def plot_cube(color_cube, fig=None, save=False, title=None, path='', normalize=True):
assert isinstance(color_cube, metrics_color.ColorDensityCube)
win = color_cube.get_win()
res = color_cube.get_res()
fig_was_none = False
if not fig:
fig_was_none = True
fig = plt.figure(dpi=200)
ax = fig.add_subplot(111, projection='3d')
else:
ax = fig.add_subplot(212, projection='3d')
ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([0.8, 1.2, 0.8, 1.1])) # burst view along y-axis
ax.view_init(azim=-25)
half_win_size = win//2
print(half_win_size)
axis = xrange(half_win_size, 256, win)
for x in axis:
for y in axis:
if normalize:
size = color_cube.get_normalized()[int(x / win)][int(y / win)] * 5000 / res
else:
size = color_cube.get_cube()[int(x / win)][int(y / win)] * 5000 / res
print(size)
color = [np.repeat(x/256, res),
np.repeat(y/256, res),
np.array(xrange(half_win_size, 256, win)) / 256.0]
color = np.swapaxes(color, 0, 1)
ec = np.where(size >= 0.0, 'w', 'r')
size = abs(size)
ax.scatter(x, y, axis, c=color, s=size, edgecolor=ec, alpha=1)
if fig_was_none:
plt.tight_layout()
fig.text(0.5, 0.975, title, ha='center')
plt.show()
else:
return
if save:
assert title is not None
fig.text(0.5, 0.975, title, ha='center')
plt.savefig(path + title + '.png')
plt.close()
def short_proj():
return np.dot(Axes3D.get_proj(ax), scale)
def plot3D_stack_with_overlay_1axes(ax,
na_3D,
overlay_image,
intensity_threshold,
trim_overlay,
overlay_type,
a_shapes,
view_init,
shape_by_shape,
number=1,
label_exterior_cell=1,
verbose=True):
z, y, x = na_3D.shape
if shape_by_shape:
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
ax.set_zlim(z, 0) # slice 27 on the bottom
#ax.get_zaxis().set_visible(False)
else:
ax.set_xlabel("x-axis")
ax.set_ylabel("y-axis")
ax.set_zlabel("z-axis")
ax.set_xlim(0, x)
ax.set_ylim(0, y)
ax.set_zlim(z, 0) # slice 27 on the bottom
#scaling the axis not supported in 3D for the moment. this oneliner solves this
#https://stackoverflow.com/questions/30223161/matplotlib-mplot3d-how-to-increase-the-size-of-an-axis-stretch-in-a-3d-plo/30315313
Z_DIM, Y_DIM, X_DIM = na_3D.shape
pixel_width = 0.1294751 #check fiji properties
voxel_depth = 1.1000000
xy_vs_z = voxel_depth / pixel_width #1 increase in z corresponds to 5 increases in x or y in absolute distance (= back of the envelope calculation)
Z_STRETCH = 3 # for plotting purposes it can be handy to stretch the z-dimension so you are better able to see the slices. 1=true size ; 5 = max otherwise it will not fit the page anymore
ax.get_proj = lambda: np.dot(
Axes3D.get_proj(ax),
np.diag([X_DIM / Y_DIM, 1, Z_DIM / Y_DIM * xy_vs_z * Z_STRETCH, 1]))
if view_init: ax.view_init(elev=view_init[0], azim=view_init[1])
Z1, Y1, X1 = np.where(na_3D > intensity_threshold)
if verbose:
if not len(Z1):
print('warning : no points selected with intensity threshold',
intensity_threshold, 'the maximum of the stack is ',
np.max(na_3D))
ax.plot3D(X1, Y1, Z1, **d_plot3D_red)
#show a levelset / overlag image over the original
if len(overlay_image):
if overlay_type == 'level_set':
print(overlay_image.dtype)
if trim_overlay:
trim_overlay(na_3D, overlay_image)
Z2, Y2, X2 = np.where(
overlay_image < 0) #interior of a level set is below zero
else:
Z2, Y2, X2 = np.where(overlay_image != 0)
ax.plot3D(X2, Y2, Z2, **d_plot3D_green)
#draw spheres inside the cell
if len(a_shapes):
for ix_stack, shape in enumerate(a_shapes):
if shape.__class__.__name__ == 'Sphere':
ax.plot3D(*shape.l_pixels[::-1],
**d_plot3D_sphere,
color=a_colors[ix_stack % len(a_colors)])
if shape.__class__.__name__ == 'Cell':
if shape.l_pixels:
ax.plot3D(*shape.l_pixels[::-1],
**d_plot3D_sphere,
color=a_colors[ix_stack % len(a_colors)])
if shape.label == label_exterior_cell:
s_title = str(shape.label) + '(exterior)'
ax.set_title(s_title)
else:
ax.set_title(shape.label)
# if len(a_spheres):
# for ix_stack, d_sphere in enumerate(a_spheres):
# ax.plot3D(*get_3D_sphere_coordinates(d_sphere['centre'], d_sphere['radius'], na_3D.shape, verbose=False)[::-1],
# **d_plot3D_sphere,color=a_colors[ix_stack%len(a_colors)])
if shape_by_shape:
pass
else:
title = 'intensity_threshold={0};view_init={1}'.format(
intensity_threshold, view_init)
ax.set_title(title, fontsize=12)
if view_init:
d_save_info['f_name'] = d_save_info['f_name'] + '_elev=' + str(
view_init[0]) + '_azim=' + str(view_init[1])
return
def short_proj():
return np.dot(Axes3D.get_proj(ax), scale)
def demo_visualize(args, demo_loader, disp_net, ego_pose_net, obj_pose_net):
global device
torch.set_printoptions(sci_mode=False)
np.set_printoptions(suppress=True)
# np.set_printoptions(formatter={'all':lambda x: str(x)})
# switch to eval mode
disp_net.eval().to(device)
ego_pose_net.eval().to(device)
obj_pose_net.eval().to(device)
ego_global_mat = np.identity(4)
ego_global_mats = [ego_global_mat]
objOs, objHs, objXs, objYs, objZs = [], [], [], [], []
objIDs = []
colors = ['yellow', 'lightskyblue', 'lime', 'magenta', 'orange', 'coral', 'gold', 'cyan']
vidx = 0
for i, (ref_img, tgt_img, ref_seg, tgt_seg, intrinsics, intrinsics_inv) in enumerate(demo_loader):
ref_img = ref_img.to(device)
tgt_img = tgt_img.to(device)
ref_seg = ref_seg.to(device)
tgt_seg = tgt_seg.to(device)
intrinsics = intrinsics.to(device)
intrinsics_inv = intrinsics_inv.to(device)
# input instance masking
ref_bg_mask = 1 - (ref_seg[:,1:].sum(dim=1, keepdim=True)>0).float()
tgt_bg_mask = 1 - (tgt_seg[:,1:].sum(dim=1, keepdim=True)>0).float()
ref_bg_img = ref_img * ref_bg_mask * tgt_bg_mask
tgt_bg_img = tgt_img * ref_bg_mask * tgt_bg_mask
num_inst = int(ref_seg[:,0,0,0])
num_insts = [[num_inst], [num_inst]]
# tracking info
if len(objIDs) == 0:
objIDs.append( np.arange(num_inst).tolist() )
else:
