class TestDownload(unittest.TestCase):
def setUp(self):
self.oo = OCP()
def test_get_cutout_type(self):
# kasthuri11/image/xy/3/1000,1100/1000,1100/1000/
numpy_download = self.oo.get_cutout('kasthuri11', 'image',
1000, 1100,
1000, 1100,
1000, 1001,
resolution=3)
self.assertEqual(type(numpy_download), numpy.ndarray)
def test_get_cutout(self):
# kasthuri11/image/xy/3/1000,1100/1000,1100/1000/
numpy_download = self.oo.get_cutout('kasthuri11', 'image',
1000, 1100,
1000, 1100,
1000, 1001,
resolution=3)
# We know this pixel is 132
self.assertEqual(numpy_download[0][0][0], 132)
def test_get_volume_type(self):
# kasthuri11/image/xy/3/1000,1100/1000,1100/1000/
ramon_download = self.oo.get_volume('kasthuri11', 'image',
1000, 1100,
1000, 1100,
1000, 1001,
resolution=3)
self.assertEqual(type(ramon_download), ndio.ramon.RAMONVolume)
def test_get_volume(self):
# kasthuri11/image/xy/3/1000,1100/1000,1100/1000/
ramon_download = self.oo.get_volume('kasthuri11', 'image',
1000, 1100,
1000, 1100,
1000, 1001,
resolution=3)
self.assertEqual(ramon_download.xyz_offset[0], 1000)
self.assertEqual(ramon_download.resolution, 3)
self.assertEqual(ramon_download.cutout[0][0][0], 132)
def test_get_image_type(self):
# kasthuri11/image/xy/3/1000,1100/1000,1100/1000/
image_download = self.oo.get_image('kasthuri11', 'image',
1000, 1100,
1000, 1100,
1000,
resolution=3)
self.assertEqual(type(image_download), numpy.ndarray)
self.assertEqual(image_download.shape, (100, 100))
def getRaw(x1,x2,y1,y2,z1,z2):
"""
Retrieve mitochondria image volume from neurodata given x,y,z parameters
Arguments:
x1/y1/z1: starting value for cut
x2/y2/z2: ending cut value
Returns:
3D Numpy Array of images (each image is in a 2D numpy array)
"""
oo = OCP()
return oo.get_cutout('kasthuri11cc', 'image', x1, x2, y1, y2, z1, z2, resolution = 3)
def getTruth(db = False):
# Careful of hardcoded filepath
if db == False:
vol = np.array([])
data = ndio.convert.png.import_png_collection('newdata/mito_anno_*')
for img in data:
vol = np.dstack([vol, img]) if vol.size else img
return vol
else:
boundaries = getConfig()
oo = OCP()
#added +12 because first 10 images in stack aren't even annotated
return oo.get_cutout('kasthuri2015_ramon_v1', 'mitochondria', 694 + boundaries[0], 694 + boundaries[1], 1750 + boundaries[2], 1750 + boundaries[3], 1004, 1154, resolution = 3)
class TestRAMONSegment(unittest.TestCase):
def setUp(self):
self.oo = OCP()
self.ramon_segment = self.oo.get_ramon('kasthuri2015_ramon_v1',
'neurons', 3, 3)
self.default_ramon_segment = ndio.ramon.RAMONSynapse()
def test_metadata(self):
self.assertEqual(str(self.ramon_segment.id), '3')
class TestDownload(unittest.TestCase):
def setUp(self):
self.oo = OCP()
def test_export_import_png(self):
# kasthuri11/image/xy/3/1000,1100/1000,1100/1000/
image_download = self.oo.get_image('kasthuri11', 'image',
1000, 1100,
1000, 1100,
1000,
resolution=3)
# if returns string, successful export
self.assertEqual(
ndpng.export_png("tests/trash/download.png", image_download),
"tests/trash/download.png")
# now confirm import works too
self.assertEqual(ndpng.import_png("tests/trash/download.png")[0][0],
image_download[0][0])
self.assertEqual(ndpng.import_png("tests/trash/download.png")[10][10],
image_download[10][10])
def test_export_import_tiff(self):
# kasthuri11/image/xy/3/1000,1100/1000,1100/1000/
image_download = self.oo.get_image('kasthuri11', 'image',
1000, 1100,
1000, 1100,
1000,
resolution=3)
# if returns string, successful export
self.assertEqual(
ndtiff.export_tiff("tests/trash/download-1.tiff", image_download),
