def put_tile(request: HttpRequest,
project_id=None,
stack_id=None) -> HttpResponse:
""" Store labels to HDF5 """
if not tile_loading_enabled:
raise ConfigurationError("HDF5 tile loading is currently disabled")
scale = float(request.POST.get('scale', '0'))
height = int(request.POST.get('height', '0'))
width = int(request.POST.get('width', '0'))
x = int(request.POST.get('x', '0'))
y = int(request.POST.get('y', '0'))
z = int(request.POST.get('z', '0'))
col = request.POST.get('col', 'y')
row = request.POST.get('row', 'x')
image = request.POST.get('image', 'x')
fpath = os.path.join(settings.HDF5_STORAGE_PATH,
f'{project_id}_{stack_id}.hdf')
with closing(h5py.File(fpath, 'a')) as hfile:
hdfpath = '/labels/scale/' + str(int(scale)) + '/data'
image_from_canvas = np.asarray(
Image.open(BytesIO(base64.decodestring(image))))
hfile[hdfpath][y:y + height, x:x + width, z] = image_from_canvas[:, :,
0]
return HttpResponse("Image pushed to HDF5.", content_type="plain/text")
def get_tile(request: HttpRequest,
project_id=None,
stack_id=None) -> HttpResponse:
if not tile_loading_enabled:
raise ConfigurationError("HDF5 tile loading is currently disabled")
scale = float(request.GET.get('scale', '0'))
height = int(request.GET.get('height', '0'))
width = int(request.GET.get('width', '0'))
x = int(request.GET.get('x', '0'))
y = int(request.GET.get('y', '0'))
z = int(request.GET.get('z', '0'))
col = request.GET.get('col', 'y')
row = request.GET.get('row', 'x')
file_extension = request.GET.get('file_extension', 'png')
basename = request.GET.get('basename', 'raw')
# need to know the stack name
fpath = os.path.join(
settings.HDF5_STORAGE_PATH,
'{0}_{1}_{2}.hdf'.format(project_id, stack_id, basename))
if not os.path.exists(fpath):
data = np.zeros((height, width))
pilImage = Image.frombuffer('RGBA', (width, height), data, 'raw', 'L',
0, 1)
response = HttpResponse(content_type="image/png")
pilImage.save(response, "PNG")
return response
# return HttpResponse(json.dumps({'error': 'HDF5 file does not exists: {0}'.format(fpath)}))
with closing(h5py.File(fpath, 'r')) as hfile:
#import math
#zoomlevel = math.log(int(scale), 2)
hdfpath = '/' + str(int(scale)) + '/' + str(z) + '/data'
if not str(int(scale)) in hfile['/'].keys():
data = np.zeros((height, width))
pilImage = Image.frombuffer('RGBA', (width, height), data, 'raw',
'L', 0, 1)
response = HttpResponse(content_type="image/png")
pilImage.save(response, "PNG")
return response
# return HttpResponse(json.dumps({'error': 'HDF5 file does not contain scale: {0}'.format(str(int(scale)))}))
image_data = hfile[hdfpath]
data = image_data[y:y + height, x:x + width]
pilImage = Image.frombuffer('RGBA', (width, height), data, 'raw', 'L',
0, 1)
response = HttpResponse(content_type="image/png")
pilImage.save(response, "PNG")
return response
return response
def get_hdf5_tile(project_id, stack_id, scale, height, width, x, y, z, col,
row, file_extension, basename):
if not tile_loading_enabled:
raise ConfigurationError("HDF5 tile loading is currently disabled")
# need to know the stack name
fpath = os.path.join(settings.HDF5_STORAGE_PATH,
f'{project_id}_{stack_id}_{basename}.hdf')
if not os.path.exists(fpath):
data = np.zeros((height, width))
pilImage = Image.frombuffer('RGBA', (width, height), data, 'raw', 'L',
0, 1)
response = HttpResponse(content_type="image/png")
pilImage.save(response, "PNG")
return response
with closing(h5py.File(fpath, 'r')) as hfile:
# import math
# zoomlevel = math.log(int(scale), 2)
hdfpath = '/' + str(int(scale)) + '/' + str(z) + '/data'
if not str(int(scale)) in hfile['/'].keys():
data = np.zeros((height, width))
pilImage = Image.frombuffer('RGBA', (width, height), data, 'raw',
'L', 0, 1)
response = HttpResponse(content_type="image/png")
pilImage.save(response, "PNG")
return response
image_data = hfile[hdfpath]
data = image_data[y:y + height, x:x + width]
pilImage = Image.frombuffer('RGBA', (width, height), data, 'raw', 'L',
0, 1)
response = HttpResponse(content_type="image/png")
pilImage.save(response, "PNG")
return response
return response
def get_cloudvolume_tile(project_id,
stack_id,
scale,
height,
width,
x,
y,
z,
col,
row,
file_extension='png',
basename=None,
fill_missing=False,
cache=True,
upscale=False):
if not cv_tile_loading_enabled:
raise ConfigurationError(
"CloudVolume tile loading is currently disabled")
if upscale:
mip = math.ceil(abs(math.log(scale) / math.log(2)))
else:
mip = math.floor(abs(math.log(scale) / math.log(2)))
scale_to_fit = False
effective_scale = 1.0
voxel_offset = (0, 0, 0)
try:
cv = cloudvolume.CloudVolume(basename,
use_https=True,
parallel=False,
cache=cache,
mip=mip,
bounded=False,
fill_missing=fill_missing)
cutout = cv[x:(x + width), y:(y + height), z]
except cloudvolume.exceptions.ScaleUnavailableError as e:
logger.info(
f'Need to use extra scaling, because mip level {mip} is not available: {e}'
)
cv_test = cloudvolume.CloudVolume(basename,
use_https=True,
parallel=False,
cache=cache,
bounded=False,
fill_missing=fill_missing)
# Find mip closest to the request
min_mip = None
min_mip_dist = float('infinity')
for ex_mip in cv_test.available_mips:
if abs(mip - ex_mip) < min_mip_dist:
min_mip = ex_mip
min_mip_dist = abs(mip - ex_mip)
if min_mip is None:
raise ValueError('No fitting scale level found')
# Get volume with best fit
cv = cloudvolume.CloudVolume(basename,
use_https=True,
parallel=False,
cache=cache,
mip=min_mip,
bounded=False,
fill_missing=fill_missing)
effective_scale = 2**mip / 2**min_mip
# TODO: Correctly walk downsample factors / scale levels in each
# dimensions for exact scaling in non power-of-two scale pyramids.
scale_to_fit = True
x, y = math.floor(x * effective_scale), math.floor(y * effective_scale)
width, height = math.ceil(width * effective_scale), math.ceil(
height * effective_scale)
cutout = cv[(x + cv.voxel_offset[0]):(x + cv.voxel_offset[0] + width),
(y + cv.voxel_offset[1]):(y + cv.voxel_offset[1] + height), z]
if cutout is None:
data = np.zeros((height, width))
else:
data = np.transpose(cutout[:, :, 0, 0])
img = Image.frombuffer('RGBA', (width, height), data, 'raw', 'L', 0, 1)
if scale_to_fit:
img = img.resize((math.ceil(width / effective_scale),
math.ceil(height / effective_scale)))
response = HttpResponse(content_type=f"image/{file_extension.lower()}")
img.save(response, file_extension.upper())
return response