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805 lines
33 KiB
Python
805 lines
33 KiB
Python
import numpy as np
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import cv2 as cv
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import math
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import argparse
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class AudioDrawing:
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'''
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Used for drawing audio graphics
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'''
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def __init__(self, args):
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self.inputType = args.inputType
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self.draw = args.draw
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self.graph = args.graph
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self.audio = cv.samples.findFile(args.audio)
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self.audioStream = args.audioStream
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self.windowType = args.windowType
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self.windLen = args.windLen
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self.overlap = args.overlap
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self.enableGrid = args.enableGrid
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self.rows = args.rows
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self.cols = args.cols
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self.xmarkup = args.xmarkup
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self.ymarkup = args.ymarkup
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self.zmarkup = args.zmarkup
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self.microTime = args.microTime
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self.frameSizeTime = args.frameSizeTime
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self.updateTime = args.updateTime
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self.waitTime = args.waitTime
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if self.initAndCheckArgs(args) is False:
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exit()
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def Draw(self):
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if self.draw == "static":
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if self.inputType == "file":
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samplingRate, inputAudio = self.readAudioFile(self.audio)
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elif self.inputType == "microphone":
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samplingRate, inputAudio = self.readAudioMicrophone()
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duration = len(inputAudio) // samplingRate
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# since the dimensional grid is counted in integer seconds,
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# if the input audio has an incomplete last second,
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# then it is filled with zeros to complete
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remainder = len(inputAudio) % samplingRate
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if remainder != 0:
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sizeToFullSec = samplingRate - remainder
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zeroArr = np.zeros(sizeToFullSec)
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inputAudio = np.concatenate((inputAudio, zeroArr), axis=0)
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duration += 1
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print("Update duration of audio to full second with ",
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sizeToFullSec, " zero samples")
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print("New number of samples ", len(inputAudio))
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if duration <= self.xmarkup:
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self.xmarkup = duration + 1
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if self.graph == "ampl":
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imgAmplitude = self.drawAmplitude(inputAudio)
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imgAmplitude = self.drawAmplitudeScale(imgAmplitude, inputAudio, samplingRate)
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cv.imshow("Display window", imgAmplitude)
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cv.waitKey(0)
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elif self.graph == "spec":
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stft = self.STFT(inputAudio)
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imgSpec = self.drawSpectrogram(stft)
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imgSpec = self.drawSpectrogramColorbar(imgSpec, inputAudio, samplingRate, stft)
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cv.imshow("Display window", imgSpec)
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cv.waitKey(0)
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elif self.graph == "ampl_and_spec":
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imgAmplitude = self.drawAmplitude(inputAudio)
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imgAmplitude = self.drawAmplitudeScale(imgAmplitude, inputAudio, samplingRate)
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stft = self.STFT(inputAudio)
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imgSpec = self.drawSpectrogram(stft)
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imgSpec = self.drawSpectrogramColorbar(imgSpec, inputAudio, samplingRate, stft)
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imgTotal = self.concatenateImages(imgAmplitude, imgSpec)
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cv.imshow("Display window", imgTotal)
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cv.waitKey(0)
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elif self.draw == "dynamic":
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if self.inputType == "file":
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self.dynamicFile(self.audio)
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elif self.inputType == "microphone":
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self.dynamicMicrophone()
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def readAudioFile(self, file):
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cap = cv.VideoCapture(file)
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params = [cv.CAP_PROP_AUDIO_STREAM, self.audioStream,
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cv.CAP_PROP_VIDEO_STREAM, -1,
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cv.CAP_PROP_AUDIO_DATA_DEPTH, cv.CV_16S]
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params = np.asarray(params)
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cap.open(file, cv.CAP_ANY, params)
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if cap.isOpened() == False:
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print("Error : Can't read audio file: '", self.audio, "' with audioStream = ", self.audioStream)
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print("Error: problems with audio reading, check input arguments")
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exit()
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audioBaseIndex = int(cap.get(cv.CAP_PROP_AUDIO_BASE_INDEX))
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numberOfChannels = int(cap.get(cv.CAP_PROP_AUDIO_TOTAL_CHANNELS))
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print("CAP_PROP_AUDIO_DATA_DEPTH: ", str((int(cap.get(cv.CAP_PROP_AUDIO_DATA_DEPTH)))))
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print("CAP_PROP_AUDIO_SAMPLES_PER_SECOND: ", cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))
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print("CAP_PROP_AUDIO_TOTAL_CHANNELS: ", numberOfChannels)
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print("CAP_PROP_AUDIO_TOTAL_STREAMS: ", cap.get(cv.CAP_PROP_AUDIO_TOTAL_STREAMS))
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frame = []
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frame = np.asarray(frame)
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inputAudio = []
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while (1):
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if (cap.grab()):
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frame = []
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frame = np.asarray(frame)
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frame = cap.retrieve(frame, audioBaseIndex)
