def generateKMeansSeries(activationsPath, labelIndexMappingPath, targetFolder):
if targetFolder.endswith('/') == False:
targetFolder += '/'
activations = utility.arrayFromFile(activationsPath)
labels = utility.getLabelStrings(labelIndexMappingPath)
neurons = activations.shape[1] - len(labels)
# test per label k-means
k = PER_LABEL_START
while k <= PER_LABEL_END:
runCounter = 0
while runCounter < RUNS_PER_TYPE:
logger.info("Calculating per label KMeans with k = " + str(k) +
".")
currentTarget = targetFolder + "perLabel_" + str(
k) + "/run_" + str(runCounter) + "/"
if not os.path.exists(os.path.dirname(currentTarget)):
os.makedirs(os.path.dirname(currentTarget))
c, i = kMeans_per_label.findKMeansPerLabel(activations, k,
len(labels),
currentTarget,
labelIndexMappingPath)
plot_cluster.plotClusters(kMeans_core.cleanUp(c), i, neurons,
currentTarget, labels[:len(labels)])
runCounter += 1
k += 1
# test mixed k-means
k = MIXED_START
while k <= MIXED_END:
runCounter = 0
while runCounter < RUNS_PER_TYPE:
logger.info("Calculating mixed KMeans with k = " + str(k) + ".")
currentTarget = targetFolder + "mixed_" + str(k) + "/run_" + str(
runCounter) + "/"
if not os.path.exists(os.path.dirname(currentTarget)):
os.makedirs(os.path.dirname(currentTarget))
[c, i] = kMeans_mixed.findKMeans(activations, k, len(labels),
currentTarget)
plot_cluster.plotClusters(kMeans_core.cleanUp(c), i,
activations.shape[1] - len(labels),
currentTarget, labels[:len(labels)])
runCounter += 1
k += MIXED_STEP
def generateKMeansSeries(activationsPath, labelIndexMappingPath, targetFolder):
if targetFolder.endswith("/") == False:
targetFolder += "/"
activations = utility.arrayFromFile(activationsPath)
labels = utility.getLabelStrings(labelIndexMappingPath)
neurons = activations.shape[1] - len(labels)
# test per label k-means
k = PER_LABEL_START
while k <= PER_LABEL_END:
runCounter = 0
while runCounter < RUNS_PER_TYPE:
logger.info("Calculating per label KMeans with k = " + str(k) + ".")
currentTarget = targetFolder + "perLabel_" + str(k) + "/run_" + str(runCounter) + "/"
if not os.path.exists(os.path.dirname(currentTarget)):
os.makedirs(os.path.dirname(currentTarget))
c, i = kMeans_per_label.findKMeansPerLabel(
activations, k, len(labels), currentTarget, labelIndexMappingPath
)
plot_cluster.plotClusters(kMeans_core.cleanUp(c), i, neurons, currentTarget, labels[: len(labels)])
runCounter += 1
k += 1
# test mixed k-means
k = MIXED_START
while k <= MIXED_END:
runCounter = 0
while runCounter < RUNS_PER_TYPE:
logger.info("Calculating mixed KMeans with k = " + str(k) + ".")
currentTarget = targetFolder + "mixed_" + str(k) + "/run_" + str(runCounter) + "/"
if not os.path.exists(os.path.dirname(currentTarget)):
os.makedirs(os.path.dirname(currentTarget))
[c, i] = kMeans_mixed.findKMeans(activations, k, len(labels), currentTarget)
plot_cluster.plotClusters(
kMeans_core.cleanUp(c), i, activations.shape[1] - len(labels), currentTarget, labels[: len(labels)]
)
runCounter += 1
k += MIXED_STEP
with open(sys.argv[2], "r") as result:
for line in result.readlines():
split = line.strip().split(' ')
imagePaths.append(split[0])
# for rootPath, subdirs, files in os.walk(sys.argv[2]):
# for f in files:
# if f.endswith('.jpg'):
# imagePaths.append(rootPath + f)
clusters = None
if len(sys.argv) == 5:
clusters = pickle.load(sys.argv[4])
activations = utility.arrayFromFile(sys.argv[1])
if clusters == None:
[clusters, iterations] = kMeans.findKMeans(activations, K, 0,
targetPath)
for clusterIndex, cluster in enumerate(clusters):
for index in cluster['memberIndices']:
currentTarget = targetPath + str(clusterIndex) + "/"
if not os.path.exists(os.path.dirname(currentTarget)):
os.makedirs(os.path.dirname(currentTarget))
copyfile(imagePaths[index],
currentTarget + os.path.basename(imagePaths[index]))
