def human_compare_with_raw_rois(ann1roipth, ann2roipth, cutoff=30):
#format ZYX, and remove any rois missaved
ann1_zyx_rois = np.asarray(
[[int(yy) for yy in xx.replace(".roi", "").split("-")]
for xx in read_roi_zip(ann1roipth, include_roi_name=True)])
ann2_zyx_rois = np.asarray(
[[int(yy) for yy in xx.replace(".roi", "").split("-")]
for xx in read_roi_zip(ann2roipth, include_roi_name=True)])
paired, tp, fp, fn = pairwise_distance_metrics(
ann1_zyx_rois, ann2_zyx_rois, cutoff
) #returns true positive = tp; false positive = fp; false negative = fn
precision = tp / (tp + fp)
recall = tp / (tp + fn) #calculating precision and recall
f1 = 2 * (
(precision * recall) / (precision + recall)) #calculating f1 score
print(
"\n Finished calculating statistics for set params\n\n\nReport:\n***************************\n\
Cutoff: {} \n\
F1 score: {}% \n\
true positives, false positives, false negatives: {} \n\
precision: {}% \n\
recall: {}%\n".format(cutoff, round(f1 * 100, 2), (tp, fp, fn),
round(precision * 100, 2), round(recall * 100, 2)))
return tp, fp, fn, f1
for sz in flds:
arrs = [os.path.join(sz, xx) for xx in os.listdir(sz) if xx[-3:] == "npy"]; arrs.sort()
#reinit
tps = []; fps = []; fns = []
print(sz)
for i in range(len(arrs)):
#load points dict
points_dict = load_dictionary("/home/wanglab/Documents/cfos_inputs/cfos_points_dictionary.p")
#set ground truth
ground_truth = list(points_dict[os.path.basename(arrs[i][:-13])+".npy"])
clearmap = np.load(arrs[i])
paired,tp,fp,fn = pairwise_distance_metrics(ground_truth, clearmap, cutoff = cutoff) #returns true positive = tp; false positive = fp; false negative = fn
tps.append(tp); fps.append(fp); fns.append(fn) #append matrix to save all values to calculate f1 score
tps_s.append(tps); fps_s.append(fps); fns_s.append(fns)
tp = sum(tps); fp = sum(fps); fn = sum(fns) #sum all the elements in the lists
precision = tp/(tp+fp); recall = tp/(tp+fn) #calculating precision and recall
f1 = 2*( (precision*recall)/(precision+recall) ) #calculating f1 score
#%%
#left y axis
#n_cells_per_size
n_cells_per_size = np.asarray(n_cells_per_size)
n_cells_per_size_av = np.mean(n_cells_per_size, axis = 1).astype(int)
fls.sort()
#init dataframe
print("brain: %s\ncutoff: %s \n\n" % (os.path.basename(brain), cutoff))
df = pd.DataFrame()
df["parameters"] = [os.path.basename(xx) for xx in fls]
df["tp"] = np.zeros(len(df))
df["fp"] = np.zeros(len(df))
df["fn"] = np.zeros(len(df))
df["f1"] = np.zeros(len(df))
for n, fl in enumerate(fls):
if n % 50 == 0: print(n)
detected_cells = np.load(fl, allow_pickle=True)
paired, tp, fp, fn = pairwise_distance_metrics(annotated_cells[br],
detected_cells,
cutoff=cutoff,
verbose=False)
try:
precision = tp / (tp + fp)
recall = tp / (tp + fn) #calculating precision and recall
f1 = 2 * ((precision * recall) /
(precision + recall)) #calculating f1 score
except Exception as e:
print(e)
f1 = np.nan #if tp, fn, etc. are 0
df.loc[df.parameters == os.path.basename(fl), "f1"] = f1
df.loc[df.parameters == os.path.basename(fl), "tp"] = tp
df.loc[df.parameters == os.path.basename(fl), "fp"] = fp
df.loc[df.parameters == os.path.basename(fl), "fn"] = fn
df["fn"] = np.zeros(len(df))
df["f1"] = np.zeros(len(df))
for n, fld in enumerate(list(dct.keys())):
if n % 100 == 0: print(n)
detected_cells = np.asarray([v for k, v in dct[fld].items()])
#cells were accidently saved as x y z instead
detected_cells_reshape = np.asarray([
np.array([[z, y, x] for x, y, z in vol]) for vol in detected_cells
])
measures = [
pairwise_distance_metrics(annotated_cells[i],
detected_cells_reshape[i],
cutoff=cutoff,
verbose=False)
for i in range(len(detected_cells_reshape))
]
tp = sum([measure[1] for measure in measures])
fp = sum([measure[2] for measure in measures])
fn = sum([measure[3] for measure in measures])
try:
precision = tp / (tp + fp)
recall = tp / (tp + fn) #calculating precision and recall
f1 = 2 * ((precision * recall) /
(precision + recall)) #calculating f1 score
except Exception as e:
print(e)
tps = []
fps = []
fns = []
#set voxel cutoff value
cutoff = 5
for i in range(len(ann2_dsets)):
#set ground truth
print(ann1_dsets[i])
ann1_ground_truth = points_dict[ann1_dsets[i]]
ann2_ground_truth = points_dict[ann2_dsets[i]]
paired, tp, fp, fn = pairwise_distance_metrics(
ann2_ground_truth, ann1_ground_truth, cutoff=30
) #returns true positive = tp; false positive = fp; false negative = fn
#f1 per dset
precision = tp / (tp + fp)
recall = tp / (tp + fn) #calculating precision and recall
f1 = 2 * (
(precision * recall) / (precision + recall)) #calculating f1 score
tps.append(tp)
fps.append(fp)
fns.append(fn) #append matrix to save all values to calculate f1 score
tp = sum(tps)
fp = sum(fps)
fn = sum(fns) #sum all the elements in the lists
precision = tp / (tp + fp)