def compute_accuracy(gt, predictions, threshold=np.inf):
"""
compare predicted rois vs ground truth rois
"""
a = neurofinder.load(
json.dumps([{
'coordinates': r.coordinates.tolist()
} for r in gt]))
b = neurofinder.load(
json.dumps([{
'coordinates': r.coordinates.tolist()
} for r in predictions]))
recall, precision = neurofinder.centers(a, b, threshold=threshold)
inclusion, exclusion = neurofinder.shapes(a, b, threshold=threshold)
if recall == 0 and precision == 0:
combined = 0
else:
combined = 2 * (recall * precision) / (recall + precision)
#result = {'combined': round(combined, 4), 'inclusion': round(inclusion, 4), 'precision': round(precision, 4), 'recall': round(recall, 4), 'exclusion': round(exclusion, 4)}
#print(json.dumps(result))
print "Combined: %f" % (combined, )
print "Precision: %f" % (precision, )
print "Recall: %f" % (recall, )
print "Inclusion: %f" % (inclusion, )
print "Exclusion: %f" % (exclusion, )
def test_accuracy():
app = QApplication([])
app.setApplicationName("cidan")
widget = MainWindow(dev=True, preload=False)
main_widget = widget.table_widget
load_new_dataset(main_widget,
file_path="test_files/small_dataset1.tif",
save_dir_path="test_files/save_dir",
load_into_mem=True)
main_widget.open_dataset_thread.wait()
time.sleep(10)
data_handler = main_widget.data_handler
data_handler.change_filter_param("median_filter", True)
data_handler.change_roi_extraction_param("roi_circ_threshold", 20)
main_widget.thread_list[1].run()
assert main_widget.tabs[1].image_view.magic_wand(70, 127)
data_handler.export()
a = neurofinder.load("test_files/roi_list.json")
b = neurofinder.load("test_files/save_dir/roi_list.json")
test = neurofinder.match(a, b)
percision, recall = neurofinder.centers(a, b)
inclusion, exclusion = neurofinder.shapes(a, b)
assert percision > .8
assert recall > .8
assert exclusion > .6
assert inclusion > .6
image_good = io.imread("test_files/embedding_norm_image.png")
image_test = io.imread(
"test_files/save_dir/embedding_norm_images/embedding_norm_image.png")
assert structural_similarity(image_good, image_test) > .90
data_handler.change_box_param("total_num_spatial_boxes", 4)
main_widget.thread_list[1].run()
data_handler.export()
image_test = io.imread(
"test_files/save_dir/embedding_norm_images/embedding_norm_image.png")
assert structural_similarity(image_good, image_test) > .60
a = neurofinder.load("test_files/roi_list.json")
b = neurofinder.load("test_files/save_dir/roi_list.json")
percision, recall = neurofinder.centers(a, b)
inclusion, exclusion = neurofinder.shapes(a, b)
assert percision > .4
assert recall > .4
assert exclusion > .4
assert inclusion > .3
app.quit()
shutil.rmtree("test_files/save_dir")
def __init__(self, name, regions):
"""
Result objects represent the result of running a model against a single example
:param name: name of the dataset corresponding to these results
:param regions: list of dictionaries specifying the ROI
Each dictionary should contain a "coordinates" key that gives a list of (x, y) coordinates
"""
self.name = name
self.raw_regions = regions
regions_json = json.dumps(regions)
self.regions = neurofinder.load(regions_json)
def lt_evaluate(file1, file2, threshold=5):
a = load(file1)
b = load(file2)
if a != 0 and b != 0:
recall, precision = centers(a, b, threshold=threshold)
inclusion, exclusion = shapes(a, b, threshold=threshold)
if recall == 0 and precision == 0:
combined = 0
else:
combined = 2 * (recall * precision) / (recall + precision)
result = {
'combined': round(combined, 4),
'inclusion': round(inclusion, 4),
'precision': round(precision, 4),
'recall': round(recall, 4),
'exclusion': round(exclusion, 4)
}
return (result)
else:
return {}
def compute_accuracy(gt, predictions, threshold=np.inf):
"""
compare predicted rois vs ground truth rois
"""
a = neurofinder.load(json.dumps([{'coordinates' : r.coordinates.tolist()} for r in gt]))
b = neurofinder.load(json.dumps([{'coordinates' : r.coordinates.tolist()} for r in predictions]))
recall, precision = neurofinder.centers(a, b, threshold=threshold)
inclusion, exclusion = neurofinder.shapes(a, b, threshold=threshold)
if recall == 0 and precision == 0:
combined = 0
else:
combined = 2 * (recall * precision) / (recall + precision)
#result = {'combined': round(combined, 4), 'inclusion': round(inclusion, 4), 'precision': round(precision, 4), 'recall': round(recall, 4), 'exclusion': round(exclusion, 4)}
