def vis_group_color(self, estDisp, gtDisp=None, leftImage=None, rightImage=None, save_path=None):
"""
Args:
estDisp, (tensor or numpy.array): in (1, 1, Height, Width) or (1, Height, Width) or (Height, Width) layout
gtDisp, (None or tensor or numpy.array): in (1, 1, Height, Width) or (1, Height, Width) or (Height, Width) layout
leftImage, (None or numpy.array), in (Height, Width, 3) layout
rightImage, (None or numpy.array), in (Height, Width, 3) layout
save_path, (None or String)
Output:
details refer to dmb.visualization.group_color
"""
assert isinstance(estDisp, (np.ndarray, torch.Tensor))
if torch.is_tensor(estDisp):
estDisp = estDisp.clone().detach().cpu().numpy()
if estDisp.ndim == 3:
estDisp = estDisp[0, :, :]
elif estDisp.ndim == 4:
estDisp = estDisp[0, 0, :, :]
if gtDisp is not None:
assert isinstance(gtDisp, (np.ndarray, torch.Tensor))
if torch.is_tensor(gtDisp):
gtDisp = gtDisp.clone().detach().cpu().numpy()
if gtDisp.ndim == 3:
gtDisp = gtDisp[0, :, :]
elif gtDisp.ndim == 4:
gtDisp = gtDisp[0, 0, :, :]
return group_color(estDisp, gtDisp, leftImage, rightImage, save_path)
def visualize_disp(result_pkl):
ori_data = result_pkl['OriginalData']
net_result = result_pkl['Result']
if 'disps' in net_result:
disps = net_result['disps']
best_disp = disps[0][0, 0, :, :].cpu().numpy()
else:
return
plt.imshow(group_color(best_disp, ori_data['leftDisp'],
ori_data['leftImage'], ori_data['rightImage']),
cmap='hot')
plt.show()