def testClassifier(classifierFilename, case):
assert case=="lena" or case=="car" , "case parameter should be lena or car"
superPixelClassifier = pomio.unpickleObject(classifierFilename)
print "\n*Loaded classifier [ " , type(superPixelClassifier) , "] from:" , classifierFilename
image = None
orientation = None
if case == "car":
print "*Loading MSRC car image::"
image = pomio.msrc_loadImages("/home/amb/dev/mrf/data/MSRC_ObjCategImageDatabase_v2", ['Images/7_3_s.bmp'] )[0].m_img
orientation = "lower"
elif case == "lena":
print "*Loading Lena.jpg"
image = skimage.data.lena()
orientation = "upper"
print "*Predicting superpixel labels in image::"
numberSuperPixels = 400
superPixelCompactness = 10
[superPixelLabels, superPixelsMask] = predictSuperPixelLabels(superPixelClassifier, image,numberSuperPixels, superPixelCompactness)
carSuperPixelLabels = getSuperPixelLabelledImage(image, superPixelsMask, superPixelLabels)
plotSuperPixelImage(image, carSuperPixelLabels, orientation)
def test():
# TODO use reference classifier
classifierName = "/home/amb/dev/mrf/classifiers/randomForest/superpixel/randyForest_superPixel_maxDepth15_0.6Data.pkl"
classifier = pomio.unpickleObject(classifierName)
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)[0]
prediction = SuperPixelClassifier.getSuperPixelLabelledImage(
car.m_img, mask, spLabels)
# save prediction to file
pomio.writeMatToCSV(
prediction, "/home/amb/dev/eval/test/predict/testPrediction1.labels")
results = evaluatePrediction(prediction, groundTruth)
print "\nINFO: Car test eval results::\n\t", results
def loadReferenceGroundTruthLabels(sourceData, imgName):
gtFile = str(sourceData) + "/GroundTruth/" + str(imgName)
if "_GT" in imgName:
imgName = imgName.replace("_GT" , "")
gtImgLabels = pomio.msrc_loadImages(sourceData , ["Images/" + imgName] )[0].m_gt
return gtImgLabels
def getSuperPixelTrainingData(msrcDataDirectory,
nbSuperPixels,
superPixelCompactness,
scale,
trainSplit=0.6,
validationSplit=0.2,
testSplit=0.2):
# Should probably make this a call to pomio in case the ordering changes in the future...
voidClassLabel = pomio.getVoidIdx()
# These could be user-specified
if scale == None:
scale = 0.05 # default to 10% of data
msrcData = pomio.msrc_loadImages(msrcDataDirectory, None)
print "Now generating superpixel classifier for MSRC data"
# splitData = pomio.splitInputDataset_msrcData(msrcData, datasetScale=scale, keepClassDistForTraining=True, trainSplit, validationSplit, testSplit )
splitData = pomio.splitInputDataset_msrcData(msrcData, scale, True,
trainSplit, validationSplit,
testSplit)
# prepare superpixel training data
trainingMsrcImages = splitData[0]
# Just use the above function to get superpixel features and labels for training data
return SuperPixelClassifier.getSuperPixelData(trainingMsrcImages,
nbSuperPixels,
superPixelCompactness)
def testClassifier(classifierFilename, case):
assert case == "lena" or case == "car", "case parameter should be lena or car"
superPixelClassifier = pomio.unpickleObject(classifierFilename)
print "\n*Loaded classifier [ ", type(
superPixelClassifier), "] from:", classifierFilename
image = None
orientation = None
if case == "car":
print "*Loading MSRC car image::"
image = pomio.msrc_loadImages(
"/home/amb/dev/mrf/data/MSRC_ObjCategImageDatabase_v2",
['Images/7_3_s.bmp'])[0].m_img
orientation = "lower"
elif case == "lena":
print "*Loading Lena.jpg"
image = skimage.data.lena()
orientation = "upper"
print "*Predicting superpixel labels in image::"
numberSuperPixels = 400
superPixelCompactness = 10
[superPixelLabels,
superPixelsMask] = predictSuperPixelLabels(superPixelClassifier, image,
numberSuperPixels,
superPixelCompactness)
carSuperPixelLabels = getSuperPixelLabelledImage(image, superPixelsMask,
superPixelLabels)
plotSuperPixelImage(image, carSuperPixelLabels, orientation)
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 loadReferenceGroundTruthLabels(sourceData, imgName):
gtFile = str(sourceData) + "/GroundTruth/" + str(imgName)
if "_GT" in imgName:
