for i in range(pr_masks.shape[0]):
pr_map_cls = [(pr_masks[i, :, :, k] > thres_cls[key])
for k, key in enumerate(thres_cls.keys())]
pr_map = pr_map_cls[0] * 1.0
for k in range(1, len(classes)):
top = pr_map_cls[k].copy() * (k + 1)
top[np.where(np.logical_and(top > 0, pr_map > 0))] = pr_map[np.where(
np.logical_and(top > 0, pr_map > 0))]
pr_map[np.where(top > 0)] = top[np.where(top > 0)]
pr_maps_roc.append(pr_map)
pr_maps_roc = np.stack(pr_maps_roc)
pr_masks_roc = np.stack([pr_maps_roc == k for k in range(1,
len(classes) + 1)],
axis=-1)
## IoU and dice coefficient
iou_classes, mIoU, dice_classes, mDice = iou_calculate(gt_masks, pr_masks_roc)
print('iou_classes: {:.4f},{:.4f},{:.4f},{:.4f}; mIoU: {:.4f}'.format(
iou_classes[-1], iou_classes[0], iou_classes[1], iou_classes[2], mIoU))
print('dice_classes: {:.4f},{:.4f},{:.4f},{:.4f}; mDice: {:.4f}'.format(
dice_classes[-1], dice_classes[0], dice_classes[1], dice_classes[2],
mDice))
def plot(file_name,
pr_masks,
pr_masks_roc,
gt_masks,
index,
classes=['G1', 'S', 'G2', 'BK']):
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg
for i in test_indices:
_, gt_mask = test_dataset[i];gt_masks.append(gt_mask)
gt_masks = np.stack(gt_masks);gt_maps = np.argmax(gt_masks,axis=-1)
## reshape
# offset = 8
# pr_masks = pr_masks[:,:,offset:-offset]
# gt_masks = gt_masks[:,:,offset:-offset,:]
# print(pr_masks.shape); print(gt_masks.shape)
## IoU and dice coefficient
pr_save_mask = np.concatenate([pr_masks[:,:,:,-1:],pr_masks[:,:,:,:-1]], axis = -1)
gt_save_mask = np.concatenate([gt_masks[:,:,:,-1:],gt_masks[:,:,:,:-1]], axis = -1)
test_ids = [test_dataset.ids[index] for index in test_indices]
## IoU and dice coefficient
iou_classes, mIoU, dice_classes, mDice = iou_calculate(gt_save_mask, pr_save_mask)
print('iou_classes: {:.4f},{:.4f},{:.4f}; mIoU: {:.4f}'.format(iou_classes[0],iou_classes[1],iou_classes[2], mIoU))
print('dice_classes: {:.4f},{:.4f},{:.4f}; mDice: {:.4f}'.format(dice_classes[0],dice_classes[1],dice_classes[2], mDice))
# save the prediction
pr_save_map = np.argmax(pr_save_mask,axis=-1)
gt_save_map = np.argmax(gt_save_mask,axis=-1)
save = True
if save:
pred_dir = os.path.join(model_folder, 'pred_{}'.format(subset))
generate_folder(pred_dir)
for pi, fn in enumerate(test_ids):
imsave(pred_dir+'/{}'.format(fn), pr_save_map[pi,:,:])
io.imsave(model_folder+'/pr_{}.png'.format(fn.split('.')[0]), pr_save_map[pi,:,:])
y_true=gt_save_map.flatten(); y_pred = pr_save_map.flatten()
cf_mat = confusion_matrix(y_true, y_pred)
## Label map prediction
pr_masks = model.predict(images_pad, batch_size=1)
## probability maps [N(num of images) x H x W x C(class)] for 0: G1, 1: S, 2: G2, 3: background/M phase
# crop images back
pr_masks = pr_masks[:, pad_widths[1][0]:-pad_widths[1][1], pad_widths[2][
0]:-pad_widths[2][
1], :] if dataset == 'cyc2_1488x1512' else pr_masks[:, :, pad_widths[
2][0]:-pad_widths[2][1], :]
pr_masks_ = np.zeros(pr_masks.shape, dtype=np.float32)
pr_masks_[:, :, :, 0] = pr_masks[:, :, :, 3]
pr_masks_[:, :, :,
1:] = pr_masks[:, :, :, :3] # 0:background/M, 1:G1, 2:S, 3:G2
pr_maps = np.argmax(pr_masks_, axis=-1) # predicted label map
## calculate the performance metrics on the testing images
iou_classes, mIoU, dice_classes, mDice = iou_calculate(gt_maps, pr_maps)
print('iou_classes: {:.4f},{:.4f},{:.4f},{:.4f}; mIoU: {:.4f}'.format(
iou_classes[0], iou_classes[1], iou_classes[2], iou_classes[-1], mIoU))
print('dice_classes: {:.4f},{:.4f},{:.4f},{:.4f}; mDice: {:.4f}'.format(
dice_classes[0], dice_classes[1], dice_classes[2], dice_classes[-1],
mDice))
# confusion matrix, precision, recall, and F1 score
y_true = gt_maps.flatten()
y_pred = pr_maps.flatten()
cf_mat = confusion_matrix(y_true, y_pred)
print('Confusion matrix:')
print(cf_mat)
prec_scores = []
recall_scores = []
f1_scores = []