# -> ref_seg의 인스턴스들이 tgt_seg_prev의 몇 번째 채널 인스턴스에 매칭되는가?
p2c_iou, p2c_idx = inst_iou(ref_seg.cpu(), tgt_seg_prev.cpu(), torch.ones(1,1,ref_seg.size(2),ref_seg.size(3)).type_as(ref_seg).cpu())
p2c_iou = p2c_iou[1:]
p2c_idx = p2c_idx[1:] - 1
newColorID = list(set(np.arange(len(colors))) - set(objIDs[-1]))
newID = []
for ii, iou in enumerate(p2c_iou):
if iou > 0.5:
newID.append( objIDs[-1][int(p2c_idx[ii])] )
elif iou != iou:
break;
else:
newID.append( newColorID[0] )
newColorID = newColorID[1:]
objIDs.append( newID )
tgt_seg_prev = tgt_seg.clone()
tgt_seg_prev[0,0] = 0
objIDs_flatten = list(itertools.chain.from_iterable(objIDs))
# pdb.set_trace()
'''
# plt.close('all')
ea1 = 4; ea2 = 5; ii = 1;
fig = plt.figure(99, figsize=(20, 10))
fig.add_subplot(ea1,ea2,ii); ii += 1;
plt.imshow(tgt_seg_prev[0,0].cpu()); plt.colorbar();
fig.add_subplot(ea1,ea2,ii); ii += 1;
plt.imshow(tgt_seg_prev[0,1].cpu()); plt.colorbar();
fig.add_subplot(ea1,ea2,ii); ii += 1;
plt.imshow(tgt_seg_prev[0,2].cpu()); plt.colorbar();
fig.add_subplot(ea1,ea2,ii); ii += 1;
plt.imshow(tgt_seg_prev[0,3].cpu()); plt.colorbar();
fig.add_subplot(ea1,ea2,ii); ii += 1;
plt.imshow(tgt_seg_prev[0,4].cpu()); plt.colorbar();
fig.add_subplot(ea1,ea2,ii); ii += 1;
plt.imshow(ref_seg[0,0].cpu()); plt.colorbar();
fig.add_subplot(ea1,ea2,ii); ii += 1;
plt.imshow(ref_seg[0,1].cpu()); plt.colorbar();
fig.add_subplot(ea1,ea2,ii); ii += 1;
plt.imshow(ref_seg[0,2].cpu()); plt.colorbar();
fig.add_subplot(ea1,ea2,ii); ii += 1;
plt.imshow(ref_seg[0,3].cpu()); plt.colorbar();
fig.add_subplot(ea1,ea2,ii); ii += 1;
plt.imshow(ref_seg[0,4].cpu()); plt.colorbar();
plt.tight_layout(); plt.ion(); plt.show()
'''
# compute depth & camera motion
ref_depth = 1 / disp_net(ref_img)
tgt_depth = 1 / disp_net(tgt_img)
ego_pose = ego_pose_net(tgt_bg_img, ref_bg_img)
ego_pose_inv = ego_pose_net(ref_bg_img, tgt_bg_img)
# ego_pose = ego_pose_net(tgt_img, ref_img)
# ego_pose_inv = ego_pose_net(ref_img, tgt_img)
ego_mat = pose_vec2mat(ego_pose).squeeze(0).cpu().detach().numpy()
ego_mat = np.concatenate([ego_mat, np.array([0, 0, 0, 1]).reshape(1,4)], axis=0)
ego_global_mat = ego_global_mat @ ego_mat
ego_global_mats.append(ego_global_mat)
### Batch-wise computing ### rtt_Is, rtt_Ms, prj_Ds, cmp_Ds --> (19.11.18) change from fw to iw for bg regions
### Outputs: warped-masked-bg-img, valid-bg-mask, valid-bg-proj-depth, valid-bg-comp-depth ###
### NumScales(1) >> NumRefs(2) >> I-M-D-D(4) >> 2B(fwd/bwd)xCxHxW ###
IMDDs = compute_batch_bg_warping(tgt_img, [ref_img, ref_img], [tgt_bg_mask, tgt_bg_mask], [ref_bg_mask, ref_bg_mask],
tgt_depth, [ref_depth, ref_depth], [ego_pose, ego_pose], [ego_pose_inv, ego_pose_inv], intrinsics)
ref_obj_img = ref_img.repeat(num_inst,1,1,1) * ref_seg[0,1:1+num_inst].unsqueeze(1)
tgt_obj_img = tgt_img.repeat(num_inst,1,1,1) * tgt_seg[0,1:1+num_inst].unsqueeze(1)
ref_obj_mask = ref_seg[0,1:1+num_inst].unsqueeze(1)
tgt_obj_mask = tgt_seg[0,1:1+num_inst].unsqueeze(1)
ref_obj_depth = ref_depth.repeat(num_inst,1,1,1) * ref_seg[0,1:1+num_inst].unsqueeze(1)
tgt_obj_depth = tgt_depth.repeat(num_inst,1,1,1) * tgt_seg[0,1:1+num_inst].unsqueeze(1)
_, _, _, _, r2t_obj_imgs, r2t_obj_masks, _, r2t_obj_sc_depths = \
compute_reverse_warp_ego([ref_depth, ref_depth], [ref_obj_img, ref_obj_img], [ref_obj_mask, ref_obj_mask], [ego_pose_inv, ego_pose_inv], intrinsics, num_insts)
_, _, _, _, t2r_obj_imgs, t2r_obj_masks, _, t2r_obj_sc_depths = \
compute_reverse_warp_ego([tgt_depth, tgt_depth], [tgt_obj_img, tgt_obj_img], [tgt_obj_mask, tgt_obj_mask], [ego_pose, ego_pose], intrinsics, num_insts)
obj_pose = obj_pose_net(tgt_obj_img, r2t_obj_imgs[0])
obj_pose_inv = obj_pose_net(ref_obj_img, t2r_obj_imgs[0])
obj_pose = torch.cat([obj_pose, torch.zeros_like(obj_pose)], dim=1)
obj_pose_inv = torch.cat([obj_pose_inv, torch.zeros_like(obj_pose_inv)], dim=1)
obj_mat = pose_vec2mat(obj_pose).cpu().detach().numpy()
obj_mat = np.concatenate([obj_mat, np.array([0, 0, 0, 1]).reshape(1,1,4).repeat(obj_pose.size(0),axis=0)], axis=1)
obj_global_mat = ego_global_mat.reshape(1,4,4).repeat(obj_pose.size(0),axis=0) @ obj_mat
obj_IMDDs, obj_ovls = compute_batch_obj_warping(tgt_img, [ref_img, ref_img], [tgt_obj_mask, tgt_obj_mask], [ref_obj_mask, ref_obj_mask], tgt_depth, [ref_depth, ref_depth],
[ego_pose, ego_pose], [ego_pose_inv, ego_pose_inv], [obj_pose, obj_pose], [obj_pose_inv, obj_pose_inv], intrinsics, num_insts)
tr_fwd, tr_bwd = compute_obj_translation(r2t_obj_sc_depths, t2r_obj_sc_depths, [tgt_obj_depth, tgt_obj_depth], [ref_obj_depth, ref_obj_depth], num_insts, intrinsics)
rtt_obj_imgs, rtt_obj_masks, rtt_obj_depths, rtt_obj_sc_depths = compute_reverse_warp_obj(r2t_obj_sc_depths, r2t_obj_imgs, r2t_obj_masks, [-obj_pose, -obj_pose], intrinsics.repeat(num_inst,1,1), num_insts)