imgName = imgName.replace("_GT", "")
gtImgLabels = pomio.msrc_loadImages(sourceData,
["Images/" + imgName])[0].m_gt
return gtImgLabels
def neighbourClassImageCount(msrcDataLocation, outputFileLocation):
# for each non-matching class pair, count images where they are neighbours
classes = pomio.msrc_classLabels
totalClasses = np.size(classes)
msrcImages = pomio.msrc_loadImages(msrcDataLocation)
print "*Imported MSRC image data using pomio.py::" , np.shape(msrcImages)
# numberOfPairs = factorial(totalClasses) / (factorial(totalClasses -2) * factorial(2))
result = None
for startClassIdx in range(0, totalClasses):
for endClassIdx in range(0, totalClasses):
if (not startClassIdx == endClassIdx) and (endClassIdx > startClassIdx):
# we have a class pair
startClassValue = classes[startClassIdx]
endClassValue = classes[endClassIdx]
neighbourCount = 0
# loop over images, increase count when an image includes startClass-endClass neighbours
for imageIdx in range(0,np.size(msrcImages)):
imageGroundTruth = msrcImages[imageIdx].m_gt
# Get binary representation of image for each class
startClassChannel = (imageGroundTruth == startClassIdx)
startClassChannel = startClassChannel.astype('int')
endClassChannel = (imageGroundTruth == endClassIdx)
endClassChannel= endClassChannel.astype('int')
# get the difference (do not expect pixel to be in both classes!)
classDifference = startClassChannel - endClassChannel
# get x and y gradient
dx_classDiff, dy_classDiff = np.gradient(classDifference)
# if absolute value of x or y gradient = 2, neighbour pixels
if(np.any(dx_classDiff == 2) or (np.any(dy_classDiff == 2))):
neighbourCount = neighbourCount + 1
print "Pair:[" + str(startClassValue) + ", " + str(endClassValue) +"] count = " + str(neighbourCount)
if result == None:
result = np.array([ startClassValue, endClassValue, neighbourCount])
else:
result = np.vstack([ result, [ startClassValue, endClassValue, str(neighbourCount)] ] )
print "Shape of result data::", np.shape(result)
print "Class pair image count result::", result
np.savetxt(outputFileLocation, result, delimiter=",", fmt="%.32f")
def areAllMsrcGroundTruthImagesTheSameSize(msrcDataLocation):
msrcImages = pomio.msrc_loadImages(msrcDataLocation)
size = int(msrcImages[0].m_gt.size)
equalSize = True
for imageIdx in range(1, np.size(msrcImages)):
imageSize = int(msrcImages[imageIdx].m_gt.size)
if not size == imageSize:
equalSize = False
print "Image#" + str(imageIdx+1) + " doesn't match first size (" + str(size) + "), imageSize=" + str(imageSize)
return equalSize
def areAllMsrcGroundTruthImagesTheSameSize(msrcDataLocation):
msrcImages = pomio.msrc_loadImages(msrcDataLocation)
size = int(msrcImages[0].m_gt.size)
equalSize = True
for imageIdx in range(1, np.size(msrcImages)):
imageSize = int(msrcImages[imageIdx].m_gt.size)
if not size == imageSize:
equalSize = False
print "Image#" + str(imageIdx +
1) + " doesn't match first size (" + str(
size) + "), imageSize=" + str(imageSize)
return equalSize
def getUniqueImageSizes(msrcDataLocation):
msrcImages = pomio.msrc_loadImages(msrcDataLocation)
uniqueImageSizes = np.array([])
firstSize = msrcImages[0].m_gt.size
uniqueImageSizes = np.append(uniqueImageSizes, firstSize)
for imageIdx in range(1, np.size(msrcImages)):
imageSize = msrcImages[imageIdx].m_gt.size
if not imageSize in uniqueImageSizes:
uniqueImageSizes = np.append([uniqueImageSizes], imageSize)
return uniqueImageSizes
def getUniqueImageSizes(msrcDataLocation):
msrcImages = pomio.msrc_loadImages(msrcDataLocation)
uniqueImageSizes = np.array([])
firstSize = msrcImages[0].m_gt.size
uniqueImageSizes = np.append(uniqueImageSizes, firstSize)
for imageIdx in range(1, np.size(msrcImages)):
imageSize = msrcImages[imageIdx].m_gt.size
if not imageSize in uniqueImageSizes:
uniqueImageSizes = np.append([uniqueImageSizes] , imageSize)
return uniqueImageSizes
def getSuperPixelTrainingData(msrcDataDirectory, nbSuperPixels,superPixelCompactness, scale, trainSplit=0.6, validationSplit=0.2, testSplit=0.2):
