def fn():
cellprofiler.utilities.jutil.attach()
with self.image_dict_lock:
generation = self.image_dict_generation
for k, v in well.iteritems():
sd = {}
with self.image_dict_lock:
if self.image_dict_generation > generation:
return
self.image_dict[k] = sd
for c, fd in enumerate(v):
if PlateData.D_CHANNEL in fd:
channel = fd[PlateData.D_CHANNEL]
else:
channel = str(c + 1)
try:
img = load_using_bioformats_url(fd[PlateData.D_FILENAME])
with self.image_dict_lock:
if self.image_dict_generation > generation:
return
sd[channel] = img
except:
traceback.print_exc()
pass
wx.CallAfter(self.update_figure)
cellprofiler.utilities.jutil.detach()
def fn():
cellprofiler.utilities.jutil.attach()
with self.image_dict_lock:
generation = self.image_dict_generation
for k, v in well.iteritems():
sd = {}
with self.image_dict_lock:
if self.image_dict_generation > generation:
return
self.image_dict[k] = sd
for c, fd in enumerate(v):
if PlateData.D_CHANNEL in fd:
channel = fd[PlateData.D_CHANNEL]
else:
channel = str(c + 1)
try:
img = load_using_bioformats_url(
fd[PlateData.D_FILENAME])
with self.image_dict_lock:
if self.image_dict_generation > generation:
return
sd[channel] = img
except:
traceback.print_exc()
pass
wx.CallAfter(self.update_figure)
cellprofiler.utilities.jutil.detach()
def test_03_03_load_using_bioformats_url(self):
url = "https://github.com/CellProfiler/python-bioformats/raw/1.0.5/bioformats/tests/Channel1-01-A-01.tif"
try:
fd = urlopen(url)
if fd.code < 200 or fd.code >= 300:
raise OSError("Http error %d" % fd.code)
except OSError as e:
def bad_url(e=e):
raise e
unittest.expectedFailure(bad_url)()
data = F.load_using_bioformats_url(url, rescale=False)
self.assertSequenceEqual(data.shape, (640, 640))
def test_03_03_load_using_bioformats_url(self):
url = "https://github.com/CellProfiler/python-bioformats/raw/1.0.5/bioformats/tests/Channel1-01-A-01.tif"
try:
fd = urlopen(url)
if fd.code < 200 or fd.code >= 300:
raise OSError("Http error %d" % fd.code)
except OSError as e:
def bad_url(e=e):
raise e
unittest.expectedFailure(bad_url)()
data = F.load_using_bioformats_url(url, rescale=False)
self.assertSequenceEqual(data.shape, (640, 640))
def test_03_03_load_using_bioformats_url(self):
data = F.load_using_bioformats_url(
"http://www.cellprofiler.org/linked_files/broad-logo.gif",
rescale=False)
self.assertSequenceEqual(data.shape, (38, 150, 3))
expected_0_10_0_10 = np.array([[
181, 176, 185, 185, 175, 175, 176, 195, 187, 185
], [25, 7, 7, 7, 2, 2, 13, 13, 0, 1], [21, 1, 1, 0, 0, 1, 0, 1, 0, 0],
[64, 13, 1, 1, 12, 12, 2, 1, 1, 1],
[22, 56, 26, 13, 1, 1, 6, 0, 0, 0],
[12, 13, 82, 57, 9, 12, 2, 6, 6, 6],
[12, 13, 20, 89, 89, 21, 11, 12, 1, 0],
[6, 1, 7, 21, 89, 102, 26, 0, 10, 1],
[26, 0, 0, 1, 20, 84, 151, 58, 12, 1],
[23, 6, 1, 1, 0, 1, 55, 166, 100, 12]],
dtype=np.uint8)
self.assertTrue(np.all(expected_0_10_0_10 == data[:10, :10, 0]))
def test_03_03_load_using_bioformats_url(self):
data = F.load_using_bioformats_url(
"http://www.cellprofiler.org/linked_files/broad-logo.gif")
