def FeedForward_UnitTest():
featureSize = 12
colorChannels = 1
# Load Centroids and Initialize KMean module
centroids = datamanipulation.LoadCentroids("C:/Images/TempCentroids_" +
str(colorChannels) + ".txt")
KMean = kmean.KMean(featureSize * featureSize * colorChannels,
len(centroids))
# Load image and slice it
image_a = datamanipulation.LoadImageData(["C:/Images/Satellite_1.png"],
colorChannels)
image_b = datamanipulation.LoadImageData(["C:/Images/Satellite_1_LQ.png"],
colorChannels)
compare_1 = datamanipulation.CompareImages(image_a, image_a)
compare_2 = datamanipulation.CompareImages(image_a, image_b)
data = datamanipulation.SliceImage(image_b, featureSize * colorChannels,
featureSize)
loadedPoints = len(data)
# Classify points
data = KMean.KmeanClassification(data, centroids)
# Sort points by centroid
classifiedPoints = 0
for centroid in range(0, len(centroids)):
points = [x for x in data if x[-3] == centroid]
classifiedPoints = classifiedPoints + len(points)
encoder = autoencoder.AutoEncoder([
featureSize * featureSize * colorChannels, 100,
featureSize * featureSize * colorChannels
])
if (encoder.LoadParameters("C:/Images/EncoderParameters_" +
str(colorChannels) + "_" + str(centroid) +
".txt")):
print("feeding points into encoder")
for point in points:
output = encoder.FeedForward(
np.array(point[:-3]).reshape(144, 1))
image_b = datamanipulation.InsertSubImage(
image_b,
output.flatten(),
featureSize,
colorChannels,
point[-2],
point[-1],
referenceImage=image_a)
else:
print("unable to load encoder")
# return False
compare_3 = datamanipulation.CompareImages(image_a, image_b)
print("Compare 1: " + str(compare_1))
print("Compare 2: " + str(compare_2))
print("Compare 3: " + str(compare_3))
if (loadedPoints == classifiedPoints):
return True
return False
def CompareImages_UnitTest():
image_a = datamanipulation.LoadImageData(["C:/Images/Satellite_1.png"])
image_b = datamanipulation.LoadImageData(["C:/Images/Satellite_1_LQ.png"])
image_c = datamanipulation.LoadImageData(["C:/Images/Satellite_1_VLQ.png"])
compare_1 = datamanipulation.CompareImages(image_a, image_a)
compare_2 = datamanipulation.CompareImages(image_a, image_b)
compare_3 = datamanipulation.CompareImages(image_a, image_c)
if (compare_1 > 0):
return False
elif (compare_2 <= compare_1):
return False
elif (compare_3 <= compare_2):
return False
return True
def KmeanClassification_UnitTest():
featureSize = 12
colorChannels = 1
# Load Centroids and Initialize KMean module
centroids = datamanipulation.LoadCentroids("C:/Images/TempCentroids_" +
str(colorChannels) + ".txt")
KMean = kmean.KMean(featureSize * featureSize * colorChannels,
len(centroids))
# Load Image and Slice it
image = datamanipulation.LoadImageData(["C:/Images/Satellite_1_LQ.png"])
data = datamanipulation.SliceImage(image, featureSize * colorChannels,
featureSize)
originalClusters = [x[-3] for x in data]
# Classify Images
data = KMean.KmeanClassification(data, centroids)
finalClusters = [x[-3] for x in data]
for a, b in zip(originalClusters, finalClusters):
if (a == b):
print("One or more points were left unclassified")
return False
return True
def CreateTrainingSetFromData_UnitTest():
featureSize = 12
colorChannels = 1
# Load Centroids and Initialize KMean module
centroids = datamanipulation.LoadCentroids("C:/Images/TempCentroids_" +
str(colorChannels) + ".txt")
KMean = kmean.KMean(featureSize * featureSize * colorChannels,
len(centroids))
# Load image and slice it
images = ("C:/Images/Satellite_1.png", "C:/Images/Satellite_2.png",
"C:/Images/Satellite_3.png", "C:/Images/Satellite_4.png",
"C:/Images/Satellite_5.png")
data = datamanipulation.SliceImage(datamanipulation.LoadImageData(images),
featureSize * colorChannels,
featureSize)
# Classify points
loadedPoints = len(data)
data = KMean.KmeanClassification(data, centroids)
print("Creating Training Sets with Threshold")
for index in range(0, len(centroids)):
trainingSet = datamanipulation.CreateTrainingSetFromData(
data, centroids, index)
if (len(trainingSet) > 0):