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for i in range(len(frame[1][0])):
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inputAudio.append(frame[1][0][i])
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else:
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break
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inputAudio = np.asarray(inputAudio)
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print("Number of samples: ", len(inputAudio))
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samplingRate = int(cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))
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return samplingRate, inputAudio
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def readAudioMicrophone(self):
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cap = cv.VideoCapture()
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params = [cv.CAP_PROP_AUDIO_STREAM, 0, cv.CAP_PROP_VIDEO_STREAM, -1]
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params = np.asarray(params)
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cap.open(0, cv.CAP_ANY, params)
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if cap.isOpened() == False:
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print("Error: Can't open microphone")
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print("Error: problems with audio reading, check input arguments")
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exit()
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audioBaseIndex = int(cap.get(cv.CAP_PROP_AUDIO_BASE_INDEX))
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numberOfChannels = int(cap.get(cv.CAP_PROP_AUDIO_TOTAL_CHANNELS))
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print("CAP_PROP_AUDIO_DATA_DEPTH: ", str((int(cap.get(cv.CAP_PROP_AUDIO_DATA_DEPTH)))))
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print("CAP_PROP_AUDIO_SAMPLES_PER_SECOND: ", cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))
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print("CAP_PROP_AUDIO_TOTAL_CHANNELS: ", numberOfChannels)
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print("CAP_PROP_AUDIO_TOTAL_STREAMS: ", cap.get(cv.CAP_PROP_AUDIO_TOTAL_STREAMS))
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cvTickFreq = cv.getTickFrequency()
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sysTimeCurr = cv.getTickCount()
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sysTimePrev = sysTimeCurr
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frame = []
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frame = np.asarray(frame)
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inputAudio = []
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while ((sysTimeCurr - sysTimePrev) / cvTickFreq < self.microTime):
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if (cap.grab()):
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frame = []
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frame = np.asarray(frame)
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frame = cap.retrieve(frame, audioBaseIndex)
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for i in range(len(frame[1][0])):
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inputAudio.append(frame[1][0][i])
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sysTimeCurr = cv.getTickCount()
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else:
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print("Error: Grab error")
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break
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inputAudio = np.asarray(inputAudio)
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print("Number of samples: ", len(inputAudio))
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samplingRate = int(cap.get(cv.CAP_PROP_AUDIO_SAMPLES_PER_SECOND))
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return samplingRate, inputAudio
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def drawAmplitude(self, inputAudio):
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color = (247, 111, 87)
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thickness = 5
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frameVectorRows = 500
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middle = frameVectorRows // 2
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# usually the input data is too big, so it is necessary
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# to reduce size using interpolation of data
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frameVectorCols = 40000
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if len(inputAudio) < frameVectorCols:
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frameVectorCols = len(inputAudio)
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img = np.zeros((frameVectorRows, frameVectorCols, 3), np.uint8)
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img += 255 # white background
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audio = np.array(0)
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audio = cv.resize(inputAudio, (1, frameVectorCols), interpolation=cv.INTER_LINEAR)
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reshapeAudio = np.reshape(audio, (-1))
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# normalization data by maximum element
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minCv, maxCv, _, _ = cv.minMaxLoc(reshapeAudio)
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maxElem = int(max(abs(minCv), abs(maxCv)))
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# if all data values are zero (silence)
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if maxElem == 0:
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maxElem = 1
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for i in range(len(reshapeAudio)):
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reshapeAudio[i] = middle - reshapeAudio[i] * middle // maxElem
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for i in range(1, frameVectorCols, 1):
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cv.line(img, (i - 1, int(reshapeAudio[i - 1])), (i, int(reshapeAudio[i])), color, thickness)
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img = cv.resize(img, (900, 400), interpolation=cv.INTER_AREA)
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return img
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def drawAmplitudeScale(self, inputImg, inputAudio, samplingRate, xmin=None, xmax=None):
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# function of layout drawing for graph of volume amplitudes
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# x axis for time
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# y axis for amplitudes
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# parameters for the new image size
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preCol = 100
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aftCol = 100
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preLine = 40
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aftLine = 50
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frameVectorRows = inputImg.shape[0]
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frameVectorCols = inputImg.shape[1]
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totalRows = preLine + frameVectorRows + aftLine
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totalCols = preCol + frameVectorCols + aftCol
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imgTotal = np.zeros((totalRows, totalCols, 3), np.uint8)
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imgTotal += 255 # white background
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imgTotal[preLine: preLine + frameVectorRows, preCol: preCol + frameVectorCols] = inputImg
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# calculating values on x axis
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if xmin is None:
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xmin = 0
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if xmax is None:
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xmax = len(inputAudio) / samplingRate
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if xmax > self.xmarkup:
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xList = np.linspace(xmin, xmax, self.xmarkup).astype(int)
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else:
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# this case is used to display a dynamic update
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tmp = np.arange(xmin, xmax, 1).astype(int) + 1
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xList = np.concatenate((np.zeros(self.xmarkup - len(tmp)), tmp[:]), axis=None)
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# calculating values on y axis
