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 test_gradient_hist(self):
feature = imfeat.GradientHistogram()
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_save_lena(self):
feat = imfeat.GradientHistogram()
out = feat.make_feature_mask(
imfeat.convert_image(cv2.imread('test_images/lena.jpg'),
feat.MODES))
try:
os.makedirs('out')
except OSError:
pass
print('m[%s]M[%s]' % (np.min(out), np.max(out)))
cv2.imwrite('out/lena_gradient.jpg', np.array(out * 255,
dtype=np.uint8))
def __init__(self, max_integral_trees=None, tp=None, tp2=None, num_classes=21):
super(TextonBase, self).__init__({'type': 'numpy', 'dtype': 'uint8', 'mode': 'bgr'})
self._max_integral_trees = max_integral_trees
self._tp = tp
self._tp2 = tp2
import imseg
if not (tp and tp2):
from imfeat._texton.msrc_model import data
tp = pickle.loads(data[1][1])
tp2 = pickle.loads(data[0][1])
self.tp = imseg.TextonPredict(tp) # NOTE(brandyn): TP = 1 and TP2 = 0 as that is how the names were sorted
if max_integral_trees is not None:
self.tp2 = imseg.IntegralPredict(tp2[:max_integral_trees])
else:
self.tp2 = imseg.IntegralPredict(tp2)
self.num_classes = num_classes
self.grad = imfeat.GradientHistogram()
os.makedirs('%s/view' % out_root)
except OSError:
pass
cv2.imwrite('%s/view/key.png' % out_root, color_key)
def convert_color(image):
image = imfeat.convert_image(image, [('opencv', 'lab', cv.IPL_DEPTH_8U)])
image = np.asarray(cv.GetMat(image)).copy()
return image
HOG = imfeat.HOGLatent(4)
LBP = imfeat.LBP()
GRAD = imfeat.GradientHistogram()
def convert_labels_to_integrals(label_mask, num_vals):
masks = []
print(label_mask.shape)
for x in range(num_vals):
m = np.asfarray(label_mask == x)
m = cv2.integral(m)
masks.append(m)
return np.ascontiguousarray(np.swapaxes(np.swapaxes(np.array(masks), 0, 2), 0, 1))
def save_feature_hists(func_data, extra_dump_vars, bins=20):
def _meta_hog_gradient():
return imfeat.MetaFeature(imfeat.HOGLatent(), imfeat.GradientHistogram())
def _gradient_hist():
return imfeat.GradientHistogram()