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# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File pram -> visualize_landmarks
@IDE PyCharm
@Author fx221@cam.ac.uk
@Date 22/03/2024 10:39
=================================================='''
import os
import os.path as osp
import numpy as np
from tqdm import tqdm
from colmap_utils.read_write_model import read_model, write_model, Point3D, Image, read_compressed_model
from recognition.vis_seg import generate_color_dic
def reconstruct_map(valid_image_ids, valid_p3d_ids, cameras, images, point3Ds, p3d_seg: dict):
new_point3Ds = {}
new_images = {}
valid_p3d_ids_ = []
for pid in tqdm(valid_p3d_ids, total=len(valid_p3d_ids)):
if pid == -1:
continue
if pid not in point3Ds.keys():
continue
if pid not in p3d_seg.keys():
continue
sid = map_seg[pid]
if sid == -1:
continue
valid_p3d_ids_.append(pid)
valid_p3d_ids = valid_p3d_ids_
print('valid_p3ds: ', len(valid_p3d_ids))
# for im_id in tqdm(images.keys(), total=len(images.keys())):
for im_id in tqdm(valid_image_ids, total=len(valid_image_ids)):
im = images[im_id]
# print('im: ', im)
# exit(0)
pids = im.point3D_ids
valid_pids = []
# for v in pids:
# if v not in valid_p3d_ids:
# valid_pids.append(-1)
# else:
# valid_pids.append(v)
new_im = Image(id=im_id, qvec=im.qvec, tvec=im.tvec, camera_id=im.camera_id, name=im.name, xys=im.xys,
point3D_ids=pids)
new_images[im_id] = new_im
for pid in tqdm(valid_p3d_ids, total=len(valid_p3d_ids)):
sid = map_seg[pid]
xyz = points3D[pid].xyz
if show_2D:
xyz[1] = 0
rgb = points3D[pid].rgb
else:
bgr = seg_color[sid + sid_start]
rgb = np.array([bgr[2], bgr[1], bgr[0]])
error = points3D[pid].error
p3d = Point3D(id=pid, xyz=xyz, rgb=rgb, error=error,
image_ids=points3D[pid].image_ids,
point2D_idxs=points3D[pid].point2D_idxs)
new_point3Ds[pid] = p3d
return cameras, new_images, new_point3Ds
if __name__ == '__main__':
save_root = '/scratches/flyer_3/fx221/exp/localizer/vis_clustering/'
seg_color = generate_color_dic(n_seg=2000)
data_root = '/scratches/flyer_3/fx221/exp/localizer/resnet4x-20230511-210205-pho-0005-gm'
show_2D = False
compress_map = False
# compress_map = True
# scene = 'Aachen/Aachenv11'
# seg_data = np.load(osp.join(data_root, scene, 'point3D_cluster_n512_xz_birch.npy'), allow_pickle=True)[()]
# sid_start = 1
# vrf_file_name = 'point3D_vrf_n512_xz_birch.npy'
#
# scene = 'CambridgeLandmarks/GreatCourt'
# seg_data = np.load(osp.join(data_root, scene, 'point3D_cluster_n32_xy_birch.npy'), allow_pickle=True)[()]
# sid_start = 1
# scene = 'CambridgeLandmarks/KingsCollege'
# seg_data = np.load(osp.join(data_root, scene, 'point3D_cluster_n32_xy_birch.npy'), allow_pickle=True)[()]
# sid_start = 33
# vrf_file_name = 'point3D_vrf_n32_xy_birch.npy'
# scene = 'CambridgeLandmarks/StMarysChurch'
# seg_data = np.load(osp.join(data_root, scene, 'point3D_cluster_n32_xz_birch.npy'), allow_pickle=True)[()]
# sid_start = 32 * 4 + 1
# vrf_file_name = 'point3D_vrf_n32_xz_birch.npy'
# scene = '7Scenes/office'
# seg_data = np.load(osp.join(data_root, scene, 'point3D_cluster_n16_xz_birch.npy'), allow_pickle=True)[()]
# sid_start = 33
# scene = '7Scenes/chess'
# seg_data = np.load(osp.join(data_root, scene, 'point3D_cluster_n16_xz_birch.npy'), allow_pickle=True)[()]
# sid_start = 1
# vrf_file_name = 'point3D_vrf_n16_xz_birch.npy'
# scene = '7Scenes/redkitchen'
# seg_data = np.load(osp.join(data_root, scene, 'point3D_cluster_n16_xz_birch.npy'), allow_pickle=True)[()]
# sid_start = 16 * 5 + 1
# vrf_file_name = 'point3D_vrf_n16_xz_birch.npy'
# scene = '12Scenes/apt1/kitchen'
# seg_data = np.load(osp.join(data_root, scene, 'point3D_cluster_n16_xy_birch.npy'), allow_pickle=True)[()]
# sid_start = 1
# vrf_file_name = 'point3D_vrf_n16_xy_birch.npy'
# data_root = '/scratches/flyer_3/fx221/exp/localizer/resnet4x-20230511-210205-pho-0005-gml2'
# scene = 'JesusCollege/jesuscollege'
# seg_data = np.load(osp.join(data_root, scene, 'point3D_cluster_n256_xy_birch.npy'), allow_pickle=True)[()]
# sid_start = 1
# vrf_file_name = 'point3D_vrf_n256_xy_birch.npy'
scene = 'DarwinRGB/darwin'
seg_data = np.load(osp.join(data_root, scene, 'point3D_cluster_n128_xy_birch.npy'), allow_pickle=True)[()]
sid_start = 1
vrf_file_name = 'point3D_vrf_n128_xy_birch.npy'
cameras, images, points3D = read_model(osp.join(data_root, scene, 'model'), ext='.bin')
print('Load {:d} 3D points from map'.format(len(points3D.keys())))
if compress_map:
vrf_data = np.load(osp.join(data_root, scene, vrf_file_name), allow_pickle=True)[()]
valid_image_ids = [vrf_data[v][0]['image_id'] for v in vrf_data.keys()]
else:
valid_image_ids = list(images.keys())
if compress_map:
_, _, compress_points3D = read_compressed_model(osp.join(data_root, scene, 'compress_model_birch'),
ext='.bin')
print('Load {:d} 3D points from compressed map'.format(len(compress_points3D.keys())))
valid_p3d_ids = list(compress_points3D.keys())
else:
valid_p3d_ids = list(points3D.keys())
save_path = osp.join(save_root, scene)
if compress_map:
save_path = save_path + '_comp'
if show_2D:
save_path = save_path + '_2D'
os.makedirs(save_path, exist_ok=True)
p3d_id = seg_data['id']
seg_id = seg_data['label']
map_seg = {p3d_id[i]: seg_id[i] for i in range(p3d_id.shape[0])}
new_cameras, new_images, new_point3Ds = reconstruct_map(valid_image_ids=valid_image_ids,
valid_p3d_ids=valid_p3d_ids, cameras=cameras, images=images,
point3Ds=points3D, p3d_seg=map_seg)
# write_model(cameras=cameras, images=images, points3D=new_point3Ds,
# path=save_path, ext='.bin')
write_model(cameras=new_cameras, images=new_images, points3D=new_point3Ds, path=save_path, ext='.bin')
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