# Should probably make this a call to pomio in case the ordering changes in the future...
voidClassLabel = pomio.getVoidIdx()
# These could be user-specified
if scale == None:
scale = 0.05 # default to 10% of data
msrcData = pomio.msrc_loadImages(msrcDataDirectory, None)
print "Now generating superpixel classifier for MSRC data"
# splitData = pomio.splitInputDataset_msrcData(msrcData, datasetScale=scale, keepClassDistForTraining=True, trainSplit, validationSplit, testSplit )
splitData = pomio.splitInputDataset_msrcData(msrcData, scale, True, trainSplit, validationSplit, testSplit )
# prepare superpixel training data
trainingMsrcImages = splitData[0]
# Just use the above function to get superpixel features and labels for training data
return SuperPixelClassifier.getSuperPixelData(trainingMsrcImages,nbSuperPixels,superPixelCompactness)
def test():
# TODO use reference classifier
classifierName = "/home/amb/dev/mrf/classifiers/randomForest/superpixel/randyForest_superPixel_maxDepth15_0.6Data.pkl"
classifier = pomio.unpickleObject(classifierName)
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)[0]
prediction = SuperPixelClassifier.getSuperPixelLabelledImage(car.m_img, mask, spLabels)
# save prediction to file
pomio.writeMatToCSV(prediction, "/home/amb/dev/eval/test/predict/testPrediction1.labels")
results = evaluatePrediction(prediction, groundTruth)
print "\nINFO: Car test eval results::\n\t" , results
def imageCountPerClass(msrcDataLocation):
classes = pomio.msrc_classLabels
totalClasses = np.size(classes)
classImageCount = np.arange(0, totalClasses)
msrcImages = pomio.msrc_loadImages(msrcDataLocation)
print "\t*Imported MSRC image data using pomio.py::", np.shape(msrcImages)
for classIdx in range(0, totalClasses):
classValue = classes[classIdx]
imageCountForClass = 0
for imageIdx in range(0, np.size(msrcImages)):
image = msrcImages[imageIdx]
imageGroundTruth = image.m_gt
if classIdx in imageGroundTruth:
imageCountForClass = imageCountForClass + 1
# add total count to the class count result
print "Class = " + str(classValue), ", image count=" + str(
imageCountForClass)
classImageCount[classIdx] = int(imageCountForClass)
result = np.array([classes, classImageCount])
print "Image count per class::\n", result
return result
def imageCountPerClass(msrcDataLocation):
classes = pomio.msrc_classLabels
totalClasses = np.size(classes)
classImageCount = np.arange(0,totalClasses)
msrcImages = pomio.msrc_loadImages(msrcDataLocation)
print "\t*Imported MSRC image data using pomio.py::" , np.shape(msrcImages)
for classIdx in range(0,totalClasses):
classValue = classes[classIdx]
imageCountForClass = 0;
for imageIdx in range(0,np.size(msrcImages)):
image = msrcImages[imageIdx]
imageGroundTruth = image.m_gt
if classIdx in imageGroundTruth:
imageCountForClass = imageCountForClass + 1
# add total count to the class count result
print "Class = " + str(classValue), ", image count=" + str(imageCountForClass)
classImageCount[classIdx] = int(imageCountForClass)
result = np.array([classes, classImageCount])
print "Image count per class::\n" , result
return result
msrcImages = pomio.msrc_loadImages(msrcData, [\
'Images/10_1_s.bmp',\
'Images/10_2_s.bmp',\
