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example_viz.py
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example_viz.py
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import os
import open3d as o3d
import torch
import numpy as np
from utils import image_proc
from model.model import DeformNet
from model import dataset
import utils.utils as utils
import utils.viz_utils as viz_utils
import utils.nnutils as nnutils
import utils.line_mesh as line_mesh_utils
import options as opt
def main():
#####################################################################################################
# Options
#####################################################################################################
# Source-target example
split = "test"
seq_id = 17
src_id = 300 # source frame
tgt_id = 600 # target frame
srt_tgt_str = "5dbd7c9104df0300f329f294_shirt_000300_000600_geodesic_0.05"
# Make sure to use the intrinsics corresponding to the seq_id above!!
intrinsics = {
"fx": 575.548,
"fy": 577.46,
"cx": 323.172,
"cy": 236.417
}
# Train set example
# Important: You need to generate graph data using create_graph_data.py first.
# split = "train"
# seq_id = 258
# src_id = 0 # source frame
# tgt_id = 110 # target frame
# srt_tgt_str = "generated_shirt_000000_000110_geodesic_0.05"
# # Make sure to use the intrinsics corresponding to the seq_id above!!
# intrinsics = {
# "fx": 575.548,
# "fy": 577.46,
# "cx": 323.172,
# "cy": 236.417
# }
# Some params for coloring the predicted correspondence confidences
weight_thr = 0.3
weight_scale = 1
# We will overwrite the default value in options.py / settings.py
opt.use_mask = True
#####################################################################################################
# Load model
#####################################################################################################
saved_model = opt.saved_model
assert os.path.isfile(saved_model), f"Model {saved_model} does not exist."
pretrained_dict = torch.load(saved_model)
# Construct model
model = DeformNet().cuda()
if "chairs_things" in saved_model:
model.flow_net.load_state_dict(pretrained_dict)
else:
if opt.model_module_to_load == "full_model":
# Load completely model
model.load_state_dict(pretrained_dict)
elif opt.model_module_to_load == "only_flow_net":
# Load only optical flow part
model_dict = model.state_dict()
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_dict.items() if "flow_net" in k}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model.load_state_dict(model_dict)
else:
print(opt.model_module_to_load, "is not a valid argument (A: 'full_model', B: 'only_flow_net')")
exit()
model.eval()
#####################################################################################################
# Load example dataset
#####################################################################################################
example_dir = os.path.join("example_data" , f"{split}/seq{str(seq_id).zfill(3)}")
image_height = opt.image_height
image_width = opt.image_width
max_boundary_dist = opt.max_boundary_dist
src_id_str = str(src_id).zfill(6)
tgt_id_str = str(tgt_id).zfill(6)
src_color_image_path = os.path.join(example_dir, "color", src_id_str + ".jpg")
src_depth_image_path = os.path.join(example_dir, "depth", src_id_str + ".png")
tgt_color_image_path = os.path.join(example_dir, "color", tgt_id_str + ".jpg")
tgt_depth_image_path = os.path.join(example_dir, "depth", tgt_id_str + ".png")
graph_nodes_path = os.path.join(example_dir, "graph_nodes", srt_tgt_str + ".bin")
graph_edges_path = os.path.join(example_dir, "graph_edges", srt_tgt_str + ".bin")
graph_edges_weights_path = os.path.join(example_dir, "graph_edges_weights", srt_tgt_str + ".bin")
graph_clusters_path = os.path.join(example_dir, "graph_clusters", srt_tgt_str + ".bin")
pixel_anchors_path = os.path.join(example_dir, "pixel_anchors", srt_tgt_str + ".bin")
pixel_weights_path = os.path.join(example_dir, "pixel_weights", srt_tgt_str + ".bin")
# Source color and depth
source, _, cropper = dataset.DeformDataset.load_image(
src_color_image_path, src_depth_image_path, intrinsics, image_height, image_width
)
# Target color and depth (and boundary mask)
target, target_boundary_mask, _ = dataset.DeformDataset.load_image(
tgt_color_image_path, tgt_depth_image_path, intrinsics, image_height, image_width, cropper=cropper,
max_boundary_dist=max_boundary_dist, compute_boundary_mask=True
)
# Graph
graph_nodes, graph_edges, graph_edges_weights, _, graph_clusters, pixel_anchors, pixel_weights = dataset.DeformDataset.load_graph_data(
