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# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File pram -> recmap
@IDE PyCharm
@Author fx221@cam.ac.uk
@Date 07/02/2024 11:02
=================================================='''
import argparse
import torch
import os
import os.path as osp
import numpy as np
import cv2
import yaml
import multiprocessing as mp
from copy import deepcopy
import logging
import h5py
from tqdm import tqdm
import open3d as o3d
from sklearn.cluster import KMeans, Birch
from collections import defaultdict
from colmap_utils.read_write_model import read_model, qvec2rotmat, write_cameras_binary, write_images_binary
from colmap_utils.read_write_model import write_points3d_binary, Image, Point3D, Camera
from colmap_utils.read_write_model import write_compressed_points3d_binary, write_compressed_images_binary
from recognition.vis_seg import generate_color_dic, vis_seg_point, plot_kpts
class RecMap:
def __init__(self):
self.cameras = None
self.images = None
self.points3D = None
self.pcd = o3d.geometry.PointCloud()
self.seg_color_dict = generate_color_dic(n_seg=1000)
def load_sfm_model(self, path: str, ext='.bin'):
self.cameras, self.images, self.points3D = read_model(path, ext)
self.name_to_id = {image.name: i for i, image in self.images.items()}
print('Load {:d} cameras, {:d} images, {:d} points'.format(len(self.cameras), len(self.images),
len(self.points3D)))
def remove_statics_outlier(self, nb_neighbors: int = 20, std_ratio: float = 2.0):
xyzs = []
p3d_ids = []
for p3d_id in self.points3D.keys():
xyzs.append(self.points3D[p3d_id].xyz)
p3d_ids.append(p3d_id)
xyzs = np.array(xyzs)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(xyzs)
new_pcd, inlier_ids = pcd.remove_statistical_outlier(nb_neighbors=nb_neighbors, std_ratio=std_ratio)
new_point3Ds = {}
for i in inlier_ids:
new_point3Ds[p3d_ids[i]] = self.points3D[p3d_ids[i]]
self.points3D = new_point3Ds
n_outlier = xyzs.shape[0] - len(inlier_ids)
ratio = n_outlier / xyzs.shape[0]
print('Remove {:d} - {:d} = {:d}/{:.2f}% points'.format(xyzs.shape[0], len(inlier_ids), n_outlier, ratio * 100))
def load_segmentation(self, path: str):
data = np.load(path, allow_pickle=True)[()]
p3d_id = data['id']
seg_id = data['label']
self.p3d_seg = {p3d_id[i]: seg_id[i] for i in range(p3d_id.shape[0])}
self.seg_p3d = {}
for pid in self.p3d_seg.keys():
sid = self.p3d_seg[pid]
if sid not in self.seg_p3d.keys():
self.seg_p3d[sid] = [pid]
else:
self.seg_p3d[sid].append(pid)
if 'xyz' not in data.keys():
all_xyz = []
for pid in p3d_id:
xyz = self.points3D[pid].xyz
all_xyz.append(xyz)
data['xyz'] = np.array(all_xyz)
np.save(path, data)
print('Add xyz to ', path)
def cluster(self, k=512, mode='xyz', min_obs=3, save_fn=None, method='kmeans', **kwargs):
if save_fn is not None:
if osp.isfile(save_fn):
print('{:s} exists.'.format(save_fn))
return
all_xyz = []
point3D_ids = []
for p3d in self.points3D.values():
track_len = len(p3d.point2D_idxs)
if track_len < min_obs:
continue
all_xyz.append(p3d.xyz)
point3D_ids.append(p3d.id)
xyz = np.array(all_xyz)
point3D_ids = np.array(point3D_ids)
if mode.find('x') < 0:
xyz[:, 0] = 0
if mode.find('y') < 0:
xyz[:, 1] = 0
if mode.find('z') < 0:
xyz[:, 2] = 0
if method == 'kmeans':
model = KMeans(n_clusters=k, random_state=0, verbose=True).fit(xyz)
elif method == 'birch':
model = Birch(threshold=kwargs.get('threshold'), n_clusters=k).fit(xyz) # 0.01 for indoor
else:
print('Method {:s} for clustering does not exist'.format(method))
exit(0)
labels = np.array(model.labels_).reshape(-1)
if save_fn is not None:
np.save(save_fn, {
'id': np.array(point3D_ids), # should be assigned to self.points3D_ids
'label': np.array(labels),
'xyz': np.array(all_xyz),
})
def assign_point3D_descriptor(self, feature_fn: str, save_fn=None, n_process=1):
'''
assign each 3d point a descriptor for localization
:param feature_fn: file name of features [h5py]
:param save_fn:
:param n_process:
:return:
'''
def run(start_id, end_id, points3D_desc):
for pi in tqdm(range(start_id, end_id), total=end_id - start_id):
p3d_id = all_p3d_ids[pi]
img_list = self.points3D[p3d_id].image_ids
kpt_ids = self.points3D[p3d_id].point2D_idxs
