### ### 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], 'y_start': membrane_group_ROI['y'][0], 'y_stop': membrane_group_ROI['y'][1], 'z_start': membrane_group_ROI['z'][0], 'z_stop': membrane_group_ROI['z'][1], 'resolution': membrane_group_ROI['resolution'],
''' tokens = oo.get_public_tokens() print len(tokens) image_token = 'kasthuri11cc' annotation_token = 'kasthuri2015_ramon_v1' segmentation_token = 'ac3ac4' ''' Next we'll download a sample of data and ground-truth annotations from this dataset. Some of you are researching membrane segmentation, and will want to use this sample: ''' channel_ROIs = nn.get_metadata(segmentation_token)['channels'] membrane_group_ROI = channel_ROIs['ac4_neuron_truth']['rois']['ac4'] 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], 'z_start': membrane_group_ROI['z'][0], 'z_stop': membrane_group_ROI['z'][1], 'resolution': membrane_group_ROI['resolution'] } '''
import ndio.remote.OCPMeta as NDLIMS nn = NDLIMS() import ndio.convert.tiff as ndtiff # For export to tiff later import numpy as np print "Done importing packages" tokens = oo.get_public_tokens() image_token = 'kasthuri11cc' annotation_token = 'cv_kasthuri11_membrane_2014' # Get channel ROI for token channel_ROI = nn.get_metadata(annotation_token)['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], 'y_start': membrane_group_ROI['y'][0], 'y_stop': membrane_group_ROI['y'][1], 'z_start': membrane_group_ROI['z'][0], 'z_stop': membrane_group_ROI['z'][1], 'resolution': membrane_group_ROI['resolution'],
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], "z_start": membrane_group_ROI["z"][0], "z_stop": membrane_group_ROI["z"][1], "resolution": membrane_group_ROI["resolution"],
that they'd fill the screen: ''' tokens = oo.get_public_tokens() print len(tokens) image_token = 'kasthuri11cc' annotation_token = 'kasthuri2015_ramon_v1' segmentation_token = 'ac3ac4' ''' Next we'll download a sample of data and ground-truth annotations from this dataset. Some of you are researching membrane segmentation, and will want to use this sample: ''' channel_ROIs = nn.get_metadata(segmentation_token)['channels'] membrane_group_ROI = channel_ROIs['ac4_neuron_truth']['rois']['ac4'] 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], 'z_start': membrane_group_ROI['z'][0], 'z_stop': membrane_group_ROI['z'][1], 'resolution': membrane_group_ROI['resolution'] } ''' Now you can retrieve your data by using your ROI bounds, and then requesting it