'Images/11_1_s.bmp',\
'Images/11_2_s.bmp',\
'Images/12_1_s.bmp',\
'Images/12_2_s.bmp',\
'Images/13_1_s.bmp',\
'Images/13_2_s.bmp',\
'Images/14_1_s.bmp',\
'Images/14_2_s.bmp',\
'Images/15_1_s.bmp',\
'Images/15_2_s.bmp',\
'Images/16_1_s.bmp',\
'Images/16_2_s.bmp',\
'Images/17_1_s.bmp',\
'Images/17_2_s.bmp',\
'Images/18_1_s.bmp',\
'Images/18_2_s.bmp',\
'Images/19_1_s.bmp',\
'Images/19_2_s.bmp',\
'Images/1_1_s.bmp',\
'Images/1_2_s.bmp',\
'Images/20_1_s.bmp',\
'Images/20_2_s.bmp',\
'Images/2_1_s.bmp',\
'Images/2_2_s.bmp',\
'Images/3_1_s.bmp',\
'Images/3_2_s.bmp',\
'Images/4_1_s.bmp',\
'Images/4_2_s.bmp',\
'Images/5_1_s.bmp',\
'Images/5_2_s.bmp',\
'Images/6_1_s.bmp',\
'Images/6_2_s.bmp',\
'Images/7_1_s.bmp',\
'Images/7_2_s.bmp',\
'Images/8_1_s.bmp',\
'Images/8_2_s.bmp',\
'Images/9_1_s.bmp',\
'Images/9_2_s.bmp'])
if __name__ == "__main__":
# Create network
print "*Creating neural net"
net = createDefaultNeuralNet()
msrcData = "/home/amb/dev/mrf/data/MSRC_ObjCategImageDatabase_v2"
print "\n*Creating training dataset"
labeledData = createTrainingSupervisedDataSet(msrcData, 0.05, True)
print "\n*Training network via backpropogation"
trainingResult = trainNetworkBackprop(net, labeledData)
net = trainingResult[0]
trainer = trainingResult[1]
predictImage = pomio.msrc_loadImages(msrcData)[1]
print "\n*Read in an image from the MSRC dataset::", np.shape(
predictImage.m_img)
# todo: replace with features.computePixelFeatures JRS
imageFeatures = FeatureGenerator.generatePixelFeaturesForImage(
predictImage.m_img)
print "\n*Using neural net to predict class label::"
prediction = predictClass(imageFeatures, net)
print prediction
#!/usr/bin/env python
import sys
import matplotlib.pyplot as plt
# Give it the path to the data set as an arg
import pomio
X = pomio.msrc_loadImages(sys.argv[1])
print X
# Display the images
plt.ion()
for img in X:
plt.figure(1)
plt.imshow(img.m_img)
plt.title("Image %s" % img.m_imgFn)
plt.figure(2)
plt.imshow(img.m_gt)
plt.title("labels")
plt.draw()
plt.pause(0.2)
# plt.ioff()
# plt.show()
def neighbourClassImageCount(msrcDataLocation, outputFileLocation):
# for each non-matching class pair, count images where they are neighbours
classes = pomio.msrc_classLabels
totalClasses = np.size(classes)
msrcImages = pomio.msrc_loadImages(msrcDataLocation)
print "*Imported MSRC image data using pomio.py::", np.shape(msrcImages)
# numberOfPairs = factorial(totalClasses) / (factorial(totalClasses -2) * factorial(2))
result = None
for startClassIdx in range(0, totalClasses):
for endClassIdx in range(0, totalClasses):
if (not startClassIdx
== endClassIdx) and (endClassIdx > startClassIdx):
# we have a class pair
startClassValue = classes[startClassIdx]
endClassValue = classes[endClassIdx]
neighbourCount = 0
# loop over images, increase count when an image includes startClass-endClass neighbours
for imageIdx in range(0, np.size(msrcImages)):
imageGroundTruth = msrcImages[imageIdx].m_gt
# Get binary representation of image for each class
startClassChannel = (imageGroundTruth == startClassIdx)
startClassChannel = startClassChannel.astype('int')
endClassChannel = (imageGroundTruth == endClassIdx)
endClassChannel = endClassChannel.astype('int')