all_descs = []
for img_id, p2d_id in zip(img_list, kpt_ids):
if img_id not in self.images.keys():
continue
img_fn = self.images[img_id].name
desc = feat_file[img_fn]['descriptors'][()].transpose()[p2d_id]
all_descs.append(desc)
if len(all_descs) == 1:
points3D_desc[p3d_id] = all_descs[0]
else:
all_descs = np.array(all_descs) # [n, d]
dist = all_descs @ all_descs.transpose() # [n, n]
dist = 2 - 2 * dist
md_dist = np.median(dist, axis=-1) # [n]
min_id = np.argmin(md_dist)
points3D_desc[p3d_id] = all_descs[min_id]
if osp.isfile(save_fn):
print('{:s} exists.'.format(save_fn))
return
p3D_desc = {}
feat_file = h5py.File(feature_fn, 'r')
all_p3d_ids = sorted(self.points3D.keys())
if n_process > 1:
if len(all_p3d_ids) <= n_process:
run(start_id=0, end_id=len(all_p3d_ids), points3D_desc=p3D_desc)
else:
manager = mp.Manager()
output = manager.dict() # necessary otherwise empty
n_sample_per_process = len(all_p3d_ids) // n_process
jobs = []
for i in range(n_process):
start_id = i * n_sample_per_process
if i == n_process - 1:
end_id = len(all_p3d_ids)
else:
end_id = (i + 1) * n_sample_per_process
p = mp.Process(
target=run,
args=(start_id, end_id, output),
)
jobs.append(p)
p.start()
for p in jobs:
p.join()
p3D_desc = {}
for k in output.keys():
p3D_desc[k] = output[k]
else:
run(start_id=0, end_id=len(all_p3d_ids), points3D_desc=p3D_desc)
if save_fn is not None:
np.save(save_fn, p3D_desc)
def reproject(self, img_id, xyzs):
qvec = self.images[img_id].qvec
Rcw = qvec2rotmat(qvec=qvec)
tvec = self.images[img_id].tvec
tcw = tvec.reshape(3, )
Tcw = np.eye(4, dtype=float)
Tcw[:3, :3] = Rcw
Tcw[:3, 3] = tcw
# intrinsics
cam = self.cameras[self.images[img_id].camera_id]
K = self.get_intrinsics_from_camera(camera=cam)
xyzs_homo = np.hstack([xyzs, np.ones(shape=(xyzs.shape[0], 1), dtype=float)])
kpts = K @ ((Tcw @ xyzs_homo.transpose())[:3, :]) # [3, N]
kpts = kpts.transpose() # [N, 3]
kpts[:, 0] = kpts[:, 0] / kpts[:, 2]
kpts[:, 1] = kpts[:, 1] / kpts[:, 2]
return kpts
def find_covisible_frame_ids(self, image_id, images, points3D):
covis = defaultdict(int)
p3d_ids = images[image_id].point3D_ids
for pid in p3d_ids:
if pid == -1:
continue
if pid not in points3D.keys():
continue
for im in points3D[pid].image_ids:
covis[im] += 1
covis_ids = np.array(list(covis.keys()))
covis_num = np.array([covis[i] for i in covis_ids])
ind_top = np.argsort(covis_num)[::-1]
sorted_covis_ids = [covis_ids[i] for i in ind_top]
return sorted_covis_ids
def create_virtual_frame_3(self, save_fn=None, save_vrf_dir=None, show_time=-1, ignored_cameras=[],
min_cover_ratio=0.9,
depth_scale=1.2,
radius=15,
min_obs=120,
topk_imgs=500,
n_vrf=10,
covisible_frame=20,
**kwargs):
def reproject(img_id, xyzs):
qvec = self.images[img_id].qvec
Rcw = qvec2rotmat(qvec=qvec)
tvec = self.images[img_id].tvec
tcw = tvec.reshape(3, )
Tcw = np.eye(4, dtype=float)
Tcw[:3, :3] = Rcw
Tcw[:3, 3] = tcw
# intrinsics
cam = self.cameras[self.images[img_id].camera_id]
K = self.get_intrinsics_from_camera(camera=cam)
xyzs_homo = np.hstack([xyzs, np.ones(shape=(xyzs.shape[0], 1), dtype=float)])
kpts = K @ ((Tcw @ xyzs_homo.transpose())[:3, :]) # [3, N]
kpts = kpts.transpose() # [N, 3]
kpts[:, 0] = kpts[:, 0] / kpts[:, 2]
kpts[:, 1] = kpts[:, 1] / kpts[:, 2]
return kpts
def find_best_vrf_by_covisibility(p3d_id_list):
all_img_ids = []
all_xyzs = []
img_ids_full = []
img_id_obs = {}
for pid in p3d_id_list:
if pid not in self.points3D.keys():
continue
all_xyzs.append(self.points3D[pid].xyz)
img_ids = self.points3D[pid].image_ids
for iid in img_ids:
if iid in all_img_ids:
continue
# valid_p3ds = [v for v in self.images[iid].point3D_ids if v > 0 and v in p3d_id_list]
if len(ignored_cameras) > 0:
ignore = False
img_name = self.images[iid].name
for c in ignored_cameras:
if img_name.find(c) >= 0:
ignore = True
break
if ignore:
continue
# valid_p3ds = np.intersect1d(np.array(self.images[iid].point3D_ids), np.array(p3d_id_list)).tolist()
valid_p3ds = [v for v in self.images[iid].point3D_ids if v > 0]
img_ids_full.append(iid)
if len(valid_p3ds) < min_obs:
continue
all_img_ids.append(iid)
img_id_obs[iid] = len(valid_p3ds)
all_xyzs = np.array(all_xyzs)