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ymin = np.min(inputAudio)
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ymax = np.max(inputAudio)
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yList = np.linspace(ymin, ymax, self.ymarkup)
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# parameters for layout drawing
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textThickness = 1
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gridThickness = 1
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gridColor = (0, 0, 0)
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textColor = (0, 0, 0)
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font = cv.FONT_HERSHEY_SIMPLEX
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fontScale = 0.5
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# horizontal axis under the graph
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cv.line(imgTotal, (preCol, totalRows - aftLine),
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(preCol + frameVectorCols, totalRows - aftLine),
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gridColor, gridThickness)
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# vertical axis for amplitude
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cv.line(imgTotal, (preCol, preLine), (preCol, preLine + frameVectorRows),
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gridColor, gridThickness)
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# parameters for layout calculation
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serifSize = 10
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indentDownX = serifSize * 2
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indentDownY = serifSize // 2
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indentLeftX = serifSize
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indentLeftY = 2 * preCol // 3
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# drawing layout for x axis
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numX = frameVectorCols // (self.xmarkup - 1)
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for i in range(len(xList)):
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a1 = preCol + i * numX
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a2 = frameVectorRows + preLine
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b1 = a1
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b2 = a2 + serifSize
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if self.enableGrid is True:
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d1 = a1
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d2 = preLine
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cv.line(imgTotal, (a1, a2), (d1, d2), gridColor, gridThickness)
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cv.line(imgTotal, (a1, a2), (b1, b2), gridColor, gridThickness)
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cv.putText(imgTotal, str(int(xList[i])), (b1 - indentLeftX, b2 + indentDownX),
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font, fontScale, textColor, textThickness)
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# drawing layout for y axis
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numY = frameVectorRows // (self.ymarkup - 1)
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for i in range(len(yList)):
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a1 = preCol
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a2 = totalRows - aftLine - i * numY
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b1 = preCol - serifSize
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b2 = a2
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if self.enableGrid is True:
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d1 = preCol + frameVectorCols
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d2 = a2
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cv.line(imgTotal, (a1, a2), (d1, d2), gridColor, gridThickness)
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cv.line(imgTotal, (a1, a2), (b1, b2), gridColor, gridThickness)
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cv.putText(imgTotal, str(int(yList[i])), (b1 - indentLeftY, b2 + indentDownY),
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font, fontScale, textColor, textThickness)
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imgTotal = cv.resize(imgTotal, (self.cols, self.rows), interpolation=cv.INTER_AREA)
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return imgTotal
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def STFT(self, inputAudio):
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"""
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The Short-time Fourier transform (STFT), is a Fourier-related transform used to determine
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the sinusoidal frequency and phase content of local sections of a signal as it changes over
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time.
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In practice, the procedure for computing STFTs is to divide a longer time signal into
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shorter segments of equal length and then compute the Fourier transform separately on each
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shorter segment. This reveals the Fourier spectrum on each shorter segment. One then usually
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plots the changing spectra as a function of time, known as a spectrogram or waterfall plot.
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https://en.wikipedia.org/wiki/Short-time_Fourier_transform
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"""
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time_step = self.windLen - self.overlap
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stft = []
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if self.windowType == "Hann":
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# https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
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Hann_wind = []
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for i in range (1 - self.windLen, self.windLen, 2):
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Hann_wind.append(i * (0.5 + 0.5 * math.cos(math.pi * i / (self.windLen - 1))))
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Hann_wind = np.asarray(Hann_wind)
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elif self.windowType == "Hamming":
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# https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
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Hamming_wind = []
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for i in range (1 - self.windLen, self.windLen, 2):
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Hamming_wind.append(i * (0.53836 - 0.46164 * (math.cos(2 * math.pi * i / (self.windLen - 1)))))
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Hamming_wind = np.asarray(Hamming_wind)
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for index in np.arange(0, len(inputAudio), time_step).astype(int):
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section = inputAudio[index:index + self.windLen]
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zeroArray = np.zeros(self.windLen - len(section))
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section = np.concatenate((section, zeroArray), axis=None)
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if self.windowType == "Hann":
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section *= Hann_wind
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elif self.windowType == "Hamming":
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section *= Hamming_wind
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dst = np.empty(0)
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dst = cv.dft(section, dst, flags=cv.DFT_COMPLEX_OUTPUT)
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reshape_dst = np.reshape(dst, (-1))
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# we need only the first part of the spectrum, the second part is symmetrical
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complexArr = np.zeros(len(dst) // 4, dtype=complex)
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for i in range(len(dst) // 4):
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complexArr[i] = complex(reshape_dst[2 * i], reshape_dst[2 * i + 1])
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stft.append(np.abs(complexArr))
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stft = np.array(stft).transpose()
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# convert elements to the decibel scale
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np.log10(stft, out=stft, where=(stft != 0.))
