def test_match():
a = many([[[0, 0], [0, 1], [1, 0], [1, 1]],
[[10, 10], [10, 11], [11, 10], [11, 11]]])
b = many([[[0, 0], [0, 1], [1, 0], [1, 1]], [[30, 30], [31, 30], [31,
31]]])
assert match(a, b) == [0, 1]
assert match(a, b, threshold=5) == [0, nan]
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")
# idcomps=np.intersect1d(pos_examples,idcomps)
# regions_CNMF=cse.utilities.nf_masks_to_json( masks_ws[idcomps],os.path.join('/tmp/regions_CNMF_2.json'))
# b=load(os.path.join('/tmp/regions_CNMF_2.json'))
# pl.imshow(np.sum(masks_ws[idcomps],0),alpha=.3,cmap='hot')
pl.imshow(np.sum(masks_2,0),alpha=.3,cmap='hot')
pl.title('M_2')
pl.subplot(2,2,4)
# pl.imshow(Cn,cmap='gray')
# pl.imshow(np.sum(masks_nf,0)+2*np.sum(masks_ws[idcomps],0))
pl.imshow(np.sum(masks_nf,0)+2*np.sum(masks_2,0))
# pl.imshow(np.sum(masks_2,0),alpha=.2,cmap='hot')
pl.title('M_overlap')
#print
mtc=match(a,b,threshold=5)
re,pr=centers(a,b,threshold=5)
incl,excl=shapes(a,b,threshold=5)
fscore=2*(pr*re)/(pr+re)
print(('Exclusion %.3f\nRecall %.3f\nCombined %.3f\nPrecision %.3f\nInclusion %.3f' % (excl,re,fscore,pr,incl)))
else:
print((ref_file + ' DO NOT EXIST!'))
#%%
from neurofinder import load, centers, shapes
results=[]
for folder_in_check in folders_in:
a=load(os.path.join(folder_in_check,'regions_CNMF.json'))
dset='.'.join(folder_in_check[:-1].split('.')[1:])
print (dset)
# idcomps=np.intersect1d(pos_examples,idcomps)
# regions_CNMF=cse.utilities.nf_masks_to_json( masks_ws[idcomps],os.path.join('/tmp/regions_CNMF_2.json'))
# b=load(os.path.join('/tmp/regions_CNMF_2.json'))
# pl.imshow(np.sum(masks_ws[idcomps],0),alpha=.3,cmap='hot')
pl.imshow(np.sum(masks_2, 0), alpha=.3, cmap='hot')
pl.title('M_2')
pl.subplot(2, 2, 4)
# pl.imshow(Cn,cmap='gray')
# pl.imshow(np.sum(masks_nf,0)+2*np.sum(masks_ws[idcomps],0))
pl.imshow(np.sum(masks_nf, 0) + 2 * np.sum(masks_2, 0))
# pl.imshow(np.sum(masks_2,0),alpha=.2,cmap='hot')
pl.title('M_overlap')
# print
mtc = match(a, b, threshold=5)
re, pr = centers(a, b, threshold=5)
incl, excl = shapes(a, b, threshold=5)
fscore = 2 * (pr * re) / (pr + re)
print((
'Exclusion %.3f\nRecall %.3f\nCombined %.3f\nPrecision %.3f\nInclusion %.3f'
% (excl, re, fscore, pr, incl)))
else:
print((ref_file + ' DO NOT EXIST!'))
#%%
from neurofinder import load, centers, shapes
results = []
for folder_in_check in folders_in:
a = load(os.path.join(folder_in_check, 'regions_CNMF.json'))
def overlay(model, image=None, compare=None, threshold=inf, correct=False):
"""
Overlay regions onto reference image, with optional comparison regions.
Parameters
----------
model : ExtractionModel
image : array-like, optional, default = None
Base image, can provide a 2d array,
if unspecified will be black.
modelCompare : ExtractionModel, default = None
Regions to be compared to if provided.
threshold : float, default = inf
Distance threshold for matching sources.