print('Find {} 3D points and {} images'.format(len(p3d_id_list), len(img_id_obs.keys())))
top_img_ids_by_obs = sorted(img_id_obs.items(), key=lambda item: item[1], reverse=True) # [(key, value), ]
all_img_ids = []
for item in top_img_ids_by_obs:
all_img_ids.append(item[0])
if len(all_img_ids) >= topk_imgs:
break
# all_img_ids = all_img_ids[:200]
if len(all_img_ids) == 0:
print('no valid img ids with obs over {:d}'.format(min_obs))
all_img_ids = img_ids_full
img_observations = {}
p3d_id_array = np.array(p3d_id_list)
for idx, img_id in enumerate(all_img_ids):
valid_p3ds = [v for v in self.images[img_id].point3D_ids if v > 0]
mask = np.array([False for i in range(len(p3d_id_list))])
for pid in valid_p3ds:
found_idx = np.where(p3d_id_array == pid)[0]
if found_idx.shape[0] == 0:
continue
mask[found_idx[0]] = True
img_observations[img_id] = mask
unobserved_p3d_ids = np.array([True for i in range(len(p3d_id_list))])
candidate_img_ids = []
total_cover_ratio = 0
while total_cover_ratio < min_cover_ratio:
best_img_id = -1
best_img_obs = -1
for idx, im_id in enumerate(all_img_ids):
if im_id in candidate_img_ids:
continue
obs_i = np.sum(img_observations[im_id] * unobserved_p3d_ids)
if obs_i > best_img_obs:
best_img_id = im_id
best_img_obs = obs_i
if best_img_id >= 0:
# keep the valid img_id
candidate_img_ids.append(best_img_id)
# update the unobserved mask
unobserved_p3d_ids[img_observations[best_img_id]] = False
total_cover_ratio = 1 - np.sum(unobserved_p3d_ids) / len(p3d_id_list)
print(len(candidate_img_ids), best_img_obs, best_img_obs / len(p3d_id_list), total_cover_ratio)
if best_img_obs / len(p3d_id_list) < 0.01:
break
if len(candidate_img_ids) >= n_vrf:
break
else:
break
return candidate_img_ids
# return [(v, img_observations[v]) for v in candidate_img_ids]
if save_vrf_dir is not None:
os.makedirs(save_vrf_dir, exist_ok=True)
seg_ref = {}
for sid in self.seg_p3d.keys():
if sid == -1: # ignore invalid segment
continue
all_p3d_ids = self.seg_p3d[sid]
candidate_img_ids = find_best_vrf_by_covisibility(p3d_id_list=all_p3d_ids)
seg_ref[sid] = {}
for can_idx, img_id in enumerate(candidate_img_ids):
cam = self.cameras[self.images[img_id].camera_id]
width = cam.width
height = cam.height
qvec = self.images[img_id].qvec
tvec = self.images[img_id].tvec
img_name = self.images[img_id].name
orig_p3d_ids = [p for p in self.images[img_id].point3D_ids if p in self.points3D.keys() and p >= 0]
orig_xyzs = []
new_xyzs = []
for pid in all_p3d_ids:
if pid in orig_p3d_ids:
orig_xyzs.append(self.points3D[pid].xyz)
else:
if pid in self.points3D.keys():
new_xyzs.append(self.points3D[pid].xyz)
if len(orig_xyzs) == 0:
continue
orig_xyzs = np.array(orig_xyzs)
new_xyzs = np.array(new_xyzs)
print('img: ', osp.join(kwargs.get('image_root'), img_name))
img = cv2.imread(osp.join(kwargs.get('image_root'), img_name))
orig_kpts = reproject(img_id=img_id, xyzs=orig_xyzs)
max_depth = depth_scale * np.max(orig_kpts[:, 2])
orig_kpts = orig_kpts[:, :2]
mask_ori = (orig_kpts[:, 0] >= 0) & (orig_kpts[:, 0] < width) & (orig_kpts[:, 1] >= 0) & (
orig_kpts[:, 1] < height)
orig_kpts = orig_kpts[mask_ori]
if orig_kpts.shape[0] == 0:
continue
img_kpt = plot_kpts(img=img, kpts=orig_kpts, radius=[3 for i in range(orig_kpts.shape[0])],
colors=[(0, 0, 255) for i in range(orig_kpts.shape[0])], thickness=-1)
if new_xyzs.shape[0] == 0:
img_all = img_kpt
else:
new_kpts = reproject(img_id=img_id, xyzs=new_xyzs)
mask_depth = (new_kpts[:, 2] > 0) & (new_kpts[:, 2] <= max_depth)
mask_in_img = (new_kpts[:, 0] >= 0) & (new_kpts[:, 0] < width) & (new_kpts[:, 1] >= 0) & (
new_kpts[:, 1] < height)
dist_all_orig = torch.from_numpy(new_kpts[:, :2])[..., None] - \
torch.from_numpy(orig_kpts[:, :2].transpose())[None]
dist_all_orig = torch.sqrt(torch.sum(dist_all_orig ** 2, dim=1)) # [N, M]
min_dist = torch.min(dist_all_orig, dim=1)[0].numpy()
mask_close_to_img = (min_dist <= radius)
mask_new = (mask_depth & mask_in_img & mask_close_to_img)
cover_ratio = np.sum(mask_ori) + np.sum(mask_new)
cover_ratio = cover_ratio / len(all_p3d_ids)
print('idx: {:d}, img: ori {:d}/{:d}/{:.2f}, new {:d}/{:d}'.format(can_idx,
orig_kpts.shape[0],
