def main(cfg):
data = dict()
out_path = pjoin(cfg.out_path, '{}'.format(cfg.exp_name),
'ksp_segmentation')
if (os.path.exists(out_path)):
print('ouput path {} already exists.'.format(out_path))
# return cfg
else:
os.makedirs(out_path)
# Set logger
print('-' * 10)
print('starting segmentation on: {}'.format(cfg.in_path))
print('2d locs filename: {}'.format(cfg.locs_fname))
print('Output path: {}'.format(out_path))
print('-' * 10)
precomp_desc_path = pjoin(cfg.in_path, cfg.precomp_dir)
if (not os.path.exists(precomp_desc_path)):
os.makedirs(precomp_desc_path)
with open(pjoin(out_path, 'cfg.yml'), 'w') as outfile:
yaml.dump(cfg.__dict__, stream=outfile, default_flow_style=False)
# ---------- Descriptors/superpixel costs
spext = SuperpixelExtractor(cfg.in_path, cfg.precomp_dir,
cfg.slic_compactness, cfg.slic_n_sp)
spext.run()
link_agent, desc_df = make_link_agent(cfg)
print('Building superpixel managers')
sps_man = spm.SuperpixelManager(cfg.in_path, cfg.precomp_dir,
link_agent.labels, desc_df,
cfg.init_radius)
print('Using foreground model from model')
pm = utls.probas_to_df(link_agent.labels, link_agent.obj_preds)
g_for = gtrack.GraphTracking(link_agent, sps_man=sps_man)
g_back = gtrack.GraphTracking(link_agent, sps_man=sps_man)
find_new_forward = True
find_new_backward = True
i = 0
pos_sp_for = []
pos_sp_back = []
list_ksp = []
pos_tls_for = None
pos_tls_back = None
dict_ksp = dict()
# make forward and backward graphs
g_back.make_graph(desc_df,
pm,
cfg.pm_thr,
cfg.norm_neighbor,
direction='backward',
labels=link_agent.labels)
g_for.make_graph(desc_df,
pm,
cfg.pm_thr,
cfg.norm_neighbor,
direction='forward',
labels=link_agent.labels)
print("Computing KSP on backward graph.")
sps = g_back.run()
dict_ksp['backward_sets'] = sps
print("Computing KSP on forward graph.")
sps = g_for.run()
dict_ksp['forward_sets'] = sps
all_sps = list(set(dict_ksp['forward_sets'] + dict_ksp['backward_sets']))
print('got ', len(all_sps), ' unique superpixels')
# Saving
fileOut = pjoin(out_path, 'results.npz')
data = dict()
data['ksp_scores_mat'] = utls.get_binary_array(link_agent.labels,
np.array(all_sps))
data['pm_scores_mat'] = utls.get_pm_array(link_agent.labels, pm)
data['paths_back'] = dict_ksp['backward_sets']
data['paths_for'] = dict_ksp['forward_sets']
data['all_sps'] = all_sps
print("Saving results and cfg to: " + fileOut)
np.savez(fileOut, **data)
print('Finished experiment: ', out_path)
write_frames_results.main(cfg, out_path)
comp_scores.main(cfg, out_path)
return cfg
def main(cfg):
data = dict()
d = datetime.datetime.now()
cfg.run_dir = pjoin(cfg.out_path, '{}'.format(cfg.exp_name))
if (os.path.exists(cfg.run_dir)):
print('run dir {} already exists.'.format(cfg.run_dir))
return cfg
else:
os.makedirs(cfg.run_dir)
# Set logger
utls.setup_logging(cfg.run_dir)
logger = logging.getLogger('ksp')
logger.info('-' * 10)
logger.info('starting experiment on: {}'.format(cfg.in_path))
logger.info('2d locs filename: {}'.format(cfg.csv_fname))