self.assertSequenceEqual(data.shape, (38, 150, 3))
expected_0_10_0_10 = np.array([
[181, 176, 185, 185, 175, 175, 176, 195, 187, 185],
[ 25, 7, 7, 7, 2, 2, 13, 13, 0, 1],
[ 21, 1, 1, 0, 0, 1, 0, 1, 0, 0],
[ 64, 13, 1, 1, 12, 12, 2, 1, 1, 1],
[ 22, 56, 26, 13, 1, 1, 6, 0, 0, 0],
[ 12, 13, 82, 57, 9, 12, 2, 6, 6, 6],
[ 12, 13, 20, 89, 89, 21, 11, 12, 1, 0],
[ 6, 1, 7, 21, 89, 102, 26, 0, 10, 1],
[ 26, 0, 0, 1, 20, 84, 151, 58, 12, 1],
[ 23, 6, 1, 1, 0, 1, 55, 166, 100, 12]],
dtype=np.uint8)
self.assertTrue(np.all(expected_0_10_0_10 == data[:10,:10, 0]))
def fn():
from bioformats.formatreader import load_using_bioformats_url
import cellprofiler.utilities.jutil
from scipy.io.matlab.mio import loadmat
from cellprofiler.modules.loadimages import url2pathname
cellprofiler.utilities.jutil.attach()
with self.image_dict_lock:
generation = self.image_dict_generation
for k, v in well.iteritems():
sd = {}
with self.image_dict_lock:
if self.image_dict_generation > generation:
return
self.image_dict[k] = sd
for c, fd in enumerate(v):
if PlateData.D_CHANNEL in fd:
channel = fd[PlateData.D_CHANNEL]
else:
channel = str(c+1)
url = fd[PlateData.D_FILENAME]
try:
if url.lower().endswith(".mat"):
img = loadmat(
url2pathname(url),
struct_as_record=True)["Image"]
else:
img = load_using_bioformats_url(url)
with self.image_dict_lock:
if self.image_dict_generation > generation:
return
sd[channel] = img
except:
traceback.print_exc()
pass
wx.CallAfter(self.update_figure)
cellprofiler.utilities.jutil.static_call(
"java/lang/System", "gc", "()V")
cellprofiler.utilities.jutil.detach()
def fn():
from bioformats.formatreader import load_using_bioformats_url
import cellprofiler.utilities.jutil
from scipy.io.matlab.mio import loadmat
from cellprofiler.modules.loadimages import url2pathname
cellprofiler.utilities.jutil.attach()
with self.image_dict_lock:
generation = self.image_dict_generation
for k, v in well.iteritems():
sd = {}
with self.image_dict_lock:
if self.image_dict_generation > generation:
return
self.image_dict[k] = sd
for c, fd in enumerate(v):
if PlateData.D_CHANNEL in fd:
channel = fd[PlateData.D_CHANNEL]
else:
channel = str(c + 1)
url = fd[PlateData.D_FILENAME]
try:
if url.lower().endswith(".mat"):
img = loadmat(url2pathname(url),
struct_as_record=True)["Image"]
else:
img = load_using_bioformats_url(url)
with self.image_dict_lock:
if self.image_dict_generation > generation:
return
sd[channel] = img
except:
traceback.print_exc()
pass
wx.CallAfter(self.update_figure)
cellprofiler.utilities.jutil.static_call("java/lang/System", "gc",
"()V")
cellprofiler.utilities.jutil.detach()
def test_02_01_compare_to_matlab(self):
path = os.path.split(__file__)[0]
png_file = 'Channel2-01-A-01Mask.png'
mat_file = 'texturemeasurements.mat'
mask_file = os.path.join(path, png_file)
if not os.path.isfile(mask_file):
github_fmt = github_url + "/cellprofiler/modules/tests/%s"
mask = load_using_bioformats_url(github_fmt % png_file) != 0
fin = urllib2.urlopen(github_fmt % mat_file)
fd, tempmat = tempfile.mkstemp(".mat")
