def run(self):
progress = 0.
results = dict()
self.set_progress_percentage(progress)
for s in self.samples:
truth = os.path.join('/groups/saalfeld/saalfeldlab/larissa/data/cremieval/', self.de,
s + '.' + self.m + '.h5')
test = os.path.join(os.path.dirname(self.input()[0].fn), s+'.'+self.m+'.h5')
truth = CremiFile(truth, 'a')
test = CremiFile(test, 'a')
synaptic_partners_eval = SynapticPartners()
print(test.read_annotations())
fscore, precision, recall, fp, fn, filtered_matches = synaptic_partners_eval.fscore(
test.read_annotations(), truth.read_annotations(), truth.read_neuron_ids(), all_stats=True)
results[s] = dict()
results[s]['fscore'] = fscore
results[s]['precision'] = precision
results[s]['recall'] = recall
results[s]['fp'] = fp
results[s]['fn'] = fn
results[s]['filtered_matches'] = filtered_matches
progress += 100. / len(self.samples)
try:
self.set_progress_percentage(progress)
except:
pass
with self.output().open('w') as done:
json.dump(results, done)
def main(argv):
del argv
npz_file = FLAGS.base_dir + 'seg_000_' + FLAGS.model_name + '.npz'
hdf_file = FLAGS.base_dir + 'seg_000_' + FLAGS.model_name + '.hdf'
gt_file = 'FIB-25/test_sample/groundtruth.h5'
pred = np.load(npz_file)
img = pred['segmentation']
print(np.unique(img))
print(img.shape)
with h5py.File(hdf_file, 'w') as f:
neuron_ids = f.create_dataset('volumes/labels/neuron_ids', data=img)
with h5py.File(hdf_file, 'r+') as f1:
f1['volumes/labels/neuron_ids'].attrs.create('resolution',
[8.0, 8.0, 8.0])
test = CremiFile(hdf_file, 'r')
truth = CremiFile(gt_file, 'r')
neuron_ids_evaluation = NeuronIds(truth.read_neuron_ids())
(voi_split, voi_merge) = neuron_ids_evaluation.voi(test.read_neuron_ids())
adapted_rand = neuron_ids_evaluation.adapted_rand(test.read_neuron_ids())
print("Neuron IDs")
print("==========")
print("\tvoi split : " + str(voi_split))
print("\tvoi merge : " + str(voi_merge))
voi_total = voi_split + voi_merge
print("\tvoi total : " + str(voi_total))
print("\tadapted RAND: " + str(adapted_rand))
cremi_score = sqrt(voi_total * adapted_rand)
print("\tcremi score : " + str(cremi_score))
print("\tmodel name : " + FLAGS.model_name)
with open(FLAGS.results_file, 'a+') as f:
f.write("\nvoi split : " + str(voi_split)+"\nvoi merge : " + str(voi_merge)+\
"\nvoi total : " + str(voi_total)+"\nadapted RAND: " + str(adapted_rand)+\
"\ncremi score : " + str(cremi_score)+\
"\ntime cost : " + FLAGS.time+'\n\n')
def run(self):
progress = 0.0
results = dict()
self.set_progress_percentage(progress)
for s in self.samples:
truth = os.path.join(
"/groups/saalfeld/saalfeldlab/larissa/data/cremieval/",
self.de,
s + "." + self.m + ".h5",
)
test = os.path.join(
os.path.dirname(self.input()[0].fn), s + "." + self.m + ".h5"
)
truth = CremiFile(truth, "a")
test = CremiFile(test, "a")
synaptic_partners_eval = SynapticPartners()
print(test.read_annotations())
fscore, precision, recall, fp, fn, filtered_matches = synaptic_partners_eval.fscore(
test.read_annotations(),
truth.read_annotations(),
truth.read_neuron_ids(),
all_stats=True,
)
results[s] = dict()
results[s]["fscore"] = fscore
results[s]["precision"] = precision
results[s]["recall"] = recall
results[s]["fp"] = fp
results[s]["fn"] = fn
results[s]["filtered_matches"] = filtered_matches
progress += 100.0 / len(self.samples)
try:
self.set_progress_percentage(progress)
except:
pass
with self.output().open("w") as done:
json.dump(results, done)
#!/usr/bin/python
from cremi.io import CremiFile
from cremi.evaluation import NeuronIds, Clefts, SynapticPartners
test = CremiFile('test.hdf', 'r')
truth = CremiFile('groundtruth.hdf', 'r')
neuron_ids_evaluation = NeuronIds(truth.read_neuron_ids())
