fn = dataset.get_img_path(cnt)
dname = os.path.dirname(fn)
fn = os.path.basename(os.path.splitext(fn)[0])
print("\nProcessing {}".format(fn))
new = dataset.var2img(out[i])
print("raw value: {} {}".format(np.min(out[i]), np.max(out[i])))
#print(new.shape)
h, w = dataset.crop
# converted image
if args.imgtype == "dcm":
if dname != prevdir:
salt = str(random.randint(1000, 999999))
prevdir = dname
for j in range(args.num_slices):
ref_dicom = dataset.overwrite(new[j], cnt, salt)
path = os.path.join(
outdir, '{:s}_{}_{}.dcm'.format(fn, args.suffix, j))
ref_dicom.save_as(path)
else:
path = os.path.join(outdir,
'{:s}_{}.jpg'.format(fn, args.suffix))
write_image(new, path)
## images for analysis
if args.output_analysis:
# original
path = os.path.join(outdir, '{:s}_0orig.png'.format(fn))
write_image((imgs[i] * 127.5 + 127.5).astype(np.uint8), path)
# cycle
path = os.path.join(outdir, '{:s}_1cycle.png'.format(fn))
out_v = gen(imgs)
if args.gpu >= 0:
imgs = xp.asnumpy(imgs.data)
out = xp.asnumpy(out_v.data)
else:
imgs = imgs.data
out = out_v.data
## output images
for i in range(len(out)):
fn = dataset.get_img_path(cnt)
print("\nProcessing {}".format(fn))
new = dataset.var2img(out[i])
print("raw value: {} {}".format(np.min(out[i]),np.max(out[i])))
path = os.path.join(outdir,os.path.basename(fn))
# converted image
if args.imgtype=="dcm":
ref_dicom = dataset.overwrite(new[0],fn,salt)
ref_dicom.save_as(path)
else:
write_image(new, path)
cnt += 1
####
elapsed_time = time.time() - start
print ("{} images in {} sec".format(cnt,elapsed_time))