try:
seg_lesion_arr[k, h1:h2, w1:w2] = seg_lesion
except:
cv2.imshow(
'Image',
utils.array_to_img(seg_lesion, clip=True).astype('uint8'))
# lesion
########
seg_lesion_final = np.copy(seg_lesion_arr[k])
y_true_lesion = np.where(mask == 2, 1., 0.)
seg_lesion_tmp = np.where(seg_lesion_final == 2, 1., 0.)
dice_lesion[n] = metric.dc(seg_lesion_tmp, y_true_lesion)
precision_lesion[n] = metric.sensitivity(seg_lesion_tmp,
y_true_lesion)
sensitivity_lesion[n] = metric.precision(seg_lesion_tmp,
y_true_lesion)
#############################################################################################
print('dice=', dice_lesion[n], ' ; precision=',
precision_lesion[n], ' ; sensitivity=',
sensitivity_lesion[n])
if isDbg:
if (n % dbg_step == 0) or dice_lesion[n] < min_dice_dbg:
# Original Image
img = utils.dbg_orig_img(data_path, filename)
cv2.waitKey(0)
# cv2.destroyAllWindows()
# liver
def calculate_metrics(pred, target):
sens = metric.sensitivity(pred, target)
spec = metric.specificity(pred, target)
dice = metric.dc(pred, target)
sum_intersection = 0
j = begin_idx
for k in range(len(val_img_paths)):
curr_sum_ground_truth = np.sum(y_true_liver[k]) # float 64
curr_sum_prediction = np.sum(pred_liver[k])
curr_sum_intersection = np.sum((pred_liver[k] * y_true_liver[k]))
# GLOBAL
sum_ground_truth += curr_sum_ground_truth
sum_prediction += curr_sum_prediction
sum_intersection += curr_sum_intersection
# LOCAL
dice_liver_local[j] = metric.dc(pred_liver[k], y_true_liver[k])
precision_liver_local[j] = metric.sensitivity(pred_liver[k],
y_true_liver[k])
sensitivity_liver_local[j] = metric.precision(pred_liver[k],
y_true_liver[k])
if isDbg:
# Original
if (j % dbg_step == 0) or dice_liver_local[j] < min_dice_dbg:
img = utils.dbg_orig_img(data_path, val_img_paths[k])
cv2.waitKey(0)
cv2.destroyAllWindows()
# Liver - gt
img = utils.apply_mask(img,
mask_arr[k], (0, 0, 255),
mask_idx=1)
img = utils.apply_mask(img,
mask_arr[k], (0, 0, 255),