def test_jug_execute_simple():
N = 1024
random.seed(232)
A = [False for i in range(N)]
def setAi(i):
A[i] = True
setall = [Task(setAi, i) for i in range(N)]
store = dict_store()
jug.task.Task.store = store
simple_execute()
assert False not in A
assert max(store.counts.values()) < 4
def test_jug_invalidate():
def setAi(i):
A[i] = True
N = 1024
A = [False for i in range(N)]
setall = [Task(setAi, i) for i in range(N)]
store = dict_store()
jug.task.Task.store = store
for t in setall: t.run()
opts = Options(default_options)
opts.invalid_name = setall[0].name
jug.subcommands.invalidate.invalidate(store, opts)
assert not list(store.store.keys()), list(store.store.keys())
jug.task.Task.store = dict_store()
def test_jug_execute_deps():
N = 256
random.seed(234)
A = [False for i in range(N)]
def setAi(i, other):
A[i] = True
idxs = list(range(N))
random.shuffle(idxs)
prev = None
for idx in idxs:
prev = Task(setAi, idx, prev)
store = dict_store()
jug.task.Task.store = store
simple_execute()
assert False not in A
assert max(store.counts.values()) < 5
_compare('mean_t',mean_t,refs)
_compare('rc_t',rc_t,refs)
#_compare('watershed:direct',water_direct,refs)
#_compare('watershed:gradient',water_gradient,refs)
#_compare('watershed:direct_rc',water_direct_rc,refs)
#_compare('watershed:gradient_rc',water_gradient_rc,refs)
_compare('watershed:direct_mean',water_direct_mean,refs)
_compare('watershed:gradient_mean',water_gradient_mean,refs)
#_compare('watershed:direct_raw',water_direct_raw,refs)
#_compare('watershed:gradient_raw',water_gradient_raw,refs)
#_compare('watershed:direct:full',water_direct,refs_full)
#_compare('watershed:gradient:full',water_gradient,refs_full)
#_compare('watershed:direct_raw:full',water_direct_raw,refs_full)
#_compare('watershed:gradient_raw:full',water_gradient_raw,refs_full)
#_compare('active_masks',active_masks,refs)
_compare('active_masks:filtered',active_masks_filtered,refs)
#_compare('active_masks2',active_masks2,refs)
#_compare('roysam',roysams,refs)
_compare('roysam_mean',roysams_mean,refs)
#_compare('roysams_mean_filter',roysams_mean_filtered,refs)
_compare('roysams_mean_filter_no_AS',roysams_mean_filtered[5:],refs[5:])
aabid_refs = ic100_ref[:5] + gnf_ref[:5]
aabids = [Task(load_aabid,'ic100',i) for i in xrange(5)] +\
[Task(load_aabid,'gnf',i) for i in xrange(5)]
compare(aabid_refs,ic100_ref[:5],'AS',aabids)
# vim: set ts=4 sts=4 sw=4 expandtab smartindent:
return _process_B(B)
def _process_B(B):
L,N = ndimage.label(~B)
bg = 0
bg_size = (L==bg).sum()
for i in xrange(1,N+1):
i_size = (L == i).sum()
if i_size > bg_size:
bg_size = i_size
bg = i
if i_size < _min_obj_size:
L[L==i] = 0
L[L==bg]=0
L,_ = ndimage.label(L!=0)
return L
def load_aabid(col,id):
from readmagick import readimg
id = ('../data/images/segmented-ashariff/%s/dna-%s.psd' % (col,id))
I = readimg(id)
B = (I[:,:,0] > I[:,:,1])
return _process_B(B)
ic100_imgs = load_ic100()
gnf_imgs = load_gnf()
ic100_ref = [Task(load_ref,'ic100',i) for i in ic100_idxs]
gnf_ref = [Task(load_ref,'gnf',i) for i in gnf_idxs]