示例#1
0
def noisyRandom(generatedFilename):


    infileString, outfolderMC, outfolderRS, gpumcmlDirectory, gpumcmlExecutable = systemPaths.initPaths()
    infile = open(infileString)

    BVFs, Vss, ds, SaO2s, rs, nrSamples, photons, wavelengths, FWHM, eHbO2, eHb, nrSimulations = simulation.noisy()


    reflectances  = np.zeros((nrSimulations, len(wavelengths)))
    parameters    = np.zeros((nrSimulations, 7))

    print('start simulations...')

    #%% start program logic
    start = time.time()



    for i in range(nrSimulations):

        print('starting simulation ' + str(i) + ' of ' + str(nrSimulations))

        BVF = random.uniform(min(BVFs), max(BVFs))
        Vs  = random.uniform(min(Vss), max(Vss))
        d   = random.uniform(min(ds), max(ds))
        SaO2= random.uniform(min(SaO2s), max(SaO2s))
        r   = random.uniform(min(rs), max(rs))

        min_sm_BVF = max(min(BVFs), 0.03)
        sm_BVF = random.uniform(min_sm_BVF, max(BVFs))
        sm_Vs  = random.uniform(min(Vss), max(Vss))

        parameters[i,:] = np.array([BVF, Vs, d, SaO2, r, sm_BVF, sm_Vs])


        for j, wavelength in enumerate(wavelengths):

            reflectanceValue = mch.runOneSimulation(
                wavelength, eHbO2, eHb,
                infile, outfolderMC, gpumcmlDirectory, gpumcmlExecutable,
                BVF, Vs, d,
                r, SaO2,
                submucosa_BVF=sm_BVF, submucosa_Vs=sm_Vs, submucosa_SaO2=SaO2,
                Fwhm = FWHM, nrPhotons=photons)


            print((BVF, Vs, d, SaO2, r, wavelength, sm_BVF, sm_Vs))

            # here, summarize result from wavelength in reflectance spectrum
            reflectances[i, j] = reflectanceValue




    infile.close()

    # save the reflectance results!
    now = datetime.datetime.now().strftime("%Y%B%d%I:%M%p")
    np.save(outfolderRS + now + generatedFilename + "reflectances" + str(photons) + "photons", reflectances)
    np.save(outfolderRS + now + generatedFilename  + str(nrSimulations) + "parameters", parameters)

    end = time.time()
    print "total time to generate noisy random data: " + str((end - start))
def estimateParametersRealImage(trainingParameters, trainingReflectances,
                                shape, image, trainsegmentation,
                                testsegmentation, activateDA):

    sourceReflectancesDA = image[np.nonzero(trainsegmentation)[0], :]
    # choose m reflectances for training DA
    m = trainingReflectances.shape[0]
    sourceReflectancesDA = np.matrix(random.sample(sourceReflectancesDA, m))

    #%% 2. determine domain adaptation weights

    trainingWeights = np.ones(trainingReflectances.shape[0])

    if (activateDA):
        trainingWeights = calculateWeights(trainingReflectances,
                                           sourceReflectancesDA)

    #%% 3. train forest

    rf = randomForest(trainingParameters, trainingReflectances,
                      trainingWeights)

    #%% 4. estimate the parameters for the image

    print "starting to estimate the tissue parameters"
    start = time.time()

    estimatedParameters = rf.predict(image)

    # set to zero if not in segmentation mask
    #estimatedParameters[np.where(0 == testsegmentation), :] = 0

    end = time.time()
    print "time necessary to estimate parameters for image [s]: " + str(
        (end - start))

    #%% save the parametric images TODO delete after everything works
    #    import Image
    #
    #    for i in np.arange(0,estimatedParameters.shape[1]):
    #        parameterImage_i = np.reshape(estimatedParameters[:,i], shape)
    #        im = Image.fromarray(parameterImage_i)
    #        im.save("data/output/" + "parameterImage_" + str(i) + ".tiff")

    #%% 6. evaluate data

    # for this, create monte carlo simulation for each
    # parameter estimate. The resulted reflectance estimate can then be compared to
    # the measured reflectance.

    from setup import systemPaths
    from setup import simulation

    import helper.monteCarloHelper as mch

    infileString, outfolderMC, outfolderRS, gpumcmlDirectory, gpumcmlExecutable = systemPaths.initPaths(
    )
    infile = open(infileString)

    BVFs, Vss, ds, SaO2s, rs, nrSamples, photons, wavelengths, FWHM, eHbO2, eHb, nrSimulations = simulation.noisy(
    )

    # the estimated parameters within the segmentation
    estimatedParametersOnlySegmented = estimatedParameters[
        np.nonzero(testsegmentation)[0], :]
    # the image reflectances from which these parameters where estimated
    inputReflectancesOnlySegmented = image[np.nonzero(testsegmentation)[0], :]