fout = os.fdopen(fd, "wb")
shutil.copyfileobj(fin, fout)
fout.close()
texture_measurements = loadmat(tempmat, struct_as_record=True)
os.remove(tempmat)
else:
mask = load_image(mask_file) != 0
texture_measurements = loadmat(
os.path.join(path, mat_file),
struct_as_record=True)
texture_measurements = texture_measurements['m'][0,0]
folder = 'ExampleSBSImages'
file_name = 'Channel1-01-A-01.tif'
image_file = os.path.join(
example_images_directory(), folder, file_name)
maybe_download_example_image([folder], file_name)
image = load_image(image_file, rescale=False).astype(float)/255.0
labels,count = scind.label(mask.astype(bool),np.ones((3,3),bool))
centers = scind.center_of_mass(np.ones(labels.shape), labels,
np.arange(count)+1)
centers = np.array(centers)
X = 1 # the index of the X coordinate
Y = 0 # the index of the Y coordinate
order_python = np.lexsort((centers[:,X],centers[:,Y]))
workspace, module = self.make_workspace(image, labels, convert = False)
module.scale_groups[0].scale.value = 3
my_angle = M.H_HORIZONTAL
module.scale_groups[0].angles.value = my_angle
module.run(workspace)
m = workspace.measurements
tm_center_x = texture_measurements['Location_Center_X'][0,0][:,0]
tm_center_y = texture_measurements['Location_Center_Y'][0,0][:,0]
order_matlab = np.lexsort((tm_center_x,tm_center_y))
for measurement in M.F_HARALICK:
mname = '%s_%s_%s_%d'%(M.TEXTURE, measurement, INPUT_IMAGE_NAME, 3)
pymname = mname + "_" + M.H_TO_A[my_angle]
pytm = m.get_current_measurement(INPUT_OBJECTS_NAME, pymname)
tm = texture_measurements[mname][0,0][:,]
error_count = 0
for i in range(count):
matlab_val = tm[order_matlab[i]]
python_val = pytm[order_python[i]]
self.assertAlmostEqual(tm[order_matlab[i]],
pytm[order_python[i]],7,
"Measurement = %s, Loc=(%.2f,%.2f), Matlab=%f, Python=%f"%
(mname, tm_center_x[order_matlab[i]],
tm_center_y[order_matlab[i]],
tm[order_matlab[i]],
pytm[order_python[i]]))
image_measurements =\
(('Texture_AngularSecondMoment_Cytoplasm_3', 0.5412),
('Texture_Contrast_Cytoplasm_3',0.1505),
('Texture_Correlation_Cytoplasm_3', 0.7740),
('Texture_Variance_Cytoplasm_3', 0.3330),
('Texture_InverseDifferenceMoment_Cytoplasm_3',0.9321),
('Texture_SumAverage_Cytoplasm_3',2.5684),
('Texture_SumVariance_Cytoplasm_3',1.1814),
('Texture_SumEntropy_Cytoplasm_3',0.9540),
('Texture_Entropy_Cytoplasm_3',1.0677),
('Texture_DifferenceVariance_Cytoplasm_3',0.1314),
('Texture_DifferenceEntropy_Cytoplasm_3',0.4147),
('Texture_InfoMeas1_Cytoplasm_3',-0.4236),
('Texture_InfoMeas2_Cytoplasm_3',0.6609))
for feature_name, value in image_measurements:
py_feature_name = feature_name + "_" + M.H_TO_A[my_angle]
pytm = m.get_current_image_measurement(py_feature_name)
self.assertAlmostEqual(pytm, value,3,
"%s failed. Python=%f, Matlab=%f" %
(feature_name, pytm, value))
def test_02_01_compare_to_matlab(self):
path = os.path.split(__file__)[0]
png_file = 'Channel2-01-A-01Mask.png'
mat_file = 'texturemeasurements.mat'
mask_file = os.path.join(path, png_file)
if not os.path.isfile(mask_file):
