def __init__(self, maxk, path, disks, circumferences):
self.knn1 = None
self.knn2 = None
self.path = path
self.maxk = maxk
self.disks = disks
self.circumferences = circumferences
self.ds1 = self.loadDataset(C.HIST_HCoS, path)
self.ds2 = self.loadDataset(C.HIST_LBP_R1P8_R3P16_CONCAT, path)
self.log = Log()
def __init__(self, maxk, path, disks, circumferences):
self.knn1 = None
self.knn2 = None
self.path = path
self.maxk = maxk
self.disks = disks
self.circumferences = circumferences
self.ds1 = self.loadDataset(C.HIST_HCoS, path)
self.ds2 = self.loadDataset(C.HIST_LBP_R1P8_R3P16_CONCAT, path)
self.log = Log()
class MobileSimulation():
def __init__(self, maxk, path, disks, circumferences):
self.knn1 = None
self.knn2 = None
self.path = path
self.maxk = maxk
self.disks = disks
self.circumferences = circumferences
self.ds1 = self.loadDataset(C.HIST_HCoS, path)
self.ds2 = self.loadDataset(C.HIST_LBP_R1P8_R3P16_CONCAT, path)
self.log = Log()
def loadDataset(self, ht, trainingPath):
dataset = DataSet()
dataset.load(ht, trainingPath)
return dataset
def addHistogramsAndLabels(self, ds, knn):
for i in range(0, ds.getTrainingSize()):
hist = ds.trainingData[i]
label = ds.trainingLabels[i]
knn.addHistogram(hist, label)
knn.train()
def train(self):
self.knn1 = SimilaritySearch(k=self.ds1.getTrainingSize())
self.knn2 = SimilaritySearch(k=self.ds2.getTrainingSize())
self.addHistogramsAndLabels(self.ds1, self.knn1)
self.addHistogramsAndLabels(self.ds2, self.knn2)
def mergeResults(self, results1, results2, maxk):
kResults={}
for p in C.PERC_COMBS:
results = {}
for key, value in results1.items():
if not key in results2:
results[key] = value * p
for key, value in results2.items():
if not key in results1:
results[key] = value * (1-p)
for key, value in results1.items():
if key in results2:
v1 = value * p
v2 = (1-p) * results2[key]
results[key] = v1 + v2
for k in range(1,maxk+1):
kResults[(p,k)]=sorted(results, key=results.__getitem__)[0:k]
return kResults
def getSpeciesDictFromSearch(self, dist, neigh, ds, expectedLabel):
labels = ds.trainingLabels
neigh = neigh.flatten()
dist = dist.flatten()
maxi = np.max(dist)
mini = np.min(dist)
rankRange = float(maxi - mini)
if rankRange==0:
rankRange = 1
results = {}
for cont in range(0,len(neigh)):
pos = neigh[cont]
item = labels[pos]
if not(expectedLabel.equals(item)):
if not item.getKey() in results:
results[item.getKey()] = (dist[cont] - mini) / rankRange
return results
def getTopK(self, neighbours1, dist1, neighbours2, dist2, maxk, expectedLabel):
results1 = self.getSpeciesDictFromSearch(dist1, neighbours1, self.ds1, expectedLabel)
results2 = self.getSpeciesDictFromSearch(dist2, neighbours2, self.ds2, expectedLabel)
return self.mergeResults(results1, results2, maxk)
def runKnn(self, knn, hist):
return knn.findk(hist)
def parseResults(self, results):
aux = ""
cont = 1
for label in results:
aux = aux + "\n" + str(cont) + "." + str(label)
cont+=1
return aux
def run(self):
self.train()
speciesFolders = cleanHiddenFiles(getFolderContents(self.path))
for speciesName in speciesFolders:
speciesFolder = getPath(self.path, speciesName) #folder for 1 species
if os.path.isdir(speciesFolder):
images = cleanHiddenFiles(getFolderContents(speciesFolder))
for imageName in images:
try:
name = getPath(speciesFolder, imageName)
img = loadImage(name)
start = time.time()
#a mobile phone would request to process the image and get the species list
speciesImage = LeafImage(inputImg=img, disks=self.disks, circumferences=self.circumferences)
neighbours1, dist1 = self.runKnn(self.knn1, speciesImage.getHistogram(C.HIST_HCoS))
neighbours2, dist2 = self.runKnn(self.knn2, speciesImage.getHistogram(C.HIST_LBP_R1P8_R3P16_CONCAT))
histPath = C.matchHistToName(C.HIST_HCoS)
expectedLabel = Label(speciesName, histPath, speciesFolder)
kResults = self.getTopK(neighbours1, dist1, neighbours2, dist2, self.maxk, expectedLabel)