logger.info('Output path: {}'.format(cfg.run_dir))
logger.info('-' * 10)
precomp_desc_path = pjoin(cfg.in_path, cfg.precomp_dir)
if (not os.path.exists(precomp_desc_path)):
os.makedirs(precomp_desc_path)
with open(pjoin(cfg.run_dir, 'cfg.yml'), 'w') as outfile:
yaml.dump(cfg.__dict__, stream=outfile, default_flow_style=False)
# ---------- Descriptors/superpixel costs
dm = DataManager(cfg.in_path, cfg.precomp_dir, feats_mode=cfg.feats_mode)
dm.calc_superpix(cfg.slic_compactness, cfg.slic_n_sp)
link_agent, desc_df = make_link_agent(cfg)
logger.info('Building superpixel managers')
sps_man = spm.SuperpixelManager(cfg.in_path,
cfg.precomp_dir,
link_agent.labels,
desc_df,
init_radius=cfg.sp_trans_init_radius,
hoof_n_bins=cfg.hoof_n_bins)
locs2d_sps = link_agent.get_all_entrance_sps(desc_df)
desc_df['positive'] = locs2d_sps
if (cfg.use_siam_pred):
pm = utls.probas_to_df(link_agent.labels, link_agent.obj_preds)
else:
pm = utls.calc_pm(desc_df, desc_df['positive'], cfg.bag_n_feats,
cfg.bag_t, cfg.bag_max_depth, cfg.bag_max_samples,
cfg.bag_jobs)
ksp_scores_mat = []
g_for = gtrack.GraphTracking(link_agent, sps_man=sps_man)
g_back = gtrack.GraphTracking(link_agent, sps_man=sps_man)
find_new_forward = True
find_new_backward = True
i = 0
pos_sp_for = []
pos_sp_back = []
list_ksp = []
pos_tls_for = None
pos_tls_back = None
dict_ksp = dict()
while ((find_new_forward or find_new_backward) and (i < cfg.n_iters_ksp)):
logger.info("i: " + str(i + 1))
if ((i > 0) & find_new_forward):
g_for.merge_tracklets_temporally(pos_tls_for, pm, desc_df,
cfg.pm_thr)
if ((i > 0) & find_new_backward):
g_back.merge_tracklets_temporally(pos_tls_back, pm, desc_df,
cfg.pm_thr)
# Make backward graph
if (find_new_backward):
g_back.makeFullGraph(desc_df,
pm,
cfg.pm_thr,
cfg.hoof_tau_u,
cfg.norm_neighbor,
direction='backward',
labels=link_agent.labels)
logger.info("Computing KSP on backward graph. (i: {}".format(i +
1))
g_back.run()
dict_ksp['backward_sets'], pos_tls_back = utls.ksp2sps(
g_back.kspSet, g_back.tracklets)
dict_ksp['backward_tracklets'] = g_back.tracklets
dict_ksp['backward_costs'] = g_back.costs
# Make forward graph
if (find_new_forward):
g_for.makeFullGraph(desc_df,
pm,
cfg.pm_thr,
cfg.hoof_tau_u,
cfg.norm_neighbor,
direction='forward',
labels=link_agent.labels)
logger.info("Computing KSP on forward graph. (i: {})".format(i +
1))
g_for.run()
dict_ksp['forward_sets'], pos_tls_for = utls.ksp2sps(
g_for.kspSet, g_for.tracklets)
dict_ksp['forward_tracklets'] = g_for.tracklets
if ((find_new_forward or find_new_backward)):
ksp_scores_mat = utls.sp_tuples_to_mat(
dict_ksp['forward_sets'] + dict_ksp['backward_sets'],
link_agent.labels)
# Update marked superpixels if graph is not "finished"
if (find_new_forward):
marked_for = [
m for sublist in dict_ksp['forward_sets'] for m in sublist
]
pos_sp_for.append(len(marked_for))
logger.info("""Forward graph. Number of positive sps
of ksp at iteration {}: {}""".format(
i + 1, len(marked_for)))
if (i > 0):
