if x == 0:
I = ISpatial
tree = treeSpatial
else:
I = INonSpatial
tree = treeNonSpatial
for cell in cells_testWeWant:
cellr = cell['id'][1]
cellc = cell['id'][2]
cellNeighbors = adj_test[cell['id']]
DTOutput = SDT.sdt_predict(tree, cell, cellNeighbors, theta)
if DTOutput == cell['class']:#Correct result
if DTOutput == 'null': # TN
pass
else : # TP
I = drawSquare(I,cellr,cellc,green)
else: # incorrect result
if DTOutput == 'null': #FN
I = drawSquare(I,cellr,cellc,yellow)
else : # FP
I = drawSquare(I,cellr,cellc,red)
if x == 0:
ISpatial = I
else:
INonSpatial = I
IFinal = np.concatenate((INonSpatial,ISpatial), axis = 1)
m.imsave(outputname,IFinal)
counter += 1
print counter
eventClass = e
dataset = d
print eventClass,dataset
headers,matches = SOLARGenImageList.image_event_matches(dataset=dataset,waves = ['0171'])
S_train,S_test, = read_data(e,n,d,w,b)
cells, adj = S_train
cells2, adj2 = S_test
print 'matches calculated'
counter = 0
total = len(matches.keys())
for x in sorted(matches.keys()): # for each image
paramsFilename = x[0]
imageFilename = paramsFilename[:-4]+'_th.png'
I = m.imread(imageFilename)
outputname = os.path.join(outputFolder,e,os.path.basename(imageFilename)[:-4]+'_'+e+'.png')
cellsWeWant = [x for x in cells if x['id'][3][0] == paramsFilename]
for cell in cellsWeWant:
cellr = cell['id'][1]
cellc = cell['id'][2]
I = drawSquare(I,cellr,cellc,colordict[e] if cell['class'] != 'null' else black )
# null training cells are colored black, non-nulls are colored according to their class
cellsWeWant2 = [x for x in cells2 if x['id'][3][0] == paramsFilename]
for cell in cellsWeWant2:
cellr = cell['id'][1]
cellc = cell['id'][2]
I = drawSquare(I,cellr,cellc,cyan) #color the test cells cyan
m.imsave(outputname,I)
counter += 1
print counter, '/', total
