def overlap_mip():
configure_logger()
client = cloud.authenticate()
bucket = client.get_bucket('elvos')
# iterate through every source directory...
for location in WHENCE:
prefix = location + '/'
logging.info(f"MIPing images from {prefix}")
# for every blob in the source directory
for in_blob in bucket.list_blobs(prefix=prefix):
# blacklist
if in_blob.name == prefix + 'LAUIHISOEZIM5ILF.npy':
continue
file_id = in_blob.name.split('/')[2]
file_id = file_id.split('.')[0]
# download the array
logging.info(f'downloading {in_blob.name}')
input_arr = cloud.download_array(in_blob)
logging.info(f"blob shape: {input_arr.shape}")
# if it's in failure analysis, do the failure analysis MIP
if file_id in FAILURE_ANALYSIS:
if location == 'numpy/axial':
cropped_arr = transforms.crop_overlap_axial_fa(
input_arr, location)
# otherwise do a normal MIP
else:
if location == 'numpy/axial':
cropped_arr = transforms.crop_overlap_axial(
input_arr, location)
else:
cropped_arr = transforms.crop_overlap_coronal(input_arr)
not_extreme_arr = transforms.remove_extremes(cropped_arr)
logging.info(f'removed array extremes')
mip_arr = transforms.mip_overlap(not_extreme_arr)
# OPTIONAL: visualize a slice
# plt.figure(figsize=(6, 6))
# plt.imshow(mip_arr[10], interpolation='none')
# plt.show()
# save to the numpy generator source directory
cloud.save_npy_to_cloud(mip_arr, file_id, location, 'overlap')
def overlap_mip():
configure_logger()
client = cloud.authenticate()
bucket = client.get_bucket('elvos')
# iterate through every source directory...
for location in WHENCE:
prefix = location + '/'
logging.info(f"MIPing images from {prefix}")
for in_blob in bucket.list_blobs(prefix=prefix):
# blacklist
if in_blob.name == prefix + 'LAUIHISOEZIM5ILF.npy':
continue
file_id = in_blob.name.split('/')[2]
file_id = file_id.split('.')[0]
# perform the normal MIPing procedure
logging.info(f'downloading {in_blob.name}')
input_arr = cloud.download_array(in_blob)
logging.info(f"blob shape: {input_arr.shape}")
if location == 'numpy/axial':
cropped_arr = transforms.crop_overlap_axial(
input_arr, location)
else:
cropped_arr = transforms.crop_overlap_coronal(
input_arr, location)
not_extreme_arr = transforms.segment_vessels(cropped_arr)
logging.info(f'removed array extremes')
mip_arr = transforms.mip_overlap(not_extreme_arr)
# plt.figure(figsize=(6, 6))
# plt.imshow(mip_arr[10], interpolation='none')
# plt.show()
# save to the numpy generator source directory
cloud.save_segmented_npy_to_cloud(mip_arr, file_id, location,
'overlap')