images = np.stack(images)
gt_masks = np.stack(gt_masks)
# scale back from args.flu_scale
gt_masks = np.uint8(gt_masks / args.scale * 255)
pr_masks = pr_masks / args.scale * 255
pr_masks = np.uint8(np.clip(pr_masks, 0, 255))
# save prediction examples
plot_fig_file = model_folder + '/pred_examples.png'
nb_images = 10
plot_flu_prediction(plot_fig_file, images, gt_masks, pr_masks, nb_images)
# output_dir = model_folder+'/pred_fl'; generate_folder(output_dir)
# plot_set_prediction(output_dir, images, gt_masks, pr_masks)
# calculate PSNR
mPSNR, psnr_scores = calculate_psnr(gt_masks, pr_masks)
print('PSNR: {:.4f}'.format(mPSNR))
# calculate Pearson correlation coefficient
mPear, pear_scores = calculate_pearsonr(gt_masks, pr_masks)
print('Pearsonr:{:.4f}'.format(mPear))
with open(model_folder + '/metric_summary.txt', 'w+') as f:
# loss
f.write("loss {}: {:.5}\n".format(metric.__name__, value))
# average psnr
for metric, value in zip(metrics, scores[1:]):
f.write("mean {}: {:.5}\n".format(metric.__name__, value))
# save PSNR over fluorescent 1 and fluorescent 2
f.write('PSNR: {:.4f}\n'.format(mPSNR))
f.write('Pearsonr:{:.4f}\n'.format(mPear))
gt_vol2 = extract_vol(gt_masks)
pr_vol2 = extract_vol(pr_masks)
else:
gt_vol2 = gt_masks
pr_vol2 = pr_masks
elif fl_ch == 'fl12':
gt_vol = gt_masks[:, :, :, 0]
pr_vol = pr_masks[:, :, :, 0]
gt_vol2 = gt_masks[:, :, :, 1]
pr_vol2 = pr_masks[:, :, :, 1]
if fl_ch == 'fl12' or fl_ch == 'fl1':
pr_vol = pr_vol[:, offset:-offset, offset:-offset]
gt_vol = gt_vol[:, offset:-offset, offset:-offset]
mse_score = np.mean(np.square(pr_vol - gt_vol))
psnr_score = calculate_psnr(pr_vol, gt_vol)
cor_score = calculate_pearsonr(pr_vol, gt_vol)
print(pr_vol.shape, gt_vol.shape)
mse_scores.append(mse_score)
psnr_scores.append(psnr_score)
cor_scores.append(cor_score)
print('{}-FL1: psnr {:.4f}, cor {:.4f}, mse {:.4f}\n'.format(
vol_fn, psnr_score, cor_score, mse_score))
if fl_ch == 'fl12' or fl_ch == 'fl2':
pr_vol2 = pr_vol2[:, offset:-offset, offset:-offset]
gt_vol2 = gt_vol2[:, offset:-offset, offset:-offset]
mse_score2 = np.mean(np.square(pr_vol2 - gt_vol2))
psnr_score2 = calculate_psnr(pr_vol2, gt_vol2)
cor_score2 = calculate_pearsonr(pr_vol2, gt_vol2)
print(pr_vol2.shape, gt_vol2.shape)
mse2_scores.append(mse_score2)
image_ids,
nb_images,
rand_seed=2)
ghf.plot_flu_prediction8(model_folder + '/grants_fl_v2',
images,
gt_masks,
pr_masks,
cut_folder,
fl1_folder,
fl2_folder,
image_ids,
nb_images,
rand_seed=2)
# calculate PSNR
f1_mPSNR, f1_psnr_scores = calculate_psnr(gt_masks[:, :, :, 0],
pr_masks[:, :, :, 0])
f2_mPSNR, f2_psnr_scores = calculate_psnr(gt_masks[:, :, :, 1],
pr_masks[:, :, :, 1])
mPSNR, f_psnr_scores = calculate_psnr(gt_masks, pr_masks)
print('PSNR: fluo1 {:.4f}, fluo2 {:.4f}, combined {:.4f}'.format(
f1_mPSNR, f2_mPSNR, mPSNR))
# calculate Pearson correlation coefficient
f1_mPear, f1_pear_scores = calculate_pearsonr(gt_masks[:, :, :, 0],
pr_masks[:, :, :, 0])
f2_mPear, f2_pear_scores = calculate_pearsonr(gt_masks[:, :, :, 1],
pr_masks[:, :, :, 1])
f_mPear, f_pear_scores = calculate_pearsonr(gt_masks, pr_masks)
