def test_ainterface(self):
"""Simple test of the basic feature interface"""
features = [
imfeat.ObjectBank(),
imfeat.GIST(),
imfeat.HOGLatent(2),
imfeat.Autocorrelogram(),
imfeat.GradientHistogram(),
imfeat.Histogram('gray'),
imfeat.RHOG(gray=False),
imfeat.RHOG(gray=True),
imfeat.Moments('rgb', 2),
imfeat.Histogram('rgb'),
imfeat.SpatialHistogram(mode='rgb', num_rows=2, num_cols=2),
imfeat.TinyImage()
]
feat_sz = {}
for image_fn in glob.glob('test_images/*'):
if image_fn in ['test_images/test3.gif']:
continue
for feat_num, feature in enumerate(features):
prev_f = None
for load_func in [
cv.LoadImage, cv.LoadImageM, cv2.imread, Image.open
]:
f = feature(
imfeat.resize_image(load_func(image_fn), 100, 100))
self.assertEqual(feat_sz.setdefault(feat_num, f.size),
f.size)
if prev_f is None:
prev_f = f
if load_func != Image.open: # Skip PIL as the jpeg loading produces different data
np.testing.assert_equal(prev_f, f)
def __init__(self, min_diff=.5, **kw):
super(DecisionTree, self).__init__([('opencv', 'gray', 8)],
min_diff=min_diff,
**kw)
self._surf = impoint.SURF()
self._feat = imfeat.Histogram('gray', num_bins=4, norm=False)
self.rfc = None
def test_gray_hist(self):
feature = imfeat.Histogram('gray')
for feat_out, image in self._run_all_images(feature):
print(feat_out)
print(len(feat_out))
self._feat_hist_zero(feature)
self._feat_hist_norm(feature)
def test_hist(self):
img = cv.LoadImage('test_images/lena.jpg')
feat = imfeat.Histogram('rgb', num_bins=8)
feat2 = imfeat.PyramidHistogram(mode='rgb', num_bins=8, levels=1)
np.testing.assert_equal(feat(img), feat2(img))
out = imfeat.PyramidHistogram(mode='lab',
num_bins=[4, 11, 11],
levels=4)(img)
print(out)
print(out.shape)
def test_hist_planar(self):
img = cv.LoadImage('test_images/lena.jpg')
for i in range(512):
for j in range(512):
img[i, j] = (random.randint(0, 255), random.randint(0, 255),
random.randint(0, 255))
for mode in modes:
print(mode)
feat = imfeat.Histogram(mode, style='planar')
b = feat(img)
print(b)
def _hist_joint():
return imfeat.Histogram('rgb', style='joint')
def _lab_hist_joint_8bins():
return imfeat.Histogram('lab', style='joint', num_bins=8)
def test_meta(self):
self._histogram(imfeat.MetaFeature(imfeat.Histogram('lab')))
def test_histogram_joint_lab(self):
self._histogram(imfeat.Histogram('lab'))
def test_histogram_joint_spatial(self):
img = Image.open('test_images/lena.ppm')
np.testing.assert_equal(
imfeat.Histogram(mode='rgb')(img),
imfeat.SpatialHistogram(mode='rgb')(img))
def test_histogram_joint(self):
self._histogram(imfeat.Histogram('rgb'))
def __init__(self, min_diff=10, **kw):
super(Histogram, self).__init__(min_diff=min_diff, **kw)
self._feat = imfeat.Histogram('gray')
def __init__(self, num_bins=8, *args, **kw):
super(GradientHistogram, self).__init__({'type': 'numpy', 'dtype': 'float32', 'mode': 'gray'})
self._hist_feat = imfeat.Histogram('gray', num_bins=num_bins)