                        xmax = (currentSamples) / samplingRate

                    if self.graph == "ampl":
                        imgAmplitude = self.drawAmplitude(section)
                        imgAmplitude = self.drawAmplitudeScale(imgAmplitude, section, samplingRate, xmin, xmax)
                        cv.imshow("Display amplitude graph", imgAmplitude)
                        cv.waitKey(self.waitTime)

                    elif self.graph == "spec":
                        stft = self.STFT(section)
                        imgSpec = self.drawSpectrogram(stft)
                        imgSpec = self.drawSpectrogramColorbar(imgSpec, section, samplingRate, stft, xmin, xmax)
                        cv.imshow("Display spectrogram", imgSpec)
                        cv.waitKey(self.waitTime)

                    elif self.graph == "ampl_and_spec":
                        imgAmplitude = self.drawAmplitude(section)
                        stft = self.STFT(section)
                        imgSpec = self.drawSpectrogram(stft)

                        imgAmplitude = self.drawAmplitudeScale(imgAmplitude, section, samplingRate, xmin, xmax)
                        imgSpec = self.drawSpectrogramColorbar(imgSpec, section, samplingRate, stft, xmin, xmax)

                        imgTotal = self.concatenateImages(imgAmplitude, imgSpec)
                        cv.imshow("Display amplitude graph and spectrogram", imgTotal)
                        cv.waitKey(self.waitTime)
            else:
                break


    def initAndCheckArgs(self, args):
        if args.inputType != "file" and args.inputType != "microphone":
            print("Error: ", args.inputType, " input method doesnt exist")
            return False
        if args.draw != "static" and args.draw != "dynamic":
            print("Error: ", args.draw, " draw type doesnt exist")
            return False
        if args.graph != "ampl" and args.graph != "spec" and args.graph != "ampl_and_spec":
            print("Error: ", args.graph, " type of graph doesnt exist")
            return False
        if args.windowType != "Rect" and args.windowType != "Hann" and args.windowType != "Hamming":
            print("Error: ", args.windowType, " type of window doesnt exist")
            return False
        if args.windLen <= 0:
            print("Error: windLen = ", args.windLen, " - incorrect value. Must be > 0")
            return False
        if args.overlap <= 0:
            print("Error: overlap = ", args.overlap, " - incorrect value. Must be > 0")
            return False
        if args.rows <= 0:
            print("Error: rows = ", args.rows, " - incorrect value. Must be > 0")
            return False
        if args.cols <= 0:
            print("Error: cols = ", args.cols, " - incorrect value. Must be > 0")
            return False
        if args.xmarkup < 2:
            print("Error: xmarkup = ", args.xmarkup, " - incorrect value. Must be >= 2")
            return False
        if args.ymarkup < 2:
            print("Error: ymarkup = ", args.ymarkup, " - incorrect value. Must be >= 2")
            return False
        if args.zmarkup < 2:
            print("Error: zmarkup = ", args.zmarkup, " - incorrect value. Must be >= 2")
            return False
        if args.microTime <= 0:
            print("Error: microTime = ", args.microTime, " - incorrect value. Must be > 0")
            return False
        if args.frameSizeTime <= 0:
            print("Error: frameSizeTime = ", args.frameSizeTime, " - incorrect value. Must be > 0")
            return False
        if args.updateTime <= 0:
            print("Error: updateTime = ", args.updateTime, " - incorrect value. Must be > 0")
            return False
        if args.waitTime < 0:
            print("Error: waitTime = ", args.waitTime, " - incorrect value. Must be >= 0")
            return False
        return True


if __name__ == "__main__":

    parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter,
                                     description='''this sample draws a volume graph and/or spectrogram of audio/video files and microphone\nDefault usage: ./Spectrogram.exe''')

    parser.add_argument("-i", "--inputType", dest="inputType", type=str, default="file", help="file or microphone")
    parser.add_argument("-d", "--draw", dest="draw", type=str, default="static",
                        help="type of drawing: static - for plotting graph(s) across the entire input audio; dynamic - for plotting graph(s) in a time-updating window")
    parser.add_argument("-g", "--graph", dest="graph", type=str, default="ampl_and_spec",
                        help="type of graph: amplitude graph or/and spectrogram. Please use tags below : ampl - draw the amplitude graph; spec - draw the spectrogram; ampl_and_spec - draw the amplitude graph and spectrogram on one image under each other")

    parser.add_argument("-a", "--audio", dest="audio", type=str, default='Megamind.avi',
                        help="name and path to file")
    parser.add_argument("-s", "--audioStream", dest="audioStream", type=int, default=1,
                        help=" CAP_PROP_AUDIO_STREAM value")

    parser.add_argument("-t", '--windowType', dest="windowType", type=str, default="Rect",
                        help="type of window for STFT. Please use tags below : Rect/Hann/Hamming")
    parser.add_argument("-l", '--windLen', dest="windLen", type=int, default=256, help="size of window for STFT")
    parser.add_argument("-o", '--overlap', dest="overlap", type=int, default=128, help="overlap of windows for STFT")

    parser.add_argument("-gd", '--grid', dest="enableGrid", type=bool, default=False, help="grid on amplitude graph(on/off)")

    parser.add_argument("-r", '--rows', dest="rows", type=int, default=400, help="rows of output image")
    parser.add_argument("-c", '--cols', dest="cols", type=int, default=900, help="cols of output image")

    parser.add_argument("-x", '--xmarkup', dest="xmarkup", type=int, default=5,
                        help="number of x axis divisions (time asix)")
    parser.add_argument("-y", '--ymarkup', dest="ymarkup", type=int, default=5,
                        help="number of y axis divisions (frequency or/and amplitude axis)")  # ?
    parser.add_argument("-z", '--zmarkup', dest="zmarkup", type=int, default=5,
                        help="number of z axis divisions (colorbar)")  # ?

    parser.add_argument("-m", '--microTime', dest="microTime", type=int, default=20,
                        help="time of recording audio with microphone in seconds")
    parser.add_argument("-f", '--frameSizeTime', dest="frameSizeTime", type=int, default=5,
                        help="size of sliding window in seconds")
    parser.add_argument("-u", '--updateTime', dest="updateTime", type=int, default=1,
                        help="update time of sliding window in seconds")
    parser.add_argument("-w", '--waitTime', dest="waitTime", type=int, default=10,
                        help="parameter to cv.waitKey() for dynamic update, takes values in milliseconds")

    args = parser.parse_args()

    AudioDrawing(args).Draw()