# get the difference (do not expect pixel to be in both classes!)
classDifference = startClassChannel - endClassChannel
# get x and y gradient
dx_classDiff, dy_classDiff = np.gradient(classDifference)
# if absolute value of x or y gradient = 2, neighbour pixels
if (np.any(dx_classDiff == 2)
or (np.any(dy_classDiff == 2))):
neighbourCount = neighbourCount + 1
print "Pair:[" + str(startClassValue) + ", " + str(
endClassValue) + "] count = " + str(neighbourCount)
if result == None:
result = np.array(
[startClassValue, endClassValue, neighbourCount])
else:
result = np.vstack([
result,
[startClassValue, endClassValue,
str(neighbourCount)]
])
print "Shape of result data::", np.shape(result)
print "Class pair image count result::", result
np.savetxt(outputFileLocation, result, delimiter=",", fmt="%.32f")
#!/usr/bin/env python
import sys
import pomio
import amntools
import matplotlib.pyplot as plt
import numpy as np
import FeatureGenerator
import matplotlib
# Load all data
print 'Loading all data...'
# first arg should be msrc data path
data = pomio.msrc_loadImages( sys.argv[1], ['Images/7_3_s.bmp'])
# get particular image we like
ex = data[0]
plt.figure()
plt.imshow(ex.m_img)
plt.title('original image')
plt.figure()
clrs = [[z/255.0 for z in c[1]] for c in pomio.msrc_classToRGB]
pomio.showLabels( ex.m_gt )
plt.title('ground truth labels' )
print 'unique class labels: ', np.unique(ex.m_gt)
# generate features
imagePixelFeatures = FeatureGenerator.generatePixelFeaturesForImage(ex.m_img)
if __name__ == "__main__":
# Create network
print "*Creating neural net"
net = createDefaultNeuralNet()
msrcData = "/home/amb/dev/mrf/data/MSRC_ObjCategImageDatabase_v2"
print "\n*Creating training dataset"
labeledData = createTrainingSupervisedDataSet(msrcData, 0.05, True)
print "\n*Training network via backpropogation"
trainingResult = trainNetworkBackprop(net, labeledData)
net = trainingResult[0]
trainer = trainingResult[1]
predictImage = pomio.msrc_loadImages(msrcData)[1]
print "\n*Read in an image from the MSRC dataset::" , np.shape(predictImage.m_img)
imageFeatures = FeatureGenerator.generatePixelFeaturesForImage(predictImage.m_img)
print "\n*Using neural net to predict class label::"
prediction = predictClass(imageFeatures, net)
print prediction
#!/usr/bin/env python
import sys
import matplotlib.pyplot as plt
# Give it the path to the data set as an arg
import pomio
X = pomio.msrc_loadImages( sys.argv[1] )
print X
# Display the images
plt.ion()
for img in X:
plt.figure(1)
plt.imshow( img.m_img )
plt.title('Image %s' % img.m_imgFn)
plt.figure(2)
plt.imshow( img.m_gt )
plt.title('labels')
plt.draw()
plt.pause(0.2)
#plt.ioff()
#plt.show()
if (trainSplit == 1.0) and (cvSplit == 0.0 and testSplit == 0.0):
writeDataType = False
else:
# we get here if we have non-default splits
writeDataType = True
# Do the expected thing if we have a non-default split
if args.v:
print "Loading data"
if writeDataType == True:
# Get train, test and cv datasets
print "\nSplitting data into sets: train =", trainSplit, "test =", testSplit, "cvSplit =", cvSplit
keepClassDistForTraining = False #!!
