== 0] = (0, 0, 0)
# ori_imgs[index,...]*=np.tile(np.expand_dims(pred_vessels[index,...], axis=3), (1,1,3))
Image.fromarray(ori_imgs[index, ...].astype(
np.uint8)).save(
os.path.join(vessels_dir,
os.path.basename(filenames[index])))
# compare with the ground truth
comp_dir = comparison_out.format(os.path.basename(dataset),
os.path.basename(result))
if not os.path.isdir(comp_dir):
os.makedirs(comp_dir)
dice_list = []
for index in range(gt_vessels.shape[0]):
diff_map, dice_coeff = utils.difference_map(
gt_vessels[index, ...], pred_vessels[index, ...],
masks[index, ...])
dice_list.append(dice_coeff)
Image.fromarray(diff_map.astype(np.uint8)).save(
os.path.join(comp_dir,
os.path.basename(filenames[index])))
# print "indices of best dice coeff : {}".format(sorted(range(len(dice_list)),key=lambda k: dice_list[k]))
# skip the ground truth
if "1st_manual" not in result:
# print metrics
print "-- {} --".format(os.path.basename(result))
print "dice coefficient : {}".format(
utils.dice_coefficient(gt_vessels, pred_vessels, masks))
print "f1 score : {}, accuracy : {}, sensitivity : {}, specificity : {}".format(
*utils.misc_measures_evaluation(gt_vessels, pred_vessels,
def save_imgs(slice_list,
save_folder,
methods,
img_size,
crop_size=(100, 80),
factor=1.5):
# initialize CT and MR folders
ct_save_folder = os.path.join(save_folder, 'raw_ct')
if not os.path.exists(ct_save_folder):
os.makedirs(ct_save_folder)
mr_save_folder = os.path.join(save_folder, 'raw_mr')
if not os.path.exists(mr_save_folder):
os.makedirs(mr_save_folder)
# Save image
for idx in range(args.number):
canvas, info_height, margin = None, None, None
if args.add_value:
font_type = cv2.FONT_HERSHEY_TRIPLEX
font_scale = 1.
font_thickness = 2
font_color = (0, 0, 0)
# Calculate height and width of the text information
info_str = '{} {:.3f}'.format(args.measure.upper(),
slice_list[idx].measurement)
info_size = cv2.getTextSize(info_str, font_type, font_scale,
font_thickness)
info_height, info_width = info_size[0][1], info_size[0][0]
margin = int(0.2 * info_height)
bottom_left = (
int((len(methods) + 2) * img_size[1] * 0.5 - info_width * 0.5),
int(img_size[0] + int(factor * crop_size[0]) + info_height +
0.5 * margin)) # row and col
canvas = 255 * np.ones(
(img_size[0] + int(factor * crop_size[0]) + info_height +
margin, (len(methods) + 2) * img_size[1], 3),
dtype=np.uint8)
# Add text information
cv2.putText(canvas,
info_str,
bottom_left,
font_type,
font_scale,
font_color,
thickness=font_thickness)
else:
canvas = 255 * np.ones((img_size[0] + int(factor * crop_size[0]),
(len(methods) + 2) * img_size[1], 3),
dtype=np.uint8)
img_name = slice_list[idx].name
ct_img = cv2.imread(os.path.join('../ct',
img_name)) # read 3 channel data
mri_img = cv2.imread(os.path.join('../gt',
img_name)) # read 3 channel data
canvas[:img_size[0], :img_size[1], :] = ct_img # save ct img
# save mri img
mri_img_rec, mri_crop = utils.central_crop(mri_img.copy(),
factor=factor)
canvas[:img_size[0],
(len(methods) + 1) * img_size[1]:, :] = mri_img_rec
canvas[img_size[0]:img_size[0] + int(factor * crop_size[0]),
-int(factor * crop_size[1]):, :] = mri_crop
for idx_method, method_name in enumerate(methods):
# Read 3 channel data
img = cv2.imread(
os.path.join('../{}'.format(method_name), img_name))
# calculate difference map
utils.difference_map(img, mri_img, save_folder, img_name,
method_name)
img, crop_img = utils.central_crop(img, factor=factor)
if args.add_value:
canvas[:-int(factor * crop_size[0]) - info_height - margin,
(idx_method + 1) * img_size[1]:(idx_method + 2) *
img_size[1], :] = img
canvas[img_size[0]:-info_height - margin,
(idx_method + 2) * img_size[1] -
int(factor * crop_size[1]):(idx_method + 2) *
img_size[1], :] = crop_img
else:
canvas[:img_size[0], (idx_method + 1) *
img_size[1]:(idx_method + 2) * img_size[1], :] = img
canvas[img_size[0]:, (idx_method + 2) * img_size[1] -
int(factor * crop_size[1]):(idx_method + 2) *
img_size[1], :] = crop_img
cv2.imwrite(os.path.join(save_folder, img_name), canvas)
cv2.imwrite(os.path.join(ct_save_folder, img_name), ct_img)
cv2.imwrite(os.path.join(mr_save_folder, img_name), mri_img)