np.sum(mask_ori),
cover_ratio * 100,
new_kpts.shape[0],
np.sum(mask_new)))
new_kpts = new_kpts[mask_new]
# img_all = img_kpt
img_all = plot_kpts(img=img_kpt, kpts=new_kpts, radius=[3 for i in range(new_kpts.shape[0])],
colors=[(0, 255, 0) for i in range(new_kpts.shape[0])], thickness=-1)
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.imshow('img', img_all)
if save_vrf_dir is not None:
cv2.imwrite(osp.join(save_vrf_dir,
'seg-{:05d}_can-{:05d}_'.format(sid, can_idx) + img_name.replace('/', '+')),
img_all)
key = cv2.waitKey(show_time)
if key == ord('q'):
cv2.destroyAllWindows()
exit(0)
covisile_frame_ids = self.find_covisible_frame_ids(image_id=img_id, images=self.images,
points3D=self.points3D)
seg_ref[sid][can_idx] = {
'image_name': img_name,
'image_id': img_id,
'qvec': deepcopy(qvec),
'tvec': deepcopy(tvec),
'camera': {
'model': cam.model,
'params': cam.params,
'width': cam.width,
'height': cam.height,
},
'original_points3d': np.array(
[v for v in self.images[img_id].point3D_ids if v >= 0 and v in self.points3D.keys()]),
'covisible_frame_ids': np.array(covisile_frame_ids[:covisible_frame]),
}
# save vrf info
if save_fn is not None:
print('Save {} segments with virtual reference image information to {}'.format(len(seg_ref.keys()),
save_fn))
np.save(save_fn, seg_ref)
def visualize_3Dpoints(self):
xyz = []
rgb = []
for point3D in self.points3D.values():
xyz.append(point3D.xyz)
rgb.append(point3D.rgb / 255)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(xyz)
pcd.colors = o3d.utility.Vector3dVector(rgb)
o3d.visualization.draw_geometries([pcd])
def visualize_segmentation(self, p3d_segs, points3D):
p3d_ids = p3d_segs.keys()
xyzs = []
rgbs = []
for pid in p3d_ids:
xyzs.append(points3D[pid].xyz)
seg_color = self.seg_color_dict[p3d_segs[pid]]
rgbs.append(np.array([seg_color[2], seg_color[1], seg_color[0]]) / 255)
xyzs = np.array(xyzs)
rgbs = np.array(rgbs)
self.pcd.points = o3d.utility.Vector3dVector(xyzs)
self.pcd.colors = o3d.utility.Vector3dVector(rgbs)
o3d.visualization.draw_geometries([self.pcd])
def visualize_segmentation_on_image(self, p3d_segs, image_path, feat_path):
vis_color = generate_color_dic(n_seg=1024)
feat_file = h5py.File(feat_path, 'r')
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
for mi in sorted(self.images.keys()):
im = self.images[mi]
im_name = im.name
p3d_ids = im.point3D_ids
p2ds = feat_file[im_name]['keypoints'][()]
image = cv2.imread(osp.join(image_path, im_name))
print('img_name: ', im_name)
sems = []
for pid in p3d_ids:
if pid in p3d_segs.keys():
sems.append(p3d_segs[pid] + 1)
else:
sems.append(0)
sems = np.array(sems)
sems = np.array(sems)
mask = sems > 0
img_seg = vis_seg_point(img=image, kpts=p2ds[mask], segs=sems[mask], seg_color=vis_color)
cv2.imshow('img', img_seg)
key = cv2.waitKey(0)
if key == ord('q'):
exit(0)
elif key == ord('r'):
# cv2.destroyAllWindows()
return
def extract_query_p3ds(self, log_fn, feat_fn, save_fn=None):
if save_fn is not None:
if osp.isfile(save_fn):
print('{:s} exists'.format(save_fn))
return
loc_log = np.load(log_fn, allow_pickle=True)[()]
fns = loc_log.keys()
feat_file = h5py.File(feat_fn, 'r')
out = {}
for fn in tqdm(fns, total=len(fns)):
matched_kpts = loc_log[fn]['keypoints_query']
matched_p3ds = loc_log[fn]['points3D_ids']
query_kpts = feat_file[fn]['keypoints'][()].astype(float)
query_p3d_ids = np.zeros(shape=(query_kpts.shape[0],), dtype=int) - 1
print('matched kpts: {}, query kpts: {}'.format(matched_kpts.shape[0], query_kpts.shape[0]))
if matched_kpts.shape[0] > 0:
# [M, 2, 1] - [1, 2, N] = [M, 2, N]
dist = torch.from_numpy(matched_kpts).unsqueeze(-1) - torch.from_numpy(
query_kpts.transpose()).unsqueeze(0)
dist = torch.sum(dist ** 2, dim=1) # [M, N]
values, idxes = torch.topk(dist, dim=1, largest=False, k=1) # find the matches kpts with dist of 0
values = values.numpy()
idxes = idxes.numpy()
for i in range(values.shape[0]):
if values[i, 0] < 1:
query_p3d_ids[idxes[i, 0]] = matched_p3ds[i]
out[fn] = query_p3d_ids
np.save(save_fn, out)
feat_file.close()
def compute_mean_scale_p3ds(self, min_obs=5, save_fn=None):
if save_fn is not None:
if osp.isfile(save_fn):
with open(save_fn, 'r') as f:
lines = f.readlines()
l = lines[0].strip().split()
self.mean_xyz = np.array([float(v) for v in l[:3]])
self.scale_xyz = np.array([float(v) for v in l[3:]])
print('{} exists'.format(save_fn))
return
all_xyzs = []
for pid in self.points3D.keys():
p3d = self.points3D[pid]
obs = len(p3d.point2D_idxs)
if obs < min_obs:
continue
all_xyzs.append(p3d.xyz)
all_xyzs = np.array(all_xyzs)
mean_xyz = np.ceil(np.mean(all_xyzs, axis=0))
all_xyz_ = all_xyzs - mean_xyz
dx = np.max(abs(all_xyz_[:, 0]))
dy = np.max(abs(all_xyz_[:, 1]))
dz = np.max(abs(all_xyz_[:, 2]))
scale_xyz = np.ceil(np.array([dx, dy, dz], dtype=float).reshape(3, ))
scale_xyz[scale_xyz < 1] = 1
scale_xyz[scale_xyz == 0] = 1
# self.mean_xyz = mean_xyz
# self.scale_xyz = scale_xyz
#
# if save_fn is not None:
# with open(save_fn, 'w') as f:
# text = '{:.4f} {:.4f} {:.4f} {:.4f} {:.4f} {:.4f}'.format(mean_xyz[0], mean_xyz[1], mean_xyz[2],
# scale_xyz[0], scale_xyz[1], scale_xyz[2])
# f.write(text + '\n')
def compute_statics_inlier(self, xyz, nb_neighbors=20, std_ratio=2.0):
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(xyz)
new_pcd, inlier_ids = pcd.remove_statistical_outlier(nb_neighbors=nb_neighbors, std_ratio=std_ratio)
return inlier_ids
def export_features_to_directory(self, feat_fn, save_dir, with_descriptors=True):
def print_grp_name(grp_name, object):
try:
n_subgroups = len(object.keys())
except:
n_subgroups = 0
dataset_list.append(object.name)
dataset_list = []
feat_file = h5py.File(feat_fn, 'r')
feat_file.visititems(print_grp_name)
all_keys = []
os.makedirs(save_dir, exist_ok=True)
for fn in dataset_list:
subs = fn[1:].split('/')[:-1] # remove the first '/'
subs = '/'.join(map(str, subs))
if subs in all_keys:
continue
all_keys.append(subs)
for fn in tqdm(all_keys, total=len(all_keys)):
feat = feat_file[fn]
data = {
# 'descriptors': feat['descriptors'][()].transpose(),
'scores': feat['scores'][()],
'keypoints': feat['keypoints'][()],
'image_size': feat['image_size'][()]
}
np.save(osp.join(save_dir, fn.replace('/', '+')), data)
feat_file.close()
def get_intrinsics_from_camera(self, camera):
if camera.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"):
fx = fy = camera.params[0]
cx = camera.params[1]
cy = camera.params[2]
elif camera.model in ("PINHOLE", "OPENCV", "OPENCV_FISHEYE", "FULL_OPENCV"):
fx = camera.params[0]
fy = camera.params[1]
cx = camera.params[2]
cy = camera.params[3]
else:
raise Exception("Camera model not supported")
# intrinsics
K = np.identity(3)
K[0, 0] = fx
K[1, 1] = fy
K[0, 2] = cx
K[1, 2] = cy
return K
def compress_map_by_projection_v2(self, vrf_path, point3d_desc_path, vrf_frames=1, covisible_frames=20, radius=20,
nkpts=-1, save_dir=None):
def sparsify_by_grid(h, w, uvs, scores):
nh = np.ceil(h / radius).astype(int)
nw = np.ceil(w / radius).astype(int)
grid = {}
for ip in range(uvs.shape[0]):
p = uvs[ip]
iw = np.rint(p[0] // radius).astype(int)
ih = np.rint(p[1] // radius).astype(int)
idx = ih * nw + iw
if idx in grid.keys():
if scores[ip] <= grid[idx]['score']:
continue
else:
grid[idx]['score'] = scores[ip]
grid[idx]['ip'] = ip
else:
grid[idx] = {
'score': scores[ip],
'ip': ip
}
retained_ips = [grid[v]['ip'] for v in grid.keys()]
retained_ips = np.array(retained_ips)
return retained_ips
def choose_valid_p3ds(current_frame_id, covisible_frame_ids, reserved_images):
curr_p3d_ids = []
curr_xyzs = []
for pid in self.images[current_frame_id].point3D_ids:
if pid == -1:
continue
if pid not in self.points3D.keys():
continue
curr_p3d_ids.append(pid)
curr_xyzs.append(self.points3D[pid].xyz)
curr_xyzs = np.array(curr_xyzs) # [N, 3]
curr_xyzs_homo = np.hstack([curr_xyzs, np.ones((curr_xyzs.shape[0], 1), dtype=curr_xyzs.dtype)]) # [N, 4]
curr_mask = np.array([True for mi in range(curr_xyzs.shape[0])]) # keep all at first
for iim in covisible_frame_ids:
cam_id = self.images[iim].camera_id
width = self.cameras[cam_id].width
height = self.cameras[cam_id].height
qvec = self.images[iim].qvec
tcw = self.images[iim].tvec
Rcw = qvec2rotmat(qvec=qvec)
Tcw = np.eye(4, dtype=float)
Tcw[:3, :3] = Rcw
Tcw[:3, 3] = tcw.reshape(3, )
uvs = reserved_images[iim]['xys']