"tests/trash/download-1.tiff")
# now confirm import works too
self.assertEqual(ndtiff.import_tiff("tests/trash/download-1.tiff")[0][0],
image_download[0][0])
self.assertEqual(ndtiff.import_tiff("tests/trash/download-1.tiff")[10][10],
image_download[10][10])
### ndio membrane segmentation imports
import ndio
import ndio.remote.OCP as OCP
import ndio.remote.OCPMeta as NDLIMS
import ndio.convert.tiff as ndtiff
###
### Create OCP / OCPMeta objects
oo = OCP()
nn = NDLIMS()
###
# Get all tokens
tokens = oo.get_public_tokens()
# Membrane group image and annotation tokens for membrane image in datamap
image_token = 'kasthuri11cc'
annotation_token = 'cv_kasthuri11_membrane_2014'
# Get channel ROI for token
channel_ROI = nn.get_metadata('cv_kasthuri11_membrane_2014')['channels']
# Get membrane ROI coordinates
membrane_group_ROI = channel_ROI['image']['rois']['ac4']
# Sets membrane query in Python
membrane_query = {
'token': 'cv_kasthuri11_membrane_2014',
'channel': 'image',
'x_start': membrane_group_ROI['x'][0],
'x_stop': membrane_group_ROI['x'][1],
### ndio membrane segmentation imports import ndio print "ndio version: %s" % (ndio.version) import ndio.remote.OCP as OCP oo = OCP() import ndio.remote.OCPMeta as NDLIMS nn = NDLIMS() ### ### Watershed segmentation imports import numpy as np import matplotlib.pyplot as plt from scipy import ndimage as ndi from skimage.morphology import watershed from skimage.feature import peak_local_max from skimage.morphology import closing from skimage.morphology import binary_dilation from skimage.morphology import binary_erosion from skimage.filters.rank import threshold from skimage.exposure import rescale_intensity from skimage.filters import threshold_otsu ### print "Done importing packages" ######### # ndio demo for membrane segmentation #########
def setUp(self):
self.oo = OCP()
def setUp(self):
self.oo = OCP()
self.ramon_segment = self.oo.get_ramon('kasthuri2015_ramon_v1',
'neurons', 3, 3)
self.default_ramon_segment = ndio.ramon.RAMONSynapse()
### ndio membrane segmentation imports
import ndio
import ndio.remote.OCP as OCP
import ndio.remote.OCPMeta as NDLIMS
import numpy as np
### Create OCP / OCPMeta objects
oo = OCP()
nn = NDLIMS()
###
print "Done importing packages"
image_token = "kasthuri11cc"
annotation_token = "kasthuri2015_ramon_v"
segmentation_token = "ac3ac4"
# Get channel ROI for token
channel_ROI = nn.get_metadata(segmentation_token)["channels"]
# Get membrane ROI coordinates
membrane_group_ROI = channel_ROI["ac4_neuron_truth"]["rois"]["ac4"]
# Sets membrane query in Python
membrane_query = {
"token": "ac3ac4",
"channel": "ac4_neuron_truth",
"x_start": membrane_group_ROI["x"][0],
"x_stop": membrane_group_ROI["x"][1],
"y_start": membrane_group_ROI["y"][0],
"y_stop": membrane_group_ROI["y"][1],
### ndio membrane segmentation imports
import ndio
import ndio.remote.OCP as OCP
import ndio.remote.OCPMeta as NDLIMS
import numpy as np
### Create OCP / OCPMeta objects
oo = OCP()
nn = NDLIMS()
###
print "Done importing packages"
image_token = 'kasthuri11cc'
annotation_token = 'kasthuri2015_ramon_v'
segmentation_token = 'ac3ac4'
# Get channel ROI for token
channel_ROI = nn.get_metadata(segmentation_token)['channels']
# Get membrane ROI coordinates
membrane_group_ROI = channel_ROI['ac4_neuron_truth']['rois']['ac4']
# Sets membrane query in Python
membrane_query = {
'token': 'ac3ac4',
'channel': 'ac4_neuron_truth',
'x_start': membrane_group_ROI['x'][0],
'x_stop': membrane_group_ROI['x'][1],
'y_start': membrane_group_ROI['y'][0],
'y_stop': membrane_group_ROI['y'][1],
