def load_data():
#save classes as ints
#create map from class->index
print("Loading images and classes")
images, classes = loader.loadTrainingAndClasses()
unique_classes = list(set(classes))
int_unique_classes={unique_classes[i]:i for i in range(len(unique_classes))}
#convert classes with map
int_classes=[int_unique_classes[cl] for cl in classes]
int_classes=numpy.array(int_classes)
#preprocess images
print("Pre-processing images")
thumbsize = 28;
thumbs = [misc.imresize(x,(thumbsize,thumbsize)) for x in images]
grays = [color.rgb2gray(x) for x in thumbs]
normies = [exposure.equalize_hist(x) for x in grays]
normies = np.array(normies)
return normies, int_classes
for i in range(splits):
for j in range(splits):
subImage = image[i * (width / splits) : (i + 1) * (width / splits), j * (height / splits) : (j+1) * (height / splits), :]
angle, magnitude = meanAngle(subImage)
result[i, j] = angle
if magnitude < threshold:
result[i,j] = -1
return result
def splitMeanAngleFeatures(image, splits = 2, threshold = 10):
result = splitMeanAngle(image, splits, threshold).flatten()
return result
print("Loading images")
#images, classes = loader.loadProblematicImagesAndClasses()
images, classes = loader.loadTrainingAndClasses()
amount = len(images)
print("Making thumbnails")
thumbsize = 50
thumbs = [misc.imresize(x,(thumbsize, thumbsize)) for x in images]
print("Calculating features")
#features = list(map(extractor.calculateNormalizedColorFeatures, images))
splits = 5
features = numpy.zeros([len(images), splits * splits])
for i in range(amount):
if(i%10 ==0):print(i, "/", amount)
features[i] = splitMeanAngleFeatures(thumbs[i], splits)
return numpy.sum(numpy.square(numpy.add(a, numpy.multiply(b, -1))))
def nearestNeighbour(xs, ys, x):
bestIndex = 0
bestDistance = distance(xs[0], x)
for i in range(len(xs)):
d = distance(xs[i], x)
if d < bestDistance:
bestIndex = i
bestDistance = d
return ys[bestIndex]
print("Loading images")
trainingImages, trainingClasses = loader.loadTrainingAndClasses()
testImages = loader.loadTest()
trainingAmount = len(trainingImages)
testAmount = len(testImages)
print("Making thumbnails")
size = 50
trainingThumbs = list(
map(lambda x: misc.imresize(x, (size, size)), trainingImages))
testThumbs = list(map(lambda x: misc.imresize(x, (size, size)), testImages))
print("Calculating features")
#features = list(map(extractor.calculateNormalizedColorFeatures, images))
splits = 5
trainingFeatures = numpy.zeros([trainingAmount, splits * splits * 3])
testFeatures = numpy.zeros([testAmount, splits * splits * 3])