graph_nodes_path, graph_edges_path, graph_edges_weights_path, None,
graph_clusters_path, pixel_anchors_path, pixel_weights_path, cropper
)
num_nodes = np.array(graph_nodes.shape[0], dtype=np.int64)
# Update intrinsics to reflect the crops
fx, fy, cx, cy = image_proc.modify_intrinsics_due_to_cropping(
intrinsics['fx'], intrinsics['fy'], intrinsics['cx'], intrinsics['cy'],
image_height, image_width, original_h=cropper.h, original_w=cropper.w
)
intrinsics = np.zeros((4), dtype=np.float32)
intrinsics[0] = fx
intrinsics[1] = fy
intrinsics[2] = cx
intrinsics[3] = cy
#####################################################################################################
# Predict deformation
#####################################################################################################
# Move to device and unsqueeze in the batch dimension (to have batch size 1)
source_cuda = torch.from_numpy(source).cuda().unsqueeze(0)
target_cuda = torch.from_numpy(target).cuda().unsqueeze(0)
target_boundary_mask_cuda = torch.from_numpy(target_boundary_mask).cuda().unsqueeze(0)
graph_nodes_cuda = torch.from_numpy(graph_nodes).cuda().unsqueeze(0)
graph_edges_cuda = torch.from_numpy(graph_edges).cuda().unsqueeze(0)
graph_edges_weights_cuda = torch.from_numpy(graph_edges_weights).cuda().unsqueeze(0)
graph_clusters_cuda = torch.from_numpy(graph_clusters).cuda().unsqueeze(0)
pixel_anchors_cuda = torch.from_numpy(pixel_anchors).cuda().unsqueeze(0)
pixel_weights_cuda = torch.from_numpy(pixel_weights).cuda().unsqueeze(0)
intrinsics_cuda = torch.from_numpy(intrinsics).cuda().unsqueeze(0)
num_nodes_cuda = torch.from_numpy(num_nodes).cuda().unsqueeze(0)
with torch.no_grad():
model_data = model(
source_cuda, target_cuda,
graph_nodes_cuda, graph_edges_cuda, graph_edges_weights_cuda, graph_clusters_cuda,
pixel_anchors_cuda, pixel_weights_cuda,
num_nodes_cuda, intrinsics_cuda,
evaluate=True, split="test"
)
# Get some of the results
rotations_pred = model_data["node_rotations"].view(num_nodes, 3, 3).cpu().numpy()
translations_pred = model_data["node_translations"].view(num_nodes, 3).cpu().numpy()
mask_pred = model_data["mask_pred"]
assert mask_pred is not None, "Make sure use_mask=True in options.py"
mask_pred = mask_pred.view(-1, opt.image_height, opt.image_width).cpu().numpy()
# Compute mask gt for mask baseline
_, source_points, valid_source_points, target_matches, \
valid_target_matches, valid_correspondences, _, \
_ = model_data["correspondence_info"]
target_matches = target_matches.view(-1, opt.image_height, opt.image_width).cpu().numpy()
valid_source_points = valid_source_points.view(-1, opt.image_height, opt.image_width).cpu().numpy()
valid_target_matches = valid_target_matches.view(-1, opt.image_height, opt.image_width).cpu().numpy()
valid_correspondences = valid_correspondences.view(-1, opt.image_height, opt.image_width).cpu().numpy()
deformed_graph_nodes = graph_nodes + translations_pred
# Delete tensors to free up memory
del source_cuda
del target_cuda
del target_boundary_mask_cuda
del graph_nodes_cuda
del graph_edges_cuda
del graph_edges_weights_cuda
del graph_clusters_cuda
del pixel_anchors_cuda
del pixel_weights_cuda
del intrinsics_cuda
del model
#####################################################################################################
# Prepare data
#####################################################################################################
#####################################################################################################
# Source
#####################################################################################################
source_flat = np.moveaxis(source, 0, -1).reshape(-1, 6)
source_points = viz_utils.transform_pointcloud_to_opengl_coords(source_flat[..., 3:])
source_colors = source_flat[..., :3]
source_pcd = o3d.geometry.PointCloud()
source_pcd.points = o3d.utility.Vector3dVector(source_points)
source_pcd.colors = o3d.utility.Vector3dVector(source_colors)
# keep only object using the mask
valid_source_mask = np.moveaxis(valid_source_points, 0, -1).reshape(-1).astype(np.bool)
valid_source_points = source_points[valid_source_mask, :]
valid_source_colors = source_colors[valid_source_mask, :]
# source object PointCloud
source_object_pcd = o3d.geometry.PointCloud()
source_object_pcd.points = o3d.utility.Vector3dVector(valid_source_points)
source_object_pcd.colors = o3d.utility.Vector3dVector(valid_source_colors)