# pdb.set_trace()
'''
sq = 0; bb = 0;
plt.close('all')
plt.figure(1); plt.imshow(r2t_obj_sc_depths[sq][bb,0].detach().cpu()); plt.colorbar(); plt.ion(); plt.show()
plt.figure(2); plt.imshow(r2t_sc_depths[sq][0,0].detach().cpu()); plt.colorbar(); plt.ion(); plt.show()
plt.figure(3); plt.imshow(rtt_obj_sc_depths[sq][bb,0].detach().cpu()); plt.colorbar(); plt.ion(); plt.show()
plt.figure(4); plt.imshow(rtt_obj_sc_depth_2[bb,0].detach().cpu()); plt.colorbar(); plt.ion(); plt.show()
plt.figure(5); plt.imshow(rev_d2f[bb,0].detach().cpu()); plt.colorbar(); plt.ion(); plt.show()
plt.figure(6); plt.imshow(d2f[bb,0].detach().cpu()); plt.colorbar(); plt.ion(); plt.show()
plt.figure(7); plt.imshow(norm[bb,0].detach().cpu()); plt.colorbar(); plt.ion(); plt.show()
plt.figure(8); plt.imshow(r2t_obj_masks[sq][bb,0].detach().cpu()); plt.colorbar(); plt.ion(); plt.show()
plt.figure(9); plt.imshow(rtt_obj_masks[sq][bb,0].detach().cpu()); plt.colorbar(); plt.ion(); plt.show()
plt.figure(10); plt.imshow(rtt_obj_imgs[sq][bb,0].detach().cpu()); plt.colorbar(); plt.ion(); plt.show()
'''
_, _, r2t_ego_projected_depth, r2t_ego_computed_depth = inverse_warp2(ref_img, tgt_depth, ego_pose, intrinsics, ref_depth)
### KITTI ###
if 'kitti' in args.data:
xlim_1 = 0.25; ylim_1 = 0.1; zlim_1 = 1.2;
xlim_2 = 0.25; ylim_2 = 0.1; zlim_2 = 1.2;
obj_vo_scale = 3.0
ego_vo_scale = 0.015
### CS ###
if 'cityscapes' in args.data:
xlim_1 = 0.1; ylim_1 = 0.1; zlim_1 = 0.4;
xlim_2 = 0.12; ylim_2 = 0.06; zlim_2 = 0.60;
obj_vo_scale = 3.0
ego_vo_scale = 0.005
ego_init_o = np.array([0,0,0,1]).reshape(4,1)
ego_init_x = np.array([ego_vo_scale*1,0,0,1]).reshape(4,1)
ego_init_y = np.array([0,ego_vo_scale*1,0,1]).reshape(4,1)
ego_init_z = np.array([0,0,ego_vo_scale*1,1]).reshape(4,1)
egoOs = np.array([mat @ ego_init_o for mat in ego_global_mats])[:,:3,0]
egoXs = np.array([mat @ ego_init_x for mat in ego_global_mats])[:,:3,0]
egoYs = np.array([mat @ ego_init_y for mat in ego_global_mats])[:,:3,0]
egoZs = np.array([mat @ ego_init_z for mat in ego_global_mats])[:,:3,0]
bbox_y = dict(boxstyle='round', facecolor='yellow', alpha=0.5)
bbox_c = dict(boxstyle='round', facecolor='coral', alpha=0.5)
bbox_m = dict(boxstyle='round', facecolor='magenta', alpha=0.5)
bbox_l = dict(boxstyle='round', facecolor='lime', alpha=0.5)
bbox_w = dict(boxstyle='round', facecolor='white', alpha=0.5)
bbox_b = dict(boxstyle='round', facecolor='deepskyblue', alpha=0.5)
# pdb.set_trace()
sq = 0; bb = 0;
r2t_objs_coords = pixel2cam(r2t_obj_sc_depths[0][:,0], intrinsics.inverse().repeat(num_inst,1,1))
rtt_objs_coords = pixel2cam(rtt_obj_sc_depths[0][:,0], intrinsics.inverse().repeat(num_inst,1,1))
tgt_objs_coords = pixel2cam(tgt_obj_depth[:,0], intrinsics.inverse().repeat(num_inst,1,1))
r2t_obj_3d_locs = []
rtt_obj_3d_locs = []
tgt_obj_3d_locs = []
for r2t_obj_coords in r2t_objs_coords: r2t_obj_3d_locs.append(torch.cat([coords[coords!=0].mean().unsqueeze(0) for coords in r2t_obj_coords]))
for rtt_obj_coords in rtt_objs_coords: rtt_obj_3d_locs.append(torch.cat([coords[coords!=0].mean().unsqueeze(0) for coords in rtt_obj_coords]))
for tgt_obj_coords in tgt_objs_coords: tgt_obj_3d_locs.append(torch.cat([coords[coords!=0].mean().unsqueeze(0) for coords in tgt_obj_coords]))
for obj_loc in tgt_obj_3d_locs: objOs.append( (ego_global_mat @ np.concatenate([obj_loc.detach().cpu().numpy(), np.array([1])]).reshape(4,1))[:3].squeeze() );
objHs_pred, objHs_comp = [], []
for ii in range(len(obj_pose_inv)): objHs_pred.append( (ego_global_mat @ np.concatenate([tgt_obj_3d_locs[ii].detach().cpu().numpy(), np.array([1])]).reshape(4,1))[:3].squeeze() + obj_vo_scale*obj_pose_inv[ii].detach().cpu().numpy()[:3] )
for ii in range(len(obj_pose_inv)): objHs_comp.append( (ego_global_mat @ np.concatenate([tgt_obj_3d_locs[ii].detach().cpu().numpy(), np.array([1])]).reshape(4,1))[:3].squeeze() - obj_vo_scale*tr_fwd[0][ii].detach().cpu().numpy() )
for pred, comp in zip(objHs_pred, objHs_comp): objHs.append( (pred + comp) / 2 )
r2t_obj_3d_loc = torch.stack(r2t_obj_3d_locs).unsqueeze(-1).unsqueeze(-1)
r2t_obj_homo, _ = cam2homo(r2t_obj_3d_loc, intrinsics.repeat(num_inst,1,1), torch.zeros([1,3,1]).cuda())
r2t_obj_tail = r2t_obj_homo.reshape(num_inst,2).detach().cpu().numpy()
r2t_obj_trans = -obj_pose[:,:3]
r2t_obj_trans_gt = -tr_fwd[0]
r2t_obj_3d_loc_tr = r2t_obj_3d_loc.reshape(num_inst,3) + r2t_obj_trans
r2t_obj_3d_loc_tr_gt = r2t_obj_3d_loc.reshape(num_inst,3) + r2t_obj_trans_gt
r2t_obj_homo_tr, _ = cam2homo(r2t_obj_3d_loc_tr.unsqueeze(-1).unsqueeze(-1), intrinsics.repeat(num_inst,1,1), torch.zeros([1,3,1]).cuda())