    # index vector for selecting n samples from this data
    indices = np.arange(0, estimatedParametersOnlySegmented.shape[0], 1)
    # choose n
    n = 20
    nSamples = random.sample(indices, n)
    estimatedParametersOnlySegmented = estimatedParametersOnlySegmented[
        nSamples]
    inputReflectancesOnlySegmented = inputReflectancesOnlySegmented[nSamples]

    # placeholder for the reflectance computed from MC with the estimated parameters
    reflectancesFromEstimatedParameters = np.zeros(
        (inputReflectancesOnlySegmented.shape[0],
         inputReflectancesOnlySegmented.shape[1] + 1))

    #wavelengths = np.delete(wavelengths, [2, 7])

    for i, (BVF, Vs, d) in enumerate(estimatedParametersOnlySegmented):

        print('starting simulation ' + str(i) + ' of ' +
              str(estimatedParametersOnlySegmented.shape[0]))

        for j, wavelength in enumerate(wavelengths):

            reflectanceValue = mch.runOneSimulation(
                wavelength,
                eHbO2,
                eHb,
                infile,
                outfolderMC,
                gpumcmlDirectory,
                gpumcmlExecutable,
                BVF,
                Vs,
                d,
                # np.mean(rs), SaO2,
                # submucosa_BVF=sm_BVF, submucosa_Vs=sm_Vs, submucosa_SaO2=SaO2,
                Fwhm=FWHM,
                nrPhotons=photons)

            #print((BVF, Vs, d, wavelength))
            reflectancesFromEstimatedParameters[i, j] = reflectanceValue

    # correct these reflectances by image quotient
    reflectancesFromEstimatedParameters = mch.normalizeImageQuotient(
        reflectancesFromEstimatedParameters)

    wavelengths = mch.removeIqWavelength(wavelengths)
    #%% plot data for nicer inspection

    from sklearn.metrics import r2_score

    r2Score = r2_score(reflectancesFromEstimatedParameters.T,
                       inputReflectancesOnlySegmented.T)

    print("r2Score for random forest estimatation of:", str(r2Score))

    #%% sort by wavelength:

    for plot_i in range(n):

        sortedIndices = sorted(range(len(wavelengths)),
                               key=lambda k: wavelengths[k])

        plt.figure()
        plt.plot(wavelengths[sortedIndices],
                 reflectancesFromEstimatedParameters[plot_i,
                                                     sortedIndices], 'g-o')
        plt.plot(wavelengths[sortedIndices],
                 inputReflectancesOnlySegmented[plot_i, sortedIndices], 'b-o')
        print(
            str(
                r2_score(reflectancesFromEstimatedParameters[plot_i, :],
                         inputReflectancesOnlySegmented[plot_i, :])))
        plt.legend(["estimated", "measurement"])
        plt.xlabel("wavelength [m]")
        plt.ylabel("normalized reflectance")
        plt.savefig("data/output/example_fit_" + str(plot_i) + '.png')

    return estimatedParameters, r2Score, reflectancesFromEstimatedParameters, inputReflectancesOnlySegmented
示例#3
0
def noisyRandom(generatedFilename):
    # the input file without the run specific parameters for ua, us and d:
    infileString = 'data/colonTemplate.mci'
    infile = open(infileString)
    # the output folder for the mc simulations
    # attention: this is relative to your gpumcml path!
    outfolderMC = 'outputMC/'
    # the output folder for the reflectance spectra
    outfolderRS = '../data/output/'
    gpumcmlDirectory = '/home/wirkert/workspace/monteCarlo/gpumcml/fast-gpumcml/'
    gpumcmlExecutable = 'gpumcml.sm_20'

    BVFs, Vss, ds, SaO2s, rs, nrSamples, photons, wavelengths, FWHM, eHbO2, eHb, nrSimulations = simulation.noisy(
    )

    reflectances = np.zeros((nrSimulations, len(wavelengths)))
    parameters = np.zeros((nrSimulations, 8))

    print('start simulations...')