# o3d.visualization.draw_geometries([source_pcd])
# o3d.visualization.draw_geometries([source_object_pcd])
#####################################################################################################
# Source warped
#####################################################################################################
warped_deform_pred_3d_np = image_proc.warp_deform_3d(
source, pixel_anchors, pixel_weights, graph_nodes, rotations_pred, translations_pred
)
source_warped = np.copy(source)
source_warped[3:, :, :] = warped_deform_pred_3d_np
# (source) warped RGB-D image
source_warped = np.moveaxis(source_warped, 0, -1).reshape(-1, 6)
warped_points = viz_utils.transform_pointcloud_to_opengl_coords(source_warped[..., 3:])
warped_colors = source_warped[..., :3]
# Filter points at (0, 0, 0)
warped_points = warped_points[valid_source_mask]
warped_colors = warped_colors[valid_source_mask]
# warped PointCloud
warped_pcd = o3d.geometry.PointCloud()
warped_pcd.points = o3d.utility.Vector3dVector(warped_points)
warped_pcd.paint_uniform_color([1, 0.706, 0]) # warped_pcd.colors = o3d.utility.Vector3dVector(warped_colors)
# o3d.visualization.draw_geometries([source_object_pcd, warped_pcd])
####################################
# TARGET #
####################################
# target RGB-D image
target_flat = np.moveaxis(target, 0, -1).reshape(-1, 6)
target_points = viz_utils.transform_pointcloud_to_opengl_coords(target_flat[..., 3:])
target_colors = target_flat[..., :3]
# target PointCloud
target_pcd = o3d.geometry.PointCloud()
target_pcd.points = o3d.utility.Vector3dVector(target_points)
target_pcd.colors = o3d.utility.Vector3dVector(target_colors)
# o3d.visualization.draw_geometries([target_pcd])
####################################
# GRAPH #
####################################
# Transform to OpenGL coords
graph_nodes = viz_utils.transform_pointcloud_to_opengl_coords(graph_nodes)
deformed_graph_nodes = viz_utils.transform_pointcloud_to_opengl_coords(deformed_graph_nodes)
# Graph nodes
rendered_graph_nodes = []
for node in graph_nodes:
mesh_sphere = o3d.geometry.TriangleMesh.create_sphere(radius=0.01)
mesh_sphere.compute_vertex_normals()
mesh_sphere.paint_uniform_color([1.0, 0.0, 0.0])
mesh_sphere.translate(node)
rendered_graph_nodes.append(mesh_sphere)
# Merge all different sphere meshes
rendered_graph_nodes = viz_utils.merge_meshes(rendered_graph_nodes)
# Graph edges
edges_pairs = []
for node_id, edges in enumerate(graph_edges):
for neighbor_id in edges:
if neighbor_id == -1:
break
edges_pairs.append([node_id, neighbor_id])
colors = [[0.2, 1.0, 0.2] for i in range(len(edges_pairs))]
line_mesh = line_mesh_utils.LineMesh(graph_nodes, edges_pairs, colors, radius=0.003)
line_mesh_geoms = line_mesh.cylinder_segments
# Merge all different line meshes
line_mesh_geoms = viz_utils.merge_meshes(line_mesh_geoms)
# o3d.visualization.draw_geometries([rendered_graph_nodes, line_mesh_geoms, source_object_pcd])
# Combined nodes & edges
rendered_graph = [rendered_graph_nodes, line_mesh_geoms]
####################################
# Mask
####################################
mask_pred_flat = mask_pred.reshape(-1)
valid_correspondences = valid_correspondences.reshape(-1).astype(np.bool)
####################################
# Correspondences
####################################
# target matches
target_matches = np.moveaxis(target_matches, 0, -1).reshape(-1, 3)
target_matches = viz_utils.transform_pointcloud_to_opengl_coords(target_matches)
################################
# "Good" matches
################################
good_mask = valid_correspondences & (mask_pred_flat >= weight_thr)
good_source_points_corresp = source_points[good_mask]
good_target_matches_corresp = target_matches[good_mask]
good_mask_pred = mask_pred_flat[good_mask]
# number of good matches
n_good_matches = good_source_points_corresp.shape[0]
# Subsample
subsample = True
if subsample:
N = 2000
sampled_idxs = np.random.permutation(n_good_matches)[:N]
good_source_points_corresp = good_source_points_corresp[sampled_idxs]
good_target_matches_corresp = good_target_matches_corresp[sampled_idxs]
good_mask_pred = good_mask_pred[sampled_idxs]
n_good_matches = N
# both good_source and good_target points together into one vector
good_matches_points = np.concatenate([good_source_points_corresp, good_target_matches_corresp], axis=0)
good_matches_lines = [[i, i + n_good_matches] for i in range(0, n_good_matches, 1)]
# --> Create good (unweighted) lines
good_matches_colors = [[201/255, 177/255, 14/255] for i in range(len(good_matches_lines))]
good_matches_set = o3d.geometry.LineSet(
points=o3d.utility.Vector3dVector(good_matches_points),
