raab/efishSignalDetector
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elimination of double detection works

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This commit is contained in:
Till Raab 2023-11-29 15:00:02 +01:00
parent f70e74f5e1
commit 866fd3081d
1 changed files with 119 additions and 40 deletions
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159
extract_from_bbox.py
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@ -37,7 +37,7 @@ def bbox_to_data(img_path, t_min, t_max, f_min, f_max):
boxes[:, 1] = boxes[:, 1] * (f_max - f_min) + f_min
boxes[:, 3] = boxes[:, 3] * (f_max - f_min) + f_min
scores = annotations[:, -1]
scores = annotations[:, 5]
return boxes, scores
@ -146,21 +146,49 @@ def main(args):
bbox_overlapping_mask, overlap_bbox_idxs = find_overlapping_bboxes(df_collect)
bbox_groups = delete_double_boxes(bbox_overlapping_mask, overlap_bbox_idxs, df_collect)
# embed()
# quit()
print('got here')
time_frequency_bboxes = pd.DataFrame(data= np.array(df_collect), columns=['file_name', 't0', 'f0', 't1', 'f1', 'score'])
###########################################
colors = np.random.rand(np.max(bbox_groups).astype(int), 3)
for file_name in time_frequency_bboxes['file_name'].unique():
fig, ax = plt.subplots()
mask = time_frequency_bboxes['file_name'] == file_name
for index, bbox in time_frequency_bboxes[mask].iterrows():
name, t0, f0, t1, f1 = (bbox[0], *bbox[1:-1].astype(float))
if bbox_groups[index] == 0:
color = 'tab:green'
elif bbox_groups[index] > 0:
color = 'tab:red'
else:
color = 'k'
ax.add_patch(
Rectangle((t0, f0),
(t1 - t0),
(f1 - f0),
fill=False, color=color, linestyle='--', linewidth=2, zorder=10)
)
# ax.set_xlim(float(time_frequency_bboxes[mask]['t0'].min()), float(time_frequency_bboxes[mask]['t1'].max()))
ax.set_xlim(0, float(time_frequency_bboxes[mask]['t1'].max()))
# ax.set_ylim(float(time_frequency_bboxes[mask]['f0'].min()), float(time_frequency_bboxes[mask]['f1'].max()))
ax.set_ylim(400, 1200)
plt.show()
exit()
###########################################
if args.tracking_data_path:
file_paths = sorted(list(pathlib.Path(args.tracking_data_path).absolute().rglob('*.raw')))
for raw_path in file_paths:
if not raw_path.parent.name in time_frequency_bboxes['file_name'].to_list():
continue
assign_rises_to_ids(raw_path, time_frequency_bboxes, bbox_overlapping_mask, bbox_groups)
pass
def delete_double_boxes(bbox_overlapping_mask, overlap_bbox_idxs, df_collect):
def delete_double_boxes(bbox_overlapping_mask, overlap_bbox_idxs, df_collect, overlap_th = 0.2):
def get_connected(non_regarded_bbox_idx, overlap_bbox_idxs):
mask = np.array((np.array(overlap_bbox_idxs) == non_regarded_bbox_idx).sum(1), dtype=bool)
affected_bbox_idxs = np.unique(overlap_bbox_idxs[mask])
@ -168,13 +196,11 @@ def delete_double_boxes(bbox_overlapping_mask, overlap_bbox_idxs, df_collect):
handled_bbox_idxs = []
bbox_groups = np.zeros(len(df_collect))
detele_bbox_idxs = []
for Coverlapping_bbox_idx in tqdm(np.unique(overlap_bbox_idxs)):
if Coverlapping_bbox_idx in handled_bbox_idxs:
continue
# if bbox_overlapping_mask[Coverlapping_bbox_idx] >= 3:
# pass
# else:
# continue
regarded_bbox_idxs = [Coverlapping_bbox_idx]
mask = np.array((np.array(overlap_bbox_idxs) == Coverlapping_bbox_idx).sum(1), dtype=bool)
@ -191,43 +217,96 @@ def delete_double_boxes(bbox_overlapping_mask, overlap_bbox_idxs, df_collect):
regarded_bbox_idxs.append(non_regarded_bbox_idx)
non_regarded_bbox_idxs = list(set(affected_bbox_idxs) - set(regarded_bbox_idxs))
bbox_idx_group = regarded_bbox_idxs
bbox_idx_group = np.array(regarded_bbox_idxs)
bbox_scores = df_collect[bbox_idx_group][:, -1].astype(float)
# bbox_idx_group = bbox_idx_group[bbox_scores.argsort()]
# bbox_scores = bbox_scores[bbox_scores.argsort()]
bbox_groups[bbox_idx_group] = np.max(bbox_groups) + 1