r2t_obj_homo_tr_gt, _ = cam2homo(r2t_obj_3d_loc_tr_gt.unsqueeze(-1).unsqueeze(-1), intrinsics.repeat(num_inst,1,1), torch.zeros([1,3,1]).cuda())
r2t_obj_head = r2t_obj_homo_tr.reshape(num_inst,2).detach().cpu().numpy()
r2t_obj_head_gt = r2t_obj_homo_tr_gt.reshape(num_inst,2).detach().cpu().numpy()
arr_scale = 1.5
tgt = (tgt_img[bb%args.batch_size]*0.5+0.5).detach().cpu().numpy().transpose(1,2,0)
tgt_inst = 1 - tgt_bg_mask[bb].repeat(3,1,1).detach().cpu().numpy().transpose(1,2,0)
tgt_masked = (tgt + 0.2 * tgt_inst).clip(max=1.0)
ref = (ref_img[bb%args.batch_size]*0.5+0.5).detach().cpu().numpy().transpose(1,2,0)
ref_inst = 1 - ref_bg_mask[bb].repeat(3,1,1).detach().cpu().numpy().transpose(1,2,0)
ref_masked = (ref + 0.2 * ref_inst).clip(max=1.0)
d_tgt = 1/tgt_depth.detach().cpu()[bb%args.batch_size,0]
d_ref = 1/ref_depth.detach().cpu()[bb%args.batch_size,0]
r2t_obj = (r2t_obj_imgs[0].sum(dim=0) * 0.5 + 0.5).detach().cpu().numpy().transpose(1,2,0) if num_inst != 0 else np.zeros([256,832,3])
tgt_obj = (tgt_obj_img.sum(dim=0) * 0.5 + 0.5).detach().cpu().numpy().transpose(1,2,0) if num_inst != 0 else np.zeros([256,832,3])
i_w_bg = (IMDDs[sq][0] * 0.5 + 0.5)[bb].detach().cpu().numpy().transpose(1,2,0)
i_w_obj = (obj_IMDDs[sq][0] * 0.5 + 0.5)[bb].detach().cpu().numpy().transpose(1,2,0)
i_w = ((IMDDs[sq][0] + obj_IMDDs[sq][0]) * 0.5 + 0.5)[bb].detach().cpu().numpy().transpose(1,2,0)
m_w = obj_IMDDs[sq][1][0].repeat(3,1,1).detach().cpu().numpy().transpose(1,2,0)
i_w_masked = i_w + 0.2 * m_w
d_diff = ( ((IMDDs[sq][3] + obj_IMDDs[sq][3]) - (IMDDs[sq][2] + obj_IMDDs[sq][2])).abs() / ((IMDDs[sq][3] + obj_IMDDs[sq][3]) + (IMDDs[sq][2] + obj_IMDDs[sq][2])).abs().clamp(min=1e-3) ).clamp(0,1)[bb,0].detach().cpu()
d_diff_ego = ( (r2t_ego_projected_depth - r2t_ego_computed_depth).abs() / (r2t_ego_projected_depth + r2t_ego_computed_depth).abs().clamp(min=1e-3) ).clamp(0,1)[bb,0].detach().cpu() * (IMDDs[sq][1] + obj_IMDDs[sq][1])[bb,0].detach().cpu()
occ = 1.5 * d_diff.unsqueeze(-1).repeat(1,1,3).numpy()
occ[:,:,2] = 0
occ[occ<0.1] = 0
i_w_occ = (i_w_masked + occ).clip(max=1.0)
tgt_diff = np.abs(i_w-tgt).mean(axis=2)
th = 5; samp = 20;
r2t_obj_coords = r2t_objs_coords.sum(dim=0, keepdim=True)
rtt_obj_coords = rtt_objs_coords.sum(dim=0, keepdim=True)
tgt_obj_coords = tgt_objs_coords.sum(dim=0, keepdim=True)
r2t_filt = np.abs(stats.zscore( r2t_obj_coords[bb,2].view(-1)[r2t_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy() )) < th
rtt_filt = np.abs(stats.zscore( rtt_obj_coords[bb,2].view(-1)[rtt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy() )) < th
tgt_filt = np.abs(stats.zscore( tgt_obj_coords[bb,2].view(-1)[tgt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy() )) < th
npts_r2t = int(r2t_filt.sum())
npts_rtt = int(rtt_filt.sum())
npts_tgt = int(tgt_filt.sum())
X_r2t = r2t_obj_coords[bb,0].view(-1)[r2t_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[r2t_filt][range(0,npts_r2t,samp)]
Y_r2t = r2t_obj_coords[bb,1].view(-1)[r2t_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[r2t_filt][range(0,npts_r2t,samp)]
Z_r2t = r2t_obj_coords[bb,2].view(-1)[r2t_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[r2t_filt][range(0,npts_r2t,samp)]
C_r2t = r2t_obj_imgs[0].sum(dim=0).view(3,-1)[:,r2t_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[:,r2t_filt][:,range(0,npts_r2t,samp)] * 0.5 + 0.5
C_r2t[0] = 1; C_r2t[1] = 0; C_r2t[2] = 0;
X_rtt = rtt_obj_coords[bb,0].view(-1)[rtt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[rtt_filt][range(0,npts_rtt,samp)]
Y_rtt = rtt_obj_coords[bb,1].view(-1)[rtt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[rtt_filt][range(0,npts_rtt,samp)]
Z_rtt = rtt_obj_coords[bb,2].view(-1)[rtt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[rtt_filt][range(0,npts_rtt,samp)]
C_rtt = rtt_obj_imgs[0].sum(dim=0).view(3,-1)[:,rtt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[:,rtt_filt][:,range(0,npts_rtt,samp)] * 0.5 + 0.5
C_rtt[0] = 1; C_rtt[1] = 1; C_rtt[2] = 0;
X_tgt = tgt_obj_coords[bb,0].view(-1)[tgt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[tgt_filt][range(0,npts_tgt,samp)]
Y_tgt = tgt_obj_coords[bb,1].view(-1)[tgt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[tgt_filt][range(0,npts_tgt,samp)]
Z_tgt = tgt_obj_coords[bb,2].view(-1)[tgt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[tgt_filt][range(0,npts_tgt,samp)]
C_tgt = tgt_obj_img.sum(dim=0).view(3,-1)[:,tgt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[:,tgt_filt][:,range(0,npts_tgt,samp)] * 0.5 + 0.5
C_tgt[0] = 0; C_tgt[1] = 0; C_tgt[2] = 1;
XYZ_global_tgt = np.expand_dims(ego_global_mat, axis=0).repeat(X_tgt.shape[0],axis=0) @ np.expand_dims(np.stack([X_tgt, Y_tgt, Z_tgt, np.ones([X_tgt.shape[0]])]).transpose(1,0), axis=-1)
