add 棋盘格验证通过
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@ -78,7 +78,7 @@ inline bool find_pattern_points(
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else
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cv::cvtColor(img, gray, cv::COLOR_BGR2GRAY);
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auto flags = cv::CALIB_CB_ADAPTIVE_THRESH | cv::CALIB_CB_NORMALIZE_IMAGE;
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auto flags = cv::CALIB_CB_ADAPTIVE_THRESH | cv::CALIB_CB_NORMALIZE_IMAGE | cv::CALIB_CB_FAST_CHECK;
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auto success = cv::findChessboardCorners(gray, board_pattern.pattern_size, points, flags);
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if (!success) return false;
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@ -1,16 +1,16 @@
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#include <fmt/core.h>
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#include <yaml-cpp/yaml.h>
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#include <filesystem>
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#include <fstream>
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#include <opencv2/opencv.hpp>
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#include "calibration/board_pattern.hpp"
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#include "src/device/camera.hpp"
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#include "src/device/cboard.hpp"
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#include "src/component/img_tools.hpp"
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#include "src/component/logger.hpp"
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#include "src/component/math_tools.hpp"
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#include "src/component/yaml.hpp"
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#include "src/device/camera.hpp"
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#include "src/device/gimbal/gimbal.hpp"
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const std::string keys =
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"{help h usage ? | | 输出命令行参数说明}"
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@ -27,10 +27,13 @@ void write_q(const std::string q_path, const Eigen::Quaterniond & q)
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}
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void capture_loop(
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const std::string & config_path, const std::string & output_folder,
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const calibration::BoardPattern & board_pattern)
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const std::string & config_path, const std::string & output_folder)
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{
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device::CBoard cboard(config_path);
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// 从配置文件加载标定板参数（支持 circles_grid 和 chessboard）
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auto yaml = YAML::LoadFile(config_path);
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auto board_pattern = calibration::load_board_pattern(yaml);
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device::Gimbal gimbal(config_path);
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device::Camera camera(config_path);
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cv::Mat img;
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std::chrono::steady_clock::time_point timestamp;
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@ -38,7 +41,7 @@ void capture_loop(
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int count = 0;
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while (true) {
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camera.read(img, timestamp);
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Eigen::Quaterniond q = cboard.imu_at(timestamp);
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Eigen::Quaterniond q = gimbal.q(timestamp);
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// 在图像上显示欧拉角，用来判断imuabs系的xyz正方向，同时判断imu是否存在零漂
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auto img_with_ypr = img.clone();
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@ -48,7 +51,26 @@ void capture_loop(
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component::draw_text(img_with_ypr, fmt::format("X {:.2f}", zyx[2]), {40, 120}, {0, 0, 255});
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std::vector<cv::Point2f> centers_2d;
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auto success = calibration::find_pattern_points(img, board_pattern, centers_2d);
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bool success;
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if (board_pattern.pattern_type == calibration::PatternType::chessboard) {
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// 棋盘格检测很慢，先在缩小图上快速检测，再映射回原图做亚像素精化
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cv::Mat small;
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double scale = 0.5;
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cv::resize(img, small, {}, scale, scale);
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std::vector<cv::Point2f> small_pts;
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success = calibration::find_pattern_points(small, board_pattern, small_pts);
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if (success) {
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for (auto & p : small_pts) { p.x /= scale; p.y /= scale; }
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cv::Mat gray;
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cv::cvtColor(img, gray, cv::COLOR_BGR2GRAY);
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cv::cornerSubPix(
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gray, small_pts, cv::Size(11, 11), cv::Size(-1, -1),
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cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30, 1e-3));
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centers_2d = std::move(small_pts);
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}
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} else {
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success = calibration::find_pattern_points(img, board_pattern, centers_2d);
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}
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cv::drawChessboardCorners(img_with_ypr, board_pattern.pattern_size, centers_2d, success);
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cv::resize(img_with_ypr, img_with_ypr, {}, 0.5, 0.5); // 显示时缩小图片尺寸
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@ -69,7 +91,7 @@ void capture_loop(
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component::logger()->info("[{}] Saved in {}", count, output_folder);
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}
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// 离开该作用域时，camera和cboard会自动关闭
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// 离开该作用域时，camera和gimbal会自动关闭
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}
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int main(int argc, char * argv[])
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@ -82,17 +104,20 @@ int main(int argc, char * argv[])
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}
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auto config_path = cli.get<std::string>(0);
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auto output_folder = cli.get<std::string>("output-folder");
