from sklearn.preprocessing import StandardScaler
basedir = 'SimpleImageDataset/'
haralick, labels, chistogram = [], [], []
print(
'This script will test (with cross-validation) classification of the simple 3 class dataset'
)
print('Computing features...')
# Use glob to get all the images
images = glob('{}/*.jpg'.format(basedir))
for fname in sorted(images):
imc = mh.imread(fname)
haralick.append(texture(mh.colors.rgb2gray(imc)))
chistogram.append(chist(imc))
labels.append(fname[:-len('xx.jpg')])
print('Finished computing features.')
haralick = np.array(haralick)
chistogram = np.array(chistogram)
labels = np.array(labels)
haralick_plus_chist = np.hstack([chistogram, haralick])
clf = Pipeline([('preproc', StandardScaler()),
('classifier', LogisticRegression())])
from sklearn import cross_validation
cv = cross_validation.LeaveOneOut(len(images))
def chist_file(fname):
from features import chist
im = mh.imread(fname)
return chist(im)
def features_for(im):
from features import chist
im = mh.imread(im)
img = mh.colors.rgb2grey(im).astype(np.uint8)
return np.concatenate([mh.features.haralick(img).ravel(), chist(im)])
haralicks = []
chists = []
print('Computing features...')
# Use glob to get all the images
images = glob('{}/*.jpg'.format(basedir))
# We sort the images to ensure that they are always processed in the same order
# Otherwise, this would introduce some variation just based on the random
# ordering that the filesystem uses
images.sort()
for fname in images:
imc = mh.imread(fname)
imc = imc[200:-200, 200:-200]
haralicks.append(texture(mh.colors.rgb2grey(imc)))
chists.append(chist(imc))
haralicks = np.array(haralicks)
chists = np.array(chists)
features = np.hstack([chists, haralicks])
print('Computing neighbors...')
sc = StandardScaler()
features = sc.fit_transform(features)
#计算距离矩阵
dists = distance.squareform(distance.pdist(features))
print('Plotting...')
fig, axes = plt.subplots(2, 9, figsize=(16, 8))
# Remove ticks from all subplots
for ax in axes.flat:
def chist_file(fname):
from features import chist
im = mh.imread(fname)
return chist(im)
def features_for(im):
from features import chist
im = mh.imread(im)
img = mh.colors.rgb2grey(im).astype(np.uint8)
return np.concatenate([mh.features.haralick(img).ravel(),
chist(im)])
.format(k, v.mean()))
to_array = TaskGenerator(np.array)
hstack = TaskGenerator(np.hstack)
haralicks = []
chists = []
lbps = []
labels = []
# Use glob to get all the images
images = glob('{0}/*.jpg'.format(basedir))
for fname in sorted(images):
haralicks.append(compute_texture(fname))
chists.append(chist(fname))
lbps.append(compute_lbp(fname))
labels.append(fname[:-len('00.jpg')]
) # The class is encoded in the filename as xxxx00.jpg
haralicks = to_array(haralicks)
chists = to_array(chists)
lbps = to_array(lbps)
labels = to_array(labels)
scores_base = accuracy(haralicks, labels)
scores_chist = accuracy(chists, labels)
scores_lbps = accuracy(lbps, labels)
combined = hstack([chists, haralicks])
scores_combined = accuracy(combined, labels)
haralicks = []
labels = []
chists = []
print('This script will test (with cross-validation) classification of the simple 3 class dataset')
print('Computing features...')
# Use glob to get all the images
images = glob('{}/*.jpg'.format(basedir))
# We sort the images to ensure that they are always processed in the same order
# Otherwise, this would introduce some variation just based on the random
# ordering that the filesystem uses
for fname in sorted(images):
imc = mh.imread(fname)
haralicks.append(texture(mh.colors.rgb2grey(imc)))
chists.append(chist(imc))
# Files are named like building00.jpg, scene23.jpg...
labels.append(fname[:-len('xx.jpg')])
print('Finished computing features.')
haralicks = np.array(haralicks)
labels = np.array(labels)
chists = np.array(chists)
haralick_plus_chists = np.hstack([chists, haralicks])
# We use Logistic Regression because it achieves high accuracy on small(ish) datasets
# Feel free to experiment with other classifiers
