def main(gt, method):
# read gt image addresses
gt_names = utils.all_files_under(os.path.join('../', gt), extension='.jpg')
# read prediction image addresses
filenames = utils.all_files_under(os.path.join('../', method),
extension='.jpg')
# print(methods[idx_method])
mae_method, rmse_method, psnr_method, ssim_method, pcc_method = [], [], [], [], []
for idx_name in range(len(gt_names)):
# read gt and prediction image
gt_img = cv2.imread(gt_names[idx_name],
cv2.IMREAD_GRAYSCALE).astype(np.float32)
pred_img = cv2.imread(filenames[idx_name],
cv2.IMREAD_GRAYSCALE).astype(np.float32)
# check gt and prediction image name
if gt_names[idx_name].split('p0')[-1] != filenames[idx_name].split(
'p0')[-1]:
sys.exit(' [!] Image name can not match!')
# calcualte mae and psnr
mae = utils.mean_absoulute_error(pred_img, gt_img)
rmse = utils.root_mean_square_error(pred_img, gt_img)
psnr = utils.peak_signal_to_noise_ratio(pred_img, gt_img)
ssim = utils.structural_similarity_index(pred_img, gt_img)
pcc = utils.pearson_correlation_coefficient(pred_img, gt_img)
if np.mod(idx_name, 300) == 0:
print('Method: {}, idx: {}'.format(method, idx_name))
# collect each image results
mae_method.append(mae)
rmse_method.append(rmse)
psnr_method.append(psnr)
ssim_method.append(ssim)
pcc_method.append(pcc)
# list to np.array
mae_method = np.asarray(mae_method)
rmse_method = np.asarray(rmse_method)
psnr_method = np.asarray(psnr_method)
ssim_method = np.asarray(ssim_method)
pcc_method = np.asarray(pcc_method)
print(' MAE - mean: {:.3f}, std: {:.3f}'.format(np.mean(mae_method),
np.std(mae_method)))
print('RMSE - mean: {:.3f}, std: {:.3f}'.format(np.mean(rmse_method),
np.std(rmse_method)))
print('PSNR - mean: {:.3f}, std: {:.3f}'.format(np.mean(psnr_method),
np.std(psnr_method)))
print('SSIM - mean: {:.3f}, std: {:.3f}'.format(np.mean(ssim_method),
np.std(ssim_method)))
print(' PCC - mean: {:.3f}, std: {:.3f}'.format(np.mean(pcc_method),
np.std(pcc_method)))
data_list = [mae_method, rmse_method, psnr_method, ssim_method, pcc_method]
write_to_csv(method, data_list, gt_names)
def read_imgs_and_measurement():
slice_list = []
# read gt image addresses
gt_names = utils.all_files_under('../gt', extension='.jpg')
for idx in range(len(gt_names)):
if np.mod(idx, 300) == 0:
print('Method: {}, Measure: {}, idx: {}'.format(
args.method, args.measure, idx))
img_name = os.path.basename(gt_names[idx])
# read gt and prediction image
gt_img = cv2.imread(gt_names[idx],
cv2.IMREAD_GRAYSCALE).astype(np.float32)
pred_img = cv2.imread(
os.path.join('../{}'.format(args.method), img_name),
cv2.IMREAD_GRAYSCALE).astype(np.float32)
# calculate measurement
measure_value = 0.