print("Training Set for centroid " + str(index) + " has length " +
str(len(trainingSet)))
else:
print("Training Set for centroid " + str(index) + " is empty")
print("Creating Training Sets without Threshold")
trainingPoints = 0
for index in range(0, len(centroids)):
trainingSet = datamanipulation.CreateTrainingSetFromData(
data, centroids, index, threshold=False)
trainingPoints = trainingPoints + len(trainingSet)
if (len(trainingSet) > 0):
print("Training Set for centroid " + str(index) + " has length " +
str(len(trainingSet)))
else:
print("Training Set for centroid " + str(index) + " is empty")
print("Loaded points vs training points: " + str(loadedPoints) + " / " +
str(trainingPoints))
if (loadedPoints == trainingPoints):
return True
return False
def InsertSubImage_UnitTest():
colorChannels = 1
featureSize = 12
image_a = datamanipulation.LoadImageData(["C:/Images/Satellite_1.png"],
colorChannels)
image_b = datamanipulation.LoadImageData(["C:/Images/Blue.png"],
colorChannels)
data = datamanipulation.SliceImage(image_a, featureSize * colorChannels,
featureSize)
difference_original, identical_original = datamanipulation.CompareImages(
image_a, image_b)
image_b = datamanipulation.InsertSubImage(image_b, data[0][:-3],
featureSize, colorChannels,
data[0][-2], data[0][-1])
difference_modified, identical_modified = datamanipulation.CompareImages(
image_a, image_b)
if (difference_modified >= difference_original):
return False
elif ((identical_modified - identical_original) !=
(featureSize * featureSize)):
return False
return True
def KMeanClustering_UnitTest():
featureSize = 12
colorChannels = 3
numberOfCentroids = 12
images = ("C:/Images/Satellite_1.png", "C:/Images/Satellite_2.png")
data = datamanipulation.SliceImage(datamanipulation.LoadImageData(images),
featureSize * colorChannels,
featureSize)
KMean = kmean.KMean(featureSize * featureSize * colorChannels,
numberOfCentroids)
data, centroids = KMean.KMeanClustering(data, KMean.GenerateCentroids())
for index in range(0, numberOfCentroids):
points = [x for x in data if x[-3] == index]
if (len(points) < 1): return False
return True
def main():
print("Image Sharpening with Machine Learning\n")
print("Sebastian Pendola, NY 2017\n\n")
print("1) Train KMean Clustering")
print("2) Train AutoEncoders")
print("3) Enhance Image")
print("4) Display Centroids")
print("5) Test on Image")
task = int(raw_input("\nChoose a Task: "))
if task == 1:
featureSize = int(raw_input("Feature Size: "))
colorChannels = int(raw_input("Color Channels: "))
numberOfCentroids = int(raw_input("Clusters: "))
epochs = int(raw_input("Epochs: "))
useLastCentroids = raw_input("Use last centroids? (y/n) ")
startTime = dt.datetime.now().replace(microsecond=0)
#images = ("images/Satellite_1.png", "images/Satellite_2.png", "images/Satellite_3.png", "images/Satellite_4.png", "images/Satellite_5.png")
images = ("images/image_1.png", "images/image_2.png",
"images/image_3.png", "images/image_4.png",
"images/image_5.png", "images/image_6.png",
"images/image_7.png", "images/image_8.png")
data = datamanipulation.SliceImage(
datamanipulation.LoadImageData(images),
featureSize * colorChannels, featureSize)
publisher.PublishMsg("KMean Clustering Started")
publisher.PublishMsg(
str(numberOfCentroids) + " clusters of size " + str(featureSize) +
"x" + str(featureSize))
publisher.PublishCmd("validationgraph")
publisher.PublishCmd("flushgraph")
KMean = kmean.KMean(featureSize * featureSize * colorChannels,
numberOfCentroids)
if (useLastCentroids == "y" or useLastCentroids == "Y"):
data, centroids = KMean.KMeanClustering(
data,
datamanipulation.LoadCentroids("images/TempCentroids_" +
str(colorChannels) + ".txt"),
limit=epochs)
else:
data, centroids = KMean.KMeanClustering(data, limit=epochs)
for centroid in range(0, len(centroids)):
points = [x for x in data if x[-3] == centroid]
print("Cluster " + str(centroid) + " has " + str(len(points)) +
" points")
print("Execution Time: " +
str(dt.datetime.now().replace(microsecond=0) - startTime))
datamanipulation.SaveCentroids(