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auto yaml = component::load(config_path);
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auto board_pattern = calibration::load_board_pattern(yaml);
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// 新建输出文件夹
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std::filesystem::create_directory(output_folder);
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// 从配置文件读取标定板类型和尺寸
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auto yaml = YAML::LoadFile(config_path);
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auto board_pattern = calibration::load_board_pattern(yaml);
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component::logger()->info(
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"标定板类型: {}, 尺寸: {}x{}", calibration::pattern_name(board_pattern.pattern_type),
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"标定板类型: {}, 尺寸: {}列{}行",
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calibration::pattern_name(board_pattern.pattern_type),
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board_pattern.pattern_size.width, board_pattern.pattern_size.height);
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// 主循环，保存图片和对应四元数
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capture_loop(config_path, output_folder, board_pattern);
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capture_loop(config_path, output_folder);
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component::logger()->warn("注意四元数输出顺序为wxyz");
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184
calibration/test/camera_calibration.py
Normal file
184
calibration/test/camera_calibration.py
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@ -0,0 +1,184 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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相机校准应用程序
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使用检测到的棋盘格参数进行图像矫正和去畸变
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"""
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import cv2
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import numpy as np
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import json
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import os
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class CameraCalibration:
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def __init__(self, calibration_file='chessboard_detection_output/calibration_result.json'):
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"""
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加载校准参数
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Args:
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calibration_file: 校准结果JSON文件路径
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"""
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self.calibration_file = calibration_file
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self.camera_matrix = None
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self.dist_coeffs = None
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self.load_calibration()
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def load_calibration(self):
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"""从JSON文件加载校准参数"""
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if not os.path.exists(self.calibration_file):
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raise FileNotFoundError(f"校准文件不存在: {self.calibration_file}")
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with open(self.calibration_file, 'r', encoding='utf-8') as f:
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data = json.load(f)
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self.camera_matrix = np.array(data['camera_matrix'])
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self.dist_coeffs = np.array(data['distortion_coefficients'])
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print("✓ 校准参数加载成功")
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print(f" 重投影误差: {data['reprojection_error']:.4f} 像素")
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print(f" 使用图像数: {data['num_images']}")
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def undistort_image(self, image):
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"""
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对图像进行去畸变处理
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Args:
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image: 输入图像
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Returns:
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undistorted: 去畸变后的图像
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"""
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h, w = image.shape[:2]
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new_camera_matrix, roi = cv2.getOptimalNewCameraMatrix(
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self.camera_matrix, self.dist_coeffs, (w, h), 1, (w, h)
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)
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# 去畸变
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undistorted = cv2.undistort(image, self.camera_matrix, self.dist_coeffs, None, new_camera_matrix)
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# 裁剪图像
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x, y, w, h = roi
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undistorted = undistorted[y:y+h, x:x+w]
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return undistorted
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def undistort_video(self, input_video, output_video='undistorted_video.avi'):
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"""
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对视频进行去畸变处理
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Args:
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input_video: 输入视频路径
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output_video: 输出视频路径
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"""
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cap = cv2.VideoCapture(input_video)
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if not cap.isOpened():
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print(f"无法打开视频: {input_video}")
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return
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# 获取视频参数
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fps = cap.get(cv2.CAP_PROP_FPS)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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# 读取第一帧获取尺寸
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ret, frame = cap.read()
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if not ret:
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print("无法读取视频帧")
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return
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h, w = frame.shape[:2]
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new_camera_matrix, roi = cv2.getOptimalNewCameraMatrix(
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self.camera_matrix, self.dist_coeffs, (w, h), 1, (w, h)
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)
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x, y, w_roi, h_roi = roi
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# 创建视频写入器
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fourcc = cv2.VideoWriter_fourcc(*'XVID')
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out = cv2.VideoWriter(output_video, fourcc, fps, (w_roi, h_roi))
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print(f"开始处理视频 (共 {total_frames} 帧)...")
