def test_03_03_load_color_frame(self):
path = os.path.join(testimages_directory(),
"DrosophilaEmbryo_GFPHistone.avi")
workspace = self.run_workspace(path, N.LOAD_AS_COLOR_IMAGE, index=3)
image = workspace.image_set.get_image(IMAGE_NAME)
pixel_data = image.pixel_data
self.assertSequenceEqual(pixel_data.shape, (264, 542, 3))
self.assertTrue(np.all(pixel_data >= 0))
self.assertTrue(np.all(pixel_data <= 1))
self.assertTrue(np.any(pixel_data[:, :, 0] != pixel_data[:, :, 1]))
self.assertTrue(np.any(pixel_data[:, :, 0] != pixel_data[:, :, 2]))
def test_03_03_load_color_frame(self):
path = os.path.join(testimages_directory(),
"DrosophilaEmbryo_GFPHistone.avi")
workspace = self.run_workspace(path, N.LOAD_AS_COLOR_IMAGE,
index = 3)
image = workspace.image_set.get_image(IMAGE_NAME)
pixel_data = image.pixel_data
self.assertSequenceEqual(pixel_data.shape, (264, 542, 3))
self.assertTrue(np.all(pixel_data >= 0))
self.assertTrue(np.all(pixel_data <= 1))
self.assertTrue(np.any(pixel_data[:, :, 0] != pixel_data[:, :, 1]))
self.assertTrue(np.any(pixel_data[:, :, 0] != pixel_data[:, :, 2]))
def test_03_06_load_monochrome_plane(self):
path = os.path.join(testimages_directory(), "5channel.tif")
for i in range(5):
workspace = self.run_workspace(path, N.LOAD_AS_GRAYSCALE_IMAGE,
index=i)
image = workspace.image_set.get_image(IMAGE_NAME)
pixel_data = image.pixel_data
self.assertSequenceEqual(pixel_data.shape, (64, 64))
if i == 0:
plane_0 = pixel_data.copy()
else:
self.assertTrue(np.any(pixel_data != plane_0))
def test_03_06_load_monochrome_plane(self):
path = os.path.join(testimages_directory(), "5channel.tif")
for i in range(5):
workspace = self.run_workspace(path,
N.LOAD_AS_GRAYSCALE_IMAGE,
index=i)
image = workspace.image_set.get_image(IMAGE_NAME)
pixel_data = image.pixel_data
self.assertSequenceEqual(pixel_data.shape, (64, 64))
if i == 0:
plane_0 = pixel_data.copy()
else:
self.assertTrue(np.any(pixel_data != plane_0))
def test_04_03_load_planes(self):
file_name = "RLM1 SSN3 300308 008015000.flex"
maybe_download_test_image(file_name)
path = testimages_directory()
pathname = os.path.join(path, file_name)
url = pathname2url(pathname)
ftrs = (cpmeas.C_URL, cpmeas.C_SERIES, cpmeas.C_FRAME)
channels = ("Channel1", "Channel2")
header = ",".join([
",".join(["_".join((ftr, channel)) for ftr in ftrs])
for channel in channels
])
csv_lines = [header]
for series in range(4):
csv_lines.append(",".join([
'"%s","%d","%d"' % (url, series, frame) for frame in range(2)
]))
csv_text = "\n".join(csv_lines)
pipeline, module, filename = self.make_pipeline(csv_text)
assert isinstance(module, L.LoadData)
m = cpmeas.Measurements()
image_set_list = cpi.ImageSetList()
try:
workspace = cpw.Workspace(pipeline, module, m, None, m,
image_set_list)
self.assertTrue(module.prepare_run(workspace))
pixel_hashes = []
for i in range(4):
m.next_image_set(i + 1)
module.run(workspace)
chashes = []
for channel in channels:
pixel_data = m.get_image(channel).pixel_data
h = hashlib.md5()
h.update(pixel_data)
chashes.append(h.digest())
self.assertNotEqual(chashes[0], chashes[1])
for j, ph in enumerate(pixel_hashes):
for k, phh in enumerate(ph):
for l, phd in enumerate(chashes):
self.assertNotEqual(phh, phd)
pixel_hashes.append(chashes)
finally:
os.remove(filename)
def test_04_03_load_planes(self):
file_name = "RLM1 SSN3 300308 008015000.flex"
maybe_download_tesst_image(file_name)
path = testimages_directory()
pathname = os.path.join(path, file_name)
url = pathname2url(pathname)
ftrs = (cpmeas.C_URL, cpmeas.C_SERIES, cpmeas.C_FRAME)
channels = ("Channel1", "Channel2")
header = ",".join([",".join(["_".join((ftr, channel)) for ftr in ftrs])
for channel in channels])
csv_lines = [header]
for series in range(4):
csv_lines.append(",".join(['"%s","%d","%d"' % (url, series, frame)
for frame in range(2)]))
csv_text = "\n".join(csv_lines)
pipeline, module, filename = self.make_pipeline(csv_text)
assert isinstance(module, L.LoadData)
m = cpmeas.Measurements()
image_set_list = cpi.ImageSetList()
try:
workspace = cpw.Workspace(pipeline, module, m, None, m,
image_set_list)
self.assertTrue(module.prepare_run(workspace))
pixel_hashes = []
for i in range(4):
m.next_image_set(i+1)
module.run(workspace)
chashes = []
for channel in channels:
pixel_data = m.get_image(channel).pixel_data
h = hashlib.md5()
h.update(pixel_data)
chashes.append(h.digest())
self.assertNotEqual(chashes[0], chashes[1])
for j, ph in enumerate(pixel_hashes):
for k, phh in enumerate(ph):
for l, phd in enumerate(chashes):
self.assertNotEqual(phh, phd)
pixel_hashes.append(chashes)
finally:
os.remove(filename)
