inference.py now also writes bbox scores; extract_from_bbox.py detects overlapping bbox groups and eliminates them based on score untill all bboxes have overlap below th... detection of groups is done using time as parameter only. implement also frequency. then sort them as stated previously
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@ -1,3 +1,4 @@
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import itertools
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import pathlib
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import argparse
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import numpy as np
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@ -26,7 +27,7 @@ def bbox_to_data(img_path, t_min, t_max, f_min, f_max):
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annotations = np.array([annotations])
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if annotations.shape[1] == 0:
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print('no rises detected in this window')
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return []
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return [], []
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boxes = np.array([[x[1] - x[3] / 2, 1 - (x[2] + x[4] / 2), x[1] + x[3] / 2, 1 - (x[2] - x[4] / 2)] for x in annotations]) # x0, y0, x1, y1
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@ -36,7 +37,9 @@ def bbox_to_data(img_path, t_min, t_max, f_min, f_max):
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boxes[:, 1] = boxes[:, 1] * (f_max - f_min) + f_min
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boxes[:, 3] = boxes[:, 3] * (f_max - f_min) + f_min
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return boxes
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scores = annotations[:, -1]
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return boxes, scores
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def load_wavetracker_data(raw_path):
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fund_v = np.load(raw_path.parent / 'fund_v.npy')
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@ -47,7 +50,7 @@ def load_wavetracker_data(raw_path):
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return fund_v, ident_v, idx_v, times
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def assign_rises_to_ids(raw_path, time_frequency_bboxes):
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def assign_rises_to_ids(raw_path, time_frequency_bboxes, overlapping_boxes, bbox_groups):
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fund_v, ident_v, idx_v, times = load_wavetracker_data(raw_path)
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fig, ax = plt.subplots()
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@ -55,19 +58,75 @@ def assign_rises_to_ids(raw_path, time_frequency_bboxes):
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mask = time_frequency_bboxes['file_name'] == raw_path.parent.name
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for index, bbox in time_frequency_bboxes[mask].iterrows():
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name, t0, f0, t1, f1 = (bbox[0], *bbox[1:].astype(float))
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name, t0, f0, t1, f1 = (bbox[0], *bbox[1:-1].astype(float))
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if bbox_groups[index] == 0:
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color = 'tab:green'
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else:
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color = 'k'
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# if overlapping_boxes[index] == 0:
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# color='tab:green'
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# elif overlapping_boxes[index] == 1:
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# color = 'tab:olive'
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# elif overlapping_boxes[index] == 2:
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# color = 'tab:orange'
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# elif overlapping_boxes[index] == 3:
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# color = 'tab:red'
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# color = 'tab:green' if overlapping_boxes[index] == 0 else 'tab:orange'
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ax.add_patch(
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Rectangle((t0, f0),
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(t1 - t0),
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(f1 - f0),
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fill=False, color="tab:green", linestyle='--', linewidth=2, zorder=10)
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fill=False, color=color, linestyle='--', linewidth=2, zorder=10)
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)
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plt.show()
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# if np.any(overlapping_boxes[mask] >= 2):
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# print('yay')
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# embed()
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# quit()
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# ToDo: eliminate double rises -- overlap
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# ToDo: double detections -- non overlap --> the one with higher probability ?!
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# ToDo: assign rises to traces --> who is at lower right corner
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def find_overlapping_bboxes(df_collect):
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file_names = np.array(df_collect)[:, 0]
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bboxes_overlapping_mask = np.zeros(len(df_collect))
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bboxes = np.array(df_collect)[:, 1:].astype(float)
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overlap_bbox_idxs = []
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for file_name in tqdm(np.unique(file_names)):
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file_bbox_idxs = np.arange(len(file_names))[file_names == file_name]
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for ind0, ind1 in itertools.combinations(file_bbox_idxs, r=2):
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bb0 = bboxes[ind0]
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bb1 = bboxes[ind1]
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t0_0, f0_0, t0_1, f0_1 = bb0[:-1]
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t1_0, f1_0, t1_1, f1_1 = bb1[:-1]
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bb_times = np.array([t0_0, t0_1, t1_0, t1_1])
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bb_time_associate = np.array([0, 0, 1, 1])
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time_helper = bb_time_associate[np.argsort(bb_times)]
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if time_helper[0] == time_helper[1]:
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# no temporal overlap
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continue
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# check freq overlap
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bb_freqs = np.array([f0_0, f0_1, f1_0, f1_1])
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bb_freq_associate = np.array([0, 0, 1, 1])
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freq_helper = bb_freq_associate[np.argsort(bb_freqs)]
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if freq_helper[0] == freq_helper[1]:
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continue
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bboxes_overlapping_mask[ind0] +=1
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bboxes_overlapping_mask[ind1] +=1
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overlap_bbox_idxs.append((ind0, ind1))
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return bboxes_overlapping_mask, np.asarray(overlap_bbox_idxs)
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def main(args):
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img_paths = sorted(list(pathlib.Path(args.annotations).absolute().rglob('*.png')))
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df_collect = []
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@ -76,14 +135,20 @@ def main(args):
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# convert to time_frequency
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file_name_str, t_min, t_max, f_min, f_max = extract_time_freq_range_from_filename(img_path)
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boxes = bbox_to_data(img_path, t_min, t_max, f_min, f_max ) # t0, t1, f0, f1
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boxes, scores = bbox_to_data(img_path, t_min, t_max, f_min, f_max ) # t0, t1, f0, f1