# bbox_scores = df_collect[bbox_idx_group][:, -1]
# overlap_pct = np.full((len(bbox_idx_group), len(bbox_idx_group)), np.nan)
#
# for i, j in itertools.product(range(len(bbox_idx_group)), repeat=2):
# if i == j:
# continue
# bb0_idx = bbox_idx_group[i]
# bb1_idx = bbox_idx_group[j]
#
# bb0_t0, bb0_t1 = df_collect[bb0_idx][1].astype(float), df_collect[bb0_idx][3].astype(float)
# bb1_t0, bb1_t1 = df_collect[bb1_idx][1].astype(float), df_collect[bb1_idx][3].astype(float)
#
# helper = np.array([0, 0, 1, 1])
# bb_times = np.array([bb0_t0, bb0_t1, bb1_t0, bb1_t1])
#
# sorted_helper = helper[bb_times.argsort()]
#
# if sorted_helper[0] == sorted_helper[1]:
# continue
#
# elif sorted_helper[1] == sorted_helper[2] == 0:
# overlap_pct[i, j] = 1
#
# elif sorted_helper[1] == sorted_helper[2] == 1:
# overlap_pct[i, j] = (bb1_t1 - bb1_t0) / (bb0_t1 - bb0_t0)
#
# else:
# overlap_pct[i, j] = np.diff(sorted(bb_times)[1:3])[0] / ((bb0_t1 - bb0_t0))
# embed()
# quit()
remove_idx_combinations = [()]
remove_idx_combinations_scores = [0]
for r in range(1, len(bbox_idx_group)):
remove_idx_combinations.extend(list(itertools.combinations(bbox_idx_group, r=r)))
remove_idx_combinations_scores.extend(list(itertools.combinations(bbox_scores, r=r)))
for enu, combi_score in enumerate(remove_idx_combinations_scores):
remove_idx_combinations_scores[enu] = np.sum(combi_score)
if len(bbox_idx_group) > 2:
print(remove_idx_combinations)
print(remove_idx_combinations_scores)
print('')
remove_idx_combinations = [remove_idx_combinations[ind] for ind in np.argsort(remove_idx_combinations_scores)]
remove_idx_combinations_scores = [remove_idx_combinations_scores[ind] for ind in np.argsort(remove_idx_combinations_scores)]
print(remove_idx_combinations)
print(remove_idx_combinations_scores)
print('')
# time_overlap_pct, freq_overlap_pct = compute_time_frequency_overlap_for_bbox_group(bbox_idx_group, df_collect)
for remove_idx in remove_idx_combinations:
select_bbox_idx_group = list(set(bbox_idx_group) - set(remove_idx))
time_overlap_pct, freq_overlap_pct = (
compute_time_frequency_overlap_for_bbox_group(select_bbox_idx_group,df_collect))
if np.all(np.min([time_overlap_pct, freq_overlap_pct], axis=0) < overlap_th):
break
#embed()
#quit()
if len(remove_idx) > 0:
bbox_groups[np.array(remove_idx)] *= -1
handled_bbox_idxs.extend(bbox_idx_group)
return bbox_groups
def compute_time_frequency_overlap_for_bbox_group(bbox_idx_group, df_collect):
time_overlap_pct = np.zeros((len(bbox_idx_group), len(bbox_idx_group)))
freq_overlap_pct = np.zeros((len(bbox_idx_group), len(bbox_idx_group)))
for i, j in itertools.product(range(len(bbox_idx_group)), repeat=2):
if i == j:
continue
bb0_idx = bbox_idx_group[i]
bb1_idx = bbox_idx_group[j]
bb0_t0, bb0_t1 = df_collect[bb0_idx][1].astype(float), df_collect[bb0_idx][3].astype(float)
bb1_t0, bb1_t1 = df_collect[bb1_idx][1].astype(float), df_collect[bb1_idx][3].astype(float)
bb0_f0, bb0_f1 = df_collect[bb0_idx][2].astype(float), df_collect[bb0_idx][4].astype(float)
bb1_f0, bb1_f1 = df_collect[bb1_idx][2].astype(float), df_collect[bb1_idx][4].astype(float)
bb_times_idx = np.array([0, 0, 1, 1])
bb_times = np.array([bb0_t0, bb0_t1, bb1_t0, bb1_t1])
sorted_bb_times_idx = bb_times_idx[bb_times.argsort()]
if sorted_bb_times_idx[0] == sorted_bb_times_idx[1]:
time_overlap_pct[i, j] = 0
elif sorted_bb_times_idx[1] == sorted_bb_times_idx[2] == 0:
time_overlap_pct[i, j] = 1
elif sorted_bb_times_idx[1] == sorted_bb_times_idx[2] == 1:
time_overlap_pct[i, j] = (bb1_t1 - bb1_t0) / (bb0_t1 - bb0_t0)
else:
time_overlap_pct[i, j] = np.diff(sorted(bb_times)[1:3])[0] / ((bb0_t1 - bb0_t0))
bb_freqs_idx = np.array([0, 0, 1, 1])
bb_freqs = np.array([bb0_f0, bb0_f1, bb1_f0, bb1_f1])
sorted_bb_freqs_idx = bb_freqs_idx[bb_freqs.argsort()]
if sorted_bb_freqs_idx[0] == sorted_bb_freqs_idx[1]:
freq_overlap_pct[i, j] = 0
elif sorted_bb_freqs_idx[1] == sorted_bb_freqs_idx[2] == 0:
freq_overlap_pct[i, j] = 1
elif sorted_bb_freqs_idx[1] == sorted_bb_freqs_idx[2] == 1:
freq_overlap_pct[i, j] = (bb1_f1 - bb1_f0) / (bb0_f1 - bb0_f0)
else:
freq_overlap_pct[i, j] = np.diff(sorted(bb_freqs)[1:3])[0] / ((bb0_f1 - bb0_f0))
return time_overlap_pct, freq_overlap_pct
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Extract time, frequency and identity association of bboxes')
parser.add_argument('annotations', nargs='?', type=str, help='path to annotations')