centroids, "images/TempCentroids_" + str(colorChannels) + ".txt")
elif task == 2:
learningRate = float(raw_input("Learning Rate: "))
lmbda = float(raw_input("Lambda: "))
epochs = int(raw_input("Epochs: "))
featureSize = int(raw_input("Feature Size: "))
colorChannels = int(raw_input("Color Channels: "))
# Load Centroids and Initialize KMean module
centroids = datamanipulation.LoadCentroids("images/TempCentroids_" +
str(colorChannels) + ".txt")
KMean = kmean.KMean(featureSize * featureSize * colorChannels,
len(centroids))
# Load points and classify them
#images = ("images/Satellite_1.png", "images/Satellite_2.png", "images/Satellite_3.png", "images/Satellite_4.png", "images/Satellite_5.png")
images = ("images/image_1.png", "images/image_2.png",
"images/image_3.png", "images/image_4.png",
"images/image_5.png", "images/image_6.png",
"images/image_7.png", "images/image_8.png")
data = datamanipulation.SliceImage(
datamanipulation.LoadImageData(images),
featureSize * colorChannels, featureSize)
data = KMean.KmeanClassification(data, centroids)
trainingCosts = []
for centroid in range(0, len(centroids)):
encoder = autoencoder.AutoEncoder([
featureSize * featureSize * colorChannels, 100,
featureSize * featureSize * colorChannels
])
encoder.LoadParameters("images/EncoderParameters_" +
str(colorChannels) + "_" + str(centroid) +
".txt")
trainingSet = datamanipulation.CreateTrainingSetFromData(
data, centroids, centroid, threshold=False)
if (len(trainingSet) > 0):
print("Training Samples of size " + str(len(trainingSet[0])))
savePath = "images/EncoderParameters_" + str(
colorChannels) + "_" + str(centroid) + ".txt"
trainingCosts.append(
encoder.Train(
trainingSet,
learningRate,
lmbda,
epochs,
len(trainingSet) if len(trainingSet) < 20 else 20,
path=savePath))
else:
print("Training Set " + str(index) + " is empty")
trainingCosts.append(-1)
for index in range(0, len(centroids)):
print("AutoEncoder for Feature " + str(index) +
" has a learning cost of " + str(trainingCosts[index]))
elif task == 3:
featureSize = int(raw_input("Feature Size: "))
colorChannels = int(raw_input("Color Channels: "))
# Load Centroids and Initialize KMean module
centroids = datamanipulation.LoadCentroids("images/TempCentroids_" +
str(colorChannels) + ".txt")
KMean = kmean.KMean(featureSize * featureSize * colorChannels,
len(centroids))
# Load reference and target images
image_a = datamanipulation.LoadImageData(["images/Satellite_1.png"],
colorChannels)
image_b = datamanipulation.LoadImageData(["images/Satellite_1_LQ.png"],
colorChannels)
compare_1 = datamanipulation.CompareImages(image_a, image_b)
# Slice reference image and classify slices
data = datamanipulation.SliceImage(image_b,
featureSize * colorChannels,
featureSize)
data = KMean.KmeanClassification(data, centroids)
for centroid in range(0, len(centroids)):
points = [x for x in data if x[-3] == centroid]
if (len(points) > 0):
encoder = autoencoder.AutoEncoder([
featureSize * featureSize * colorChannels, 100,
featureSize * featureSize * colorChannels
])
if (encoder.LoadParameters("images/EncoderParameters_" +
str(colorChannels) + "_" +
str(centroid) + ".txt")):
for point in points:
input = np.array(point[:-3]).reshape(144, 1)
output = encoder.FeedForward(input / 256.0) * 256.0
image_b = datamanipulation.InsertSubImage(
image_b,
output.flatten(),
featureSize,
colorChannels,
point[-2],
point[-1],
reference=image_a)
else:
print("unable to load encoder")
compare_2 = datamanipulation.CompareImages(image_a, image_b)
print("Initial Comparation: " + str(compare_1))
print("Final Comparation: " + str(compare_2))
elif task == 4:
centroids = datamanipulation.LoadCentroids("images/TempCentroids.txt")
datamanipulation.DisplayCentroids(centroids)
elif task == 5:
featureSize = int(raw_input("Feature Size: "))
colorChannels = int(raw_input("Color Channels: "))
centroids = datamanipulation.LoadCentroids("images/TempCentroids_" +
str(colorChannels) + ".txt")
datamanipulation.MarkImage("images/Satellite_2.png", centroids,
featureSize, colorChannels)