def __init__(self, isTrain = False, isTest = False, isClassify = False):
## Mode
logic = isTrain + isTest + isClassify
if logic == 0:
isClassify = True # Default
elif logic > 1:
print 'Warning! Must select either training mode or classifying mode'
if isTrain:
print 'Training Mode'
elif isTest:
print 'Testing Mode'
elif isClassify:
print 'Classifying Mode'
self.isTrain = isTrain
self.isTest = isTest
self.isClassify = isClassify
self.validImageFormat = ['jpg', 'tif', 'bmp', 'png', 'tiff']
#Threshold
self.thresholds = {'splitThres': 0.999, 'varThres': 3, 'var2Thres': 100}
## Dismantler
##### Split #####
self.isPreClassified = False
## SVM Classifier Parameter
##### Training #####
# self.tuned_parameters = [{'kernel': ['rbf'], 'gamma': [0, 1e-3, 1e-4], 'C': [1, 10]},]
self.tuned_parameters = [{'kernel': ['rbf'], 'gamma': [0, 1e-3, 1e-4], 'C': [1]},]
# self.tuned_parameters = [{'kernel': ['rbf', 'linear', 'poly'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]},]
self.classNames = ['standalone', 'auxiliary']
if self.isTrain:
## New Model Name
self.modelName = 'dismantler_matsplit_matsvm_ceil'
## Corpus Path
self.trainCorpusPath = "/Users/sephon/Desktop/Research/VizioMetrics/Dismantler/Corpus/train_corpus"
# self.trainCorpusPath = '/home/ec2-user/VizioMetrics/Corpus/Dismantler/train_corpus/ee_cat1_multi_subimages'
## Model Saving Path
self.modelSavingPath = "/Users/sephon/Desktop/Research/VizioMetrics/Model/Dismantler"
# self.modelSavingPath = '/home/ec2-user/VizioMetrics/Model/Dismantler'
## New Model Path
self.modelPath = Common.getModelPath(self.modelSavingPath, self.modelName)
##### Classifying #####
if self.isClassify:
## Model ID
self.modelName = 'dismantler_matsplit_matsvm_ceil2015-10-01'
## Model Saving Path
self.modelSavingPath = '/Users/sephon/Desktop/Research/VizioMetrics/Model/Dismantler'
# self.modelSavingPath = '/home/ec2-user/VizioMetrics/Model/Dismantler'
## Default Dictionary Path
self.dicPath = os.path.join(self.modelSavingPath, self.modelName)
## Default SVM Model Path
self.svmModelPath = os.path.join(self.modelSavingPath, self.modelName)
print 'Options set!\n'
def __init__(self, isTrain = False, isTest = False, isClassify = False):
## Mode
logic = isTrain + isTest + isClassify
if logic == 0:
isClassify = True # Default
elif logic > 1:
print 'Warning! Must select either training mode or classifying mode'
if isTrain:
print 'Training Mode'
elif isTest:
print 'Testing Mode'
elif isClassify:
print 'Classifying Mode'
self.isTrain = isTrain
self.isTest = isTest
self.isClassify = isClassify
self.validImageFormat = ['jpg', 'tif', 'bmp', 'png', 'tiff']
self.classNames = ['single', 'composite']
self.classNames = sorted(self.classNames)
self.classIDs = range(1, len(self.classNames)+1) # Start from 1
self.classInfo = dict(zip(self.classNames, self.classIDs))
if self.isTrain or self.isClassify:
print 'Warning! Must select either training mode or classifying mode'
## Descriptor Parameter
# Size Normalization
self.offset_dim = (512, 581)
# Firelane approach
self.num_cut = 12
self.thresholds = {'splitThres': 0.999, 'varThres': 3, 'var2Thres': 100}
# Firelane map approach
self.division = (10, 10)
## Classifiers
self.availableClassifiers = ['SVM', 'CNN']
self.activatedClassifiers = 'SVM'
self.tuned_parameters = [{'kernel': ['rbf', 'linear', 'poly'], 'gamma': [0, 1e-3, 1e-4], 'C': [1, 10, 100, 1000]},]
##### Training #####
if self.isTrain:
## New Model Name
self.modelName = 'nClass_%d_' % len(self.classNames)
## Corpus Path
self.trainCorpusPath = "/Users/sephon/Desktop/Research/VizioMetrics/Corpus/Dismantler/ee_cat1_multi_subimages"
# self.trainCorpusPath = "/home/ec2-user/VizioMetrics/Corpus/Dismantler/ee_cat1_multi_subimages"
## Model Saving Path
self.modelSavingPath = "/Users/sephon/Desktop/Research/VizioMetrics/Model/Dismantler"
# self.modelSavingPath = "/home/ec2-user/VizioMetrics/Model/Dismantler"
## New Model Path
self.modelPath = Common.getModelPath(self.modelSavingPath, self.modelName)
##### Classifying #####
if self.isClassify:
## Model ID
# self.modelName = 'nClass_6_2014-10-30'
self.modelName = 'composite_detector_firelanemap'
## Model Saving Path
self.modelSavingPath = '/Users/sephon/Desktop/Research/VizioMetrics/Model/Dismantler'
# self.modelSavingPath = '/home/ec2-user/VizioMetrics/Model/Classifier'
## Default SVM Model Path
self.modelPath = os.path.join(self.modelSavingPath, self.modelName)
print 'Options set!\n'
def __init__(self, isTrain = False, isTest = False, isClassify = False):