    #%% start program logic
    start = time.time()

    for i in range(nrSimulations):

        print('starting simulation ' + str(i) + ' of ' + str(nrSimulations))

        BVF = random.uniform(min(BVFs), max(BVFs))
        Vs = random.uniform(min(Vss), max(Vss))
        d = random.uniform(min(ds), max(ds))
        r = random.uniform(min(rs), max(rs))
        SaO2 = random.uniform(min(SaO2s), max(SaO2s))

        sm_BVF = random.uniform(min(BVFs), max(BVFs))
        sm_Vs = random.uniform(min(Vss), max(Vss))
        sm_SaO2 = random.uniform(min(SaO2s), max(SaO2s))

        parameters[i, :] = np.array(
            [BVF, Vs, d, r, SaO2, sm_BVF, sm_Vs, sm_SaO2])

        for j, wavelength in enumerate(wavelengths):

            reflectanceValue = mch.runOneSimulation(wavelength,
                                                    eHbO2,
                                                    eHb,
                                                    infile,
                                                    outfolderMC,
                                                    gpumcmlDirectory,
                                                    gpumcmlExecutable,
                                                    BVF,
                                                    Vs,
                                                    d,
                                                    r,
                                                    SaO2,
                                                    submucosa_BVF=sm_BVF,
                                                    submucosa_Vs=sm_Vs,
                                                    submucosa_SaO2=sm_SaO2,
                                                    Fwhm=FWHM,
                                                    nrPhotons=photons)

            print((BVF, Vs, d, SaO2, r, wavelength, sm_BVF, sm_Vs, sm_SaO2))

            # here, summarize result from wavelength in reflectance spectrum
            reflectances[i, j] = reflectanceValue

    infile.close()

    # save the reflectance results!
    now = datetime.datetime.now().strftime("%Y%B%d%I:%M%p")
    np.save(
        outfolderRS + now + generatedFilename + "reflectances" + str(photons) +
        "photons", reflectances)
    np.save(outfolderRS + now + generatedFilename + "parameters", parameters)

    end = time.time()
    print "total time to generate noisy random data: " + str((end - start))
示例#4
0
def estimateParametersRealImage(trainingParameters, trainingReflectances, shape, image, trainsegmentation, testsegmentation, activateDA):

    sourceReflectancesDA = image[np.nonzero(trainsegmentation)[0], :]
    # choose m reflectances for training DA
    m = trainingReflectances.shape[0]
    sourceReflectancesDA = np.matrix(random.sample(sourceReflectancesDA, m))

    #%% 2. determine domain adaptation weights

    trainingWeights = np.ones(trainingReflectances.shape[0])

    if (activateDA):
        trainingWeights = calculateWeights(trainingReflectances, sourceReflectancesDA)

    #%% 3. train forest

    rf = randomForest(trainingParameters, trainingReflectances, trainingWeights)

    #%% 4. estimate the parameters for the image

    print "starting to estimate the tissue parameters"
    start = time.time()

    estimatedParameters = rf.predict(image)

    # set to zero if not in segmentation mask
    #estimatedParameters[np.where(0 == testsegmentation), :] = 0

    end = time.time()
    print "time necessary to estimate parameters for image [s]: " + str((end - start))


    #%% save the parametric images TODO delete after everything works
#    import Image
#
#    for i in np.arange(0,estimatedParameters.shape[1]):
#        parameterImage_i = np.reshape(estimatedParameters[:,i], shape)
#        im = Image.fromarray(parameterImage_i)
#        im.save("data/output/" + "parameterImage_" + str(i) + ".tiff")


    #%% 6. evaluate data

    # for this, create monte carlo simulation for each
    # parameter estimate. The resulted reflectance estimate can then be compared to
    # the measured reflectance.

    from setup import systemPaths
    from setup import simulation

    import helper.monteCarloHelper as mch



    infileString, outfolderMC, outfolderRS, gpumcmlDirectory, gpumcmlExecutable = systemPaths.initPaths()
    infile = open(infileString)

    BVFs, Vss, ds, SaO2s, rs, nrSamples, photons, wavelengths, FWHM, eHbO2, eHb, nrSimulations = simulation.noisy()

    # the estimated parameters within the segmentation
    estimatedParametersOnlySegmented    = estimatedParameters[np.nonzero(testsegmentation)[0], :]
    # the image reflectances from which these parameters where estimated
    inputReflectancesOnlySegmented      = image[np.nonzero(testsegmentation)[0], :]

    # index vector for selecting n samples from this data
    indices = np.arange(0, estimatedParametersOnlySegmented.shape[0], 1)
    # choose n
    n = 20
    nSamples = random.sample(indices, n)
    estimatedParametersOnlySegmented = estimatedParametersOnlySegmented[nSamples]
    inputReflectancesOnlySegmented   = inputReflectancesOnlySegmented[nSamples]

    # placeholder for the reflectance computed from MC with the estimated parameters
    reflectancesFromEstimatedParameters = np.zeros((inputReflectancesOnlySegmented.shape[0], inputReflectancesOnlySegmented.shape[1]+1))