#print(json.dumps(result))
print "Combined: %f" % (combined,)
print "Precision: %f" % (precision,)
print "Recall: %f" % (recall,)
print "Inclusion: %f" % (inclusion,)
print "Exclusion: %f" % (exclusion,)
#%%
def tomask(coords,dims):
mask = np.zeros(dims)
mask[list(zip(*coords))] = 1
return mask
#%%
import json
from neurofinder import load, centers, shapes,match
for folder_in in base_folders[-2:-1]:
#%
ref_file=os.path.join(folder_in,'regions','regions_CNMF_1.json')
if os.path.exists(ref_file):
print(folder_in)
b=load(ref_file)
# a=load(os.path.join(folder_in,'regions','regions_wesley.json'))
a=load(os.path.join(folder_in,'regions/regions_ben.json'))
#
with open(os.path.join(folder_in,'regions/regions_ben.json')) as f:
regions = json.load(f)
masks_nf = np.array([tomask(s['coordinates'],dims) for s in regions])
with open(os.path.join(folder_in,'regions/regions_CNMF_1.json')) as f:
regions = json.load(f)
masks_1 = np.array([tomask(s['coordinates'],dims) for s in regions])
with open(os.path.join(folder_in,'regions/regions_CNMF_2.json')) as f:
regions = json.load(f)
def tomask(coords, dims):
mask = np.zeros(dims)
mask[list(zip(*coords))] = 1
return mask
#%%
import json
from neurofinder import load, centers, shapes, match
for folder_in in base_folders[-2:-1]:
#%
ref_file = os.path.join(folder_in, 'regions', 'regions_CNMF_1.json')
if os.path.exists(ref_file):
print(folder_in)
b = load(ref_file)
# a=load(os.path.join(folder_in,'regions','regions_wesley.json'))
a = load(os.path.join(folder_in, 'regions/regions_ben.json'))
#
with open(os.path.join(folder_in, 'regions/regions_ben.json')) as f:
regions = json.load(f)
masks_nf = np.array([tomask(s['coordinates'], dims) for s in regions])
with open(os.path.join(folder_in, 'regions/regions_CNMF_1.json')) as f:
regions = json.load(f)
masks_1 = np.array([tomask(s['coordinates'], dims) for s in regions])
with open(os.path.join(folder_in, 'regions/regions_CNMF_2.json')) as f:
regions = json.load(f)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--task_list",
type=str,
default='',
required=True,
help="Path to all datasets")
parser.add_argument(
"-odir",
"--output_dir",
type=str,
default='',
required=True,
help="Where to output all eigen vector images and csv data",
)
parser.add_argument("--task_log", type=str, default='', required=True)
args = parser.parse_args()
task_list = []
with open(args.task_list, newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
for row in reader:
task_list.append(row[0])
parameters_to_search = {
"median_filter": [True, False],
"hist_eq": [True, False],
"pca": [True, False],
"total_num_spatial_boxes": [1, 4, 9],
"num_eig": [50],
"trial_split": [True],
"z_score": [True, False],
"trial_length": [250, 500, 1000],
"localSpatialDenoising": [True],
"knn": [7],
"normalize_w_k": [1E-2, 1E-3, 1E-4, 1E-5]
}
total_parameters_combinations = reduce(
lambda x, y: x * y,
[len(parameters_to_search[x]) for x in parameters_to_search])
print(total_parameters_combinations)
parameter_keys = list(parameters_to_search)
parameter_remainders = []
for num, key in enumerate(parameter_keys):
remainder = 1
for x in range(num + 1):
remainder *= len(parameters_to_search[parameter_keys[x]])
parameter_remainders.append(remainder)
rows = []
for num, path in enumerate([os.path.dirname(x) for x in task_list]):
if os.path.isfile(os.path.join(path, "roi_list.json")):
a = neurofinder.load(os.path.join(path, "roi_true.json"))
b = neurofinder.load(os.path.join(path, "roi_list.json"))
percision, recall = neurofinder.centers(a, b)
inclusion, exclusion = neurofinder.shapes(a, b)
else:
percision, recall, inclusion, exclusion = -1, -1, -1, -1
current_row = [num + 1, percision, recall, inclusion, exclusion]
for remainder, key in zip(parameter_remainders, parameter_keys):
val = parameters_to_search[key][num % remainder //
(remainder //
len(parameters_to_search[key]))]
current_row.append(val)
rows.append(current_row)
if os.path.isfile(
os.path.join(
path, "embedding_norm_images/embedding_norm_image.png")):
Image.open(
os.path.join(
path,
"embedding_norm_images/embedding_norm_image.png")).save(
os.path.join(args.output_dir,
os.path.basename(path) + ".png"))
df = pandas.DataFrame(
rows,
columns=["Seq", "Percision", "Recall", "Inclusion", "Exclusion"] +
parameter_keys)
task_log = pandas.read_csv(args.task_log)
result = pandas.concat([df, task_log], axis=1)
result.to_csv(os.path.join(args.output_dir, "out.csv"))