def genStim(lesion=False):
# initialize imgDict and labelDict
imgDict={}
labelDict={}
commandVect=['line','triangle','quadrilateral','circle','twoLines','lineCircle']
#
commandDict={'line': 'util.genLineFig(16,16)',
# 'rectangle':'util.genRectangleFig(8,3,3)',
'triangle':'util.genTriangleFig(16,3,3,5)',# last arg is always equal to 4k+5,the 5 are -sqrt(3) -sqrt(3)/2, 0, sqrt(3)/2, sqrt(3), which corresponds to the obtuse triangel, right triangle, isosceles triangle, right triangle, obtuse triangel. the 4n: between 0 to sqrt(3)/2, uniformly draw n points to be position of X, so you got some acute triangle and obtuse triangles.
'twoLines':'util.genTwoLines(16, 3, 3, 3)',
'circle':'util.genCircle(16)',
'lineCircle':'util.genLineCircle(16,5,5)',# last arg is always equal to 2k+1, these indicate the distance between the line and circle center. E.g., if k=1, it would be [0.5r, r, 1.5r].
'quadrilateral':'util.genQuadrilateralFig(16,3,3,3)'} # last arg is always equal to 2k+1,these are xOffset compare to the original rectangle, the 1 is 0, k is +- offset. E.g., if k=1, the offset factor is -0.5,0, +0.5 times the original horizontal half length.
sampleFig=util.geomFig()
sizeN=sampleFig.sizeN
lesionRange=range((sizeN+1)/2-3,(sizeN+1)/2+3)
lesionVect=[(i,j) for i in lesionRange for j in lesionRange]
inputStatsDict={}
for command in commandVect:
labelDictCurr,imgDictCurr=eval(commandDict[command])
if lesion==True:
for key in imgDictCurr.keys():
imgDictCurr[key][zip(*lesionVect)[0],zip(*lesionVect)[1]]=0
imgDict.update(imgDictCurr)
labelDict.update(labelDictCurr)
mySubType=[str(v['subType']) for k, v in labelDictCurr.iteritems()]
inputStatsDict[command]=[len(labelDictCurr),collections.Counter(mySubType)]
inputStatsDict['all']=sum([i[0] for i in inputStatsDict.values()])
print inputStatsDict
# generate grandIndex
i=0
for item in labelDict:
labelDict[item]['grandIndex']=i
i +=1
# save py dict
pickle.dump( imgDict, open( "imgDict.p", "wb" ) )
pickle.dump( labelDict, open( "labelDict.p", "wb" ) )
pickle.dump( inputStatsDict, open( "inputStatsDict.p", "wb" ) )
# save mat for matlab
scipy.io.savemat('imgDict.mat', {'imgDict': imgDict})
scipy.io.savemat('labelDict.mat', { 'labelDict': labelDict})
def genStim_test_train(testSize=0.25, lesion=False):
# initialize imgDict and labelDict
imgDict_train = {}
imgDict_test = {}
labelDict_train = {}
labelDict_test = {}
inputStatsDict_train = {}
inputStatsDict_test = {}
commandVect=['line','triangle','quadrilateral','circle','twoLines','lineCircle']
#
commandDict={'line': 'util.genLineFig(16,16)',
# 'rectangle':'util.genRectangleFig(8,3,3)',
'triangle':'util.genTriangleFig(16,3,3,5)',# last arg is always equal to 4k+5,the 5 are -sqrt(3) -sqrt(3)/2, 0, sqrt(3)/2, sqrt(3), which corresponds to the obtuse triangel, right triangle, isosceles triangle, right triangle, obtuse triangel. the 4n: between 0 to sqrt(3)/2, uniformly draw n points to be position of X, so you got some acute triangle and obtuse triangles.
'twoLines':'util.genTwoLines(16, 3, 3, 3)',
'circle':'util.genCircle(16)',
'lineCircle':'util.genLineCircle(16,5,5)',# last arg is always equal to 2k+1, these indicate the distance between the line and circle center. E.g., if k=1, it would be [0.5r, r, 1.5r].
