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bb = breakpoint
import torch
import numpy as np
from utils.utils import to_tensor, to_numpy
import open3d as o3d
import rerun as rr
OPENCV2OPENGL = (1,-1,-1,1)
def pts_to_opengl(pts):
return pts*OPENCV2OPENGL[:3]
def save_pointmaps(data,path='debug',view=False,color='novelty',frustrum_scale=20):
# debug function to save points to a ply file
import open3d as o3d
pointmaps = data['pointmaps']
B = pointmaps.shape[0]
W, H = pointmaps.shape[-3:-1]
n_cams = data['c2ws'].shape[1]
geometries = []
for b in range(B):
geometry_b = []
points = torch.cat([p.flatten(start_dim=0,end_dim=1) for p in pointmaps[b]],dim=0)
if view:
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(to_numpy(points))
if color == 'novelty':
colors = torch.ones_like(points)
pts_p_cam = W*H
# make all novel points red
colors[pts_p_cam:,1:]*=0.1
# make all points from first camera blue
colors[:pts_p_cam,0]*=0.1
colors[:pts_p_cam,2]*=0.1
colors*=255.0
else:
colors = torch.cat([p.flatten(start_dim=0,end_dim=1) for p in data['imgs'][b]],dim=0)
pcd.colors = o3d.utility.Vector3dVector(to_numpy(colors)/255.0)
geometry_b.append(pcd)
origin = o3d.geometry.TriangleMesh.create_coordinate_frame(
size=10, origin=[0,0,0])
geometry_b.append(origin)
for i in range(n_cams):
K = data['Ks'][b,i].cpu().numpy()
K = o3d.camera.PinholeCameraIntrinsic(W,H,K)
P = data['c2ws'][b,i].cpu().numpy()
cam_frame = o3d.geometry.LineSet.create_camera_visualization(intrinsic=K,extrinsic=P,scale=frustrum_scale)
geometry_b.append(cam_frame)
o3d.visualization.draw_geometries(geometry_b)
# add point at the origin
o3d.io.write_point_cloud(f"{path}_{b}.ply", pcd)
breakpoint()
geometries.append(geometry_b)
return geometries
def just_load_viz(pred_dict,gt_dict,batch,name='just_load_viz',addr='localhost:9000',fused_meshes=None,n_points=None):
rr.init(name)
rr.connect(addr)
rr.set_time_seconds("stable_time", 0)
context_views = batch['input_cams']['pointmaps']
context_rgbs = batch['input_cams']['imgs']
gt_pred_views = gt_dict['pointmaps']
pred_views = pred_dict['pointmaps']
# FIX this weird shape
pred_masks = batch['new_cams']['valid_masks']
context_masks = batch['input_cams']['valid_masks']
B = batch['new_cams']['pointmaps'].shape[0]
W,H = context_views.shape[-3:-1]
n_pred_cams = pred_views.shape[1]
for b in range(B):
rr.set_time_seconds("stable_time", b)
# Set world transform to identity (normal origin)
rr.log("world", rr.Transform3D(translation=[0, 0, 0], mat3x3=np.eye(3)))
## show context views
context_rgb = to_numpy(context_rgbs[b])
for i in range(n_pred_cams):
if 'conf_pointmaps' in pred_dict:
conf_pts = pred_dict['conf_pointmaps'][b,i]
#print(f"view {i} mean conf: {mean_conf}, std conf: {std_conf}")
conf_pts = (conf_pts - conf_pts.min())/(conf_pts.max() - conf_pts.min())
conf_pts = to_numpy(conf_pts)
rr.log(f"view_{i}/pred_conf", rr.Image(conf_pts))
if pred_masks[b,i].sum() == 0:
continue
if gt_pred_views is not None:
gt_pred_pts = gt_pred_views[b,i][pred_masks[b,i]]
gt_pred_pts = to_numpy(gt_pred_pts)
else:
gt_pred_pts = None
# red is color for gt points
if gt_pred_pts is not None:
color = np.array([1,0,0])
colors = np.ones_like(gt_pred_pts)
colors[:,0] = color[0]
colors[:,1] = color[1]
colors[:,2] = color[2]
rr.log(
