def test_image_fidelity():
point = (142195, 64376, 3130)
cv = CloudVolume('gs://seunglab-test/sharded')
img = cv.download_point(point, mip=0, size=128)
N_labels = np.unique(img).shape[0]
assert N_labels == 144
from cloudvolume import CloudVolume, view
# 1. Initialize a CloudVolume object which will know how to read from this dataset layer.
cv = CloudVolume(
'https://storage.googleapis.com/neuroglancer-public-data/kasthuri2011/image_color_corrected',
progress=True, # shows progress bar
cache=True, # cache to disk to avoid repeated downloads
# parallel=True, # uncomment to try parallel download!
)
# 2. Download context around the point in the Neuroglancer link above
# into a numpy array.
# argument one is the (x,y,z) coordinate from neuroglancer
# mip=resolution level (smaller mips are higher resolution, highest is 0)
# size is in voxels
img = cv.download_point((5188, 9096, 1198), mip=0, size=(512, 512, 64))
# 3. Visualize the image!
# Open your browser to https://localhost:8080 to view
# Press ctrl-C to continue script execution.
view(img)
# 4. When you're done experimenting, clean up the space we used on disk.
cv.cache.flush()
from cloudvolume import CloudVolume, view
# 1. Initialize a CloudVolume object which will know how to read from this dataset layer.
cv = CloudVolume(
's3://https://d2zu5izn76slwn.cloudfront.net/precomputed_volumes/brain1',
progress=True, # shows progress bar
cache=True, # cache to disk to avoid repeated downloads
# parallel=True, # uncomment to try parallel download!
)
# 2. Download context around the point in the Neuroglancer link above
# into a numpy array.
# argument one is the (x,y,z) coordinate from neuroglancer
# mip=resolution level (smaller mips are higher resolution, highest is 0)
# size is in voxels
img = cv.download_point((2054, 1227, 1131), mip=1, size=(500, 500, 500))
# 3. Visualize the image!
# Open your browser to https://localhost:8080 to view
# Press ctrl-C to continue script execution.
view(img)
# 4. When you're done experimenting, clean up the space we used on disk.
cv.cache.flush()