if args.measure.lower() == 'mae':
measure_value = utils.mean_absoulute_error(pred_img, gt_img)
elif args.measure.lower() == 'rmse':
measure_value = utils.root_mean_square_error(pred_img, gt_img)
elif args.measure.lower() == 'psnr':
measure_value = utils.peak_signal_to_noise_ratio(pred_img, gt_img)
elif args.measure.lower() == 'ssim':
measure_value = utils.structural_similarity_index(pred_img, gt_img)
slice_list.append(SliceExample(img_name, args.method, measure_value))
return slice_list
def cal_meausre(methods, measure, case_dict, num_cases_require):
mean_bar = np.zeros((len(methods), num_cases_require), dtype=np.float32)
var_bar = np.zeros((len(methods), num_cases_require), dtype=np.float32)
case_idx = 0
for key_idx in sorted(case_dict.keys()):
if case_idx < num_cases_require:
print('key_idx: {}'.format(key_idx))
img_paths = case_dict[key_idx]
measure_result = []
for method in methods:
method_measure = []
for _, img_path in enumerate(img_paths):
gt_img = cv2.imread(os.path.join('../gt', img_path),
cv2.IMREAD_GRAYSCALE).astype(
np.float32)
pred_img = cv2.imread(
os.path.join('../{}'.format(method), img_path),
cv2.IMREAD_GRAYSCALE).astype(np.float32)
if measure.lower() == 'mae':
measure_val = utils.mean_absoulute_error(
pred_img, gt_img)
elif measure.lower() == 'rmse':
measure_val = utils.root_mean_square_error(
pred_img, gt_img)
elif measure.lower() == 'psnr':
measure_val = utils.peak_signal_to_noise_ratio(
pred_img, gt_img)
elif measure.lower() == 'ssim':
measure_val = utils.structural_similarity_index(
pred_img, gt_img)
elif measure.lower() == 'pcc':
measure_val = utils.pearson_correlation_coefficient(
pred_img, gt_img)
else:
raise NotImplementedError
# Save one slice results
method_measure.append(measure_val)
# Save whole slice results
measure_result.append(method_measure)
measure_array = np.asarray(measure_result)
mean_bar[:, case_idx] = np.mean(measure_array, axis=1)
var_bar[:, case_idx] = np.std(measure_array, axis=1)
case_idx += 1
else:
break
return mean_bar, var_bar
def read_imgs_and_measurement(target=None):
slice_list = []
# read gt image addresses
gt_names = all_files_under('../gt', extension='.jpg')
for idx in range(len(gt_names)):
if np.mod(idx, 300) == 0:
print('idx: {}'.format(idx))
img_name = os.path.basename(gt_names[idx])
# read gt and prediction image
gt_img = cv2.imread(gt_names[idx],
cv2.IMREAD_GRAYSCALE).astype(np.float32)
pred_img = cv2.imread(os.path.join('../{}'.format(target), img_name),
cv2.IMREAD_GRAYSCALE).astype(np.float32)
# calculate measurement
measure_value = structural_similarity_index(pred_img, gt_img)
slice_list.append(SliceExample(img_name, measure_value))
return slice_list
def main(gt, methods, display_names):
# read gt image addresses
gt_names = utils.all_files_under(os.path.join('../', gt), extension='.jpg')
# read prediction image addresses
filenames_list = []
for method in methods:
filenames = utils.all_files_under(os.path.join('../', method),
extension='.jpg')
filenames_list.append(filenames)
mae_overall, rmse_overall, psnr_overall, ssim_overall, pcc_overall = [], [], [], [], []
for idx_method in range(len(methods)):
# print(methods[idx_method])
mae_method, rmse_method, psnr_method, ssim_method, pcc_method = [], [], [], [], []
for idx_name in range(len(gt_names)):
# read gt and prediction image
gt_img = cv2.imread(gt_names[idx_name],
cv2.IMREAD_GRAYSCALE).astype(np.float32)
pred_img = cv2.imread(filenames_list[idx_method][idx_name],
cv2.IMREAD_GRAYSCALE).astype(np.float32)
# check gt and prediction image name
if gt_names[idx_name].split('p0')[-1] != filenames_list[
idx_method][idx_name].split('p0')[-1]:
sys.exit(' [!] Image name can not match!')
# calcualte mae and psnr
mae = utils.mean_absoulute_error(pred_img, gt_img)
rmse = utils.root_mean_square_error(pred_img, gt_img)
psnr = utils.peak_signal_to_noise_ratio(pred_img, gt_img)
ssim = utils.structural_similarity_index(pred_img, gt_img)
pcc = utils.pearson_correlation_coefficient(pred_img, gt_img)
if np.mod(idx_name, 300) == 0:
print('Method: {}, idx: {}'.format(methods[idx_method],
idx_name))
# collect each image results
mae_method.append(mae)
rmse_method.append(rmse)
psnr_method.append(psnr)
ssim_method.append(ssim)
pcc_method.append(pcc)
# list to np.array
mae_method = np.asarray(mae_method)
rmse_method = np.asarray(rmse_method)
psnr_method = np.asarray(psnr_method)
ssim_method = np.asarray(ssim_method)
pcc_method = np.asarray(pcc_method)
# collect all methods results
mae_overall.append(mae_method)
rmse_overall.append(rmse_method)
psnr_overall.append(psnr_method)
ssim_overall.append(ssim_method)
pcc_overall.append(pcc_method)
# list to np.array
mae_overall = np.asarray(mae_overall)
rmse_overall = np.asarray(rmse_overall)
psnr_overall = np.asarray(psnr_overall)
ssim_overall = np.asarray(ssim_overall)
pcc_overall = np.asarray(pcc_overall)
# draw boxplot
utils.draw_box_plot(
[mae_overall, rmse_overall, psnr_overall, ssim_overall, pcc_overall],
display_names)
# write to csv file
utils.write_to_csv(
[mae_overall, rmse_overall, psnr_overall, ssim_overall, pcc_overall],
methods, gt_names)