def getOriginalImageInfo(originalImageFileList):
originalImageInfo = {}
for filename in originalImageFileList:
img = cv.imread(filename)
originalImageInfo[getImageID(filename)] = SubImageClassifier.getImageInfo(img)
return originalImageInfo
def getImageFeatureFromImagePath(imagePath, originalImageInfo, thresholds, num_cut = 5):
data = np.zeros([len(imagePath), 6+num_cut*2])
for (i,filename) in enumerate(imagePath):
id = getImageID(filename)
img = cv.imread(filename)
thisOriginalImageInfo = originalImageInfo[id]
data[i, :] = SubImageClassifier.getImageFeature(thisOriginalImageInfo, img, thresholds, num_cut)
print '%d images has been collected.' %len(imagePath)
return data
# originalImagePath = "/home/ec2-user/VizioMetrics/Corpus/Dismantler/train_corpus/ee_cat1_multi"
# standaloneImagePath = "/home/ec2-user/VizioMetrics/Corpus/Dismantler/train_corpus/ee_cat1_multi_subimages/standalone"
# auxiliaryImagePath = "/home/ec2-user/VizioMetrics/Corpus/Dismantler/train_corpus/ee_cat1_multi_subimages/auxiliary"
print 'Loading images...'
originalImageFileList = ImgLoader.getFileNamesFromPath(originalImagePath)
standaloneImageFileList = ImgLoader.getFileNamesFromPath(standaloneImagePath)
auxiliaryImageFileList = ImgLoader.getFileNamesFromPath(auxiliaryImagePath)
auxiliaryImageFileList = getBatchOfImageFileList(auxiliaryImageFileList, 1)
originalImageInfo = getOriginalImageInfo(originalImageFileList)
allFeatures = np.vstack([getImageFeatureFromImagePath(standaloneImageFileList, originalImageInfo, Opt_train.thresholds), getImageFeatureFromImagePath(auxiliaryImageFileList, originalImageInfo, Opt_train.thresholds)])
print allFeatures.shape
allLabeledNames = getLabeledName(Opt_train.classNames, [len(standaloneImageFileList), len(auxiliaryImageFileList)])
print 'All images are loaded'
SVM_train = SubImageClassifier(Opt_train, isTrain = True)
SVM_train.trainModel(allFeatures, allLabeledNames)
#
#
# SVM_train = SVMClassifier(Opt_train, isTrain = True)
# SVM_train.trainModel(allFeatures, allLabeledNames)
print 'Model has been trained'