(voi_split, voi_merge) = neuron_ids_evaluation.voi(test.read_neuron_ids())
adapted_rand = neuron_ids_evaluation.adapted_rand(test.read_neuron_ids())
print "Neuron IDs"
print "=========="
print "\tvoi split : " + str(voi_split)
print "\tvoi merge : " + str(voi_merge)
print "\tadapted RAND: " + str(adapted_rand)
clefts_evaluation = Clefts(test.read_clefts(), truth.read_clefts())
false_positive_count = clefts_evaluation.count_false_positives()
false_negative_count = clefts_evaluation.count_false_negatives()
false_positive_stats = clefts_evaluation.acc_false_positives()
false_negative_stats = clefts_evaluation.acc_false_negatives()
print "Clefts"
print "======"
print "\tfalse positives: " + str(false_positive_count)
def Reading(filename, isTest=False):
# # Read the data into dataset
# print "Filename: ", filename
# # with h5py.File('sample_A_20160501.hdf', 'r') as f:
# with h5py.File(filename, 'r') as f:
# print f["volumes"]
# imageDataSet = f["volumes/raw"][:]
# labelDataSet = f["volumes/labels/neuron_ids"][:]
# imageDataSet = imageDataSet.astype(np.float32)
# labelDataSet = labelDataSet.astype(np.float32)
# return imageDataSet, labelDataSet
file = CremiFile(filename, "r")
print filename
# Check the content of the datafile
print "Has raw : " + str(file.has_raw())
print "Has neuron ids : " + str(file.has_neuron_ids())
print "Has clefts : " + str(file.has_clefts())
print "Has annotations : " + str(file.has_annotations())
# Read everything there is.
#
# If you are using the padded versions of the datasets (where raw is larger to
# provide more context), the offsets of neuron_ids, clefts, and annotations tell
# you where they are placed in nm relative to (0,0,0) of the raw volume.
#
# In other words, neuron_ids, clefts, and annotations are exactly the same
# between the padded and unpadded versions, except for the offset attribute.
raw = file.read_raw()
if not isTest:
neuron_ids = file.read_neuron_ids()
clefts = file.read_clefts()
annotations = file.read_annotations()
print "Read raw: " + str(raw) + \
", resolution " + str(raw.resolution) + \
", offset " + str(raw.offset) + \
("" if raw.comment == None else ", comment \"" + raw.comment + "\"")
if not isTest:
print "Read neuron_ids: " + str(neuron_ids) + \
", resolution " + str(neuron_ids.resolution) + \
", offset " + str(neuron_ids.offset) + \
("" if neuron_ids.comment == None else ", comment \"" + neuron_ids.comment + "\"")
# neuron_ids.offset will contain the starting point of neuron_ids inside the raw volume.
# Note that these numbers are given in nm.
# print "Read clefts: " + str(clefts) + \
# ", resolution " + str(clefts.resolution) + \
# ", offset " + str(clefts.offset) + \
# ("" if clefts.comment == None else ", comment \"" + clefts.comment + "\"")
# print "Read annotations:"
# for (id, type, location) in zip(annotations.ids(), annotations.types(), annotations.locations()):
# print str(id) + " of type " + type + " at " + str(np.array(location)+np.array(annotations.offset))
# print "Pre- and post-synaptic partners:"
# for (pre, post) in annotations.pre_post_partners:
# print str(pre) + " -> " + str(post)
with h5py.File(filename, 'r') as f:
print f["volumes"]
imageDataSet = f["volumes/raw"][:]
if not isTest:
labelDataSet = f["volumes/labels/neuron_ids"][:]
imageDataSet = imageDataSet.astype(np.float32)
if not isTest:
labelDataSet = labelDataSet.astype(np.float32)
if not isTest:
return imageDataSet, labelDataSet
return imageDataSet
# Check the content of the datafile
print "Has raw: " + str(file.has_raw())
print "Has neuron ids: " + str(file.has_neuron_ids())
print "Has clefts: " + str(file.has_clefts())
print "Has annotations: " + str(file.has_annotations())
