Python OCPMeta.get_metadata示例

示例#1

文件： membrane_img.py 项目： Connectomics-Classes/NeuroXplorer

###

### 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'],

示例#2

文件： data-anno.py 项目： Connectomics-Classes/NeuroXplorer

'''

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']
}

'''

示例#3

文件： save_data_to_file.py 项目： Connectomics-Classes/NeuroXplorer

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'],

示例#4

文件： save_ground_to_file.py 项目： Connectomics-Classes/NeuroXplorer

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"],

示例#5

文件： data-anno.py 项目： Connectomics-Classes/NeuroXplorer

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

示例#6

文件： save_data_to_file.py 项目： Connectomics-Classes/NeuroXplorer

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'],