third_party/DarkFeat/pose_estimation.py

import argparse
import cv2
import numpy as np
import os
import math
import subprocess
from tqdm import tqdm


def compute_essential(matched_kp1, matched_kp2, K):
    pts1 = cv2.undistortPoints(
        matched_kp1,
        cameraMatrix=K,
        distCoeffs=(-0.117918271740560, 0.075246403574314, 0, 0),
    )
    pts2 = cv2.undistortPoints(
        matched_kp2,
        cameraMatrix=K,
        distCoeffs=(-0.117918271740560, 0.075246403574314, 0, 0),
    )
    K_1 = np.eye(3)
    # Estimate the homography between the matches using RANSAC
    ransac_model, ransac_inliers = cv2.findEssentialMat(
        pts1, pts2, K_1, method=cv2.RANSAC, prob=0.999, threshold=0.001, maxIters=10000
    )
    if ransac_inliers is None or ransac_model.shape != (3, 3):
        ransac_inliers = np.array([])
        ransac_model = None
    return ransac_model, ransac_inliers, pts1, pts2


def compute_error(R_GT, t_GT, E, pts1_norm, pts2_norm, inliers):
    """Compute the angular error between two rotation matrices and two translation vectors.
    Keyword arguments:
    R -- 2D numpy array containing an estimated rotation
    gt_R -- 2D numpy array containing the corresponding ground truth rotation
    t -- 2D numpy array containing an estimated translation as column
    gt_t -- 2D numpy array containing the corresponding ground truth translation
    """

    inliers = inliers.ravel()
    R = np.eye(3)
    t = np.zeros((3, 1))
    sst = True
    try:
        _, R, t, _ = cv2.recoverPose(E, pts1_norm, pts2_norm, np.eye(3), inliers)
    except:
        sst = False
    # calculate angle between provided rotations
    #
    if sst:
        dR = np.matmul(R, np.transpose(R_GT))
        dR = cv2.Rodrigues(dR)[0]
        dR = np.linalg.norm(dR) * 180 / math.pi

        # calculate angle between provided translations
        dT = float(np.dot(t_GT.T, t))
        dT /= float(np.linalg.norm(t_GT))

        if dT > 1 or dT < -1:
            print("Domain warning! dT:", dT)
            dT = max(-1, min(1, dT))
        dT = math.acos(dT) * 180 / math.pi
        dT = np.minimum(dT, 180 - dT)  # ambiguity of E estimation
    else:
        dR, dT = 180.0, 180.0
    return dR, dT


def pose_evaluation(result_base_dir, dark_name1, dark_name2, enhancer, K, R_GT, t_GT):
    try:
        m_kp1 = np.load(result_base_dir + enhancer + "/DarkFeat/POINT_1/" + dark_name1)
        m_kp2 = np.load(result_base_dir + enhancer + "/DarkFeat/POINT_2/" + dark_name2)
    except:
        return 180.0, 180.0
    try:
        E, inliers, pts1, pts2 = compute_essential(m_kp1, m_kp2, K)
    except:
        E, inliers, pts1, pts2 = np.zeros((3, 3)), np.array([]), None, None
    dR, dT = compute_error(R_GT, t_GT, E, pts1, pts2, inliers)
    return dR, dT


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--histeq", action="store_true")
    parser.add_argument("--dataset_dir", type=str, default="/data/hyz/MID/")
    opt = parser.parse_args()

    sizer = (960, 640)
    focallength_x = 4.504986436499113e03 / (6744 / sizer[0])
    focallength_y = 4.513311442889859e03 / (4502 / sizer[1])
    K = np.eye(3)
    K[0, 0] = focallength_x
    K[1, 1] = focallength_y
    K[0, 2] = 3.363322177533149e03 / (6744 / sizer[0])
    K[1, 2] = 2.291824660547715e03 / (4502 / sizer[1])
    Kinv = np.linalg.inv(K)
    Kinvt = np.transpose(Kinv)

    PE_MT = np.zeros((6, 8))

    enhancer = "None" if not opt.histeq else "HistEQ"

    for scene in ["Indoor", "Outdoor"]:
        dir_base = opt.dataset_dir + "/" + scene + "/"
        base_save = "result_errors/" + scene + "/"
        pair_list = sorted(os.listdir(dir_base))

        os.makedirs(base_save, exist_ok=True)

        for pair in tqdm(pair_list):
            opention = 1
            if scene == "Outdoor":
                pass
            else:
                if int(pair[4::]) <= 17:
                    opention = 0
                else:
                    pass
            name = []
            files = sorted(os.listdir(dir_base + pair))
            for file_ in files:
                if file_.endswith(".cr2"):
                    name.append(file_[0:9])
            ISO = [
                "00100",
                "00200",
                "00400",
                "00800",
                "01600",
                "03200",
                "06400",
                "12800",
            ]
            if opention == 1:
                Shutter_speed = ["0.005", "0.01", "0.025", "0.05", "0.17", "0.5"]
            else:
                Shutter_speed = ["0.01", "0.02", "0.05", "0.1", "0.3", "1"]

            E_GT = np.load(dir_base + pair + "/GT_Correspondence/" + "E_estimated.npy")
            F_GT = np.dot(np.dot(Kinvt, E_GT), Kinv)
            R_GT = np.load(dir_base + pair + "/GT_Correspondence/" + "R_GT.npy")
            t_GT = np.load(dir_base + pair + "/GT_Correspondence/" + "T_GT.npy")
            result_base_dir = "result/" + scene + "/" + pair + "/"
            for iso in ISO:
                for ex in Shutter_speed:
                    dark_name1 = name[0] + iso + "_" + ex + "_" + scene + ".npy"
                    dark_name2 = name[1] + iso + "_" + ex + "_" + scene + ".npy"

                    dr, dt = pose_evaluation(
                        result_base_dir, dark_name1, dark_name2, enhancer, K, R_GT, t_GT
                    )
                    PE_MT[Shutter_speed.index(ex), ISO.index(iso)] = max(dr, dt)

                    subprocess.check_output(
                        ["mkdir", "-p", base_save + pair + f"/{enhancer}/"]
                    )
                    np.save(
                        base_save + pair + f"/{enhancer}/Pose_error_DarkFeat.npy", PE_MT
                    )