C_global_tgt = (tgt_obj_img.sum(dim=0).view(3,-1)[:,tgt_obj_coords[bb].mean(dim=0).view(-1)!=0].detach().cpu().numpy()[:,tgt_filt][:,range(0,npts_tgt,samp)] * 0.5 + 0.5).clip(min=0.0, max=1.0)
plt.close('all')
fig = plt.figure(1, figsize=(1920/100, 1080/100), dpi=100) # figsize=(23, 13)
gs = GridSpec(nrows=5, ncols=6)
text_xy = [7, -16]
text_fd = {'family': 'sans', 'size': 13, 'color': 'black', 'style': 'italic'}
fig.add_subplot(gs[0, 0:2])
plt.imshow(ref_masked, vmax=1); plt.text(text_xy[0], text_xy[1], "$I_{t}$", fontdict=text_fd);
plt.xticks([]) and plt.yticks([]) if args.save_fig else plt.grid(linestyle=':', linewidth=0.4)
if not args.save_fig: plt.grid(linestyle=':', linewidth=0.4);
plt.text(55, -29, "Scene: {}, Iter: {}".format(args.data, i), fontsize=6.5);
plt.text(55, -9, "Model: {}".format(args.pretrained_disp), fontsize=6.5);
plt.xlim(0, 832-1); plt.ylim(256-1, 0);
fig.add_subplot(gs[0, 2:4])
plt.imshow(d_ref, cmap='turbo', vmax=14); plt.text(text_xy[0], text_xy[1], "$D_{t}$", fontdict=text_fd);
plt.xticks([]) and plt.yticks([]) if args.save_fig else plt.grid(linestyle=':', linewidth=0.4)
plt.xlim(0, 832-1); plt.ylim(256-1, 0);
fig.add_subplot(gs[1, 0:2])
plt.imshow(tgt_masked, vmax=1); plt.text(text_xy[0], text_xy[1], "$I_{t+1}$", fontdict=text_fd);
plt.xticks([]) and plt.yticks([]) if args.save_fig else plt.grid(linestyle=':', linewidth=0.4)
plt.xlim(0, 832-1); plt.ylim(256-1, 0);
fig.add_subplot(gs[1, 2:4])
plt.imshow(d_tgt, cmap='turbo', vmax=14); plt.text(text_xy[0], text_xy[1], "$D_{t+1}$", fontdict=text_fd);
plt.xticks([]) and plt.yticks([]) if args.save_fig else plt.grid(linestyle=':', linewidth=0.4)
plt.xlim(0, 832-1); plt.ylim(256-1, 0);
fig.add_subplot(gs[2, 0:2])
plt.imshow(r2t_obj, vmax=1); plt.text(text_xy[0], text_xy[1], "Ego-warped objects with motion", fontdict=text_fd, size=10);
plt.xticks([]) and plt.yticks([]) if args.save_fig else plt.grid(linestyle=':', linewidth=0.4)
plt.text(130, 250, "*ego speed {:0.4f}, 6-DoF {}".format(float(ego_pose[0,:3].pow(2).sum().sqrt()), ego_pose[0].detach().cpu().numpy().round(4)), fontsize=7, bbox=bbox_b, ha='left', va='bottom');
if num_inst > 0: plt.text(7, 7, "Obj-1: {:0.4f} {}".format(float(obj_pose[0,:3].pow(2).sum().sqrt()), obj_pose[0][:3].detach().cpu().numpy().round(4)), fontsize=7, bbox=bbox_m, ha='left', va='top');
if num_inst > 0 and not args.save_fig: plt.text(330, 7, "#1: {:0.4f} {}".format(float(tr_fwd[0].pow(2).sum(dim=1).sqrt()[0]), tr_fwd[0][0].detach().cpu().numpy().round(4)), fontsize=7, bbox=bbox_c, ha='left', va='top');
if num_inst > 1: plt.text(7, 31, "Obj-2: {:0.4f} {}".format(float(obj_pose[1,:3].pow(2).sum().sqrt()), obj_pose[1][:3].detach().cpu().numpy().round(4)), fontsize=7, bbox=bbox_m, ha='left', va='top');
if num_inst > 1 and not args.save_fig: plt.text(330, 31, "#2: {:0.4f} {}".format(float(tr_fwd[0].pow(2).sum(dim=1).sqrt()[1]), tr_fwd[0][1].detach().cpu().numpy().round(4)), fontsize=7, bbox=bbox_c, ha='left', va='top');
if num_inst > 2: plt.text(7, 55, "Obj-3: {:0.4f} {}".format(float(obj_pose[2,:3].pow(2).sum().sqrt()), obj_pose[2][:3].detach().cpu().numpy().round(4)), fontsize=7, bbox=bbox_m, ha='left', va='top');
if num_inst > 2 and not args.save_fig: plt.text(330, 55, "#3: {:0.4f} {}".format(float(tr_fwd[0].pow(2).sum(dim=1).sqrt()[2]), tr_fwd[0][2].detach().cpu().numpy().round(4)), fontsize=7, bbox=bbox_c, ha='left', va='top');
if num_inst > 3: plt.text(7, 79, "Obj-4: {:0.4f} {}".format(float(obj_pose[3,:3].pow(2).sum().sqrt()), obj_pose[3][:3].detach().cpu().numpy().round(4)), fontsize=7, bbox=bbox_m, ha='left', va='top');
if num_inst > 3 and not args.save_fig: plt.text(330, 79, "#4: {:0.4f} {}".format(float(tr_fwd[0].pow(2).sum(dim=1).sqrt()[3]), tr_fwd[0][3].detach().cpu().numpy().round(4)), fontsize=7, bbox=bbox_c, ha='left', va='top');
if num_inst > 0 and not args.save_fig: plt.arrow(r2t_obj_tail[0,0], r2t_obj_tail[0,1], arr_scale*(-r2t_obj_tail[0,0]+r2t_obj_head_gt[0,0]), arr_scale*(-r2t_obj_tail[0,1]+r2t_obj_head_gt[0,1]), width=2, head_width=9, head_length=9, color='red', alpha=1);
if num_inst > 0: plt.arrow(r2t_obj_tail[0,0], r2t_obj_tail[0,1], arr_scale*(-r2t_obj_tail[0,0]+r2t_obj_head[0,0]), arr_scale*(-r2t_obj_tail[0,1]+r2t_obj_head[0,1]), width=3, head_width=10, head_length=9, color='magenta', alpha=1);
if num_inst > 0: plt.text(r2t_obj_tail[0,0]-30, r2t_obj_tail[0,1]+25, "1: {:0.4f}".format(float(obj_pose[0,:3].pow(2).sum().sqrt())), fontsize=7, bbox=bbox_l);
if num_inst > 1 and not args.save_fig: plt.arrow(r2t_obj_tail[1,0], r2t_obj_tail[1,1], arr_scale*(-r2t_obj_tail[1,0]+r2t_obj_head_gt[1,0]), arr_scale*(-r2t_obj_tail[1,1]+r2t_obj_head_gt[1,1]), width=2, head_width=9, head_length=9, color='red', alpha=1);