end = time.time()
elapsed = end - start
self.log.write(name + "\t" + str(elapsed))
aux = self.parseResults(kResults) + "\n"
print(name + "\t" + str(elapsed))
except Exception as e:
print(e)
self.log.toDisk("/Users/maeotaku/Documents/time_results_noisy.txt")
class MobileSimulation():
def __init__(self, maxk, path, disks, circumferences):
self.knn1 = None
self.knn2 = None
self.path = path
self.maxk = maxk
self.disks = disks
self.circumferences = circumferences
self.ds1 = self.loadDataset(C.HIST_HCoS, path)
self.ds2 = self.loadDataset(C.HIST_LBP_R1P8_R3P16_CONCAT, path)
self.log = Log()
def loadDataset(self, ht, trainingPath):
dataset = DataSet()
dataset.load(ht, trainingPath)
return dataset
def addHistogramsAndLabels(self, ds, knn):
for i in range(0, ds.getTrainingSize()):
hist = ds.trainingData[i]
label = ds.trainingLabels[i]
knn.addHistogram(hist, label)
knn.train()
def train(self):
self.knn1 = SimilaritySearch(k=self.ds1.getTrainingSize())
self.knn2 = SimilaritySearch(k=self.ds2.getTrainingSize())
self.addHistogramsAndLabels(self.ds1, self.knn1)
self.addHistogramsAndLabels(self.ds2, self.knn2)
def mergeResults(self, results1, results2, maxk):
kResults = {}
for p in C.PERC_COMBS:
results = {}
for key, value in results1.items():
if not key in results2:
results[key] = value * p
for key, value in results2.items():
if not key in results1:
results[key] = value * (1 - p)
for key, value in results1.items():
if key in results2:
v1 = value * p
v2 = (1 - p) * results2[key]
results[key] = v1 + v2
for k in range(1, maxk + 1):
kResults[(p, k)] = sorted(results,
key=results.__getitem__)[0:k]
return kResults
def getSpeciesDictFromSearch(self, dist, neigh, ds, expectedLabel):
labels = ds.trainingLabels
neigh = neigh.flatten()
dist = dist.flatten()
maxi = np.max(dist)
mini = np.min(dist)
rankRange = float(maxi - mini)
if rankRange == 0:
rankRange = 1
results = {}
for cont in range(0, len(neigh)):
pos = neigh[cont]
item = labels[pos]
if not (expectedLabel.equals(item)):
if not item.getKey() in results:
results[item.getKey()] = (dist[cont] - mini) / rankRange
return results
def getTopK(self, neighbours1, dist1, neighbours2, dist2, maxk,
expectedLabel):
results1 = self.getSpeciesDictFromSearch(dist1, neighbours1, self.ds1,
expectedLabel)
results2 = self.getSpeciesDictFromSearch(dist2, neighbours2, self.ds2,
expectedLabel)
return self.mergeResults(results1, results2, maxk)
def runKnn(self, knn, hist):
return knn.findk(hist)
def parseResults(self, results):
aux = ""
cont = 1
for label in results:
aux = aux + "\n" + str(cont) + "." + str(label)
cont += 1
return aux
def run(self):
self.train()
speciesFolders = cleanHiddenFiles(getFolderContents(self.path))
for speciesName in speciesFolders:
speciesFolder = getPath(self.path,
speciesName) #folder for 1 species
if os.path.isdir(speciesFolder):
images = cleanHiddenFiles(getFolderContents(speciesFolder))
for imageName in images:
try:
name = getPath(speciesFolder, imageName)
img = loadImage(name)
start = time.time()
#a mobile phone would request to process the image and get the species list
speciesImage = LeafImage(
inputImg=img,
disks=self.disks,
circumferences=self.circumferences)
neighbours1, dist1 = self.runKnn(
self.knn1, speciesImage.getHistogram(C.HIST_HCoS))
neighbours2, dist2 = self.runKnn(
self.knn2,
speciesImage.getHistogram(
C.HIST_LBP_R1P8_R3P16_CONCAT))
histPath = C.matchHistToName(C.HIST_HCoS)
expectedLabel = Label(speciesName, histPath,
speciesFolder)
kResults = self.getTopK(neighbours1, dist1,
neighbours2, dist2, self.maxk,
expectedLabel)
end = time.time()
elapsed = end - start
self.log.write(name + "\t" + str(elapsed))
aux = self.parseResults(kResults) + "\n"
print(name + "\t" + str(elapsed))
except Exception as e:
print(e)
self.log.toDisk("/Users/maeotaku/Documents/time_results_noisy.txt")
