def test_imsave_multi():
im = imread(file_path('arange512_16bit.png'))
im2 = im[::4, ::4]
ims = [im, im2]
imsave_multi(_filename, ims)
ims2 = imread_multi(_filename)
assert len(ims) == len(ims2)
for a,b in zip(ims, ims2):
assert np.all(a == b)
def test_imsave_multi():
im = imread(file_path('arange512_16bit.png'))
im2 = im[::4, ::4]
ims = [im, im2]
imsave_multi(_filename, ims)
ims2 = imread_multi(_filename)
assert len(ims) == len(ims2)
for a, b in zip(ims, ims2):
assert np.all(a == b)
def test_multi():
assert len(imread_multi(file_path('stack.tiff'))) == 2
def test_multi():
assert len(imread_multi('imread/tests/data/stack.tiff')) == 2
def test_multi():
assert len(imread_multi(file_path('stack.tiff'))) == 2
valid_set = (np.zeros((nvalid, imgd * imgd),
dtype=np.uint8), np.zeros(nvalid, dtype=np.uint8))
test_set = (np.zeros((ntest, imgd * imgd),
dtype=np.uint8), np.zeros(ntest, dtype=np.uint8))
random.seed(seed)
train_i = 0
valid_i = 0
test_i = 0
sample_count = 0
for imgi in range(zrad, nimages - zrad):
file_index = start_image + imgi
input_img = normalize_image(imread.imread_multi(image_input)[imgi])
#print img_files[file_index]
label_img = mahotas.imread(label_input)[imgi]
#print seg_files[file_index]
if len(label_img.shape) == 3:
label_img = label_img[:, :,
0] * 2**16 + label_img[:, :,
1] * 2**8 + label_img[:, :,
2]
input_vol = np.zeros((input_img.shape[0], input_img.shape[1]),
dtype=np.uint8)
input_vol[:, :] = input_img
blur_img = scipy.ndimage.gaussian_filter(input_img, gblur_sigma)
def test_multi():
assert len(imread_multi('imread/tests/data/stack.tiff')) == 2
def open_file(self, fname):
from imread import imread_multi
return imread_multi(fname)
for seed in random_seeds:
train_set = (np.zeros((ntrain, imgd*imgd), dtype=np.uint8), np.zeros(ntrain, dtype=np.uint8))
valid_set = (np.zeros((nvalid, imgd*imgd), dtype=np.uint8), np.zeros(nvalid, dtype=np.uint8))
test_set = (np.zeros((ntest, imgd*imgd), dtype=np.uint8), np.zeros(ntest, dtype=np.uint8))
random.seed(seed)
train_i = 0;
valid_i = 0;
test_i = 0;
sample_count = 0;
for imgi in range (zrad, nimages-zrad):
file_index = start_image + imgi
input_img = normalize_image(imread.imread_multi(image_input)[imgi])
#print img_files[file_index]
label_img = mahotas.imread(label_input)[imgi]
#print seg_files[file_index]
if len( label_img.shape ) == 3:
label_img = label_img[ :, :, 0 ] * 2**16 + label_img[ :, :, 1 ] * 2**8 + label_img[ :, :, 2 ]
input_vol = np.zeros((input_img.shape[0], input_img.shape[1]), dtype=np.uint8)
input_vol[:,:] = input_img
blur_img = scipy.ndimage.gaussian_filter(input_img, gblur_sigma)
membrane = label_img==0;
non_membrane = ~membrane