data[0, 15:20] = cbv_img[z - 2:z + 3]
pred = pred_fn(data).transpose((0, 2, 3, 1)).reshape(
(t1km_img.shape[1] - t1km_img.shape[1] % 16,
t1km_img.shape[2] - t1km_img.shape[2] % 16, 5))
res[z] = pred
z += 1
res = res[2:t1km_img.shape[0] - 2]
seg_combined = seg_combined[2:t1km_img.shape[0] - 2]
res_img = res.argmax(-1)
print "calculating metrics"
acc, metrics_by_class = calculate_validation_metrics(
res, seg_combined, {
0: 'background',
1: 'brain',
2: 'edema',
3: 'ce_tumor',
4: 'necrosis'
})
with open(os.path.join(output_folder, "validation_metrics.txt"), 'w') as f:
f.write("The overall accuracy on this dataset was: \t%f\n\n" % acc)
for c in metrics_by_class.keys():
f.write("Results for label: %s\n" % c)
for metric in metrics_by_class[c].keys():
f.write("%s: \t%f\n" % (metric, metrics_by_class[c][metric]))
f.write("\n")
output_folder_images = os.path.join(output_folder, "segmentation_slices")
if not os.path.isdir(output_folder_images):
os.mkdir(output_folder_images)
res = np.zeros((t1km_img.shape[0], output_shape[0], output_shape[1], 6))
while z < t1km_img.shape[0]-2:
data[0,0:5] = t1_img[z-2:z+3]
data[0,5:10] = t1km_img[z-2:z+3]
data[0,10:15] = t2_img[z-2:z+3]
data[0,15:20] = flair_img[z-2:z+3]
pred = pred_fn(data).transpose((0,2,3,1)).reshape((output_shape[0], output_shape[1], 6))
res[z] = pred
z += 1
res = res[2:t1km_img.shape[0]-2]
seg_combined = seg_combined[2:t1km_img.shape[0]-2]
res_img = res.argmax(-1)
print "calculating metrics"
acc, metrics_by_class = calculate_validation_metrics(res, seg_combined, {0: 'background', 1: 'brain', 2: 'necrosis', 3: 'edema', 4: 'non-enhancing tumor', 5:'enhancing tumor'}, 6)
with open(os.path.join(output_folder, "validation_metrics.txt"), 'w') as f:
f.write("The overall accuracy on this dataset was: \t%f\n\n" % acc)
for c in metrics_by_class.keys():
f.write("Results for label: %s\n" % c)
for metric in metrics_by_class[c].keys():
f.write("%s: \t%f\n" % (metric, metrics_by_class[c][metric]))
f.write("\n")
output_folder_images = os.path.join(output_folder, "segmentation_slices")
if not os.path.isdir(output_folder_images):
os.mkdir(output_folder_images)
np.save(os.path.join(output_folder, "seg_gt.npy"), seg_combined)
np.save(os.path.join(output_folder, "seg_pred.npy"), res_img)