if num_inst > 1: plt.arrow(r2t_obj_tail[1,0], r2t_obj_tail[1,1], arr_scale*(-r2t_obj_tail[1,0]+r2t_obj_head[1,0]), arr_scale*(-r2t_obj_tail[1,1]+r2t_obj_head[1,1]), width=3, head_width=10, head_length=9, color='magenta', alpha=1);
if num_inst > 1: plt.text(r2t_obj_tail[1,0]-30, r2t_obj_tail[1,1]+25, "2: {:0.4f}".format(float(obj_pose[1,:3].pow(2).sum().sqrt())), fontsize=7, bbox=bbox_l);
if num_inst > 2 and not args.save_fig: plt.arrow(r2t_obj_tail[2,0], r2t_obj_tail[2,1], arr_scale*(-r2t_obj_tail[2,0]+r2t_obj_head_gt[2,0]), arr_scale*(-r2t_obj_tail[2,1]+r2t_obj_head_gt[2,1]), width=2, head_width=9, head_length=9, color='red', alpha=1);
if num_inst > 2: plt.arrow(r2t_obj_tail[2,0], r2t_obj_tail[2,1], arr_scale*(-r2t_obj_tail[2,0]+r2t_obj_head[2,0]), arr_scale*(-r2t_obj_tail[2,1]+r2t_obj_head[2,1]), width=3, head_width=10, head_length=9, color='magenta', alpha=1);
if num_inst > 2: plt.text(r2t_obj_tail[2,0]-30, r2t_obj_tail[2,1]+25, "3: {:0.4f}".format(float(obj_pose[2,:3].pow(2).sum().sqrt())), fontsize=7, bbox=bbox_l);
if num_inst > 3 and not args.save_fig: plt.arrow(r2t_obj_tail[3,0], r2t_obj_tail[3,1], arr_scale*(-r2t_obj_tail[3,0]+r2t_obj_head_gt[3,0]), arr_scale*(-r2t_obj_tail[3,1]+r2t_obj_head_gt[3,1]), width=2, head_width=9, head_length=9, color='red', alpha=1);
if num_inst > 3: plt.arrow(r2t_obj_tail[3,0], r2t_obj_tail[3,1], arr_scale*(-r2t_obj_tail[3,0]+r2t_obj_head[3,0]), arr_scale*(-r2t_obj_tail[3,1]+r2t_obj_head[3,1]), width=3, head_width=10, head_length=9, color='magenta', alpha=1);
if num_inst > 3: plt.text(r2t_obj_tail[3,0]-30, r2t_obj_tail[3,1]+25, "4: {:0.4f}".format(float(obj_pose[3,:3].pow(2).sum().sqrt())), fontsize=7, bbox=bbox_l);
plt.xlim(0, 832-1); plt.ylim(256-1, 0);
fig.add_subplot(gs[3, 0:2])
plt.imshow(i_w_occ, vmax=1); plt.text(text_xy[0], text_xy[1], "Final synthesis (yellow: dis/occlusion)", fontdict=text_fd, size=10);
plt.xticks([]) and plt.yticks([]) if args.save_fig else plt.grid(linestyle=':', linewidth=0.4)
plt.xlim(0, 832-1); plt.ylim(256-1, 0);
fig.add_subplot(gs[4, 0:2])
plt.imshow(tgt_diff, cmap='bone', vmax=0.5); plt.text(text_xy[0], text_xy[1], "$I_{diff}$", fontdict=text_fd);
plt.xticks([]) and plt.yticks([]) if args.save_fig else plt.grid(linestyle=':', linewidth=0.4)
plt.xlim(0, 832-1); plt.ylim(256-1, 0);
### 3d plot 1: cam-coord ###
ax1 = fig.add_subplot(gs[3:5, 2:4], projection='3d')
ax1_axfont = {'family': 'sans', 'size': 12, 'weight': 'heavy', 'style': 'italic', 'color': 'gray'}
ax1_titlefont = {'family': 'sans', 'size': 12, 'color': 'black', 'ha': 'center', 'va': 'bottom', 'linespacing': 2}
ax1_annotfont = {'family': 'sans', 'size': 8, 'color': 'black', 'ha': 'center', 'va': 'center'}
ax1.scatter(X_r2t, Y_r2t, Z_r2t, c=C_r2t.transpose(1,0), s=1, alpha=0.4)
ax1.scatter(X_rtt, Y_rtt, Z_rtt, c=C_rtt.transpose(1,0), s=1, alpha=0.6)
ax1.scatter(X_tgt, Y_tgt, Z_tgt, c=C_tgt.transpose(1,0), s=1, alpha=0.4)
ax1.set_xlabel('X', fontdict=ax1_axfont); ax1.set_zlabel('Z', fontdict=ax1_axfont);
ax1.axes.yaxis.set_ticklabels([])
ax1.set_xlim(-xlim_1, xlim_1)
ax1.set_ylim(-ylim_1, ylim_1)
ax1.set_zlim(0, zlim_1)
ax1.text(0, 0, zlim_1*1.20, "[Top-view] Objects in {$t+1$} frame on camera coordinate\n(red: ego-warped $t$→$t+1$, yellow: final-warped $t$→$t+1$, blue: $t+1$)", fontdict=ax1_titlefont)
if num_inst > 0: ax1.text(-xlim_1/2, 0, zlim_1*1.10, "1: XYZ {}".format(r2t_obj_3d_locs[0].detach().cpu().numpy().round(4)), fontdict=ax1_annotfont, bbox=bbox_c);
if num_inst > 0: ax1.text(-xlim_1/2, 0, zlim_1*1.05, "1: XYZ {}".format(rtt_obj_3d_locs[0].detach().cpu().numpy().round(4)), fontdict=ax1_annotfont, bbox=bbox_y);
if num_inst > 0: ax1.text(-xlim_1/2, 0, zlim_1*1.00, "1: XYZ {}".format(tgt_obj_3d_locs[0].detach().cpu().numpy().round(4)), fontdict=ax1_annotfont, bbox=bbox_b);
if num_inst > 1: ax1.text(+xlim_1/2, 0, zlim_1*1.10, "2: XYZ {}".format(r2t_obj_3d_locs[1].detach().cpu().numpy().round(4)), fontdict=ax1_annotfont, bbox=bbox_c);
if num_inst > 1: ax1.text(+xlim_1/2, 0, zlim_1*1.05, "2: XYZ {}".format(rtt_obj_3d_locs[1].detach().cpu().numpy().round(4)), fontdict=ax1_annotfont, bbox=bbox_y);
if num_inst > 1: ax1.text(+xlim_1/2, 0, zlim_1*1.00, "2: XYZ {}".format(tgt_obj_3d_locs[1].detach().cpu().numpy().round(4)), fontdict=ax1_annotfont, bbox=bbox_b);
ax1.get_proj = lambda: np.dot(Axes3D.get_proj(ax1), np.diag([2, 1, 2, 1]))
ax1.view_init(elev=0, azim=-90)
### 3d plot 2: world-coord ###
ax2 = fig.add_subplot(gs[1:5, 4:6], projection='3d')
ax2_axfont = {'family': 'sans', 'size': 14, 'weight': 'heavy', 'style': 'italic', 'color': 'gray'}
ax2_titlefont = {'family': 'sans', 'size': 12, 'color': 'black', 'ha': 'center', 'va': 'center'}