K = self.get_intrinsics_from_camera(camera=self.cameras[cam_id])
proj_xys = K @ (Tcw @ curr_xyzs_homo.transpose())[:3, :] # [3, ]
proj_xys = proj_xys.transpose()
depth = proj_xys[:, 2]
proj_xys[:, 0] = proj_xys[:, 0] / depth
proj_xys[:, 1] = proj_xys[:, 1] / depth
mask_in_image = (proj_xys[:, 0] >= 0) * (proj_xys[:, 0] < width) * (proj_xys[:, 1] >= 0) * (
proj_xys[:, 1] < height)
mask_depth = proj_xys[:, 2] > 0
dist_proj_uv = torch.from_numpy(proj_xys[:, :2])[..., None] - \
torch.from_numpy(uvs[:, :2].transpose())[None]
dist_proj_uv = torch.sqrt(torch.sum(dist_proj_uv ** 2, dim=1)) # [N, M]
min_dist = torch.min(dist_proj_uv, dim=1)[0].numpy()
mask_close_to_img = (min_dist <= radius)
mask = mask_in_image * mask_depth * mask_close_to_img # p3ds to be discarded
curr_mask = curr_mask * (1 - mask)
chosen_p3d_ids = []
for mi in range(curr_mask.shape[0]):
if curr_mask[mi]:
chosen_p3d_ids.append(curr_p3d_ids[mi])
return chosen_p3d_ids
vrf_data = np.load(vrf_path, allow_pickle=True)[()]
p3d_ids_in_vrf = []
image_ids_in_vrf = []
for sid in vrf_data.keys():
svrf = vrf_data[sid]
svrf_keys = [vi for vi in range(vrf_frames)]
for vi in svrf_keys:
if vi not in svrf.keys():
continue
image_id = svrf[vi]['image_id']
if image_id in image_ids_in_vrf:
continue
image_ids_in_vrf.append(image_id)
for pid in svrf[vi]['original_points3d']:
if pid in p3d_ids_in_vrf:
continue
p3d_ids_in_vrf.append(pid)
print('Find {:d} images and {:d} 3D points in vrf'.format(len(image_ids_in_vrf), len(p3d_ids_in_vrf)))
# first_vrf_images_covis = {}
retained_image_ids = {}
for frame_id in image_ids_in_vrf:
observed = self.images[frame_id].point3D_ids
xys = self.images[frame_id].xys
covis = defaultdict(int)
valid_xys = []
valid_p3d_ids = []
for xy, pid in zip(xys, observed):
if pid == -1:
continue
if pid not in self.points3D.keys():
continue
valid_xys.append(xy)
valid_p3d_ids.append(pid)
for img_id in self.points3D[pid].image_ids:
covis[img_id] += 1
retained_image_ids[frame_id] = {
'xys': np.array(valid_xys),
'p3d_ids': valid_p3d_ids,
}
print('Find {:d} valid connected frames'.format(len(covis.keys())))
covis_ids = np.array(list(covis.keys()))
covis_num = np.array([covis[i] for i in covis_ids])
if len(covis_ids) <= covisible_frames:
sel_covis_ids = covis_ids[np.argsort(-covis_num)]
else:
ind_top = np.argpartition(covis_num, -covisible_frames)
ind_top = ind_top[-covisible_frames:] # unsorted top k
ind_top = ind_top[np.argsort(-covis_num[ind_top])]
sel_covis_ids = [covis_ids[i] for i in ind_top]
covis_frame_ids = [frame_id]
for iim in sel_covis_ids:
if iim == frame_id:
continue
if iim in retained_image_ids.keys():
covis_frame_ids.append(iim)
continue
chosen_p3d_ids = choose_valid_p3ds(current_frame_id=iim, covisible_frame_ids=covis_frame_ids,
reserved_images=retained_image_ids)
if len(chosen_p3d_ids) == 0:
continue
xys = []
for xy, pid in zip(self.images[iim].xys, self.images[iim].point3D_ids):
if pid in chosen_p3d_ids:
xys.append(xy)
xys = np.array(xys)
covis_frame_ids.append(iim)
retained_image_ids[iim] = {
'xys': xys,
'p3d_ids': chosen_p3d_ids,
}
new_images = {}
new_point3Ds = {}
new_cameras = {}
for iim in retained_image_ids.keys():
p3d_ids = retained_image_ids[iim]['p3d_ids']
''' this step reduces the performance
for v in self.images[iim].point3D_ids:
if v == -1 or v not in self.points3D:
continue
if v in p3d_ids:
continue
p3d_ids.append(v)
'''
xyzs = np.array([self.points3D[pid].xyz for pid in p3d_ids])
obs = np.array([len(self.points3D[pid].point2D_idxs) for pid in p3d_ids])
xys = self.images[iim].xys
cam_id = self.images[iim].camera_id
name = self.images[iim].name
qvec = self.images[iim].qvec
tvec = self.images[iim].tvec
if nkpts > 0 and len(p3d_ids) > nkpts:
proj_uvs = self.reproject(img_id=iim, xyzs=xyzs)
width = self.cameras[cam_id].width
height = self.cameras[cam_id].height
sparsified_idxs = sparsify_by_grid(h=height, w=width, uvs=proj_uvs[:, :2], scores=obs)
print('org / new kpts: ', len(p3d_ids), sparsified_idxs.shape)
p3d_ids = [p3d_ids[k] for k in sparsified_idxs]
new_images[iim] = Image(id=iim, qvec=qvec, tvec=tvec,
camera_id=cam_id,
name=name,
xys=np.array([]),
point3D_ids=np.array(p3d_ids))
if cam_id not in new_cameras.keys():
