def test_shape_modeling(self, dir_annot=PATH_ANNOT):
""" """
list_paths = sorted(glob.glob(os.path.join(dir_annot, '*.png')))
logging.info('nb images: %i SAMPLES: %s', len(list_paths),
repr([os.path.basename(p) for p in list_paths[:5]]))
list_segms = []
for path_seg in list_paths:
seg, _ = tl_data.load_image_2d(path_seg)
list_segms.append(seg)
list_rays, _ = seg_rg.compute_object_shapes(list_segms,
ray_step=25,
smooth_coef=1,
interp_order='spline')
logging.info('nb eggs: %i nb rays: %i', len(list_rays),
len(list_rays[0]))
model, list_mean_cdf = seg_rg.transform_rays_model_sets_mean_cdf_mixture(
list_rays, 2)
np.savez(PATH_PKL_MODEL,
data={
'name': 'set_cdfs',
'cdfs': list_mean_cdf,
'mix_model': model
})
# with open(PATH_PKL_MODEL, 'w') as fp:
# pickle.dump({'name': 'set_cdfs',
# 'cdfs': list_mean_cdf,
# 'mix_model': model}, fp)
self.assertTrue(os.path.exists(PATH_PKL_MODEL))
max_len = max(
[np.asarray(l_cdf).shape[1] for _, l_cdf in list_mean_cdf])
fig, axarr = plt.subplots(nrows=len(list_mean_cdf),
ncols=2,
figsize=(12, 3.5 * len(list_mean_cdf)))
for i, (_, list_cdf) in enumerate(list_mean_cdf):
cdist = np.zeros((len(list_cdf), max_len))
cdist[:, :len(list_cdf[0])] = np.array(list_cdf)
axarr[i, 0].set_title('Inverse cumulative distribution')
axarr[i, 0].imshow(cdist, aspect='auto')
axarr[i, 0].set_xlim([0, max_len])
axarr[i, 0].set_ylabel('Ray steps')
axarr[i, 0].set_xlabel('Distance [px]')
axarr[i, 1].set_title('Reconstructions')
axarr[i, 1].imshow(compute_prior_map(cdist, step=10))
fig.savefig(os.path.join(PATH_OUTPUT, 'RG2Sp_shape-modeling.pdf'),
bbox_inches='tight',
pad_inches=0)
def main(path_annot, path_out, nb_comp=5):
list_paths = sorted(glob.glob(path_annot))
print('nb images:', len(list_paths), 'SAMPLES:',
[os.path.basename(p) for p in list_paths[:5]])
list_segms = []
for path_seg in list_paths:
seg = tl_data.io_imread(path_seg)
list_segms.append(seg)
list_rays, _ = tl_rg.compute_object_shapes(list_segms,
ray_step=RAY_STEP,
interp_order='spline',
smooth_coef=1)
logging.info('nb eggs: %i, nb rays: %i', len(list_rays), len(list_rays[0]))
x_axis = np.linspace(0, 360, len(list_rays[0]), endpoint=False)
df = pd.DataFrame(np.array(list_rays), columns=x_axis.astype(int))
path_csv = os.path.join(path_out, NAME_CSV_RAY_ALL)
logging.info('exporting all Rays: %s', path_csv)
df.to_csv(path_csv)
# SINGLE MODEL
model, list_cdf = tl_rg.transform_rays_model_cdf_mixture(list_rays, 1)
cdf = np.array(np.array(list_cdf))
# path_model = os.path.join(path_out, NAME_NPZ_MODEL_SINGLE)
# logging.info('exporting model: %s', path_model)
# np.savez(path_model, name='cdf', cdfs=cdf, mix_model=model)
path_model = os.path.join(path_out, NAME_PKL_MODEL_SINGLE)
logging.info('exporting model: %s', path_model)
with open(path_model, 'wb') as fp:
pickle.dump({'name': 'cdf', 'cdfs': cdf, 'mix_model': model}, fp)
# MIXTURE MODEL
model, list_mean_cdf = tl_rg.transform_rays_model_sets_mean_cdf_mixture(
list_rays, nb_comp)
# path_model = os.path.join(path_out, NAME_NPZ_MODEL_MIXTURE)
# logging.info('exporting model: %s', path_model)
# np.savez(path_model, name='set_cdfs', cdfs=list_mean_cdf,
# mix_model=model)
path_model = os.path.join(path_out, NAME_PKL_MODEL_MIXTURE)
logging.info('exporting model: %s', path_model)
with open(path_model, 'wb') as fp:
pickle.dump(
{
'name': 'set_cdfs',
'cdfs': list_mean_cdf,
'mix_model': model
}, fp)
logging.info('Done')
