Ejemplo n.º 1
0
def test():

    classifierLocation = "/home/amb/dev/mrf/classifiers/logisticRegression/superpixel/logReg_miniMSRC.pkl"

    classifier = pomio.unpickleObject(classifierLocation)
    carFile = "7_3_s.bmp"
    msrcData = "/home/amb/dev/mrf/data/MSRC_ObjCategImageDatabase_v2"

    car = pomio.msrc_loadImages(msrcData, ["Images/" + carFile])[0]
    groundTruth = car.m_gt

    mask = superPixels.getSuperPixels_SLIC(car.m_img, 400, 10)

    spLabels = SuperPixelClassifier.predictSuperPixelLabels(
        classifier, car.m_img, 400, 10, True)[0]

    prediction = SuperPixelClassifier.getSuperPixelLabelledImage(
        car.m_img, mask, spLabels)

    # save prediction to file
    pomio.writeMatToCSV(prediction,
                        "/home/amb/dev/mrf/eval/testPrediction1.labels")

    results = evaluatePrediction(prediction, groundTruth, carFile)
    print "\nINFO: Car test eval results::\n\t", results

    classResults = evaluateClassPerformance(prediction, groundTruth)
    print "\nINFO: Car test eval class results::\n\t", classResults

    confusionResults = evaluateConfusionMatrix(prediction, groundTruth)
    print "\nINFO: Car test eval confusion matrix results::\n\t", "Just sum up entries... ", np.sum(
        confusionResults)
def test_superPixel_pixelFeatures():
    sourceImage = readImageFileRGB("ship-at-sea.jpg")
    superPixelMask = superPixels.getSuperPixels_SLIC(sourceImage, 400, 10)
    
    superPixelRegionFeatures = getSuperPixelFeatures_pixel(sourceImage, superPixelMask)
    
    print "\nINFO: Shape of featuresBySuperPixel =" , np.shape(superPixelRegionFeatures)
    return superPixelRegionFeatures
def test_superPixelFeatures():
    sourceImage = readImageFileRGB("ship-at-sea.jpg")
    superPixelMask = superPixels.getSuperPixels_SLIC(sourceImage, 400, 10)
    
    spFeatures = generateSuperPixelFeatures(sourceImage, superPixelMask, [])
    
    print "Shape of super pixel features =" , np.shape(spFeatures)
    
    numNanValues = np.sum( ((np.isnan(spFeatures) == True)[0]).astype('int') )
    print "\n\nPost-processing total NaN values =", numNanValues
def test_superPixelFeatures():
    sourceImage = readImageFileRGB("ship-at-sea.jpg")
    superPixelMask = superPixels.getSuperPixels_SLIC(sourceImage, 400, 10)

    spFeatures = generateSuperPixelFeatures(sourceImage, superPixelMask, [])

    print "Shape of super pixel features =", np.shape(spFeatures)

    numNanValues = np.sum(((np.isnan(spFeatures) == True)[0]).astype('int'))
    print "\n\nPost-processing total NaN values =", numNanValues
def test_superPixel_pixelFeatures():
    sourceImage = readImageFileRGB("ship-at-sea.jpg")
    superPixelMask = superPixels.getSuperPixels_SLIC(sourceImage, 400, 10)

    superPixelRegionFeatures = getSuperPixelFeatures_pixel(
        sourceImage, superPixelMask)

    print "\nINFO: Shape of featuresBySuperPixel =", np.shape(
        superPixelRegionFeatures)
    return superPixelRegionFeatures