RGB 摄像头

摄像头会给出它前方景物的图像。你需要用它来寻找和识别墙面标识。入门机器人有一个摄像头，名字是 camera，分辨率为 64 × 40 像素。

读取像素

图像是由像素组成的网格。每个像素都有红、绿、蓝三个值，范围是 0 到 255，其中 (0, 0, 0) 是黑色，(255, 255, 255) 是白色。

Python

from controller import Robot

robot = Robot()
timeStep = int(robot.getBasicTimeStep())

camera = robot.getDevice("camera")
camera.enable(timeStep)
width = camera.getWidth()
height = camera.getHeight()
print("The camera has", width, "x", height, "pixels")

while robot.step(timeStep) != -1:
    image = camera.getImage()
    # The colour of the pixel in the center of the image
    r = camera.imageGetRed(image, width, width // 2, height // 2)
    g = camera.imageGetGreen(image, width, width // 2, height // 2)
    b = camera.imageGetBlue(image, width, width // 2, height // 2)
    print("Center:", r, g, b)

C/C++

#include <iostream>
#include <webots/Robot.hpp>
#include <webots/Camera.hpp>

using namespace webots;
using namespace std;

int main() {
  Robot *robot = new Robot();
  int timeStep = (int)robot->getBasicTimeStep();

  Camera *camera = robot->getCamera("camera");
  camera->enable(timeStep);
  int width = camera->getWidth();
  int height = camera->getHeight();
  cout << "The camera has " << width << " x " << height << " pixels" << endl;

  while (robot->step(timeStep) != -1) {
    const unsigned char *image = camera->getImage();
    // The colour of the pixel in the center of the image
    int r = Camera::imageGetRed(image, width, width / 2, height / 2);
    int g = Camera::imageGetGreen(image, width, width / 2, height / 2);
    int b = Camera::imageGetBlue(image, width, width / 2, height / 2);
    cout << "Center: " << r << " " << g << " " << b << endl;
  }

  delete robot;
  return 0;
}

使用 OpenCV

OpenCV 是一个图像处理库：可以寻找形状、比较颜色，还能做很多其他事情。Webots 图像的每个像素有 4 个值，顺序为 BGRA，OpenCV 可以直接使用。下面的程序会在两个窗口中分别显示图像和图像中较亮的部分：

Python

from controller import Robot
import cv2
import numpy as np

robot = Robot()
timeStep = int(robot.getBasicTimeStep())

camera = robot.getDevice("camera")
camera.enable(timeStep)

while robot.step(timeStep) != -1:
    # Webots images have 4 channels per pixel, in BGRA order
    image = np.frombuffer(camera.getImage(), np.uint8).reshape((camera.getHeight(), camera.getWidth(), 4))
    frame = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)  # Bright parts in white

    cv2.imshow("camera", cv2.resize(frame, None, fx=4, fy=4, interpolation=cv2.INTER_NEAREST))
    cv2.imshow("threshold", cv2.resize(thresh, None, fx=4, fy=4, interpolation=cv2.INTER_NEAREST))
    cv2.waitKey(1)  # Shows the windows

C/C++

#include <opencv2/opencv.hpp>
#include <webots/Robot.hpp>
#include <webots/Camera.hpp>

using namespace webots;

int main() {
  Robot *robot = new Robot();
  int timeStep = (int)robot->getBasicTimeStep();

  Camera *camera = robot->getCamera("camera");
  camera->enable(timeStep);

  while (robot->step(timeStep) != -1) {
    // Webots images have 4 channels per pixel, in BGRA order
    cv::Mat image(camera->getHeight(), camera->getWidth(), CV_8UC4, (void *)camera->getImage());
    cv::Mat frame, gray, thresh;
    cv::cvtColor(image, frame, cv::COLOR_BGRA2BGR);
    cv::cvtColor(frame, gray, cv::COLOR_BGR2GRAY);
    cv::threshold(gray, thresh, 200, 255, cv::THRESH_BINARY); // Bright parts in white

    cv::Mat big;
    cv::resize(frame, big, cv::Size(), 4, 4, cv::INTER_NEAREST);
    cv::imshow("camera", big);
    cv::resize(thresh, big, cv::Size(), 4, 4, cv::INTER_NEAREST);
    cv::imshow("threshold", big);
    cv::waitKey(1); // Shows the windows
  }

  delete robot;
  return 0;
}

左边是入门机器人在字母受害者前方拍到的摄像头图像，右边是这个程序找出的图像中较亮的部分。

注意

对于 Python，OpenCV 会和其他 Python 库一起安装。对于 C++，你需要自行安装 OpenCV，并在编译时添加它。C++ 示例已在 OpenCV 4 上测试过。

这类窗口只用于在家测试：比赛时请把它们关掉。

分辨率

分辨率越高，能在越远的地方看到标识，但成本更高，处理时间也更长。你可以在 Robot Customizer 中为每个摄像头选择分辨率。