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visualize_live_meshing.py
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visualize_live_meshing.py
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import os
import pickle
from pathlib import Path
import numpy as np
import pyrender
import torch
import torch.nn.functional as F
import trimesh
from PIL import Image
from tqdm import tqdm
from experiment_modules.depth_model import DepthModel
import options
from tools import fusers_helper
from tools.mesh_renderer import (DEFAULT_CAM_FRUSTUM_MATERIAL,
DEFAULT_MESH_MATERIAL, Renderer,
SmoothBirdsEyeCamera, camera_marker,
create_light_array, get_image_box,
transform_trimesh)
from utils.dataset_utils import get_dataset
from utils.generic_utils import to_gpu
from utils.visualization_utils import colormap_image, save_viz_video_frames
import modules.cost_volume as cost_volume
def main(opts):
print("Setting batch size to 1.")
opts.batch_size = 1
# get dataset
dataset_class, scans = get_dataset(opts.dataset,
opts.dataset_scan_split_file, opts.single_debug_scan_id)
model = DepthModel.load_from_checkpoint(
opts.load_weights_from_checkpoint, args=None)
if (opts.fast_cost_volume and
isinstance(model.cost_volume, cost_volume.FeatureVolumeManager)):
model.cost_volume = model.cost_volume.to_fast()
model = model.cuda().eval()
# path where results for this model, dataset, and tuple type are.
results_path = os.path.join(opts.output_base_path, opts.name,
opts.dataset, opts.frame_tuple_type)
mesh_output_folder_name = f"{opts.fusion_resolution}_{opts.fusion_max_depth}_{opts.depth_fuser}"
if opts.mask_pred_depth:
mesh_output_folder_name += "_masked"
if opts.fuse_color:
mesh_output_folder_name += "_color"
incremental_mesh_output_dir = os.path.join(
results_path, "incremental_meshes", mesh_output_folder_name)
Path(incremental_mesh_output_dir).mkdir(parents=True, exist_ok=True)
print(f"".center(80, "#"))
print(f" Running Fusion! Using {opts.depth_fuser} ".center(80, "#"))
print(f"Incremental Mesh Output directory:"
f"\n{incremental_mesh_output_dir} ".center(80, "#"))
if opts.use_precomputed_partial_meshes:
print(f" Loading precomputed incremental meshes. ".center(80, "#"))
print(f"".center(80, "#"))
print("")
# path where cached depth maps are
depth_output_dir = os.path.join(results_path, "depths")
Path(os.path.join(depth_output_dir)).mkdir(parents=True, exist_ok=True)
print(f"".center(80, "#"))
print(f" Reading cached depths if they exist. ".center(80, "#"))
print(f"Directory:\n{depth_output_dir} ".center(80, "#"))
if opts.cache_depths:
print(f" Caching depths if we need to compute them. ".center(80, "#"))
print(f"".center(80, "#"))
print("")
video_output_dir = os.path.join(results_path, "viz",
"reconstruction_videos", mesh_output_folder_name)
Path(os.path.join(video_output_dir)).mkdir(parents=True, exist_ok=True)
print(f"".center(80, "#"))
print(f" Outputting videos. ".center(80, "#"))
print(f"Video Output directory:\n{video_output_dir} ".center(80, "#"))
print(f"".center(80, "#"))
print("")
fpv_renderer = Renderer(height=192, width=256)
birdseye_renderer = Renderer(height=192, width=256)
with torch.inference_mode():
for scan in tqdm(scans):
smooth_birdseye = SmoothBirdsEyeCamera()
Path(os.path.join(incremental_mesh_output_dir,
scan)).mkdir(parents=True, exist_ok=True)
# initialize fuser if we need to fuse
if opts.run_fusion:
fuser = fusers_helper.get_fuser(opts, scan)
# set up dataset with current scan
dataset = dataset_class(
opts.dataset_path,
split=opts.split,
mv_tuple_file_suffix=opts.mv_tuple_file_suffix,
limit_to_scan_id=scan,
include_full_res_depth=True,
tuple_info_file_location=opts.tuple_info_file_location,