[trainData, cvData, testData] = pomio.splitInputDataset_msrcData(
pomio.msrc_loadImages(msrcDataDirectory, subset=None),
scaleFrac,
keepClassDistForTraining,
trainSplit,
cvSplit,
testSplit,
)
assert trainData != None, "Training data object is null"
assert len(trainData) > 0, "Training data contains no data"
if testData == None or len(testData) == 0:
print "WARNING: Testing data contains no data"
print "Creating training set from %d images, and test set from %d images" % (len(trainData), len(testData))
if (trainSplit == 1.0) and (cvSplit == 0.0 and testSplit == 0.0):
writeDataType = False
else:
# we get here if we have non-default splits
writeDataType = True
# Do the expected thing if we have a non-default split
if args.v:
print 'Loading data'
if writeDataType == True:
# Get train, test and cv datasets
print "\nSplitting data into sets: train =", trainSplit, "test =", testSplit, "cvSplit =", cvSplit
keepClassDistForTraining = False #!!
[trainData, cvData, testData] = pomio.splitInputDataset_msrcData(\
pomio.msrc_loadImages(msrcDataDirectory, subset=None),
scaleFrac, keepClassDistForTraining, trainSplit , cvSplit , testSplit)
assert trainData != None, "Training data object is null"
assert len(trainData) > 0, "Training data contains no data"
if testData == None or len(testData) == 0:
print 'WARNING: Testing data contains no data'
print "Creating training set from %d images, and test set from %d images" % (
len(trainData), len(testData))
# Process and persist feature and labels for superpixels in image dataset
print "Create & save training feature and label superpixel data"
createAndSaveFeatureLabelData(trainData, outfileBase, "train", outfileType,
numberSuperPixels, superPixelCompactness,
msrcImages = pomio.msrc_loadImages(msrcData, [\
'Images/10_1_s.bmp',\
'Images/10_2_s.bmp',\
'Images/11_1_s.bmp',\
'Images/11_2_s.bmp',\
'Images/12_1_s.bmp',\
'Images/12_2_s.bmp',\
'Images/13_1_s.bmp',\
'Images/13_2_s.bmp',\
'Images/14_1_s.bmp',\
'Images/14_2_s.bmp',\
'Images/15_1_s.bmp',\
'Images/15_2_s.bmp',\
'Images/16_1_s.bmp',\
'Images/16_2_s.bmp',\
'Images/17_1_s.bmp',\
'Images/17_2_s.bmp',\
'Images/18_1_s.bmp',\
'Images/18_2_s.bmp',\
'Images/19_1_s.bmp',\
'Images/19_2_s.bmp',\
'Images/1_1_s.bmp',\
'Images/1_2_s.bmp',\
'Images/20_1_s.bmp',\
'Images/20_2_s.bmp',\
'Images/2_1_s.bmp',\
'Images/2_2_s.bmp',\
'Images/3_1_s.bmp',\
'Images/3_2_s.bmp',\
'Images/4_1_s.bmp',\
'Images/4_2_s.bmp',\
'Images/5_1_s.bmp',\
'Images/5_2_s.bmp',\
'Images/6_1_s.bmp',\
'Images/6_2_s.bmp',\
'Images/7_1_s.bmp',\
'Images/7_2_s.bmp',\
'Images/8_1_s.bmp',\
'Images/8_2_s.bmp',\
'Images/9_1_s.bmp',\
'Images/9_2_s.bmp'])
#!/usr/bin/env python
import pomio
import amntools
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
import FeatureGenerator
import matplotlib
# Load all data
print 'Loading all data...'
data = pomio.msrc_loadImages('/home/jamie/data/MSRC_ObjCategImageDatabase_v2', \
['Images/7_3_s.bmp'])
# get particular image we like
ex = data[0]
plt.figure()
plt.imshow(ex.m_img)
plt.title('original image')
plt.figure()
clrs = [[z / 255.0 for z in c[1]] for c in pomio.msrc_classToRGB]
pomio.showLabels(ex.m_gt)
plt.title('ground truth labels')
print 'unique class labels: ', np.unique(ex.m_gt)
# generate features
imagePixelFeatures = FeatureGenerator.generatePixelFeaturesForImage(ex.m_img)
# For each feature, how many distinct values?