print('Pearsonr: fluo1 {:.4f}, fluo2 {:.4f}, combined {:.4f}'.format(
f1_mPear, f2_mPear, f_mPear))
# scale back
gt_masks = gt_masks / scale_factor
pr_masks = pr_masks / scale_factor
# crop
if args.dataset == 'bone_marrow' or args.dataset == 'colorectal':
offset1, offset2 = int((val_dim - img_dim) / 2), val_dim - int(
(val_dim - img_dim) / 2)
gt_masks = gt_masks[:, offset1:offset2, offset1:offset2]
pr_masks = pr_masks[:, offset1:offset2, offset1:offset2]
images = images[:, offset1:offset2, offset1:offset2]
print('output: {}'.format(pr_masks.shape))
# save prediction examples
plot_fig_file = model_folder + '/pred_examples.png'
nb_images = 4
plot_reg_prediction(plot_fig_file,
images,
gt_masks,
pr_masks,
nb_images,
rand_seed=6)
# calculate PSNR
mPSNR, psnr_scores = calculate_psnr(gt_masks, pr_masks, max_val=1.0)
print('PSNR: {:.4f}'.format(mPSNR))
with open(model_folder + '/metric_psnr.txt', 'w+') as f:
# save PSNR over fluorescent 1 and fluorescent 2
f.write('PSNR: {:.4f}'.format(mPSNR))
return np.mean(np.square(vol1-vol2))
# prediction
val_loss = [];
gts = []; prs = []
for i in range(len(valid_dataloader)):
img_input = valid_dataloader[i][0]
prs.append(model.predict(valid_dataloader[i]))
gts.append(valid_dataloader[i][1])
gts_arr = np.concatenate(gts,axis =0)
prs_arr = np.concatenate(prs, axis =0)
cor_score = calculate_pearsonr(gts_arr, prs_arr)
mse_score = calculate_mse(gts_arr, prs_arr)
psnr_score = calculate_psnr(gts_arr, prs_arr)
print('mMSE {:4f}, mCor:{:.4f}, mPSNR {:.4f}'.format(mse_score, cor_score, psnr_score))
# scale back and compute
gts_arr_1 = gts_arr/scale*255
prs_arr_1 = prs_arr/scale*255
cor_score_1 = calculate_pearsonr(gts_arr_1, prs_arr_1)
mse_score_1 = calculate_mse(gts_arr_1, prs_arr_1)
psnr_score_1 = calculate_psnr(gts_arr_1, prs_arr_1)
## mse
mse_scores = [calculate_mse(gts_arr_1[i,:], prs_arr_1[i,:]) for i in range(prs_arr_1.shape[0])]
cor_scores = [calculate_pearsonr(gts_arr_1[i,:], prs_arr_1[i,:]) for i in range(prs_arr_1.shape[0])]
psnr_scores = [calculate_psnr(gts_arr_1[i,:], prs_arr_1[i,:]) for i in range(prs_arr_1.shape[0])]
# extract the target prediction
# offset = 12
ph_vol = extract_vol(images)
if cho == 3:
gt_vol = extract_vol(gt_masks)
pr_vol = extract_vol(pr_masks)
else:
gt_vol = gt_masks
pr_vol = pr_masks
pr_vol = pr_vol[:, offset:-offset, offset:-offset]
gt_vol = gt_vol[:, offset:-offset, offset:-offset]
ph_vol = ph_vol[:, offset:-offset, offset:-offset]
# psnr and pearsonr correlation
mse_score = np.mean(np.square(pr_vol - gt_vol))
psnr_score = calculate_psnr(pr_vol, gt_vol)
cor_score = calculate_pearsonr(pr_vol, gt_vol)
mse_scores.append(mse_score)
psnr_scores.append(psnr_score)
cor_scores.append(cor_score)
# save prediction
pred_save = False
if pred_save:
pr_vol_dir = model_folder + '/pred_vols'
generate_folder(pr_vol_dir)
np.save(os.path.join(pr_vol_dir, 'Pr_{}.npy'.format(vol_fn)), pr_vol)
np.save(os.path.join(pr_vol_dir, 'GT_{}.npy'.format(vol_fn)), gt_vol)
print(pr_vol.shape)
print('{}: psnr {:.4f}, cor {:.4f}, mse {:.4f}\n'.format(