github_fmt = github_url + "/cellprofiler/modules/tests/%s"
mask = load_using_bioformats_url(github_fmt % png_file) != 0
fin = urllib2.urlopen(github_fmt % mat_file)
fd, tempmat = tempfile.mkstemp(".mat")
fout = os.fdopen(fd, "wb")
shutil.copyfileobj(fin, fout)
fout.close()
texture_measurements = loadmat(tempmat, struct_as_record=True)
os.remove(tempmat)
else:
mask = load_image(mask_file) != 0
texture_measurements = loadmat(os.path.join(path, mat_file),
struct_as_record=True)
texture_measurements = texture_measurements['m'][0, 0]
folder = 'ExampleSBSImages'
file_name = 'Channel1-01-A-01.tif'
image_file = os.path.join(example_images_directory(), folder,
file_name)
maybe_download_example_image([folder], file_name)
image = load_image(image_file, rescale=False).astype(float) / 255.0
labels, count = scind.label(mask.astype(bool), np.ones((3, 3), bool))
centers = scind.center_of_mass(np.ones(labels.shape), labels,
np.arange(count) + 1)
centers = np.array(centers)
X = 1 # the index of the X coordinate
Y = 0 # the index of the Y coordinate
order_python = np.lexsort((centers[:, X], centers[:, Y]))
workspace, module = self.make_workspace(image, labels, convert=False)
module.scale_groups[0].scale.value = 3
my_angle = M.H_HORIZONTAL
module.scale_groups[0].angles.value = my_angle
module.run(workspace)
m = workspace.measurements
tm_center_x = texture_measurements['Location_Center_X'][0, 0][:, 0]
tm_center_y = texture_measurements['Location_Center_Y'][0, 0][:, 0]
order_matlab = np.lexsort((tm_center_x, tm_center_y))
for measurement in M.F_HARALICK:
mname = '%s_%s_%s_%d' % (M.TEXTURE, measurement, INPUT_IMAGE_NAME,
3)
pymname = mname + "_" + M.H_TO_A[my_angle]
pytm = m.get_current_measurement(INPUT_OBJECTS_NAME, pymname)
tm = texture_measurements[mname][0, 0][:, ]
error_count = 0
for i in range(count):
matlab_val = tm[order_matlab[i]]
python_val = pytm[order_python[i]]
self.assertAlmostEqual(
tm[order_matlab[i]], pytm[order_python[i]], 7,
"Measurement = %s, Loc=(%.2f,%.2f), Matlab=%f, Python=%f" %
(mname, tm_center_x[order_matlab[i]],
tm_center_y[order_matlab[i]], tm[order_matlab[i]],
pytm[order_python[i]]))
image_measurements =\
(('Texture_AngularSecondMoment_Cytoplasm_3', 0.5412),
('Texture_Contrast_Cytoplasm_3',0.1505),
('Texture_Correlation_Cytoplasm_3', 0.7740),
('Texture_Variance_Cytoplasm_3', 0.3330),
('Texture_InverseDifferenceMoment_Cytoplasm_3',0.9321),
('Texture_SumAverage_Cytoplasm_3',2.5684),
('Texture_SumVariance_Cytoplasm_3',1.1814),
('Texture_SumEntropy_Cytoplasm_3',0.9540),
('Texture_Entropy_Cytoplasm_3',1.0677),
('Texture_DifferenceVariance_Cytoplasm_3',0.1314),
('Texture_DifferenceEntropy_Cytoplasm_3',0.4147),
('Texture_InfoMeas1_Cytoplasm_3',-0.4236),
('Texture_InfoMeas2_Cytoplasm_3',0.6609))
for feature_name, value in image_measurements:
py_feature_name = feature_name + "_" + M.H_TO_A[my_angle]
pytm = m.get_current_image_measurement(py_feature_name)
self.assertAlmostEqual(
pytm, value, 3, "%s failed. Python=%f, Matlab=%f" %
(feature_name, pytm, value))