# Read everything there is.
#
# If you are using the padded versions of the datasets (where raw is larger to
# provide more context), the offsets of neuron_ids, clefts, and annotations tell
# you where they are placed in nm relative to (0,0,0) of the raw volume.
#
# In other words, neuron_ids, clefts, and annotations are exactly the same
# between the padded and unpadded versions, except for the offset attribute.
raw = file.read_raw()
neuron_ids = file.read_neuron_ids()
clefts = file.read_clefts()
annotations = file.read_annotations()
print "Read raw: " + str(raw) + \
", resolution " + str(raw.resolution) + \
", offset " + str(raw.offset) + \
("" if raw.comment == None else ", comment \"" + raw.comment + "\"")
print "Read neuron_ids: " + str(neuron_ids) + \
", resolution " + str(neuron_ids.resolution) + \
", offset " + str(neuron_ids.offset) + \
("" if neuron_ids.comment == None else ", comment \"" + neuron_ids.comment + "\"")
print "Read clefts: " + str(clefts) + \
", resolution " + str(clefts.resolution) + \
# Check the content of the datafile
print "Has raw: " + str(file.has_raw())
print "Has neuron ids: " + str(file.has_neuron_ids())
print "Has clefts: " + str(file.has_clefts())
print "Has annotations: " + str(file.has_annotations())
# Read everything there is.
#
# If you are using the padded versions of the datasets (where raw is larger to
# provide more context), the offsets of neuron_ids, clefts, and annotations tell
# you where they are placed in nm relative to (0,0,0) of the raw volume.
#
# In other words, neuron_ids, clefts, and annotations are exactly the same
# between the padded and unpadded versions, except for the offset attribute.
raw = file.read_raw()
neuron_ids = file.read_neuron_ids()
clefts = file.read_clefts()
annotations = file.read_annotations()
print "Read raw: " + str(raw) + \
", resolution " + str(raw.resolution) + \
", offset " + str(raw.offset) + \
("" if raw.comment == None else ", comment \"" + raw.comment + "\"")
print "Read neuron_ids: " + str(neuron_ids) + \
", resolution " + str(neuron_ids.resolution) + \
", offset " + str(neuron_ids.offset) + \
("" if neuron_ids.comment == None else ", comment \"" + neuron_ids.comment + "\"")
print "Read clefts: " + str(clefts) + \
", resolution " + str(clefts.resolution) + \
#!/usr/bin/python
from cremi.io import CremiFile
from cremi.evaluation import NeuronIds, Clefts, SynapticPartners
test = CremiFile('test.hdf', 'r')
truth = CremiFile('groundtruth.hdf', 'r')
neuron_ids_evaluation = NeuronIds(truth.read_neuron_ids())
(voi_split, voi_merge) = neuron_ids_evaluation.voi(test.read_neuron_ids())
adapted_rand = neuron_ids_evaluation.adapted_rand(test.read_neuron_ids())
print "Neuron IDs"
print "=========="
print "\tvoi split : " + str(voi_split)
print "\tvoi merge : " + str(voi_merge)
print "\tadapted RAND: " + str(adapted_rand)
clefts_evaluation = Clefts(test.read_clefts(), truth.read_clefts())
false_positive_count = clefts_evaluation.count_false_positives()
false_negative_count = clefts_evaluation.count_false_negatives()
false_positive_stats = clefts_evaluation.acc_false_positives()
false_negative_stats = clefts_evaluation.acc_false_negatives()
print "Clefts"
print "======"
print "\tfalse positives: " + str(false_positive_count)
