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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()
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