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return 10 * stft
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def drawSpectrogram(self, stft):
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frameVectorRows = stft.shape[0]
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frameVectorCols = stft.shape[1]
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# Normalization of image values from 0 to 255 to get more contrast image
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# and this normalization will be taken into account in the scale drawing
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colormapImageRows = 255
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imgSpec = np.zeros((frameVectorRows, frameVectorCols, 3), np.uint8)
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stftMat = np.zeros((frameVectorRows, frameVectorCols), np.float64)
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cv.normalize(stft, stftMat, 1.0, 0.0, cv.NORM_INF)
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for i in range(frameVectorRows):
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for j in range(frameVectorCols):
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imgSpec[frameVectorRows - i - 1, j] = int(stftMat[i][j] * colormapImageRows)
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imgSpec = cv.applyColorMap(imgSpec, cv.COLORMAP_INFERNO)
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imgSpec = cv.resize(imgSpec, (900, 400), interpolation=cv.INTER_LINEAR)
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return imgSpec
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def drawSpectrogramColorbar(self, inputImg, inputAudio, samplingRate, stft, xmin=None, xmax=None):
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# function of layout drawing for the three-dimensional graph of the spectrogram
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# x axis for time
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# y axis for frequencies
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# z axis for magnitudes of frequencies shown by color scale
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# parameters for the new image size
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preCol = 100
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aftCol = 100
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preLine = 40
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aftLine = 50
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colColor = 20
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ind_col = 20
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frameVectorRows = inputImg.shape[0]
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frameVectorCols = inputImg.shape[1]
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totalRows = preLine + frameVectorRows + aftLine
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totalCols = preCol + frameVectorCols + aftCol + colColor
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imgTotal = np.zeros((totalRows, totalCols, 3), np.uint8)
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imgTotal += 255 # white background
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imgTotal[preLine: preLine + frameVectorRows, preCol: preCol + frameVectorCols] = inputImg
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# colorbar image due to drawSpectrogram(..) picture has been normalised from 255 to 0,
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# so here colorbar has values from 255 to 0
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colorArrSize = 256
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imgColorBar = np.zeros((colorArrSize, colColor, 1), np.uint8)
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for i in range(colorArrSize):
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imgColorBar[i] += colorArrSize - 1 - i
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imgColorBar = cv.applyColorMap(imgColorBar, cv.COLORMAP_INFERNO)
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imgColorBar = cv.resize(imgColorBar, (colColor, frameVectorRows), interpolation=cv.INTER_AREA) #
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imgTotal[preLine: preLine + frameVectorRows,
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preCol + frameVectorCols + ind_col:
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preCol + frameVectorCols + ind_col + colColor] = imgColorBar
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# calculating values on x axis
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if xmin is None:
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xmin = 0
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if xmax is None:
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xmax = len(inputAudio) / samplingRate
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if xmax > self.xmarkup:
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xList = np.linspace(xmin, xmax, self.xmarkup).astype(int)
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else:
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# this case is used to display a dynamic update
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tmpXList = np.arange(xmin, xmax, 1).astype(int) + 1
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xList = np.concatenate((np.zeros(self.xmarkup - len(tmpXList)), tmpXList[:]), axis=None)
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# calculating values on y axis
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# according to the Nyquist sampling theorem,
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# signal should posses frequencies equal to half of sampling rate
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ymin = 0
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ymax = int(samplingRate / 2.)
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yList = np.linspace(ymin, ymax, self.ymarkup).astype(int)
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# calculating values on z axis
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zList = np.linspace(np.min(stft), np.max(stft), self.zmarkup)
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# parameters for layout drawing
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textThickness = 1
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textColor = (0, 0, 0)
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gridThickness = 1
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gridColor = (0, 0, 0)
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font = cv.FONT_HERSHEY_SIMPLEX
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fontScale = 0.5
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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()
|