Spaces:
Running
Running
File size: 6,479 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 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
import os
import glob
import pickle
from tqdm import trange
import numpy as np
import h5py
from numpy.core.fromnumeric import reshape
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 fmbench(BaseDumper):
def get_seqs(self):
data_dir = os.path.join(self.config["rawdata_dir"])
self.split_list = []
for seq in self.config["data_seq"]:
cur_split_list = np.unique(
np.loadtxt(
os.path.join(data_dir, seq, "pairs_which_dataset.txt"), dtype=str
)
)
self.split_list.append(cur_split_list)
for split in cur_split_list:
split_dir = os.path.join(data_dir, seq, split)
dump_dir = os.path.join(self.config["feature_dump_dir"], seq, split)
cur_img_seq = glob.glob(os.path.join(split_dir, "Images", "*.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_index in range(len(self.config["data_seq"])):
seq_dir = os.path.join(
self.config["feature_dump_dir"], self.config["data_seq"][seq_index]
)
if not os.path.exists(seq_dir):
os.mkdir(seq_dir)
for split in self.split_list[seq_index]:
split_dir = os.path.join(seq_dir, split)
if not os.path.exists(split_dir):
os.mkdir(split_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 seq_index in range(len(self.config["data_seq"])):
seq = self.config["data_seq"][seq_index]
print(seq)
pair_list = np.loadtxt(
os.path.join(self.config["rawdata_dir"], seq, "pairs_with_gt.txt"),
dtype=float,
)
which_split_list = np.loadtxt(
os.path.join(
self.config["rawdata_dir"], seq, "pairs_which_dataset.txt"
),
dtype=str,
)
for pair_index in trange(len(pair_list)):
cur_pair = pair_list[pair_index]
cur_split = which_split_list[pair_index]
index1, index2 = int(cur_pair[0]), int(cur_pair[1])
# get intrinsic
camera = np.loadtxt(
os.path.join(
self.config["rawdata_dir"], seq, cur_split, "Camera.txt"
),
dtype=float,
)
K1, K2 = camera[index1].reshape([3, 3]), camera[index2].reshape([3, 3])
# get pose
pose = np.loadtxt(
os.path.join(
self.config["rawdata_dir"], seq, cur_split, "Poses.txt"
),
dtype=float,
)
pose1, pose2 = pose[index1].reshape([3, 4]), pose[index2].reshape(
[3, 4]
)
R1, R2, t1, t2 = (
pose1[:3, :3],
pose2[:3, :3],
pose1[:3, 3][:, np.newaxis],
pose2[:3, 3][:, np.newaxis],
)
dR = np.dot(R2, R1.T)
dt = t2 - np.dot(dR, t1)
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 = cur_pair[2:].reshape([3, 3])
f_gt = f / np.linalg.norm(f)
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)
img_path1, img_path2 = os.path.join(
seq, cur_split, "Images", str(index1).zfill(8) + ".jpg"
), os.path.join(seq, cur_split, "Images", str(index1).zfill(8) + ".jpg")
fea_path1, fea_path2 = os.path.join(
self.config["feature_dump_dir"],
seq,
cur_split,
str(index1).zfill(8)
+ ".jpg"
+ "_"
+ self.config["extractor"]["name"]
+ "_"
+ str(self.config["extractor"]["num_kpt"])
+ ".hdf5",
), os.path.join(
self.config["feature_dump_dir"],
seq,
cur_split,
str(index2).zfill(8)
+ ".jpg"
+ "_"
+ 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()
|