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main_pipeline_no_lidar.py
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/
main_pipeline_no_lidar.py
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import numpy as np
from wrappers import Colmap, FFMpeg, PDraw, ETH3D, PCLUtil
from cli_utils import set_full_argparser, print_step, print_workflow
from video_localization import localize_video, generate_GT, generate_GT_individual_pictures
import meshlab_xml_writer as mxw
import prepare_images as pi
import prepare_workspace as pw
def main():
args = set_full_argparser().parse_args()
env = vars(args)
if args.show_steps:
print_workflow()
return
if args.add_new_videos:
env["resume_work"] = True
args.skip_step += [1, 3, 4]
if args.begin_step is not None:
args.skip_step += list(range(args.begin_step))
pw.check_input_folder(args.input_folder, with_lidar=False)
args.workspace = args.workspace.abspath()
pw.prepare_workspace(args.workspace, env, with_lidar=False)
colmap = Colmap(db=env["thorough_db"],
image_path=env["colmap_img_root"],
mask_path=env["mask_path"],
dense_workspace=env["dense_workspace"],
binary=args.colmap,
verbose=args.verbose,
logfile=args.log)
env["colmap"] = colmap
ffmpeg = FFMpeg(args.ffmpeg, verbose=args.verbose, logfile=args.log)
env["ffmpeg"] = ffmpeg
pdraw = PDraw(args.nw, verbose=args.verbose, logfile=args.log)
env["pdraw"] = pdraw
eth3d = ETH3D(args.eth3d, args.raw_output_folder / "Images", args.max_occlusion_depth,
verbose=args.verbose, logfile=args.log, splat_radius=args.eth3d_splat_radius)
env["eth3d"] = eth3d
pcl_util = PCLUtil(args.pcl_util, verbose=args.verbose, logfile=args.log)
env["pcl_util"] = pcl_util
env["videos_list"] = sum((list((args.input_folder/"Videos").walkfiles('*{}'.format(ext))) for ext in args.vid_ext), [])
no_gt_folder = args.input_folder/"Videos"/"no_groundtruth"
if no_gt_folder.isdir():
env["videos_to_localize"] = [v for v in env["videos_list"] if not str(v).startswith(no_gt_folder)]
else:
env["videos_to_localize"] = env["videos_list"]
i = 1
if i not in args.skip_step:
print_step(i, "Pictures preparation")
env["individual_pictures"] = pi.extract_pictures_to_workspace(**env)
else:
full_paths = sum((list(env["individual_pictures_path"].walkfiles('*{}'.format(ext))) for ext in env["pic_ext"]), [])
env["individual_pictures"] = [path.relpath(env["colmap_img_root"]) for path in full_paths]
i += 1
# Get already existing_videos
env["videos_frames_folders"] = {}
by_name = {v.stem: v for v in env["videos_list"]}
for folder in env["video_path"].walkdirs():
video_name = folder.basename()
if video_name in by_name.keys():
env["videos_frames_folders"][by_name[video_name]] = folder
if i not in args.skip_step:
print_step(i, "Extracting Videos and selecting optimal frames for a thorough scan")
new_video_frame_folders = pi.extract_videos_to_workspace(fps=args.lowfps, **env)
# Concatenate both already treated videos and newly detected videos
env["videos_frames_folders"] = {**env["videos_frames_folders"], **new_video_frame_folders}
env["videos_workspaces"] = {}
for v, frames_folder in env["videos_frames_folders"].items():
env["videos_workspaces"][v] = pw.prepare_video_workspace(v, frames_folder, **env)
i += 1
if i not in args.skip_step:
print_step(i, "First thorough photogrammetry")
env["thorough_recon"].makedirs_p()
colmap.extract_features(image_list=env["video_frame_list_thorough"], more=args.more_sift_features)
colmap.index_images(vocab_tree_output=env["indexed_vocab_tree"], vocab_tree_input=args.vocab_tree)
if env["match_method"] == "vocab_tree":
colmap.match(method="vocab_tree", vocab_tree=env["indexed_vocab_tree"], max_num_matches=env["max_num_matches"])
else:
colmap.match(method="exhaustive", max_num_matches=env["max_num_matches"])
colmap.map(output=env["thorough_recon"], multiple_models=env["multiple_models"])
thorough_model = pi.choose_biggest_model(env["thorough_recon"])
colmap.adjust_bundle(thorough_model, thorough_model,
num_iter=100, refine_extra_params=True)
else:
thorough_model = pi.choose_biggest_model(env["thorough_recon"])
i += 1