correct : bool, default = False
If True and a comparision given will only show correct regions
"""
if image is not None:
if image.max() > 1:
im = norm(image)
else:
im = image
size = im.shape
else:
size = (max([r.bbox[2] for r in model.regions]) + 1,
max([r.bbox[3] for r in model.regions]) + 1)
if compare is not None:
sizeCompare = (max([r.bbox[2] for r in compare.regions]) + 1,
max([r.bbox[3] for r in compare.regions]) + 1)
size = (maximum(size[0],
sizeCompare[0]), maximum(size[1], sizeCompare[1]))
im = full(size, 0.0)
if compare is not None:
matches = match(model.regions, compare.regions, threshold)
matchesCompare = full(compare.regions.count, nan)
for ii in where(~isnan(matches))[0]:
matchesCompare[matches[ii]] = ii
if any(~isnan(matches)):
hits = many([model.regions[i] for i in where(~isnan(matches))[0]])
h = hits.mask(size,
background='black',
fill=None,
stroke=[0, 0.7, 0])
else:
h = full((size[0], size[1], 3), 0.0)
if any(isnan(matches)):
falseAlarms = many(
[model.regions[i] for i in where(isnan(matches))[0]])
fA = falseAlarms.mask(size,
background='black',
fill=None,
stroke=[0.7, 0, 0])
else:
fA = full((size[0], size[1], 3), 0.0)
if any(~isnan(matchesCompare)):
truePositives = many(
[compare.regions[i] for i in where(~isnan(matchesCompare))[0]])
tP = truePositives.mask(size,
background='black',
fill=None,
stroke=[0, 0, 0.7])
else:
tP = full((size[0], size[1], 3), 0.0)
if any(isnan(matchesCompare)):
misses = many(
[compare.regions[i] for i in where(isnan(matchesCompare))[0]])
m = misses.mask(size,
background='black',
fill=None,
stroke=[0.7, 0.7, 0])
else:
m = full((size[0], size[1], 3), 0.0)
if correct:
mask = maximum(tP, h)
else:
mask = maximum(maximum(maximum(tP, fA), h), m)
else:
mask = model.regions.mask(size,
background='black',
fill=None,
stroke=[0, 0.7, 0])
base = tile(im, (3, 1, 1)).transpose(1, 2, 0)
return maximum(base, mask)
def overlay(model, image=None, compare=None, threshold=inf, correct=False):
"""
Overlay regions onto reference image, with optional comparison regions.
Parameters
----------
model : ExtractionModel
image : array-like, optional, default = None
Base image, can provide a 2d array,
if unspecified will be black.
modelCompare : ExtractionModel, default = None
Regions to be compared to if provided.
threshold : float, default = inf
Distance threshold for matching sources.
correct : bool, default = False
If True and a comparision given will only show correct regions
"""
if image is not None:
if image.max() > 1:
im = norm(image)
else:
im = image
size = im.shape
else:
size = (max([r.bbox[2] for r in model.regions])+1, max([r.bbox[3] for r in model.regions])+1)
if compare is not None:
sizeCompare = (max([r.bbox[2] for r in compare.regions])+1, max([r.bbox[3] for r in compare.regions])+1)
size = (maximum(size[0], sizeCompare[0]), maximum(size[1], sizeCompare[1]))
im = full(size, 0.0)
if compare is not None:
matches = match(model.regions, compare.regions, threshold)
matchesCompare = full(compare.regions.count,nan)
for ii in where(~isnan(matches))[0]:
matchesCompare[matches[ii]] = ii
if any(~isnan(matches)):
hits = many([model.regions[i] for i in where(~isnan(matches))[0]])
h = hits.mask(size, background='black', fill=None, stroke=[0, 0.7, 0])
else:
h = full((size[0], size[1], 3), 0.0)
if any(isnan(matches)):
falseAlarms = many([model.regions[i] for i in where(isnan(matches))[0]])
fA = falseAlarms.mask(size, background='black', fill=None, stroke=[0.7, 0, 0])
else:
fA = full((size[0], size[1], 3), 0.0)
if any(~isnan(matchesCompare)):
truePositives = many([compare.regions[i] for i in where(~isnan(matchesCompare))[0]])
tP = truePositives.mask(size, background='black', fill=None, stroke=[0, 0, 0.7])
else:
tP = full((size[0], size[1], 3), 0.0)
if any(isnan(matchesCompare)):
misses = many([compare.regions[i] for i in where(isnan(matchesCompare))[0]])
m = misses.mask(size, background='black', fill=None, stroke=[0.7, 0.7, 0])
else:
m = full((size[0], size[1], 3), 0.0)
if correct:
mask = maximum(tP, h)
else:
mask = maximum(maximum(maximum(tP, fA), h), m)
else:
mask = model.regions.mask(size, background='black', fill=None, stroke=[0, 0.7, 0])
base = tile(im,(3,1,1)).transpose(1,2,0)
return maximum(base, mask)