new_cameras[cam_id] = self.cameras[cam_id]
for pid in p3d_ids:
if pid in new_point3Ds.keys():
new_point3Ds[pid]['image_ids'].append(iim)
else:
xyz = self.points3D[pid].xyz
rgb = self.points3D[pid].rgb
error = self.points3D[pid].error
new_point3Ds[pid] = {
'image_ids': [iim],
'rgb': rgb,
'xyz': xyz,
'error': error
}
new_point3Ds_to_save = {}
for pid in new_point3Ds.keys():
image_ids = new_point3Ds[pid]['image_ids']
if len(image_ids) == 0:
continue
xyz = new_point3Ds[pid]['xyz']
rgb = new_point3Ds[pid]['rgb']
error = new_point3Ds[pid]['error']
new_point3Ds_to_save[pid] = Point3D(id=pid, xyz=xyz, rgb=rgb, error=error, image_ids=np.array(image_ids),
point2D_idxs=np.array([]))
print('Retain {:d}/{:d} images and {:d}/{:d} 3D points'.format(len(new_images), len(self.images),
len(new_point3Ds), len(self.points3D)))
if save_dir is not None:
os.makedirs(save_dir, exist_ok=True)
# write_images_binary(images=new_image_ids,
# path_to_model_file=osp.join(save_dir, 'images.bin'))
# write_points3d_binary(points3D=new_point3Ds,
# path_to_model_file=osp.join(save_dir, 'points3D.bin'))
write_compressed_images_binary(images=new_images,
path_to_model_file=osp.join(save_dir, 'images.bin'))
write_cameras_binary(cameras=new_cameras,
path_to_model_file=osp.join(save_dir, 'cameras.bin'))
write_compressed_points3d_binary(points3D=new_point3Ds_to_save,
path_to_model_file=osp.join(save_dir, 'points3D.bin'))
# Save 3d descriptors
p3d_desc = np.load(point3d_desc_path, allow_pickle=True)[()]
comp_p3d_desc = {}
for k in new_point3Ds_to_save.keys():
if k not in p3d_desc.keys():
print(k)
continue
comp_p3d_desc[k] = deepcopy(p3d_desc[k])
np.save(osp.join(save_dir, point3d_desc_path.split('/')[-1]), comp_p3d_desc)
print('Save data to {:s}'.format(save_dir))
def process_dataset(dataset, dataset_dir, sfm_dir, save_dir, feature='sfd2', matcher='gml'):
# dataset_dir = '/scratches/flyer_3/fx221/dataset'
# sfm_dir = '/scratches/flyer_2/fx221/localization/outputs' # your sfm results (cameras, images, points3D) and features
# save_dir = '/scratches/flyer_3/fx221/exp/localizer'
# local_feat = 'sfd2'
# matcher = 'gml'
# hloc_results_dir = '/scratches/flyer_2/fx221/exp/sgd2'
# config_path = 'configs/datasets/CUED.yaml'
# config_path = 'configs/datasets/7Scenes.yaml'
# config_path = 'configs/datasets/12Scenes.yaml'
# config_path = 'configs/datasets/CambridgeLandmarks.yaml'
# config_path = 'configs/datasets/Aachen.yaml'
# config_path = 'configs/datasets/Aria.yaml'
# config_path = 'configs/datasets/DarwinRGB.yaml'
# config_path = 'configs/datasets/ACUED.yaml'
# config_path = 'configs/datasets/JesusCollege.yaml'
# config_path = 'configs/datasets/CUED2Kings.yaml'
config_path = 'configs/datasets/{:s}.yaml'.format(dataset)
with open(config_path, 'rt') as f:
configs = yaml.load(f, Loader=yaml.Loader)
print(configs)
dataset = configs['dataset']
all_scenes = configs['scenes']
for scene in all_scenes:
n_cluster = configs[scene]['n_cluster']
cluster_mode = configs[scene]['cluster_mode']
cluster_method = configs[scene]['cluster_method']
# if scene not in ['heads']:
# continue
print('scene: ', scene, cluster_mode, cluster_method)
# hloc_path = osp.join(hloc_root, dataset, scene)
sfm_path = osp.join(sfm_dir, scene)
save_path = osp.join(save_dir, feature + '-' + matcher, dataset, scene)
n_vrf = 1
n_cov = 30
radius = 20
n_kpts = 0
if dataset in ['Aachen']:
image_path = osp.join(dataset_dir, scene, 'images/images_upright')
min_obs = 250
filtering_outliers = True
threshold = 0.2
radius = 32
elif dataset in ['CambridgeLandmarks', ]:
image_path = osp.join(dataset_dir, scene)
min_obs = 250
filtering_outliers = True
threshold = 0.2
radius = 64
elif dataset in ['Aria']:
image_path = osp.join(dataset_dir, scene)
min_obs = 150
filtering_outliers = False
threshold = 0.01
radius = 15
elif dataset in ['DarwinRGB']:
image_path = osp.join(dataset_dir, scene)
min_obs = 150
filtering_outliers = True
threshold = 0.2
radius = 16
elif dataset in ['ACUED']:
image_path = osp.join(dataset_dir, scene)
min_obs = 250
filtering_outliers = True
threshold = 0.2
radius = 32
elif dataset in ['7Scenes', '12Scenes']:
image_path = osp.join(dataset_dir, scene)