## Mode
logic = isTrain + isTest + isClassify
if logic == 0:
isClassify = True # Default
elif logic > 1:
print 'Warning! Must select either training mode or classifying mode'
if isTrain:
print 'Training Mode'
elif isTest:
print 'Testing Mode'
elif isClassify:
print 'Classifying Mode'
self.isTrain = isTrain
self.isTest = isTest
self.isClassify = isClassify
if self.isTrain or self.isClassify:
print 'Warning! Must select either training mode or classifying mode'
## Classifiers
self.availableClassifiers = ['SVM', 'CNN']
self.activatedClassifiers = 'SVM'
## S3 Data Read Parameter
self.keyPath = '/Users/sephon/Desktop/Research/VizioMetrics/keys.txt'
# self.keyPath = '/home/ec2-user/VizioMetrics/keys.txt'
self.host = 'escience.washington.edu.viziometrics'
## Data Read Parameter
self.finalDim = [32, 32, 1] # Final image dimensions
# self.Ntrain = 1 #/ Number of training images per category
# self.Ntest = 1 #/ Number of test images per category
self.validImageFormat = ['jpg', 'tif', 'bmp', 'png', 'tiff'] # Valid image formats
self.classNames = self.getTextDirNames(1,40)
# self.classNames = ['bar', 'boxplot', 'heatmap', 'line', 'pie', 'scatter']
self.classNames = sorted(self.classNames)
self.classIDs = range(1, len(self.classNames)+1) # Start from 1
self.classInfo = dict(zip(self.classNames, self.classIDs))
## Dictionary Parameter
self.Npatches = 200000; # Number of patches
self.Ncentroids = 40; # Number of centroids
self.rfSize = 8; # Receptor Field Size (i.e. Patch Size)
self.kmeansIterations = 100 # Iterations for kmeans centroid computation
self.whitening = True # Whether to use whitening
self.normContrast = True # Whether to normalize patches for contrast
self.minibatch = True # Use minibatch to train SVM
self.MIN_PATCH_VAR = float(38)/255 # Minimum Patch Variance for accepting as potential centroid (empirically set to about 25% quartile of var)
self.MAX_TRY = 30 # Maximum number of try to find a qualify patch from an image
self.kmeansIterations = 50
## SVM Classifier Parameter
##### Training #####
if self.isTrain:
## New Model Name
self.modelName = 'nClass_%d_' % len(self.classNames)
## Corpus Path
self.trainCorpusPath = "/Users/sephon/Desktop/Research/VizioMetrics/Corpus/TextRecognizer/English/Fnt"
# self.trainCorpusPath = "/home/ec2-user/VizioMetrics/Corpus/TextRecognizer/English/Fnt"
## Model Saving Path
self.modelSavingPath = "/Users/sephon/Desktop/Research/VizioMetrics/Model/TextRecognizer"
# self.modelSavingPath = "/home/ec2-user/VizioMetrics/Model/TextRecognizer"
## New Model Path
self.modelPath = Common.getModelPath(self.modelSavingPath, self.modelName)
##### Testing ######
if self.isTest:
## Model ID
self.modelName = 'nClass_7_2014-10-19_4cat'
## Model Saving Path
self.modelSavingPath = '/Users/sephon/Desktop/Research/VizioMetrics/Model'
# self.modelSavingPath = '/home/ec2-user/VizioMetrics/Model'
## Corpus Path
self.testCorpusPath = "/Users/sephon/Desktop/Research/VizioMetrics/Corpus/VizSet_pm_ee_cat014_test"
# self.testCorpusPath = "/home/ec2-user/VizioMetrics/Corpus/VizSet_pm_ee_cat014_test"
## Result Directory
self.resultSavingPath = '/Users/sephon/Desktop/Research/VizioMetrics/class_result'
# self.resultSavingPath = '/home/ec2-user/VizioMetrics/class_result'
## Default Dictionary Path
self.dicPath = os.path.join(self.modelSavingPath, self.modelName)
## Default SVM Model Path
self.svmModelPath = os.path.join(self.modelSavingPath, self.modelName)
## Assign new folder as result directory
self.resultPath = Common.getModelPath(self.resultSavingPath, '')
##### Classifying #####
if self.isClassify:
## Model ID
# self.modelName = 'nClass_6_2014-10-30'
self.modelName = 'nClass_7_2014-10-19_4cat'
## Model Saving Path
self.modelSavingPath = '/Users/sephon/Desktop/Research/VizioMetrics/Model/TextRecognizer'
# self.modelSavingPath = '/home/ec2-user/VizioMetrics/Model'
## Corpus Path
self.classifyCorpusPath = '/Users/sephon/Desktop/Research/VizioMetrics/Corpus/sketchCorpus'
# self.classifyCorpusPath = "/home/ec2-user/VizioMetrics/Corpus/VizSet_pm_ee_cat014_test"
## Result Directory
self.resultSavingPath = '/Users/sephon/Desktop/Research/VizioMetrics/class_result'
# self.resultSavingPath = '/home/ec2-user/VizioMetrics/class_result'
## Default Dictionary Path
self.dicPath = os.path.join(self.modelSavingPath, self.modelName)
## Default SVM Model Path
self.svmModelPath = os.path.join(self.modelSavingPath, self.modelName)
## Assign new folder as result directory
self.resultPath = Common.getModelPath(self.resultSavingPath, '')
print 'Options set!\n'