ax2.scatter(XYZ_global_tgt[:,0,0], XYZ_global_tgt[:,1,0], XYZ_global_tgt[:,2,0], c=C_global_tgt.transpose(1,0), s=6, zorder=vidx+1)
for ii in range(len(egoOs)-1): dR.drawVector(ax2, egoOs[ii], egoOs[ii+1], mutation_scale=1, alpha=0.5, arrowstyle='-', lineStyle=':', lineWidth=1, lineColor='k', zorder=vidx+2)
for ii in range(len(egoOs)):
if ii >= len(egoOs) - 1:
dR.drawPointWithAxis(ax2, egoOs[ii], egoXs[ii]-egoOs[ii], egoYs[ii]-egoOs[ii], egoZs[ii]-egoOs[ii], mutation_scale=3, alpha=1.0, arrowstyle='-', lineWidth=2.0, vectorLength=2, zorder=vidx+4)
ax2.text(egoOs[ii,0]-xlim_2/4, egoOs[ii,1], egoOs[ii,2], "{:0.4f}".format( np.linalg.norm(egoOs[ii]-egoOs[ii-1]).round(4) ), fontsize=8, bbox=bbox_w, zorder=vidx+5);
else:
dR.drawPointWithAxis(ax2, egoOs[ii], egoXs[ii]-egoOs[ii], egoYs[ii]-egoOs[ii], egoZs[ii]-egoOs[ii], mutation_scale=1, alpha=0.4, arrowstyle='-', lineWidth=1.5, zorder=vidx+3)
for ii in range(len(objOs)):
if ii >= len(objOs) - len(obj_pose):
if np.linalg.norm(objOs[ii]-objHs[ii], 2) < 0.1 and np.linalg.norm(objOs[ii]-objHs[ii], 2) > 0.002:
dR.drawVector(ax2, objOs[ii], objHs[ii], mutation_scale=20, arrowstyle='fancy', pointEnable=False, lineWidth=0.5, faceColor=colors[objIDs_flatten[ii]], edgeColor='k', zorder=vidx+20);
ax2.text(objHs[ii][0]-xlim_2/4, objHs[ii][1], objHs[ii][2], "{:0.4f}".format( (np.linalg.norm(objOs[ii]-objHs[ii])/obj_vo_scale).round(4) ), fontsize=8, bbox=bbox_w, zorder=vidx+30);
else:
if np.linalg.norm(objOs[ii]-objHs[ii], 2) < 0.1 and np.linalg.norm(objOs[ii]-objHs[ii], 2) > 0.002:
dR.drawVector(ax2, objOs[ii], objHs[ii], mutation_scale=20, alpha=0.3, arrowstyle='fancy', pointEnable=False, lineWidth=0.5, faceColor=colors[objIDs_flatten[ii]], edgeColor='k', zorder=vidx+10);
ax2.text(-xlim_2*0.9, 0, zlim_2*0.02, "Speed", fontsize=9, style='italic', bbox=bbox_w, zorder=vidx+25);
ax2.set_xlabel('X', fontdict=ax2_axfont); ax2.set_zlabel('Z', fontdict=ax2_axfont);
ax2.axes.yaxis.set_ticklabels([])
ax2.set_xlim(-xlim_2, xlim_2)
ax2.set_ylim(-ylim_2, ylim_2)
ax2.set_zlim(0, zlim_2)
ax2.get_proj = lambda: np.dot(Axes3D.get_proj(ax2), np.diag([1.2, 0.6, 2.4, 1]))
ax2.xaxis._axinfo['juggled'] = (2,0,1)
ax2.yaxis._axinfo['juggled'] = (2,1,0)
ax2.zaxis._axinfo['juggled'] = (0,2,1)
elv = 2; azm = 1;
if 'cityscapes' in args.data:
ax2.view_init(elev=-0.01-elv*vidx, azim=-90+azm*vidx)
# ax2.view_init(elev=-0.01-elv*vidx, azim=-90.01-azm*vidx)
else:
if vidx <= 10: ax2.view_init(elev=-0.01-elv*vidx, azim=-90+azm*vidx) # elev: -0~-40, azim: -90~-70
if 10 < vidx and vidx <= 20: ax2.view_init(elev= -0.01-elv*10 + elv*(vidx-10), azim= -90+azm*10 - azm*(vidx-10)) # elev: -40~-0, azim: -70~-90
if 20 < vidx and vidx <= 30: ax2.view_init(elev= -0.01 - elv*(vidx-20), azim= -90 - azm*(vidx-20)) # elev: -0~-40, azim: -90~-110
if 30 < vidx and vidx <= 40: ax2.view_init(elev= -0.01-elv*10 + elv*(vidx-30), azim= -90-azm*10 + azm*(vidx-30)) # elev: -40~-0, azim: -110~-90
if 40 < vidx and vidx <= 50: ax2.view_init(elev= -0.01 - elv*(vidx-40), azim= -90 + azm*(vidx-40)) # elev: -0~-40, azim: -90~-70
if 50 < vidx and vidx <= 60: ax2.view_init(elev= -0.01-elv*10 + elv*(vidx-50), azim= -90+azm*10 - azm*(vidx-50)) # elev: -40~-0, azim: -70~-90
ax2.dist = 10 + 0.1*vidx
ax2_title = fig.add_subplot(gs[4, 4:6])
ax2_title.axis('off')
ax2_title.text(0.5, 0.5, '[Top-view] Unified visual odometry on world coordinate', fontdict=ax2_titlefont)
plt.tight_layout();
if args.save_fig:
print('>> Saving image #{:02d}'.format(vidx))
# plt.savefig('{:}/{:}_{:04d}.png'.format(args.save_path, Path(args.data).basename(), i), dpi=100)
plt.savefig('{:}/{:04d}.png'.format(args.save_path, vidx), dpi=100)
plt.close('all')
else:
plt.ion(); plt.show();
print('>> Type \'c\' to continue')
pdb.set_trace()
plt.close('all')
vidx += 1
return 0
def MFP3D_bmax_time_all_comp (fig, MFP_stacked_thres, MFP_thres_poly, MFP_poly_bmax, depths, xx, yy, xmin, ymin, xmax, ymax, mofettes, comp_colors):
grid = fig.add_gridspec(nrows = 5, ncols = 7)
####################### Bmax vs time Plots
# create dict containing the row index for each polygon
# A: third column (because 3D plot will be in first&second column), B: fourth column etc.
# Depth 100m: first row (i.e. row "0"), depth 200m: second row etc.
# Example: 300B will be in third row (i.e. "2"), fourth column (i.e. "3")
plot_axes = {}
for row_idx, depth in enumerate(np.arange (100,600,100)): # 5 depths in total (100,200,300,400,500m)
for column_idx, signature_idx in enumerate(np.arange (0,4,1)): # 4 signature letters in total (A,B,C,D)
letter = chr(ord('A') + signature_idx)
plot_axes[str(depth) + letter] = {}
plot_axes[str(depth) + letter]["row"] = row_idx
plot_axes[str(depth) + letter]["col"] = column_idx+2 # i add "2" to column idx because first&second column is for the 3d plot