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# store values in df
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if not len(boxes) == 0:
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for x0, y0, x1, y1 in boxes:
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df_collect.append([file_name_str, x0, y0, x1, y1])
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for (t0, f0, t1, f1), s in zip(boxes, scores):
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df_collect.append([file_name_str, t0, f0, t1, f1, s])
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df_collect = np.array(df_collect)
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time_frequency_bboxes = pd.DataFrame(data= np.array(df_collect), columns=['file_name', 't0', 'f0', 't1', 'f1'])
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bbox_overlapping_mask, overlap_bbox_idxs = find_overlapping_bboxes(df_collect)
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bbox_groups = delete_double_boxes(bbox_overlapping_mask, overlap_bbox_idxs, df_collect)
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# embed()
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# quit()
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time_frequency_bboxes = pd.DataFrame(data= np.array(df_collect), columns=['file_name', 't0', 'f0', 't1', 'f1', 'score'])
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if args.tracking_data_path:
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file_paths = sorted(list(pathlib.Path(args.tracking_data_path).absolute().rglob('*.raw')))
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@ -91,10 +156,78 @@ def main(args):
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if not raw_path.parent.name in time_frequency_bboxes['file_name'].to_list():
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continue
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assign_rises_to_ids(raw_path, time_frequency_bboxes)
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assign_rises_to_ids(raw_path, time_frequency_bboxes, bbox_overlapping_mask, bbox_groups)
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pass
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def delete_double_boxes(bbox_overlapping_mask, overlap_bbox_idxs, df_collect):
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def get_connected(non_regarded_bbox_idx, overlap_bbox_idxs):
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mask = np.array((np.array(overlap_bbox_idxs) == non_regarded_bbox_idx).sum(1), dtype=bool)
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affected_bbox_idxs = np.unique(overlap_bbox_idxs[mask])
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return affected_bbox_idxs
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handled_bbox_idxs = []
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bbox_groups = np.zeros(len(df_collect))
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for Coverlapping_bbox_idx in tqdm(np.unique(overlap_bbox_idxs)):
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if Coverlapping_bbox_idx in handled_bbox_idxs:
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continue
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# if bbox_overlapping_mask[Coverlapping_bbox_idx] >= 3:
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# pass
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# else:
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# continue
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regarded_bbox_idxs = [Coverlapping_bbox_idx]
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mask = np.array((np.array(overlap_bbox_idxs) == Coverlapping_bbox_idx).sum(1), dtype=bool)
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affected_bbox_idxs = np.unique(overlap_bbox_idxs[mask])
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non_regarded_bbox_idxs = list(set(affected_bbox_idxs) - set(regarded_bbox_idxs))
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# non_regarded_bbox_idxs = list(set(non_regarded_bbox_idxs) - set(handled_bbox_idxs))
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while len(non_regarded_bbox_idxs) > 0:
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non_regarded_bbox_idxs_cp = np.copy(non_regarded_bbox_idxs)
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for non_regarded_bbox_idx in non_regarded_bbox_idxs_cp:
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Caffected_bbox_idxs = get_connected(non_regarded_bbox_idx, overlap_bbox_idxs)
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affected_bbox_idxs = np.unique(np.append(affected_bbox_idxs, Caffected_bbox_idxs))
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regarded_bbox_idxs.append(non_regarded_bbox_idx)
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non_regarded_bbox_idxs = list(set(affected_bbox_idxs) - set(regarded_bbox_idxs))
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bbox_idx_group = regarded_bbox_idxs
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bbox_groups[bbox_idx_group] = np.max(bbox_groups) + 1
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# bbox_scores = df_collect[bbox_idx_group][:, -1]
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# overlap_pct = np.full((len(bbox_idx_group), len(bbox_idx_group)), np.nan)
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#
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# for i, j in itertools.product(range(len(bbox_idx_group)), repeat=2):
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# if i == j:
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# continue
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# bb0_idx = bbox_idx_group[i]
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# bb1_idx = bbox_idx_group[j]
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#
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# bb0_t0, bb0_t1 = df_collect[bb0_idx][1].astype(float), df_collect[bb0_idx][3].astype(float)
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# bb1_t0, bb1_t1 = df_collect[bb1_idx][1].astype(float), df_collect[bb1_idx][3].astype(float)
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#
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# helper = np.array([0, 0, 1, 1])
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# bb_times = np.array([bb0_t0, bb0_t1, bb1_t0, bb1_t1])
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#
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# sorted_helper = helper[bb_times.argsort()]
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#
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# if sorted_helper[0] == sorted_helper[1]:
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# continue
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#
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# elif sorted_helper[1] == sorted_helper[2] == 0:
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# overlap_pct[i, j] = 1
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#
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# elif sorted_helper[1] == sorted_helper[2] == 1:
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# overlap_pct[i, j] = (bb1_t1 - bb1_t0) / (bb0_t1 - bb0_t0)
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#
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# else:
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# overlap_pct[i, j] = np.diff(sorted(bb_times)[1:3])[0] / ((bb0_t1 - bb0_t0))
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# embed()
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# quit()
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handled_bbox_idxs.extend(bbox_idx_group)
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return bbox_groups
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Extract time, frequency and identity association of bboxes')
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parser.add_argument('annotations', nargs='?', type=str, help='path to annotations')
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@ -73,7 +73,7 @@ def infere_model(inference_loader, model, dataset_name, detection_th=0.8):
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rel_width = rel_x1 - rel_x0
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rel_height = rel_y1 - rel_y0
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yolo_labels.append([1, rel_x_center, rel_y_center, rel_width, rel_height])
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yolo_labels.append([1, rel_x_center, rel_y_center, rel_width, rel_height, score])
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label_path = Path('data') / dataset_name / 'labels' / Path(img_name).with_suffix('.txt')
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np.savetxt(label_path, yolo_labels)
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