third_party/SGMNet/datadump/dumper/fmbench.py

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()