def auto(session_location, options, master_logger):
master_logger.info("Reading gt_stack")
gt_stack = imio.read_image_stack(options.gt_stack)
master_logger.info("Reading test_stack")
test_stack = imio.read_image_stack(options.test_stack)
master_logger.info("Finished reading stacks")
master_logger.info("Loading graph json")
pairprob_list = load_graph_json(options.ragprob_file)
master_logger.info("Finished loading graph json")
master_logger.info("Matching bodies to GT")
body2gtbody = find_gt_bodies(gt_stack, test_stack)
master_logger.info("Finished matching bodies to GT")
body2body = {}
for (node1, node2, dummy) in pairprob_list:
body2body[node1] = node1
body2body[node2] = node2
for (node1, node2, dummy) in pairprob_list:
if body2gtbody[node1] == body2gtbody[node2]:
print "merge: " , node1, node2
node2 = body2body[node2]
node1 = body2body[node1]
body2body[node1] = node2
remap_list = []
for b1, b2 in body2body.items():
if b2 == node1:
remap_list.append(b1)
for b1 in remap_list:
body2body[b1] = node2
else:
print "split: " , node1, node2
f1 = h5py.File('proofread.h5', 'w')
f1.create_dataset('stack', data=test_stack)
arr = numpy.array(body2body.items())
f1.create_dataset('transforms', data=arr)
def auto(session_location, options, master_logger):
master_logger.info("Reading gt_stack")
gt_stack = imio.read_image_stack(options.gt_stack)
master_logger.info("Reading test_stack")
test_stack = imio.read_image_stack(options.test_stack)
master_logger.info("Finished reading stacks")
master_logger.info("Loading graph json")
pairprob_list = load_graph_json(options.ragprob_file)
master_logger.info("Finished loading graph json")
master_logger.info("Matching bodies to GT")
body2gtbody = find_gt_bodies(gt_stack, test_stack)
master_logger.info("Finished matching bodies to GT")
body2body = {}
for (node1, node2, dummy) in pairprob_list:
body2body[node1] = node1
body2body[node2] = node2
for (node1, node2, dummy) in pairprob_list:
if body2gtbody[node1] == body2gtbody[node2]:
print "merge: ", node1, node2
node2 = body2body[node2]
node1 = body2body[node1]
body2body[node1] = node2
remap_list = []
for b1, b2 in body2body.items():
if b2 == node1:
remap_list.append(b1)
for b1 in remap_list:
body2body[b1] = node2
else:
print "split: ", node1, node2
f1 = h5py.File("proofread.h5", "w")
f1.create_dataset("stack", data=test_stack)
arr = numpy.array(body2body.items())
f1.create_dataset("transforms", data=arr)
def valprob(session_location, options, master_logger):
master_logger.info("Reading gt_stack")
gt_stack = imio.read_image_stack(options.gt_stack)
master_logger.info("Reading test_stack")
test_stack = imio.read_image_stack(options.test_stack)
master_logger.info("Finished reading stacks")
master_logger.info("Loading graph json")
pairprob_list = load_graph_json(options.ragprob_file)
master_logger.info("Finished loading graph json")
master_logger.info("Matching bodies to GT")
body2gtbody = find_gt_bodies(gt_stack, test_stack)
master_logger.info("Finished matching bodies to GT")
nomerge_hist = []
tot_hist = []
for iter1 in range(0, 101):
nomerge_hist.append(0)
tot_hist.append(0)
for (node1, node2, prob) in pairprob_list:
tot_hist[int(prob*100)] += 1
if body2gtbody[node1] != body2gtbody[node2]:
nomerge_hist[int(prob*100)] += 1
master_logger.info("Probability Agreement with Groundtruth")
for iter1 in range(0, 101):
if tot_hist[iter1] == 0:
per = 0
else:
per = (float(nomerge_hist[iter1])/float(tot_hist[iter1]) * 100)
print iter1, ", ", per , ", " , tot_hist[iter1]
auto_proofread(body2gtbody, options.ragprob_file, options.size_threshold, master_logger, options.test_stack, session_location)
type=float,
metavar='SIGMA',
help='Apply a gaussian filter before watershed.')
parser.add_argument('-P',
'--show-progress',
action='store_true',
default=True,
help='Show a progress bar for the agglomeration.')
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Print runtime information about execution.')
args = parser.parse_args()
probs = read_image_stack(*args.fin)
if args.invert_image:
probs = probs.max() - probs
if args.median_filter:
probs = median_filter(probs, 3)
elif args.gaussian_filter is not None:
probs = gaussian_filter(probs, args.gaussian_filter)
if args.watershed is None:
args.watershed = watershed(probs, show_progress=args.show_progress)
ws = args.watershed
thickness = args.thickness
zcrop1 = [0, thickness]
overlaps = [2**i + 1 for i in range(1, 8)]
results_table = zeros([len(args.thresholds), len(range(1, 8))], dtype=bool)
parser.add_argument("-T", "--thickness", type=int, default=250, help="How thick each substack should be.")
parser.add_argument(
"-m", "--median-filter", action="store_true", default=False, help="Run a median filter on the input image."
)
parser.add_argument(
"-g", "--gaussian-filter", type=float, metavar="SIGMA", help="Apply a gaussian filter before watershed."
)
parser.add_argument(
"-P", "--show-progress", action="store_true", default=True, help="Show a progress bar for the agglomeration."
)
parser.add_argument(
"-v", "--verbose", action="store_true", default=False, help="Print runtime information about execution."
)
args = parser.parse_args()
probs = read_image_stack(*args.fin)
if args.invert_image:
probs = probs.max() - probs
if args.median_filter:
probs = median_filter(probs, 3)
elif args.gaussian_filter is not None:
probs = gaussian_filter(probs, args.gaussian_filter)
if args.watershed is None:
args.watershed = watershed(probs, show_progress=args.show_progress)
ws = args.watershed
thickness = args.thickness
zcrop1 = [0, thickness]
overlaps = [2 ** i + 1 for i in range(1, 8)]
results_table = zeros([len(args.thresholds), len(range(1, 8))], dtype=bool)
)
parser.add_argument('fout', help='output filename (.h5)')
parser.add_argument('-I', '--invert-image', action='store_true',
help='invert the image before applying watershed'
)
parser.add_argument('-m', '--median-filter', action='store_true',
help='Apply a median filter before watershed.'
)
parser.add_argument('-g', '--gaussian-filter', type=float, metavar='SIGMA',
help='Apply a gaussian filter before watershed.'
)
parser.add_argument('-P', '--show-progress', action='store_true',
help='Show a progress bar for the watershed transform.'
)
args = parser.parse_args()
v = imio.read_image_stack(*args.fin)
if args.invert_image:
v = v.max() - v
if args.median_filter:
v = filters.median_filter(v, 3)
if args.gaussian_filter is not None:
v = filters.gaussian_filter(v, args.gaussian_filter)
if args.seed is not None:
args.seed, _ = label(args.seed == 0, diamondse(3, args.seed.ndim))
ws = watershed(v, seeds=args.seed, dams=args.build_dams,
show_progress=args.show_progress)
if os.access(args.fout, os.F_OK):
os.remove(args.fout)
imio.write_h5_stack(ws, args.fout)