'quadrilateral':'util.genQuadrilateralFig(16,3,3,3)'} # last arg is always equal to 2k+1,these are xOffset compare to the original rectangle, the 1 is 0, k is +- offset. E.g., if k=1, the offset factor is -0.5,0, +0.5 times the original horizontal half length.
sampleFig=util.geomFig()
total_len = 0
sizeN=sampleFig.sizeN
lesionRange=range((sizeN+1)/2-3,(sizeN+1)/2+3)
lesionVect=[(i,j) for i in lesionRange for j in lesionRange]
for command in commandVect:
labelDictCurr, imgDictCurr=eval(commandDict[command])
if lesion == True:
for key in imgDictCurr.keys():
imgDictCurr[key][zip(*lesionVect)[0],zip(*lesionVect)[1]] = 0
# train test split, for evenly splitting among each category
# Splitting the data into training and testing
imgDict_train_keys, imgDict_test_keys, labelDict_train_keys, labelDict_test_keys = train_test_split(imgDictCurr.keys(),
labelDictCurr.keys(),
test_size=testSize,
random_state=42)
print len(imgDict_train_keys), len(imgDictCurr.keys()), len(labelDict_train_keys), len(labelDictCurr.keys())
train_len = len(imgDict_train_keys)
total_len += train_len
# Re insert the splitted keys to train and test dicts
imgDict_train_curr = {}
imgDict_test_curr = {}
labelDict_train_curr = {}
labelDict_test_curr = {}
for key in imgDict_train_keys:
imgDict_train_curr[key] = imgDictCurr[key]
for key in imgDict_test_keys:
imgDict_test_curr[key] = imgDictCurr[key]
for key in labelDict_train_keys:
labelDict_train_curr[key] = labelDictCurr[key]
for key in labelDict_test_keys:
labelDict_test_curr[key] = labelDictCurr[key]
imgDict_train.update(imgDict_train_curr)
labelDict_train.update(labelDict_train_curr)
imgDict_test.update(imgDict_test_curr)
labelDict_test.update(labelDict_test_curr)
mySubType_train = [str(v['subType']) for k, v in labelDict_train_curr.iteritems()]
mySubType_test = [str(v['subType']) for k, v in labelDict_test_curr.iteritems()]
inputStatsDict_train[command] = [len(labelDict_train_curr), collections.Counter(mySubType_train)]
inputStatsDict_test[command] = [len(labelDict_test_curr), collections.Counter(mySubType_test)]
inputStatsDict_train['all'] = sum([i[0] for i in inputStatsDict_train.values()])
inputStatsDict_test['all'] = sum([i[0] for i in inputStatsDict_test.values()])
# generate grandIndex
i=0
for item in labelDict_train:
labelDict_train[item]['grandIndex'] = i
i +=1
i=0
for item in labelDict_test:
labelDict_test[item]['grandIndex'] = i
i += 1
# save py dict
pickle.dump(imgDict_train, open("imgDict_train.p", "wb"))
pickle.dump(labelDict_train, open("labelDict_train.p", "wb"))
pickle.dump(imgDict_test, open("imgDict_test.p", "wb"))
pickle.dump(labelDict_test, open("labelDict_test.p", "wb"))
pickle.dump(inputStatsDict_train, open("inputStatsDict_train.p", "wb"))
pickle.dump(inputStatsDict_test, open("inputStatsDict_test.p", "wb"))
# save mat for matlab
scipy.io.savemat('imgDict_train.mat', {'imgDict_train': imgDict_train})
scipy.io.savemat('imgDict_test.mat', {'imgDict_test': imgDict_test})
scipy.io.savemat('labelDict_train.mat', { 'labelDict_train': labelDict_train})
scipy.io.savemat('labelDict_test.mat', { 'labelDict_test': labelDict_test})
print "Total Length =", total_len
return total_len
