def plotSegmentationResults(flagsInd, flagsIndGT, classNames, mtStep, ONLY_EVALUATE=False):
    '''
    This function plots statistics on the classification-segmentation results produced either by the fix-sized supervised method or the HMM method.
    It also computes the overall accuracy achieved by the respective method if ground-truth is available.
    '''
    flags = [classNames[int(f)] for f in flagsInd]
    (segs, classes) = flags2segs(flags, mtStep)

    minLength = min(flagsInd.shape[0], flagsIndGT.shape[0])
    if minLength > 0:
        accuracy = numpy.count_nonzero(flagsInd[0:minLength] == flagsIndGT[0:minLength]) / float(minLength)
    else:
        accuracy = -1

    if not ONLY_EVALUATE:
        Duration = segs[-1, 1]
        SPercentages = numpy.zeros((len(classNames), 1))
        Percentages = numpy.zeros((len(classNames), 1))
        AvDurations = numpy.zeros((len(classNames), 1))

        for iSeg in range(segs.shape[0]):
            SPercentages[classNames.index(classes[iSeg])] += (segs[iSeg, 1]-segs[iSeg, 0])

        for i in range(SPercentages.shape[0]):
            Percentages[i] = 100.0 * SPercentages[i] / Duration
            S = sum(1 for c in classes if c == classNames[i])
            if S > 0:
                AvDurations[i] = SPercentages[i] / S
            else:
                AvDurations[i] = 0.0

        for i in range(Percentages.shape[0]):
            print classNames[i], Percentages[i], AvDurations[i]

        font = {'family': 'fantasy', 'size': 10}
        plt.rc('font', **font)

        fig = plt.figure()
        ax1 = fig.add_subplot(211)
        ax1.set_yticks(numpy.array(range(len(classNames))))
        ax1.axis((0, Duration, -1, len(classNames)))
        ax1.set_yticklabels(classNames)
        ax1.plot(numpy.array(range(len(flagsInd))) * mtStep + mtStep / 2.0, flagsInd)
        if flagsIndGT.shape[0] > 0:
            ax1.plot(numpy.array(range(len(flagsIndGT))) * mtStep + mtStep / 2.0, flagsIndGT + 0.05, '--r')
        plt.xlabel("time (seconds)")
        if accuracy >= 0:
            plt.title('Accuracy = {0:.1f}%'.format(100.0 * accuracy))

        ax2 = fig.add_subplot(223)
        plt.title("Classes percentage durations")
        ax2.axis((0, len(classNames) + 1, 0, 100))
        ax2.set_xticks(numpy.array(range(len(classNames) + 1)))
        ax2.set_xticklabels([" "] + classNames)
        ax2.bar(numpy.array(range(len(classNames))) + 0.5, Percentages)

        ax3 = fig.add_subplot(224)
        plt.title("Segment average duration per class")
        ax3.axis((0, len(classNames)+1, 0, AvDurations.max()))
        ax3.set_xticks(numpy.array(range(len(classNames) + 1)))
        ax3.set_xticklabels([" "] + classNames)
        ax3.bar(numpy.array(range(len(classNames))) + 0.5, AvDurations)
        fig.tight_layout()
        plt.show()
    return accuracy
dataset/ETHPy150Open tyiannak/pyAudioAnalysis/audioSegmentation.py/plotSegmentationResults/original