#for key in rd.res_dirs_dict_ksp.keys(): # Types
for key in rd.types: # Types
ims.append([])
ksp.append([])
vilar.append([])
g2s.append([])
mic17.append([])
wtp.append([])
dsets_to_plot = np.asarray(rd.best_dict_ksp[key][0:n_sets_per_type])
for dset, gset in zip(dsets_to_plot[:, 0], dsets_to_plot[:, 1]):
confs = [rd.confs_dict_ksp[key][dset][g] for g in range(5)]
# Load config
dataset = learning_dataset.LearningDataset(confs[0])
gt = dataset.gt
f = rd.self_frames_dict[key][dset]
# Image
cont_gt = segmentation.find_boundaries(gt[..., f], mode='thick')
idx_cont_gt = np.where(cont_gt)
im = utls.imread(confs[0].frameFileNames[f])
im[idx_cont_gt[0], idx_cont_gt[1], :] = (255, 0, 0)
locs2d = csv.readCsv(
os.path.join(confs[0].root_path, confs[0].ds_dir,
confs[0].locs_dir, confs[0].csvFileName_fg))
im = csv.draw2DPoint(locs2d, f, im, radius=7)
ims[-1].append(im)
print(dir_)
# Get h5 file
path_ = os.path.join(rd.root_dir,
dir_)
# Get config
conf = cfg.load_and_convert(os.path.join(path_, 'cfg.yml'))
conf.precomp_desc_path = adjust_path(rd.root_dir,
conf.precomp_desc_path)
conf.frameFileNames = [adjust_path(rd.root_dir, f) for f in conf.frameFileNames]
conf.root_path = rd.root_dir
conf.dataOutDir = adjust_path(rd.root_dir, conf.dataOutDir)
l_dataset = learning_dataset.LearningDataset(conf, pos_thr=0.5)
gt = l_dataset.gt
file_ = os.path.join(path_,
'nn_objectness_g1',
'predictions.h5')
f = h5py.File(file_, 'r')
a_group_key = list(f.keys())[0]
preds = np.squeeze(np.asarray(list(f[a_group_key])))
preds = preds.transpose((1,2,0))
gt = l_dataset.gt
if(gt[...,0].shape != preds[...,0].shape):
print('Resizing preds...')
out_shape = gt[...,0].shape
preds = np.asarray([resize(preds[...,i], out_shape) for i in range(preds.shape[-1])]).transpose((1,2,0))
pr, rc, _ = precision_recall_curve(gt.ravel(),
"""
Calculate F1 score for ratio of positive pixels in superpixels
"""
save_path = os.path.join(rd.root_dir, 'plots_results', 'sp_thr_f1s.npz')
ratios = [0.25, 0.5, 0.75, 1.0]
res = dict()
# Self-learning
for key in rd.types:
res[key] = dict()
for seq in rd.res_dirs_dict_ksp[key][0]:
f1s = list()
cfg_ = cfg.load_and_convert(os.path.join(rd.root_dir,
seq,
'cfg.yml'))
dset = learning_dataset.LearningDataset(cfg_)
gt = dset.gt
sp_gt = dset.make_y_map_true(gt)
for r in ratios:
f1 = f1_score(gt.ravel(), (sp_gt >= r).ravel())
f1s.append(f1)
res[key][cfg_.ds_dir] = f1s
data = {'res': res, 'ratios': ratios}
np.savez(save_path, **data)
for key in rd.types:
dsets_to_plot = np.asarray(rd.best_dict_ksp[key][0:n_sets_per_type])
for dset, gset in zip(dsets_to_plot[:, 0], dsets_to_plot[:, 1]):
path_out = os.path.join(rd.root_dir, 'plots_results',
(key + '_{}_{}').format(ind, dset))
ims = []
# Make images/gts/gaze-point
confs = rd.confs_dict_ksp[key][dset]
conf = confs[0]
dataset = ld.LearningDataset(conf)
gt = dataset.gt
ksp_mat = np.load(
os.path.join(rd.root_dir, rd.res_dirs_dict_ksp[key][dset][gset],
'results.npz'))['ksp_scores_mat']
for f in range(len(conf.frameFileNames)):
cont_gt = segmentation.find_boundaries(gt[..., f], mode='thick')
idx_cont_gt = np.where(cont_gt)
im = utls.imread(conf.frameFileNames[f])
im[idx_cont_gt[0], idx_cont_gt[1], :] = (255, 0, 0)
myGaze_fg = utls.readCsv(
os.path.join(conf.root_path, conf.ds_dir, conf.locs_dir,
conf.csvFileName_fg))
