def test(name, ckpt, image_pack_name=None, output_filename=None):
external_image = image_pack_name.index('.') != -1
if image_pack_name is None:
data = None
elif not external_image:
data = load_data(image_pack_name.split(','))
with get_session() as sess:
fcn = FCN(sess=sess, name=name)
if ckpt != "-1":
fcn.load(ckpt)
else:
fcn.load_absolute(name)
if not external_image:
errors, _, _, _, ret, conf = fcn.test(scales=[0.5],
summary=True,
summary_key=123,
data=data,
eval_speed=False,
visualize=True)
if output_filename is not None:
with open('outputs/%s.pkl' % output_filename, 'wb') as f:
pickle.dump(ret, f)
with open('outputs/%s_err.pkl' % output_filename, 'wb') as f:
pickle.dump(errors, f)
with open('outputs/%s_conf.pkl' % output_filename, 'wb') as f:
pickle.dump(conf, f)
print ret
print 'results dumped'
else:
img = cv2.imread(image_pack_name)
# reverse gamma correction for sRGB
img = (img / 255.0)**2.2 * 16384
images = [img]
fcn.test_external(images=images, fns=[image_pack_name])
if __name__ == '__main__':
target_dir = sys.argv[1]
if len(sys.argv) > 2:
start_ind = int(sys.argv[2])
else:
start_ind = 0
image_dir = osp.join(target_dir, "*")
print(image_dir)
imgpaths = glob(image_dir)[start_ind:]
print(len(imgpaths))
save_dir = osp.join(osp.dirname(target_dir),
osp.basename(target_dir) + "_colorcorrected")
print(save_dir)
ckpt = "pretrained/colorchecker_fold1and2.ckpt"
with get_session() as sess:
fcn = FCN(sess=sess, name=ckpt)
fcn.load_absolute(ckpt)
errors = test_external(fcn,
imgpaths=imgpaths,
save_prefix=save_dir,
show=False,
write_compare=False)
with open(osp.join(target_dir, "errored_images.txt"), "w") as f:
for p in errors:
f.write(p)