num_images_in_tuple=None,
shuffle_tuple=opts.shuffle_tuple,
include_high_res_color=opts.fuse_color and opts.run_fusion,
include_full_depth_K=True,
skip_frames=opts.skip_frames,
skip_to_frame=opts.skip_to_frame,
image_width=opts.image_width,
image_height=opts.image_height,
pass_frame_id=True,
)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=opts.batch_size,
shuffle=False,
num_workers=opts.num_workers,
drop_last=False,
)
mesh_render_fpv_frames = []
mesh_render_birdeye_frames = []
viz_depth_panel = True
all_meshes_precomputed = True
for batch_ind, batch in enumerate(tqdm(dataloader)):
# get data, move to GPU
cur_data, src_data = batch
if "frame_id_string" in cur_data:
frame_id = cur_data["frame_id_string"][0]
else:
frame_id = f"{str(batch_ind):6d}"
cur_data = to_gpu(cur_data, key_ignores=["frame_id_string"])
src_data = to_gpu(src_data, key_ignores=["frame_id_string"])
# To save time and compute , we should load meshes if they've
# all been computed and stored. We don't currently have a
# mechanism for picking up fusion from a partial mesh. We should
# only load and continue vizzing if we have a continious stream
# of saved meshes. If this panics, run this script without
# loading partial meshes
trimesh_path = os.path.join(incremental_mesh_output_dir, scan,
f"{frame_id}.ply")
if not Path(trimesh_path).is_file():
all_meshes_precomputed=False
if all_meshes_precomputed and opts.use_precomputed_partial_meshes:
scene_trimesh_mesh = trimesh.load(trimesh_path, force='mesh')
if viz_depth_panel:
pickled_depths_path = os.path.join(depth_output_dir,
scan, f"{frame_id}.pickle")
if Path(pickled_depths_path).is_file():
with open(pickled_depths_path, 'rb') as handle:
outputs = pickle.load(handle)
else:
outputs = model(
"test",
cur_data,
src_data,
unbatched_matching_encoder_forward=True,
return_mask=True,
)
depth_pred = outputs["depth_pred_s0_b1hw"]
else:
if not opts.run_fusion:
raise Exception("No precomputed partial mesh found and "
"run_fusion is disabled.")
# check if depths are precomputed.
pickled_depths_path = os.path.join(depth_output_dir, scan,
f"{frame_id}.pickle")
if Path(pickled_depths_path).is_file():
with open(pickled_depths_path, 'rb') as handle:
outputs = pickle.load(handle)
else:
outputs = model(
"test",
cur_data,
src_data,
unbatched_matching_encoder_forward=True,
return_mask=True,
)
if opts.cache_depths:
Path(os.path.join(depth_output_dir,
scan)).mkdir(parents=True, exist_ok=True)
output_path = os.path.join(depth_output_dir, scan,
f"{frame_id}.pickle")
outputs["K_full_depth_b44"] = cur_data["K_full_depth_b44"]
outputs["K_s0_b44"] = cur_data["K_s0_b44"]
outputs["frame_id"] = frame_id
if "frame_id" in src_data:
outputs["src_ids"] = src_data["frame_id_string"]
with open(output_path, 'wb') as handle:
pickle.dump(outputs, handle)
depth_pred = outputs["depth_pred_s0_b1hw"]
if opts.mask_pred_depth:
overall_mask_b1hw = outputs[
"overall_mask_bhw"
].cuda().unsqueeze(1).float()
overall_mask_b1hw = F.interpolate(
overall_mask_b1hw,
size=(192, 256),
mode="nearest"
).bool()
depth_pred[~overall_mask_b1hw] = 0
color_frame = (cur_data["high_res_color_b3hw"]
if "high_res_color_b3hw" in cur_data
else cur_data["image_b3hw"])
fuser.fuse_frames(depth_pred, cur_data["K_s0_b44"],
cur_data["cam_T_world_b44"],
color_frame)
Path(os.path.join(incremental_mesh_output_dir,
scan)).mkdir(parents=True, exist_ok=True)
mesh_path=os.path.join(incremental_mesh_output_dir, scan,
f"{frame_id}.ply")
fuser.export_mesh(path=mesh_path)
if opts.fuse_color:
scene_trimesh_mesh = trimesh.load(trimesh_path,
force='mesh')
else:
scene_trimesh_mesh = fuser.get_mesh(
convert_to_trimesh=True)
