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