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# 重置到开头
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cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
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frame_count = 0
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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frame_count += 1
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# 去畸变
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undistorted = cv2.undistort(frame, self.camera_matrix, self.dist_coeffs, None, new_camera_matrix)
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undistorted = undistorted[y:y+h_roi, x:x+w_roi]
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out.write(undistorted)
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if frame_count % 50 == 0:
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print(f" 处理进度: {frame_count}/{total_frames} ({100*frame_count/total_frames:.1f}%)")
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cap.release()
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out.release()
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print(f"\n✓ 视频处理完成，已保存到: {output_video}")
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def compare_images(self, image_path, output_path='comparison.jpg'):
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"""
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生成原始图像和去畸变图像的对比图
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Args:
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image_path: 输入图像路径
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output_path: 输出对比图路径
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"""
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image = cv2.imread(image_path)
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if image is None:
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print(f"无法读取图像: {image_path}")
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return
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undistorted = self.undistort_image(image)
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# 调整尺寸以便并排显示
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h1, w1 = image.shape[:2]
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h2, w2 = undistorted.shape[:2]
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h = min(h1, h2)
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image_resized = cv2.resize(image, (int(w1 * h / h1), h))
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undistorted_resized = cv2.resize(undistorted, (int(w2 * h / h2), h))
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# 并排拼接
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comparison = np.hstack([image_resized, undistorted_resized])
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# 添加文字标注
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cv2.putText(comparison, 'Original', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 3)
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cv2.putText(comparison, 'Undistorted', (w1 + 50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 0), 3)
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cv2.imwrite(output_path, comparison)
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print(f"✓ 对比图已保存到: {output_path}")
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def main():
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"""主函数 - 演示如何使用校准参数"""
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print("=== 相机校准应用程序 ===\n")
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# 加载校准参数
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calib = CameraCalibration()
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# 示例1: 对检测结果图像进行去畸变
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output_dir = 'chessboard_detection_output'
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detected_images = [f for f in os.listdir(output_dir) if f.startswith('detected_') and f.endswith('.jpg')]
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if detected_images:
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print(f"\n找到 {len(detected_images)} 张检测图像")
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sample_image = os.path.join(output_dir, detected_images[0])
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print(f"生成对比图: {sample_image}")
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calib.compare_images(sample_image, os.path.join(output_dir, 'comparison.jpg'))
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# 示例2: 对原始视频进行去畸变
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print("\n是否要对原始视频进行去畸变处理？")
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print("注意: 这将处理整个视频，可能需要一些时间")
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print("如需处理，请取消注释下面的代码行：")
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print("# calib.undistort_video('Video_20260303114232727.avi', 'undistorted_video.avi')")
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if __name__ == '__main__':
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main()
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221
calibration/test/chessboard_detector.py
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221
calibration/test/chessboard_detector.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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棋盘格检测程序
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用于检测视频中的11x8内角点棋盘格，并用于相机校准
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"""
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import cv2
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import numpy as np
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import os
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import json
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from datetime import datetime
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class ChessboardDetector:
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def __init__(self, pattern_size=(11, 8), square_size=1.0):
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"""
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初始化棋盘格检测器
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Args:
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pattern_size: 棋盘格内角点数量 (列, 行)
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square_size: 棋盘格方格实际尺寸(单位:mm或其他)
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"""
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self.pattern_size = pattern_size
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self.square_size = square_size
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# 准备棋盘格的3D坐标点
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self.objp = np.zeros((pattern_size[0] * pattern_size[1], 3), np.float32)
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self.objp[:, :2] = np.mgrid[0:pattern_size[0], 0:pattern_size[1]].T.reshape(-1, 2)
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self.objp *= square_size
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# 存储所有图像的角点
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self.obj_points = [] # 3D点
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self.img_points = [] # 2D点
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# 角点检测参数
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self.criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
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def detect_chessboard(self, image):
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"""
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检测单张图像中的棋盘格
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Args:
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image: 输入图像
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Returns:
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ret: 是否检测成功
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corners: 角点坐标
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gray: 灰度图
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"""
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# 查找棋盘格角点
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ret, corners = cv2.findChessboardCorners(gray, self.pattern_size, None)
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if ret:
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# 亚像素精度优化
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corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), self.criteria)
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return ret, corners, gray
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def process_video(self, video_path, output_dir='output', sample_interval=30):
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"""
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处理视频文件，检测棋盘格
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Args:
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video_path: 视频文件路径
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output_dir: 输出目录
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sample_interval: 采样间隔(帧数)
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"""
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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print(f"无法打开视频: {video_path}")
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return False
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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fps = cap.get(cv2.CAP_PROP_FPS)
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print(f"视频信息: 总帧数={total_frames}, FPS={fps}")
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print(f"开始检测棋盘格 (内角点: {self.pattern_size[0]}x{self.pattern_size[1]})...")