if i not in args.skip_step:
print_step(i, "Alignment of photogrammetric reconstruction with GPS")
env["georef_recon"].makedirs_p()
env["georef_full_recon"].makedirs_p()
colmap.align_model(output=env["georef_recon"],
input=thorough_model,
ref_images=env["georef_frames_list"])
if not (env["georef_recon"]/"images.bin").isfile():
# GPS alignment failed, possibly because not enough GPS referenced images
# Copy the original model without alignment
print("Warning, model alignment failed, the model will be normalized, and thus the depth maps too")
thorough_model.merge_tree(env["georef_recon"])
env["georef_recon"].merge_tree(env["georef_full_recon"])
if args.inspect_dataset:
print("FIRST DATASET INSPECTION")
print("Inspection of localisalization of frames used in thorough mapping "
"w.r.t Sparse reconstruction")
colmap.export_model(output=env["georef_recon"] / "georef_sparse.ply",
input=env["georef_recon"])
georef_mlp = env["georef_recon"]/"georef_recon.mlp"
mxw.create_project(georef_mlp, [env["georef_recon"] / "georef_sparse.ply"])
colmap.export_model(output=env["georef_recon"],
input=env["georef_recon"],
output_type="TXT")
eth3d.inspect_dataset(scan_meshlab=georef_mlp,
colmap_model=env["georef_recon"],
image_path=env["colmap_img_root"])
i += 1
if i not in args.skip_step:
print_step(i, "Video localization with respect to reconstruction")
for j, v in enumerate(env["videos_to_localize"]):
print("\n\nNow working on video {} [{}/{}]".format(v, j + 1, len(env["videos_to_localize"])))
video_env = env["videos_workspaces"][v]
localize_video(video_name=v,
video_frames_folder=env["videos_frames_folders"][v],
step_index=i, video_index=j+1,
num_videos=len(env["videos_to_localize"]),
**video_env, **env)
i += 1
if i not in args.skip_step:
print_step(i, "Full reconstruction point cloud densificitation")
env["georef_full_recon"].makedirs_p()
colmap.undistort(input=env["georef_full_recon"])
colmap.dense_stereo(min_depth=env["stereo_min_depth"], max_depth=env["stereo_max_depth"])
colmap.stereo_fusion(output=env["georefrecon_ply"])
i += 1
if i not in args.skip_step:
print_step(i, "Reconstruction cleaning")
filtered = env["georefrecon_ply"].stripext() + "_filtered.ply"
pcl_util.filter_cloud(input_file=env["georefrecon_ply"],
output_file=filtered,
knn=args.SOR[0], std=args.SOR[1])
mxw.create_project(env["aligned_mlp"], [filtered])
i += 1
if i not in args.skip_step:
print_step(i, "Occlusion Mesh computing")
colmap.delaunay_mesh(env["occlusion_ply"], input_ply=env["georefrecon_ply"])
if args.splats:
eth3d.create_splats(env["splats_ply"], env["georefrecon_ply"].stripext() + "_filtered.ply",
env["occlusion_ply"], env["splat_threshold"],
env["max_splat_size"])
if args.inspect_dataset:
print("SECOND DATASET INSPECTION")
print("Inspection of localisalization of frames used in thorough mapping "
"w.r.t Dense reconstruction")
eth3d.inspect_dataset(scan_meshlab=env["aligned_mlp"],
colmap_model=env["georef_recon"],
image_path=env["colmap_img_root"])
print("SECOND DATASET INSPECTION")
print("Inspection of localisalization of frames used in thorough mapping "
"w.r.t Dense reconstruction and Occlusion Meshes")
eth3d.inspect_dataset(scan_meshlab=env["aligned_mlp"],
colmap_model=env["georef_recon"],
image_path=env["colmap_img_root"],
occlusions=env["occlusion_ply"],
splats=env["splats_ply"])
i += 1
if i not in args.skip_step:
print_step(i, "Groud Truth generation")
for j, v in enumerate(env["videos_to_localize"]):
video_env = env["videos_workspaces"][v]
generate_GT(video_name=v, GT_already_done=video_env["GT_already_done"],
video_index=j+1,
step_index=i,
num_videos=len(env["videos_to_localize"]),
metadata_path=video_env["metadata_path"],
global_registration_matrix=np.eye(4),
**video_env["output_env"], **env)
if env["generate_groundtruth_for_individual_images"]:
by_folder = pi.group_pics_by_folder(env["individual_pictures"])
for folder, pic_list in by_folder.items():
generate_GT_individual_pictures(input_colmap_model=env["georef_full_recon"],
individual_pictures_list=pic_list,
relpath=folder,
step_index=i, **env)
if __name__ == '__main__':
main()