min_obs = 150
filtering_outliers = False
threshold = 0.01
radius = 15
else:
image_path = osp.join(dataset_dir, scene)
min_obs = 250
filtering_outliers = True
threshold = 0.2
radius = 32
# comp_map_sub_path = 'comp_model_n{:d}_{:s}_{:s}_vrf{:d}_cov{:d}_r{:d}_np{:d}_projection_v2'.format(n_cluster,
# cluster_mode,
# cluster_method,
# n_vrf,
# n_cov,
# radius,
# n_kpts)
comp_map_sub_path = 'compress_model_{:s}'.format(cluster_method)
seg_fn = osp.join(save_path,
'point3D_cluster_n{:d}_{:s}_{:s}.npy'.format(n_cluster, cluster_mode, cluster_method))
vrf_fn = osp.join(save_path,
'point3D_vrf_n{:d}_{:s}_{:s}.npy'.format(n_cluster, cluster_mode, cluster_method))
vrf_img_dir = osp.join(save_path,
'point3D_vrf_n{:d}_{:s}_{:s}'.format(n_cluster, cluster_mode, cluster_method))
# p3d_query_fn = osp.join(save_path,
# 'point3D_query_n{:d}_{:s}_{:s}.npy'.format(n_cluster, cluster_mode, cluster_method))
comp_map_path = osp.join(save_path, comp_map_sub_path)
os.makedirs(save_path, exist_ok=True)
rmap = RecMap()
rmap.load_sfm_model(path=osp.join(sfm_path, 'sfm_{:s}-{:s}'.format(feature, matcher)))
if filtering_outliers:
rmap.remove_statics_outlier(nb_neighbors=20, std_ratio=2.0)
# extract keypoints to train the recognition model (descriptors are recomputed from augmented db images)
# we do this for ddp training (reading h5py file is not supported)
rmap.export_features_to_directory(feat_fn=osp.join(sfm_path, 'feats-{:s}.h5'.format(feature)),
save_dir=osp.join(save_path, 'feats')) # only once for training
rmap.cluster(k=n_cluster, mode=cluster_mode, save_fn=seg_fn, method=cluster_method, threshold=threshold)
# rmap.visualize_3Dpoints()
rmap.load_segmentation(path=seg_fn)
# rmap.visualize_segmentation(p3d_segs=rmap.p3d_seg, points3D=rmap.points3D)
# Assign each 3D point a desciptor and discard all 2D images and descriptors - for localization
rmap.assign_point3D_descriptor(
feature_fn=osp.join(sfm_path, 'feats-{:s}.h5'.format(feature)),
save_fn=osp.join(save_path, 'point3D_desc.npy'.format(n_cluster, cluster_mode)),
n_process=32) # only once
# exit(0)
# rmap.visualize_segmentation_on_image(p3d_segs=rmap.p3d_seg, image_path=image_path, feat_path=feat_path)
# for query images only - for evaluation
# rmap.extract_query_p3ds(
# log_fn=osp.join(hloc_path, 'hloc_feats-{:s}_{:s}_loc.npy'.format(local_feat, matcher)),
# feat_fn=osp.join(sfm_path, 'feats-{:s}.h5'.format(local_feat)),
# save_fn=p3d_query_fn)
# continue
# up-to-date
rmap.create_virtual_frame_3(
save_fn=vrf_fn,
save_vrf_dir=vrf_img_dir,
image_root=image_path,
show_time=5,
min_cover_ratio=0.9,
radius=radius,
depth_scale=2.5, # 1.2 by default
min_obs=min_obs,
n_vrf=10,
covisible_frame=n_cov,
ignored_cameras=[])
# up-to-date
rmap.compress_map_by_projection_v2(
vrf_frames=n_vrf,
vrf_path=vrf_fn,
point3d_desc_path=osp.join(save_path, 'point3D_desc.npy'),
save_dir=comp_map_path,
covisible_frames=n_cov,
radius=radius,
nkpts=n_kpts,
)
# exit(0)
# soft_link_compress_path = osp.join(save_path, 'compress_model_{:s}'.format(cluster_method))
os.chdir(save_path)
# if osp.isdir(soft_link_compress_path):
# os.unlink(soft_link_compress_path)
# os.symlink(comp_map_sub_path, 'compress_model_{:s}'.format(cluster_method))
# create a soft link of the full model for training
if not osp.isdir('model'):
os.symlink(osp.join(sfm_path, 'sfm_{:s}-{:s}'.format(feature, matcher)), '3D-models')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, required=True, help='dataset name')
parser.add_argument('--dataset_dir', type=str, required=True, help='dataset dir')
parser.add_argument('--sfm_dir', type=str, required=True, help='sfm dir')
parser.add_argument('--save_dir', type=str, required=True, help='dir to save the landmarks data')
parser.add_argument('--feature', type=str, default='sfd2', help='feature name e.g., SP, SFD2')
parser.add_argument('--matcher', type=str, default='gml', help='matcher name e.g., SG, LSG, gml')
args = parser.parse_args()
process_dataset(
dataset=args.dataset,
dataset_dir=args.dataset_dir,
sfm_dir=args.sfm_dir,
save_dir=args.save_dir,
feature=args.feature,
matcher=args.matcher)
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