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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()
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