def vs_display_flow(r_flow, c_flow):
if r_flow.size(0) != c_flow.size(0):
# sanity check
raise ValueError("Unequal flow lengths!")
if not isinstance(r_flow, np.ndarray):
r_flow = r_flow.numpy()
if not isinstance(c_flow, np.ndarray):
c_flow = c_flow.numpy()
r_flow = [torch.from_numpy(flow_to_image(flow)) for flow in r_flow]
c_flow = [torch.from_numpy(flow_to_image(flow)) for flow in c_flow]
r_flow = torch.stack(r_flow, dim=0)
c_flow = torch.stack(c_flow, dim=0)
# display frames
flow = torch.cat([r_flow, c_flow], dim=0)
flow = make_grid(flow, nrow=r_flow.size(0), padding=1)
img_t.imshow(flow)
return
def display_frames(frames):
if isinstance(frames, np.ndarray):
frames = torch.from_numpy(frames).permute(0, 3, 1, 2)
# plot frames consecutively
frames = make_grid(frames, nrow=frames.size(0), padding=2, pad_value=255)
img_t.imshow(frames)
return
def vs_display_frames(r_frames, c_frames):
if r_frames.size(0) != c_frames.size(0):
# sanity check
raise ValueError("Unequal clip lengths!")
# display frames
frames = torch.cat([r_frames, c_frames], dim=0)
img_t.imshow(frames)
return
def display_flow(flows):
if not isinstance(flows, np.ndarray):
flows = flows.numpy()
flows = [torch.from_numpy(flow_to_image(flow)) for flow in flows]
flows = torch.stack(flows, dim=0)
# plot frames consecutively
flows = make_grid(flows, nrow=flows.size(0), padding=1)
img_t.imshow(flows)
return
def imshow_callback(ref, comp, i):
# display images alongside one another
img = make_grid([ref[i], comp[i]], nrow=2, padding=1)
img_t.imshow(img)
return
def imshow_callback(f, i):
# display image
img_t.imsave(f[i], "i_" + str(i) + ".png")
img_t.imshow(f[i])
return