def test_cover3D(block_size, context, grid):
lbl = real_image3d()[1]
lbl = lbl.astype(np.int32)
max_sizes = tuple(calculate_extents(lbl, func=np.max))
min_overlap = tuple(1 + v for v in max_sizes)
lbl = repeat(lbl, (2, 4, 4))
assert max_sizes == tuple(calculate_extents(lbl, func=np.max))
reassemble(lbl, 'ZYX', block_size, min_overlap, context, grid)
print(conf)
vars(conf)
if use_gpu:
from csbdeep.utils.tf import limit_gpu_memory
# adjust as necessary: limit GPU memory to be used by TensorFlow to leave some to OpenCL-based computations
limit_gpu_memory(args.limit_gpu_mem)
model = StarDist2D(conf, name=args.model_name, basedir=args.model_dir)
median_size = calculate_extents(list(Y), np.median)
fov = np.array(model._axes_tile_overlap('YX'))
if any(median_size > fov):
print("WARNING: median object size larger than field of view of the neural network.")
augmenter = None
# def augmenter(x, y):
# """Augmentation of a single input/label image pair.
# x is an input image
# y is the corresponding ground-truth label image
# """
# # modify a copy of x and/or y...
# return x, y
def compute_anisotropy_from_data(data):
extents = calculate_extents(data)
anisotropy = tuple(np.max(extents) / extents)
return anisotropy