f"world/new_views_gt/view_{i}", rr.Points3D(gt_pred_pts,colors=colors)
)
# green is color for pred points
pred_pts = pred_views[b,i][pred_masks[b,i]]
pred_pts = to_numpy(pred_pts)
depth = pred_views[b,i][:,:,2]
depth -= depth[pred_masks[b,i]].min()
depth[~pred_masks[b,i]] = 0
depth /= depth.max()
depth = to_numpy(depth)
rr.log(f"world/new_views_pred/view_{i}/image", rr.Image(depth))
if 'classifier' in pred_dict:
classifier = (pred_dict['classifier'][b,i] > 0.0).float() # this is assuming the classifier is a sigmoid output
classifier = to_numpy(classifier)
rr.log(f"view_{i}/pred_mask", rr.Image(classifier))
color = np.array([0,1,0])
colors = np.ones_like(pred_pts)
colors[:,0] = color[0]
colors[:,1] = color[1]
colors[:,2] = color[2]
if n_points is None:
rr.log(
f"world/new_views_pred/view_{i}/pred_points", rr.Points3D(pred_pts,colors=colors)
)
else:
# randomly sample n_points from pred_pts
n_points = min(n_points, pred_pts.shape[0])
inds = np.random.choice(pred_pts.shape[0], n_points, replace=False)
rr.log(
f"world/new_views_pred/view_{i}/pred_points", rr.Points3D(pred_pts[inds],colors=colors[inds])
)
K = batch['new_cams']['Ks'][b,i].cpu().numpy()
P = batch['new_cams']['c2ws'][b,i].cpu().numpy()
P = np.linalg.inv(P)
rr.log(f"world/new_views_pred/view_{i}", rr.Transform3D(translation=P[:3,3], mat3x3=P[:3,:3], from_parent=True))
rr.log(f"world/new_views_gt/view_{i}", rr.Transform3D(translation=P[:3,3], mat3x3=P[:3,:3], from_parent=True))
if 'classifier' in pred_dict:
classifier = gt_dict['valid_masks'][b,i].float()
classifier = to_numpy(classifier)
rr.log(f"view_{i}/gt_mask", rr.Image(classifier))
rr.log(
f"world/new_views_pred/view_{i}/image",
rr.Pinhole(
resolution=[H, W],
focal_length=[K[0,0], K[1,1]],
principal_point=[K[0,2], K[1,2]],
),
)
rr.log(f"world/new_views_pred/view_{i}/image", rr.Image(to_numpy(pred_masks[b,i].float())))
n_input_cams = context_masks.shape[1]
for i in range(n_input_cams):
context_pts = context_views[b][i][context_masks[b][i]]
context_pts = to_numpy(context_pts)
context_pts_rgb = context_rgbs[b][i][context_masks[b][i]]
context_pts_rgb = to_numpy(context_pts_rgb)
# depth imgs
#context_depths = batch['input_cams']['depths'][b][i]
#context_depths = (context_depths / context_depths.max() * 255.0).clamp(0,255)
#context_depths = to_numpy(context_depths).astype(np.uint8)
rr.log(
f"world/context_views/view_{i}_points", rr.Points3D(context_pts,colors=(context_pts_rgb/255.0))
)
K = batch['input_cams']['Ks'][b,i].cpu().numpy()
P = batch['input_cams']['c2ws'][b,i].cpu().numpy()
P = np.linalg.inv(P)
rr.log(f"world/context_views/view_{i}", rr.Transform3D(translation=P[:3,3], mat3x3=P[:3,:3], from_parent=True))
rr.log(
f"world/context_views/view_{i}/image",
rr.Pinhole(
resolution=[H, W],
focal_length=[K[0,0], K[1,1]],
principal_point=[K[0,2], K[1,2]],
),
)
context_rgb_i = context_rgb[i]
rr.log(
f"world/context_views/view_{i}/image", rr.Image(context_rgb_i)
)
rr.log(
f"world/context_camera_{i}/mask", rr.Image(to_numpy(context_masks[b,i].float()))
)
if fused_meshes is not None:
rr.log(f"world/fused_mesh", rr.Mesh3D(vertex_positions=fused_meshes[b]['verts'], vertex_normals=fused_meshes[b]['norms'], vertex_colors=fused_meshes[b]['colors'], triangle_indices=fused_meshes[b]['faces']))
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