for fb_noext, fig in zip(ffiles, figs):
for ext in FIGS_EXTENSIONS:
if ext == 'pdf':
pass
fig.savefig(pp, format='pdf')
else:
if mode == 'infer':
noext = fb_noext + '-infer'
else:
noext = fb_noext + '-train'
fig.savefig(
fname=os.path.join(dirs['figures'], noext + "." + ext),
transparent=TRANSPARENT,
)
pass
if pp:
pp.close()
if 'show' in FIGS_SAVESHOW:
plt.show()
#FIGS_ESXTENSIONS
qsf.evaluateAllResults(result_files=files['result'],
absolute_disparity=ABSOLUTE_DISPARITY,
cluster_radius=CLUSTER_RADIUS)
print("All done")
exit(0)
if TEST_TITLES is None:
TEST_TITLES = qsf.defaultTestTitles(files)
partials = None
partials = qsf.concentricSquares(CLUSTER_RADIUS)
PARTIALS_WEIGHTS = [
1.0 * pw / sum(PARTIALS_WEIGHTS) for pw in PARTIALS_WEIGHTS
]
if not USE_PARTIALS:
partials = partials[0:1]
PARTIALS_WEIGHTS = [1.0]
qsf.evaluateAllResults(result_files=files['result'],
absolute_disparity=ABSOLUTE_DISPARITY,
cluster_radius=CLUSTER_RADIUS,
labels=SLICE_LABELS,
logpath=LOGPATH)
image_data = qsf.initImageData(files=files,
max_imgs=MAX_IMGS_IN_MEM,
cluster_radius=CLUSTER_RADIUS,
tile_layers=TILE_LAYERS,
tile_side=TILE_SIDE,
width=IMG_WIDTH,
replace_nans=True)
corr2d_len, target_disparity_len, _ = qsf.get_lengths(CLUSTER_RADIUS,
TILE_LAYERS, TILE_SIDE)
train_next, dataset_train, datasets_test = qsf.initTrainTestData(