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frame_count = 0
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detected_count = 0
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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frame_count += 1
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# 按间隔采样
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if frame_count % sample_interval != 0:
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continue
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# 检测棋盘格
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success, corners, gray = self.detect_chessboard(frame)
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if success:
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detected_count += 1
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self.obj_points.append(self.objp)
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self.img_points.append(corners)
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# 绘制角点
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vis_img = frame.copy()
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cv2.drawChessboardCorners(vis_img, self.pattern_size, corners, success)
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# 保存结果图像
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output_path = os.path.join(output_dir, f'detected_{detected_count:03d}_frame{frame_count}.jpg')
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cv2.imwrite(output_path, vis_img)
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print(f"✓ 帧 {frame_count}: 检测成功 (已保存 {detected_count} 张)")
|
||||
else:
|
||||
print(f"✗ 帧 {frame_count}: 未检测到棋盘格")
|
||||
|
||||
cap.release()
|
||||
|
||||
print(f"\n检测完成: 共处理 {frame_count} 帧, 成功检测 {detected_count} 张图像")
|
||||
return detected_count > 0
|
||||
|
||||
def calibrate_camera(self, image_size):
|
||||
"""
|
||||
执行相机校准
|
||||
|
||||
Args:
|
||||
image_size: 图像尺寸 (width, height)
|
||||
|
||||
Returns:
|
||||
ret: 标定误差
|
||||
camera_matrix: 相机内参矩阵
|
||||
dist_coeffs: 畸变系数
|
||||
rvecs: 旋转向量
|
||||
tvecs: 平移向量
|
||||
"""
|
||||
if len(self.obj_points) < 3:
|
||||
print("错误: 需要至少3张成功检测的图像进行校准")
|
||||
return None
|
||||
|
||||
print(f"\n开始相机校准 (使用 {len(self.obj_points)} 张图像)...")
|
||||
|
||||
ret, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(
|
||||
self.obj_points, self.img_points, image_size, None, None
|
||||
)
|
||||
|
||||
print(f"标定完成! 重投影误差: {ret:.4f} 像素")
|
||||
|
||||
return ret, camera_matrix, dist_coeffs, rvecs, tvecs
|
||||
|
||||
def save_calibration_results(self, camera_matrix, dist_coeffs, ret, output_path='calibration_result.json'):
|
||||
"""
|
||||
保存校准结果到JSON文件
|
||||
"""
|
||||
result = {
|
||||
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||||
'pattern_size': self.pattern_size,
|
||||
'square_size': self.square_size,
|
||||
'num_images': len(self.obj_points),
|
||||
'reprojection_error': float(ret),
|
||||
'camera_matrix': camera_matrix.tolist(),
|
||||
'distortion_coefficients': dist_coeffs.tolist()
|
||||
}
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(result, f, indent=4, ensure_ascii=False)
|
||||
|
||||
print(f"\n校准结果已保存到: {output_path}")
|
||||
|
||||
# 打印结果
|
||||
print("\n=== 相机校准结果 ===")
|
||||
print(f"重投影误差: {ret:.4f} 像素")
|
||||
print(f"\n相机内参矩阵:")
|
||||
print(camera_matrix)
|
||||
print(f"\n畸变系数:")
|
||||
print(dist_coeffs.ravel())
|
||||
|
||||
|
||||
def main():
|
||||
# 配置参数
|
||||
VIDEO_PATH = 'Video_20260303114232727.avi'
|
||||
OUTPUT_DIR = 'chessboard_detection_output'
|
||||
PATTERN_SIZE = (11, 8) # 11x8 内角点
|
||||
SQUARE_SIZE = 25.0 # 假设每个方格25mm，根据实际情况调整
|
||||
SAMPLE_INTERVAL = 30 # 每30帧采样一次
|
||||
|
||||
# 创建检测器
|
||||
detector = ChessboardDetector(pattern_size=PATTERN_SIZE, square_size=SQUARE_SIZE)
|
||||
|
||||
# 处理视频
|
||||
success = detector.process_video(VIDEO_PATH, OUTPUT_DIR, SAMPLE_INTERVAL)
|
||||
|
||||
if not success:
|
||||
print("未能检测到任何棋盘格，程序退出")
|
||||
return
|
||||
|
||||
# 获取图像尺寸
|
||||
cap = cv2.VideoCapture(VIDEO_PATH)
|
||||
ret, frame = cap.read()
|
||||
if ret:
|
||||
image_size = (frame.shape[1], frame.shape[0])
|
||||
cap.release()
|
||||
|
||||
# 执行相机校准
|
||||
calib_result = detector.calibrate_camera(image_size)
|
||||
|
||||
if calib_result:
|
||||