world_T_cam_44 = cur_data["world_T_cam_b44"].squeeze().cpu().numpy()
K_33 = cur_data["K_s0_b44"].squeeze().cpu().numpy()
render_height = opts.viz_render_height
render_width = opts.viz_render_width
K_33[0] *= (render_width/depth_pred.shape[-1])
K_33[1] *= (render_height/depth_pred.shape[-2])
light_pos = world_T_cam_44.copy()
light_pos[2, 3] += 5.0
lights = create_light_array(
pyrender.PointLight(intensity=30.0),
light_pos,
x_length=12,
y_length=12,
num_x=6,
num_y=6,
)
meshes = ([] if scene_trimesh_mesh is None
else [scene_trimesh_mesh])
render_fpv = fpv_renderer.render_mesh(
meshes,
render_height, render_width,
world_T_cam_44, K_33,
True,
lights=lights,
)
meshes = ([] if scene_trimesh_mesh is None
else [scene_trimesh_mesh])
mesh_materials = ([None] if opts.fuse_color
else [DEFAULT_MESH_MATERIAL])
fpv_camera = trimesh.scene.Camera(
resolution=(render_height, render_width),
focal=(K_33[0][0], K_33[1][1])
)
cam_marker_size = 0.7
cam_marker_mesh = camera_marker(fpv_camera,
cam_marker_size=cam_marker_size)[1]
np_vertices = np.array(cam_marker_mesh.vertices)
np_vertices = (world_T_cam_44 @ np.concatenate([np_vertices,
np.ones((np_vertices.shape[0], 1))], 1).T).T
np_vertices = np_vertices/np_vertices[:,3][:,None]
cam_marker_mesh = trimesh.Trimesh(vertices=np_vertices[:,:3],
faces=cam_marker_mesh.faces)
meshes.append(cam_marker_mesh)
mesh_materials.append(DEFAULT_CAM_FRUSTUM_MATERIAL)
our_depth_3hw = colormap_image(depth_pred.squeeze(0),
vmin=0, vmax=3.0)
our_depth_hw3 = our_depth_3hw.permute(1,2,0)
pil_depth = Image.fromarray(
np.uint8(
our_depth_hw3.cpu().detach().numpy() * 255))
image_mesh = get_image_box(
pil_depth,
cam_marker_size=cam_marker_size,
fovs=(fpv_camera.fov[0], fpv_camera.fov[1])
)
image_mesh = transform_trimesh(image_mesh, world_T_cam_44)
meshes.append(image_mesh)
mesh_materials.append(None)
image_mesh = get_image_box(
pil_depth,
cam_marker_size=cam_marker_size,
flip=True,
fovs=(fpv_camera.fov[0], fpv_camera.fov[1])
)
image_mesh.vertices[:,2] += 0.01
image_mesh = transform_trimesh(image_mesh, world_T_cam_44)
meshes.append(image_mesh)
mesh_materials.append(None)
birdeye_world_T_cam_44 = smooth_birdseye.get_bird_eye_trans(
scene_trimesh_mesh,
fpv_pose=world_T_cam_44
)
if opts.back_face_alpha:
render_birdseye = birdseye_renderer.render_mesh_cull_composite(
meshes=meshes,
height=render_height,
width=render_width,
world_T_cam=birdeye_world_T_cam_44,
K=K_33,
get_colour=True,
mesh_materials=mesh_materials,
lights=lights,
alpha=opts.back_face_alpha,
)
else:
render_birdseye = birdseye_renderer.render_mesh(
meshes,
render_height, render_width,
birdeye_world_T_cam_44,
K_33, True, mesh_materials=mesh_materials,
lights=lights,
)
mesh_render_fpv_frames.append(render_fpv)
mesh_render_birdeye_frames.append(render_birdseye)
fps = (opts.standard_fps if opts.skip_frames is None
else round(opts.standard_fps/opts.skip_frames))
save_viz_video_frames(mesh_render_fpv_frames,
os.path.join(video_output_dir,
scan.replace("/", "_") + "_fpv.mp4"), fps=fps)
save_viz_video_frames(mesh_render_birdeye_frames,
os.path.join(video_output_dir,
scan.replace("/", "_") + "_birdseye.mp4"), fps=fps)
del(dataloader)
del(dataset)
if __name__ == '__main__':
# don't need grad for test.
torch.set_grad_enabled(False)
# get an instance of options and load it with config file(s) and cli args.
option_handler = options.OptionsHandler()
option_handler.parse_and_merge_options()
option_handler.pretty_print_options()
print("\n")
opts = option_handler.options
# if no GPUs are available for us then, use the 32 bit on CPU
if opts.gpus == 0:
print("Setting precision to 32 bits since --gpus is set to 0.")
opts.precision = 32
main(opts)