def test_dog():
im = mh.demos.load('lena')
im = im.mean(2)
edges = mh.dog(im)
assert edges.shape == im.shape
assert edges.any()
edges1 = mh.dog(im, sigma1=1.)
assert np.any(edges != edges1)
def test_dog():
im = mh.demos.load('lena')
im = im.mean(2)
edges = mh.dog(im)
assert edges.shape == im.shape
assert edges.any()
edges1 = mh.dog(im, sigma1=1.)
assert np.any(edges != edges1)
def extractFeatures(self,img,feature_indices=None):
if self.compute_dog:
img = mh.dog(img)
features= mh.features.haralick(img).mean(0)
if feature_indices == None:
feature_indices = self.selected_features
retval = [features[i] for i in feature_indices]
return retval
def extractFeaturesWithHaralick(self,img):
kpts = self.keypoints(img)
features = np.asarray(self.haralickFromKeypoint(img,kpts[0])).reshape(-1,5)
if self.compute_dog:
img = mh.dog(img)
for kpt_index in range(1,len(kpts)):
new_features = np.asarray(self.haralickFromKeypoint(img,kpts[kpt_index])).reshape(-1,5)
features = np.append(features,new_features,axis=0)
return features
f = mh.imread('/home/zo/PycharmProjects/cal-app/person/zj/learn/python-ml/vision4op/image/IMG_1962.JPG') #treegroup_1.JPG')
#'/home/zo/Downloads/mahotas-master/mahotas/demos/data/nuclear.png')
#mh.demos.nuclear_image()
f = f[:213,380:650,1]
f[50,:] = 0
# ashe f[:213,10:271,0]
#line:f[326:415,172:250,2]
print f.shape, f.mean() ,f.std()
pylab.imshow(f)
pylab.show()
#pylab.hist(f)
#pylab.show()
edges = mh.dog(f) # , just_filter=True) #sobel
pylab.imshow(edges)
pylab.show()
print edges.shape, edges.mean() ,edges.std()
#edges = mh.gaussian_filter(edges,1)
thred = edges.mean()
edges = (edges > thred )
pylab.imshow(edges)
pylab.show()
import mahotas
import numpy as np
from pylab import imshow, gray, show, subplot
from os import path
lena_image = path.join(path.dirname(path.abspath(__file__)), 'data',
'lena.jpg')
photo = mahotas.imread(lena_image, as_grey=True)
photo = photo.astype(np.uint8)
gray()
subplot(131)
imshow(photo)
edge_sobel = mahotas.sobel(photo)
subplot(132)
imshow(edge_sobel)
edge_dog = mahotas.dog(photo)
subplot(133)
imshow(edge_dog)
show()
import mahotas
import numpy as np
from pylab import imshow, gray, show, subplot
from os import path
lena_image = path.join(
path.dirname(path.abspath(__file__)),
'data',
'lena.jpg')
photo = mahotas.imread(lena_image, as_grey=True)
photo = photo.astype(np.uint8)
gray()
subplot(131)
imshow(photo)
edge_sobel = mahotas.sobel(photo)
subplot(132)
imshow(edge_sobel)
edge_dog = mahotas.dog(photo)
subplot(133)
imshow(edge_dog)
show()