if (pos_sp_for[-1] <= pos_sp_for[-2]):
find_new_forward = False
if (find_new_backward):
marked_back = [
m for sublist in dict_ksp['backward_sets'] for m in sublist
]
pos_sp_back.append(len(marked_back))
logger.info("""Backward graph. Number of positive sps of
ksp at iteration {}: {} """.format(
i + 1, len(marked_back)))
if (i > 0):
if (pos_sp_back[-1] <= pos_sp_back[-2]):
find_new_backward = False
list_ksp.append(dict_ksp)
n_pix_ksp = np.sum((ksp_scores_mat > 0).ravel())
logger.info("""Number hit pixels of ksp at iteration {}:
{}""".format(i + 1, n_pix_ksp))
fileOut = pjoin(cfg.run_dir, 'pm_scores_iter_{}.npz'.format(i))
data = dict()
data['ksp_scores_mat'] = ksp_scores_mat
np.savez(fileOut, **data)
# Recompute PM values
if (i + 1 < cfg.n_iters_ksp):
desc_df = merge_positives(desc_df, marked_for, marked_back)
pm = utls.calc_pm(desc_df, desc_df['positive'],
cfg.bag_n_feats, cfg.bag_t,
cfg.bag_max_depth, cfg.bag_max_samples,
cfg.bag_jobs)
i += 1
# Saving
fileOut = pjoin(cfg.run_dir, 'results.npz')
data = dict()
data['n_iters_ksp'] = cfg.n_iters_ksp
data['ksp_scores_mat'] = ksp_scores_mat
data['pm_scores_mat'] = utls.get_pm_array(link_agent.labels, pm)
data['paths_back'] = dict_ksp['backward_sets']
data['paths_for'] = dict_ksp['forward_sets']
logger.info("Saving results and cfg to: " + fileOut)
np.savez(fileOut, **data)
logger.info("done")
logger.info('Finished experiment: ' + cfg.run_dir)
write_frames_results.main(cfg, cfg.run_dir, logger)
comp_scores.main(cfg)
return cfg
]
# all_datasets = [
# 'Dataset12', 'Dataset13'
# ]
# all_datasets = [
# 'Dataset21', 'Dataset22', 'Dataset23', 'Dataset24', 'Dataset25'
# ]
# all_datasets = [
# 'Dataset32', 'Dataset33', 'Dataset34', 'Dataset35'
# ]
all_confs = []
confs_fold = []
# Run KSP on all seqs with first gaze-set and make prediction experiment
for i in range(len(all_datasets)):
set_confs = []
extra_cfg['ds_dir'] = all_datasets[i]
print("dset: " + all_datasets[i])
for k in [1]:
#for k in ['_missing']:
#for k in [1, 2, 3, 4, 5]:
extra_cfg['csvFileName_fg'] = 'video' + str(k) + '.csv'
conf, logger = iterative_ksp.main(extra_cfg)
writef.main(conf, logger=logger)
#conf = test_trans_costs.main(extra_cfg)
import os
from ksptrack import iterative_ksp
import numpy as np
import datetime
import numpy as np
import matplotlib.pyplot as plt
from ksptrack.utils import write_frames_results as writef
from ksptrack.cfgs import cfg
dir_ = [
'/home/laurent.lejeune/medical-labeling/Dataset10/results/2017-12-06_14-26-17_exp',
'/home/krakapwa/otlshare/laurent.lejeune/medical-labeling/Dataset11/results/2017-12-06_16-16-27_exp',
'/home/krakapwa/otlshare/laurent.lejeune/medical-labeling/Dataset12/results/2017-12-06_17-28-07_exp',
'/home/krakapwa/otlshare/laurent.lejeune/medical-labeling/Dataset13/results/2017-12-06_20-50-10_exp'
]
for d in dir_:
conf_path = os.path.join(d, 'cfg.yml')
conf = cfg.load_and_convert(conf_path)
conf.dataOutImageResultDir = 'results'
# Write result frames
writef.main(conf)