def get_iou(y, y_c, y_m, gt):
segmentation = config['post'].getboolean('segmentation')
remove_objects = config['post'].getboolean('remove_objects')
min_object_size = config['post'].getint('min_object_size')
only_contour = config['contour'].getboolean('exclusive')
if segmentation:
y, markers = partition_instances(y, y_m, y_c)
if remove_objects:
y = remove_small_objects(y, min_size=min_object_size)
if only_contour:
iou = iou_metric(y, label(gt > 0))
else:
iou = iou_metric(y, gt)
return iou
def show_groundtruth(uid, x, y, y_c, y_m, gt, gt_s, gt_c, gt_m, save=False):
threshold = config['param'].getfloat('threshold')
segmentation = config['post'].getboolean('segmentation')
remove_objects = config['post'].getboolean('remove_objects')
min_object_size = config['post'].getint('min_object_size')
only_contour = config['contour'].getboolean('exclusive')
view_color_equalize = config['valid'].getboolean('view_color_equalize')
print_table = config['valid'].getboolean('print_table')
model_name = config['param']['model']
if model_name == 'camunet':
threshold_edge = config[model_name].getfloat('threshold_edge')
threshold_mark = config[model_name].getfloat('threshold_mark')
elif model_name == 'dcan' or model_name == 'caunet':
threshold_edge = config[model_name].getfloat('threshold_edge')
fig, (ax1, ax2, ax3) = plt.subplots(3, 4, sharey=True, figsize=(12, 8))
fig.suptitle(uid, y=1)
y_s = y # to show pure semantic predict later
if view_color_equalize:
x = clahe(x)
ax1[0].set_title('Image')
ax1[0].imshow(x, aspect='auto')
if segmentation:
y, markers = partition_instances(y, y_m, y_c)
if remove_objects:
y = remove_small_objects(y, min_size=min_object_size)
_, count = label(y, return_num=True)
ax1[1].set_title('Final Pred, #={}'.format(count))
ax1[1].imshow(y, cmap='gray', aspect='auto')
# overlay contour to semantic ground truth (another visualized view for instance ground truth, eg. gt)
_, count = label(gt, return_num=True)
ax1[2].set_title('Instance Lbls, #={}'.format(count))
ax1[2].imshow(gt_s, cmap='gray', aspect='auto')
gt_c2, cmap = _make_overlay(gt_c)
ax1[2].imshow(gt_c2, cmap=cmap, alpha=0.7, aspect='auto')
if only_contour: # can not tell from instances in this case
iou = iou_metric(y, label(gt > 0), print_table)
else:
iou = iou_metric(y, gt, print_table)
ax1[3].set_title('Overlay, IoU={:.3f}'.format(iou))
ax1[3].imshow(gt_s, cmap='gray', aspect='auto')
y, cmap = _make_overlay(y)
ax1[3].imshow(y, cmap=cmap, alpha=0.3, aspect='auto')
y_s = y_s > threshold
_, count = label(y_s, return_num=True)
ax2[0].set_title('Semantic Predict, #={}'.format(count))
ax2[0].imshow(y_s, cmap='gray', aspect='auto')
_, count = label(gt_s, return_num=True)
ax2[1].set_title('Semantic Lbls, #={}'.format(count))
ax2[1].imshow(gt_s, cmap='gray', aspect='auto')
if y_c is not None:
y_c = y_c > threshold_edge
_, count = label(y_c, return_num=True)
ax2[2].set_title('Contour Predict, #={}'.format(count))
ax2[2].imshow(y_c, cmap='gray', aspect='auto')
_, count = label(gt_c, return_num=True)
ax2[3].set_title('Contour Lbls, #={}'.format(count))
ax2[3].imshow(gt_c, cmap='gray', aspect='auto')
_, count = label(markers, return_num=True)
ax3[0].set_title('Final Markers, #={}'.format(count))
ax3[0].imshow(markers, cmap='gray', aspect='auto')
if y_m is not None:
y_m = y_m > threshold_mark
_, count = label(y_m, return_num=True)
ax3[1].set_title('Marker Predict, #={}'.format(count))
ax3[1].imshow(y_m, cmap='gray', aspect='auto')
_, count = label(gt_m, return_num=True)
ax3[2].set_title('Marker Lbls, #={}'.format(count))
ax3[2].imshow(gt_m, cmap='gray', aspect='auto')
plt.tight_layout()
if save:
dir = predict_save_folder()
fp = os.path.join(dir, uid + '.png')
plt.savefig(fp)
else:
show_figure()