ret, camera_matrix, dist_coeffs, rvecs, tvecs = calib_result
|
||||
|
||||
# 保存校准结果
|
||||
detector.save_calibration_results(
|
||||
camera_matrix, dist_coeffs, ret,
|
||||
os.path.join(OUTPUT_DIR, 'calibration_result.json')
|
||||
)
|
||||
|
||||
print("\n✓ 校准完成！可以使用生成的校准参数进行图像矫正")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
127
calibration/test/visualize_video.py
Normal file
127
calibration/test/visualize_video.py
Normal file
@ -0,0 +1,127 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
可视化视频中的棋盘格检测
|
||||
实时显示每一帧的检测结果
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
|
||||
class ChessboardVisualizer:
|
||||
def __init__(self, pattern_size=(11, 8)):
|
||||
"""
|
||||
初始化可视化器
|
||||
|
||||
Args:
|
||||
pattern_size: 棋盘格内角点数量 (列, 行)
|
||||
"""
|
||||
self.pattern_size = pattern_size
|
||||
self.criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
|
||||
|
||||
def visualize_video(self, video_path, output_path='visualized_video.avi', show_window=False):
|
||||
"""
|
||||
可视化整个视频的棋盘格检测
|
||||
|
||||
Args:
|
||||
video_path: 输入视频路径
|
||||
output_path: 输出视频路径
|
||||
show_window: 是否显示实时窗口
|
||||
"""
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
if not cap.isOpened():
|
||||
print(f"无法打开视频: {video_path}")
|
||||
return
|
||||
|
||||
# 获取视频参数
|
||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
|
||||
print(f"视频信息: {width}x{height}, {fps:.2f} FPS, {total_frames} 帧")
|
||||
|
||||
# 创建视频写入器
|
||||
fourcc = cv2.VideoWriter_fourcc(*'XVID')
|
||||
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
|
||||
|
||||
frame_count = 0
|
||||
detected_count = 0
|
||||
|
||||
print(f"\n开始处理视频...")
|
||||
print("绿色角点 = 检测成功, 红色文字 = 未检测到")
|
||||
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
|
||||
frame_count += 1
|
||||
|
||||
# 转换为灰度图
|
||||
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
# 检测棋盘格
|
||||
ret_detect, corners = cv2.findChessboardCorners(gray, self.pattern_size, None)
|
||||
|
||||
# 创建可视化图像
|
||||
vis_frame = frame.copy()
|
||||
|
||||
if ret_detect:
|
||||
detected_count += 1
|
||||
# 亚像素精度优化
|
||||
corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), self.criteria)
|
||||
|
||||
# 绘制角点
|
||||
cv2.drawChessboardCorners(vis_frame, self.pattern_size, corners, ret_detect)
|
||||
|
||||
# 添加成功标记
|
||||
cv2.putText(vis_frame, f'DETECTED #{detected_count}', (20, 40),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||||
else:
|
||||
# 添加未检测标记
|
||||
cv2.putText(vis_frame, 'NOT DETECTED', (20, 40),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
|
||||
|
||||
# 添加帧信息
|
||||
cv2.putText(vis_frame, f'Frame: {frame_count}/{total_frames}', (20, height - 20),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
|
||||
|
||||
# 写入输出视频
|
||||
out.write(vis_frame)
|
||||
|
||||
# 显示窗口（可选）
|
||||
if show_window:
|
||||
cv2.imshow('Chessboard Detection', vis_frame)
|
||||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
||||
print("\n用户中断")
|
||||
break
|
||||
|
||||
# 进度显示
|
||||
if frame_count % 50 == 0:
|
||||
progress = 100 * frame_count / total_frames
|
||||
print(f" 进度: {frame_count}/{total_frames} ({progress:.1f}%) - 已检测: {detected_count}")
|
||||
|
||||
cap.release()
|
||||
out.release()
|
||||
if show_window:
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
print(f"\n✓ 处理完成!")
|
||||
print(f" 总帧数: {frame_count}")
|
||||
print(f" 检测成功: {detected_count} 帧 ({100*detected_count/frame_count:.1f}%)")
|
||||
print(f" 输出文件: {output_path}")
|
||||
|
||||
|
||||
def main():
|
||||
VIDEO_PATH = 'Video_20260303114232727.avi'
|
||||
OUTPUT_PATH = 'visualized_chessboard_detection.avi'
|
||||
PATTERN_SIZE = (11, 8)
|
||||
|
||||
visualizer = ChessboardVisualizer(pattern_size=PATTERN_SIZE)
|
||||
visualizer.visualize_video(VIDEO_PATH, OUTPUT_PATH, show_window=False)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -18,3 +18,7 @@ can_interface: "can0"
|
||||
#####-----gimbal参数-----#####
|
||||
com_port: "/dev/ttyUSB0"
|
||||
baudrate: 115200
|
||||
|
||||
# 重投影误差: 0.1791px
|
||||
camera_matrix: [1827.8294221039337, 0, 716.86057740384501, 0, 1828.9736207357851, 613.69509305531699, 0, 0, 1]
|
||||
distort_coeffs: [-0.083642708058668358, 0.18891600176175308, -0.00030362184648520616, -0.00066798903909152669, 0]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user