global_max = 79
global_min = 69
###### Plot Bmax & Lok./Min.
for poly_sig, poly_bmax in MFP_poly_bmax.items():
row_idx = plot_axes[poly_sig]["row"]
col_idx = plot_axes[poly_sig]["col"]
ax = plt.subplot (grid[row_idx, col_idx])
# ax2 = ax.twinx()
# Get bmax data for current polygon
x = MFP_poly_bmax[poly_sig][:,0]
y = MFP_poly_bmax[poly_sig][:,1]
# Sort the points by bmax, so that the lowest bmax are plotted last
idx = y.argsort()
x_, y_ = x[idx], y[idx]
# Plot Bmax
colormap = plt.cm.gist_stern_r
normalize = matplotlib.colors.Normalize(vmin = y.min(), vmax = y.max())
ax.scatter(x_, y_, c = y_, s = 80, cmap = colormap, norm = normalize, edgecolor = '')
###### Layout
# general tick settings
ax.tick_params(axis = 'both', which = 'major', labelsize = 16, length = 5, width = 1)
ax.set_xlim(left = 0, right = 540)
ax.set_ylim(top = global_max, bottom = global_min)
ax.set_xticks(np.arange(0, 540 + 60, 60))
ax.set_yticks(np.arange(global_min, global_max + 5, 5))
ax.set_axisbelow(True)
ax.xaxis.grid(True, lw = 2, alpha = 0.5, zorder = -5)
ticklabels = ax.get_xticklabels()
ticklabels = ax.get_yticklabels()
ticklabels[0].set_va("bottom")
ticklabels[-1].set_va("top")
ax.text(0, 1.02, poly_sig, fontsize = 19, weight = "bold", transform = ax.transAxes)
# axis labels
if poly_sig == "500B":
ax.set_ylabel("Bmax [dB]", fontsize = 18, color = "black", weight = "bold")
if poly_sig == "500B":
ax.set_xlabel ("Uhrzeit", fontsize = 20, weight = "bold")
# ticklabels
labelbottom = ["100C", "400A", "500B", "500C", "500D"]
if poly_sig in labelbottom:
ax.tick_params(labelbottom = True)
a = ax.get_xticks().tolist()
for tickidx, tick in enumerate(a):
a[tickidx] = ""
a[0] = "22"
a[1] = "23"
a[2] = "0"
a[3] = "1"
a[4] = "2"
a[5] = "3"
a[6] = "4"
a[7] = "5"
a[8] = "6"
a[9] = "7"
ax.set_xticklabels(a)
else:
ax.tick_params(labelbottom = False)
labelleft = ["100A", "200A", "300A", "400A", "500B"]
if poly_sig in labelleft:
ax.tick_params(labelleft = True)
else:
ax.tick_params(labelleft = False)
####################### 3D Plots
ax_3d = plt.subplot (grid[0:, 0:2], projection='3d')
# stretch z-axis (by 0.9)
x_scale, y_scale, z_scale = 0.6, 0.6, 0.9
from mpl_toolkits.mplot3d.axes3d import Axes3D
ax_3d.get_proj = lambda: np.dot(Axes3D.get_proj(ax_3d), np.diag([x_scale, y_scale, z_scale, 1]))
# plot surface for every depth
for component in MFP_stacked_thres:
global_max = max([array.max() for array in MFP_stacked_thres[component].values()])
for depth, array in MFP_stacked_thres[component].items():
# add white rectangle as base for current depth
rect = patches.Rectangle((0, 0), 500, 500, facecolor = (1, 1, 1, 0.5)) # color white (1, 1, 1) with alpha 0.3
ax_3d.add_patch(rect)
art3d.pathpatch_2d_to_3d(rect, z = depth, zdir = "z")
array_ = np.where(array == 0, "nan", array)
PlotMFPLoczCountSubplot_3D (fig, ax_3d, array_, global_max, xx, yy, component, depth, modus = [], unique_areas = [])
# plot border of areas with color according to component
array = np.where(array != 0, 1, array)
ax_3d.contour(xx, yy, array, offset = depth, zdir = 'z', colors = comp_colors[component])
for component in MFP_thres_poly:
for depth in MFP_thres_poly[component]:
for poly_signature, poly in MFP_thres_poly[component][depth].items():
poly_txt = ax_3d.text(poly.centroid.x + 30, poly.centroid.y, depth, poly_signature[-1:], color = 'white', fontsize = 32, weight = "bold", transform = ax_3d.transData, zorder = 1000000)
poly_txt.set_path_effects([PathEffects.Stroke(linewidth = 2, foreground = "k"), PathEffects.Normal()])
# plot mofettes
ax_3d.text(mofettes[0,0],mofettes[0,1], 0, u'\u2605', color = 'red', fontsize = 40, transform = ax_3d.transData, zorder = 2000)
ax_3d.text(mofettes[1,0],mofettes[1,1], 0, u'\u2605', color = 'red', fontsize = 40, transform = ax_3d.transData, zorder = 2000)
ax_3d.text(mofettes[2,0],mofettes[2,1], 0, u'\u2605', color = 'red', fontsize = 40, transform = ax_3d.transData, zorder = 2000)
# draw north arrow
arrow3d(ax_3d, length = 150, width = 2, head = 0.3, headwidth = 3, offset = [-200, 200, 500], theta_x = 270, theta_z = 0, color = "black")
ax_3d.text(-200, 300, 530, "N", size = 24, zorder = 1, color='k')
# set axis limits
ax_3d.set_zlim(min(depths),max(depths))
ax_3d.set_xlim(0,500)
ax_3d.set_ylim(0,500)
# define distance titles to plots
rcParams['axes.titlepad'] = -10
# invert z- & x-axis
ax_3d.invert_zaxis()
# set x-, y- & z-axislabel
ax_3d.set_xlabel('x [m]', labelpad = 0, fontsize = 20, verticalalignment = 'center')
ax_3d.set_ylabel('y [m]', labelpad = 1, fontsize = 20, verticalalignment = 'center')
ax_3d.set_zlabel('Tiefe [m]', labelpad = 28, fontsize = 24, rotation = 90)
ax_3d.zaxis.set_rotate_label(False) # disable automatic axislabel rotation
# define ticklabels
xy_labels = np.arange(xmin, xmax+1, 100)
xy_labels = xy_labels.astype(int)
z_labels = depths
# set ticks and ticklabels
# x
ax_3d.set_xticks(xy_labels) # create ticks
ax_3d.set_xticklabels(xy_labels, verticalalignment = 'baseline', horizontalalignment = 'center') # label ticks
ax_3d.tick_params(axis = 'x', which = 'major', pad = 2, labelsize = 20) # adjust ticklabel positions
# y
ax_3d.set_yticks(xy_labels)
ax_3d.set_yticklabels(xy_labels, verticalalignment = 'bottom', horizontalalignment = 'right')
ax_3d.tick_params(axis = 'y', which = 'major', pad = 2, labelsize = 20)
# z
ax_3d.set_zticks(z_labels)
ax_3d.set_zticklabels(z_labels, verticalalignment = 'center')
ax_3d.tick_params(axis = 'z', which = 'major', pad = 12, labelsize = 20)
# rotate view
ax_3d.view_init(elev = 10, azim = 225)
line_z = Line2D([0,1],[0,1], linewidth = 8, linestyle = '-', color = comp_colors["Z"])
line_ns = Line2D([0,1],[0,1], linewidth = 8, linestyle = '-', color = comp_colors["N"])
line_ew = Line2D([0,1],[0,1], linewidth = 8, linestyle = '-', color = comp_colors["E"])
plt.figlegend((line_z, line_ns, line_ew), ('Z', 'N', 'E'), loc = "center left",
fontsize = 24, borderaxespad = 0.1, bbox_to_anchor = (0.23, 0.12))
grid.update(wspace = 0.15)
ax_3d.dist = 6
# sort states by index
state_ci.sort(key = lambda x: x[1], reverse=True)
ci_thresholds = [0, 48.5, 52.7, 100] # thresholds determine where to start new plot
for x in range(3):
# parameters for 3D plot
fig = plt.figure(figsize=(12, 8))
ax0 = plt.figure().gca()
ax = fig.gca(projection='3d')
ax.elev = 35
ax.azim = 270
ax.dist = 14
x_scale = 1
y_scale = 2.1
z_scale = 1
ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([x_scale, y_scale, z_scale, 1]))
j = 0
state_list = []
# generate list of states for each plot based on index thresholds
for i in range(len(state_code)):
state = state_ci[i][0]
if state_ci[i][1] < ci_thresholds[x+1] and state_ci[i][1] >= ci_thresholds[x]:
state_list.append(state[3:])
# plot states on above list
for i in range(len(state_code)):
state = state_ci[i][0]
if state_ci[i][1] < ci_thresholds[x+1] and state_ci[i][1] >= ci_thresholds[x]:
plot_state_by_index(globals()["{0}_epi_df".format(state)], globals()["{0}_demo_df".format(state)],
state, ax0, ax, j, state_list, zlim=200)
j += 1
def scale3d(ax, x_scale=1, y_scale=1, z_scale=1):
scale = np.diag([x_scale, y_scale, z_scale, 1.0])
scale = scale * (1.0 / scale.max())
scale[3, 3] = 1.0
short_proj = np.dot(Axes3D.get_proj(ax), scale)
ax.get_proj = short_proj
