Spaces:
Running
Running
File size: 5,002 Bytes
a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
import os
import glob
import pickle
from posixpath import basename
import numpy as np
import h5py
from .base_dumper import BaseDumper
import sys
ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
sys.path.insert(0, ROOT_DIR)
import utils
class scannet(BaseDumper):
def get_seqs(self):
self.pair_list = np.loadtxt("../assets/scannet_eval_list.txt", dtype=str)
self.seq_list = np.unique(
np.asarray([path.split("/")[0] for path in self.pair_list[:, 0]], dtype=str)
)
self.dump_seq, self.img_seq = [], []
for seq in self.seq_list:
dump_dir = os.path.join(self.config["feature_dump_dir"], seq)
cur_img_seq = glob.glob(
os.path.join(
os.path.join(self.config["rawdata_dir"], seq, "img", "*.jpg")
)
)
cur_dump_seq = [
os.path.join(dump_dir, path.split("/")[-1])
+ "_"
+ self.config["extractor"]["name"]
+ "_"
+ str(self.config["extractor"]["num_kpt"])
+ ".hdf5"
for path in cur_img_seq
]
self.img_seq += cur_img_seq
self.dump_seq += cur_dump_seq
def format_dump_folder(self):
if not os.path.exists(self.config["feature_dump_dir"]):
os.mkdir(self.config["feature_dump_dir"])
for seq in self.seq_list:
seq_dir = os.path.join(self.config["feature_dump_dir"], seq)
if not os.path.exists(seq_dir):
os.mkdir(seq_dir)
def format_dump_data(self):
print("Formatting data...")
self.data = {
"K1": [],
"K2": [],
"R": [],
"T": [],
"e": [],
"f": [],
"fea_path1": [],
"fea_path2": [],
"img_path1": [],
"img_path2": [],
}
for pair in self.pair_list:
img_path1, img_path2 = pair[0], pair[1]
seq = img_path1.split("/")[0]
index1, index2 = int(img_path1.split("/")[-1][:-4]), int(
img_path2.split("/")[-1][:-4]
)
ex1, ex2 = np.loadtxt(
os.path.join(
self.config["rawdata_dir"], seq, "extrinsic", str(index1) + ".txt"
),
dtype=float,
), np.loadtxt(
os.path.join(
self.config["rawdata_dir"], seq, "extrinsic", str(index2) + ".txt"
),
dtype=float,
)
K1, K2 = np.loadtxt(
os.path.join(
self.config["rawdata_dir"], seq, "intrinsic", str(index1) + ".txt"
),
dtype=float,
), np.loadtxt(
os.path.join(
self.config["rawdata_dir"], seq, "intrinsic", str(index2) + ".txt"
),
dtype=float,
)
relative_extrinsic = np.matmul(np.linalg.inv(ex2), ex1)
dR, dt = relative_extrinsic[:3, :3], relative_extrinsic[:3, 3]
dt /= np.sqrt(np.sum(dt**2))
e_gt_unnorm = np.reshape(
np.matmul(
np.reshape(
utils.evaluation_utils.np_skew_symmetric(
dt.astype("float64").reshape(1, 3)
),
(3, 3),
),
np.reshape(dR.astype("float64"), (3, 3)),
),
(3, 3),
)
e_gt = e_gt_unnorm / np.linalg.norm(e_gt_unnorm)
f_gt_unnorm = np.linalg.inv(K2.T) @ e_gt @ np.linalg.inv(K1)
f_gt = f_gt_unnorm / np.linalg.norm(f_gt_unnorm)
self.data["K1"].append(K1), self.data["K2"].append(K2)
self.data["R"].append(dR), self.data["T"].append(dt)
self.data["e"].append(e_gt), self.data["f"].append(f_gt)
dump_seq_dir = os.path.join(self.config["feature_dump_dir"], seq)
fea_path1, fea_path2 = os.path.join(
dump_seq_dir,
img_path1.split("/")[-1]
+ "_"
+ self.config["extractor"]["name"]
+ "_"
+ str(self.config["extractor"]["num_kpt"])
+ ".hdf5",
), os.path.join(
dump_seq_dir,
img_path2.split("/")[-1]
+ "_"
+ self.config["extractor"]["name"]
+ "_"
+ str(self.config["extractor"]["num_kpt"])
+ ".hdf5",
)
self.data["img_path1"].append(img_path1), self.data["img_path2"].append(
img_path2
)
self.data["fea_path1"].append(fea_path1), self.data["fea_path2"].append(
fea_path2
)
self.form_standard_dataset()
|