    #wavelengths = np.delete(wavelengths, [2, 7])

    for i, (BVF, Vs, d) in enumerate(estimatedParametersOnlySegmented):


        print('starting simulation ' + str(i) + ' of ' + str(estimatedParametersOnlySegmented.shape[0]))

        for j, wavelength in enumerate(wavelengths):

            reflectanceValue = mch.runOneSimulation(
                wavelength, eHbO2, eHb,
                infile, outfolderMC, gpumcmlDirectory, gpumcmlExecutable,
                BVF, Vs, d,
                # np.mean(rs), SaO2,
                # submucosa_BVF=sm_BVF, submucosa_Vs=sm_Vs, submucosa_SaO2=SaO2,
                Fwhm = FWHM, nrPhotons=photons)


            #print((BVF, Vs, d, wavelength))
            reflectancesFromEstimatedParameters[i, j] = reflectanceValue


    # correct these reflectances by image quotient
    reflectancesFromEstimatedParameters = mch.normalizeImageQuotient(reflectancesFromEstimatedParameters)

    wavelengths = mch.removeIqWavelength(wavelengths)
    #%% plot data for nicer inspection

    from sklearn.metrics      import r2_score

    r2Score = r2_score(reflectancesFromEstimatedParameters.T, inputReflectancesOnlySegmented.T)


    print("r2Score for random forest estimatation of:", str(r2Score))


    #%% sort by wavelength:

    for plot_i in range(n):

        sortedIndices = sorted(range(len(wavelengths)), key=lambda k: wavelengths[k])

        plt.figure()
        plt.plot(wavelengths[sortedIndices], reflectancesFromEstimatedParameters[plot_i,sortedIndices], 'g-o')
        plt.plot(wavelengths[sortedIndices], inputReflectancesOnlySegmented[plot_i,sortedIndices], 'b-o')
        print(str(r2_score(reflectancesFromEstimatedParameters[plot_i, :], inputReflectancesOnlySegmented[plot_i, :])))
        plt.legend(["estimated", "measurement"])
        plt.xlabel("wavelength [m]")
        plt.ylabel("normalized reflectance")
        plt.savefig("data/output/example_fit_" + str(plot_i) + '.png')

    return estimatedParameters, r2Score, reflectancesFromEstimatedParameters, inputReflectancesOnlySegmented
示例#5
0
def noisyRandom(generatedFilename):
    # the input file without the run specific parameters for ua, us and d:
    infileString = 'data/colonTemplate.mci'
    infile       = open(infileString)
    # the output folder for the mc simulations
    # attention: this is relative to your gpumcml path!
    outfolderMC ='outputMC/'
    # the output folder for the reflectance spectra
    outfolderRS = '../data/output/'
    gpumcmlDirectory = '/home/wirkert/workspace/monteCarlo/gpumcml/fast-gpumcml/'
    gpumcmlExecutable = 'gpumcml.sm_20'

    BVFs, Vss, ds, SaO2s, rs, nrSamples, photons, wavelengths, FWHM, eHbO2, eHb, nrSimulations = simulation.noisy()


    reflectances  = np.zeros((nrSimulations, len(wavelengths)))
    parameters    = np.zeros((nrSimulations, 8))

    print('start simulations...')

    #%% start program logic
    start = time.time()



    for i in range(nrSimulations):

        print('starting simulation ' + str(i) + ' of ' + str(nrSimulations))

        BVF = random.uniform(min(BVFs), max(BVFs))
        Vs  = random.uniform(min(Vss), max(Vss))
        d   = random.uniform(min(ds), max(ds))
        r   = random.uniform(min(rs), max(rs))
        SaO2= random.uniform(min(SaO2s), max(SaO2s))

        sm_BVF = random.uniform(min(BVFs), max(BVFs))
        sm_Vs  = random.uniform(min(Vss), max(Vss))
        sm_SaO2= random.uniform(min(SaO2s), max(SaO2s))



        parameters[i,:] = np.array([BVF, Vs, d, r, SaO2, sm_BVF, sm_Vs, sm_SaO2])


        for j, wavelength in enumerate(wavelengths):

            reflectanceValue = mch.runOneSimulation(
                wavelength, eHbO2, eHb,
                infile, outfolderMC, gpumcmlDirectory, gpumcmlExecutable,
                BVF, Vs, d,
                r, SaO2,
                submucosa_BVF=sm_BVF, submucosa_Vs=sm_Vs, submucosa_SaO2=sm_SaO2,
                Fwhm = FWHM, nrPhotons=photons)


            print((BVF, Vs, d, SaO2, r, wavelength, sm_BVF, sm_Vs, sm_SaO2))

            # here, summarize result from wavelength in reflectance spectrum
            reflectances[i, j] = reflectanceValue




    infile.close()

    # save the reflectance results!
    now = datetime.datetime.now().strftime("%Y%B%d%I:%M%p")
    np.save(outfolderRS + now + generatedFilename + "reflectances" + str(photons) + "photons", reflectances)
    np.save(outfolderRS + now + generatedFilename + "parameters", parameters)

    end = time.time()
    print "total time to generate noisy random data: " + str((end - start))