lines=o3d.utility.Vector2iVector(good_matches_lines),
)
good_matches_set.colors = o3d.utility.Vector3dVector(good_matches_colors)
# --> Create good weighted lines
# first, we need to get the proper color coding
high_color, low_color = np.array([0.0, 0.8, 0]), np.array([0.8, 0, 0.0])
good_weighted_matches_colors = np.ones_like(good_source_points_corresp)
weights_normalized = np.maximum(np.minimum(0.5 + (good_mask_pred - weight_thr) / weight_scale, 1.0), 0.0)
weights_normalized_opposite = 1 - weights_normalized
good_weighted_matches_colors[:, 0] = weights_normalized * high_color[0] + weights_normalized_opposite * low_color[0]
good_weighted_matches_colors[:, 1] = weights_normalized * high_color[1] + weights_normalized_opposite * low_color[1]
good_weighted_matches_colors[:, 2] = weights_normalized * high_color[2] + weights_normalized_opposite * low_color[2]
good_weighted_matches_set = o3d.geometry.LineSet(
points=o3d.utility.Vector3dVector(good_matches_points),
lines=o3d.utility.Vector2iVector(good_matches_lines),
)
good_weighted_matches_set.colors = o3d.utility.Vector3dVector(good_weighted_matches_colors)
################################
# "Bad" matches
################################
bad_mask = valid_correspondences & (mask_pred_flat < weight_thr)
bad_source_points_corresp = source_points[bad_mask]
bad_target_matches_corresp = target_matches[bad_mask]
bad_mask_pred = mask_pred_flat[bad_mask]
# number of good matches
n_bad_matches = bad_source_points_corresp.shape[0]
# both good_source and good_target points together into one vector
bad_matches_points = np.concatenate([bad_source_points_corresp, bad_target_matches_corresp], axis=0)
bad_matches_lines = [[i, i + n_bad_matches] for i in range(0, n_bad_matches, 1)]
# --> Create bad (unweighted) lines
bad_matches_colors = [[201/255, 177/255, 14/255] for i in range(len(bad_matches_lines))]
bad_matches_set = o3d.geometry.LineSet(
points=o3d.utility.Vector3dVector(bad_matches_points),
lines=o3d.utility.Vector2iVector(bad_matches_lines),
)
bad_matches_set.colors = o3d.utility.Vector3dVector(bad_matches_colors)
# --> Create bad weighted lines
# first, we need to get the proper color coding
high_color, low_color = np.array([0.0, 0.8, 0]), np.array([0.8, 0, 0.0])
bad_weighted_matches_colors = np.ones_like(bad_source_points_corresp)
weights_normalized = np.maximum(np.minimum(0.5 + (bad_mask_pred - weight_thr) / weight_scale, 1.0), 0.0)
weights_normalized_opposite = 1 - weights_normalized
bad_weighted_matches_colors[:, 0] = weights_normalized * high_color[0] + weights_normalized_opposite * low_color[0]
bad_weighted_matches_colors[:, 1] = weights_normalized * high_color[1] + weights_normalized_opposite * low_color[1]
bad_weighted_matches_colors[:, 2] = weights_normalized * high_color[2] + weights_normalized_opposite * low_color[2]
bad_weighted_matches_set = o3d.geometry.LineSet(
points=o3d.utility.Vector3dVector(bad_matches_points),
lines=o3d.utility.Vector2iVector(bad_matches_lines),
)
bad_weighted_matches_set.colors = o3d.utility.Vector3dVector(bad_weighted_matches_colors)
####################################
# Generate info for aligning source to target (by interpolating between source and warped source)
####################################
assert warped_points.shape[0] == valid_source_points.shape[0]
line_segments = warped_points - valid_source_points
line_segments_unit, line_lengths = line_mesh_utils.normalized(line_segments)
line_lengths = line_lengths[:, np.newaxis]
line_lengths = np.repeat(line_lengths, 3, axis=1)
####################################
# Draw
####################################
geometry_dict = {
"source_pcd": source_pcd,
"source_obj": source_object_pcd,
"target_pcd": target_pcd,
"graph": rendered_graph
# "deformed_graph": rendered_deformed_graph
}
alignment_dict = {
"valid_source_points": valid_source_points,
"line_segments_unit": line_segments_unit,
"line_lengths": line_lengths
}
matches_dict = {
"good_matches_set": good_matches_set,
"good_weighted_matches_set": good_weighted_matches_set,
"bad_matches_set": bad_matches_set,
"bad_weighted_matches_set": bad_weighted_matches_set
}
#####################################################################################################
# Open viewer
#####################################################################################################
manager = viz_utils.CustomDrawGeometryWithKeyCallback(
geometry_dict, alignment_dict, matches_dict
)
manager.custom_draw_geometry_with_key_callback()
if __name__ == "__main__":
main()