def getDictionary(imgPaths, alpha, K, method):
ncores = 4
data = pickle.load(open('../data/traintest.pkl', 'rb'))
train = data['train_imagenames']
filters = createFilterBank()
testset = train[0:2]
#whatname = __name__
print(('Starting a pool of workers with %d cores\n') % ncores)
if __name__ == 'getDictionary':
results = Parallel(n_jobs=ncores, verbose=11)(
delayed(filterPoints)(method, filters, alpha, K, imgPaths, name)
for name in train)
pixelResponses = np.vstack((results))
d = sklearn.cluster.KMeans(n_clusters=K).fit(pixelResponses)
dictionary = d.cluster_centers_
fout = os.path.join(imgPaths, 'dictionary' + method + '.npz')
np.savez(fout, dictionary=dictionary, filterBank=filters)
return dictionary
from createFilterBank import createFilterBank
#from extractFilterResponses import extractFilterResponses
from getRandomPoints import getRandomPoints
from getHarrisPoints import getHarrisPoints
alpha = 50 #Number of points desired
k = 0.04 #Should be 0.04-0.06 is good
K = 100 #Number of clusters
method = 'Random'
#testin Q1.3
imgPaths = '../data/'
ncores = 4
data = pickle.load(open('../data/traintest.pkl', 'rb'))
train = data['train_imagenames']
filters = createFilterBank()
testset = train[0:4]
#whatname = __name__
print(('Starting a pool of workers with %d cores\n') % ncores)
results = Parallel(n_jobs=ncores, verbose=11)(
delayed(filterPoints)(method, filters, alpha, K, imgPaths, name)
for name in testset)
#results = Parallel(n_jobs=ncores, verbose=11)(map(delayed(filterPoints),testset))
pixelResponses = np.vstack((results))
d = sklearn.cluster.KMeans(n_clusters=K).fit(pixelResponses)
dictionary = d.cluster_centers_
wordMap = getVisualWords(img, filterBank, dictionary)
np.savez(fname2, wordMap=wordMap)
# number of cores to use
# set this value appropriately
ncores = 4
source = '../data/'
methods = ['Random', 'Harris']
# load the files and texton dictionary
data = pickle.load(open('../data/traintest.pkl', 'rb'))
mapping = data['mapping']
all_imagenames = data['all_imagenames']
nimages = len(all_imagenames)
filterBank = createFilterBank()
for method in methods:
destination = os.path.join('../intermediate/', method)
dictionary = np.load('dictionary' + method + '.npz')
dictionary = dictionary['dictionary']
if os.path.isdir(destination) is False:
os.mkdir(destination)
for category in mapping:
if os.path.isdir(os.path.join(destination, category)) is False:
os.mkdir(os.path.join(destination, category))
print(('Starting a pool of workers with %d cores\n') % ncores)
results = Parallel(n_jobs=ncores)(
def rgb2gray(rgb):
return np.dot(rgb[..., :3], [0.299, 0.587, 0.114])
ncores = 4
source = '../data/'
method = 'Harris'
# load the files and texton dictionary
data = pickle.load(open('../data/traintest.pkl', 'rb'))
mapping = data['mapping']
all_imagenames = data['all_imagenames']
nimages = len(all_imagenames)
filterz = createFilterBank()
dictionary = np.load('dictionary' + method + '.npz')
dictionary = dictionary['dictionary']
fname = all_imagenames[0]
fname1 = os.path.join(source, fname)
#fname2 = os.path.join(destination, os.path.splitext(fname)[0] + '.npz')
img = sp.ndimage.imread(fname1)
#if img.ndim == 3:
# img = rgb2gray(img)
#img = np.float64(img) / 255.0
wordMap = getVisualWords(img, dictionary, filterz)
done = 1