本文整理汇总了C++中cv::Point2f方法的典型用法代码示例。如果您正苦于以下问题：C++ cv::Point2f方法的具体用法？C++ cv::Point2f怎么用？C++ cv::Point2f使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cv的用法示例。
在下文中一共展示了cv::Point2f方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: dst
int
main(int argc, char *argv[]) {
cv::Mat src = cv::imread("0.bmp", 1);
vector<Point2f> corners(4);
corners[0] = Point2f(277,61);
corners[1] = Point2f(385,52);
corners[2] = Point2f(410,195);
corners[3] = Point2f(273,215);
Mat dst(400, 390, src.type());
qua_to_rect(src, corners, dst);
draw_points(src, corners);
cv::imshow("original", src);
cv::imshow("result", dst);
cv::waitKey(0);
return 0;
}示例2: main
//.........这里部分代码省略.........
labeledImageSource->next();
start = std::chrono::system_clock::now();
appLogger.info("Starting to process " + labeledImageSource->getName().string());
img = labeledImageSource->getImage();
LandmarkCollection lms = labeledImageSource->getLandmarks();
LandmarkCollection didLms = landmarkMapper.convert(lms);
landmarks.clear();
Mat landmarksImage = img.clone(); // blue rect = the used landmarks
for (const auto& lm : didLms.getLandmarks()) {
lm->draw(landmarksImage);
landmarks.emplace_back(imageio::ModelLandmark(lm->getName(), lm->getPosition2D()));
cv::rectangle(landmarksImage, cv::Point(cvRound(lm->getX() - 2.0f), cvRound(lm->getY() - 2.0f)), cv::Point(cvRound(lm->getX() + 2.0f), cvRound(lm->getY() + 2.0f)), cv::Scalar(255, 0, 0));
}
// Start affine camera estimation (Aldrian paper)
Mat affineCamLandmarksProjectionImage = landmarksImage.clone(); // the affine LMs are currently not used (don't know how to render without z-vals)
// Convert the landmarks to clip-space
vector<imageio::ModelLandmark> landmarksClipSpace;
for (const auto& lm : landmarks) {
cv::Vec2f clipCoords = render::utils::screenToClipSpace(lm.getPosition2D(), img.cols, img.rows);
imageio::ModelLandmark lmcs(lm.getName(), Vec3f(clipCoords[0], clipCoords[1], 0.0f), lm.isVisible());
landmarksClipSpace.push_back(lmcs);
}
Mat affineCam = fitting::estimateAffineCamera(landmarksClipSpace, morphableModel);
// Render the mean-face landmarks projected using the estimated camera:
for (const auto& lm : landmarks) {
Vec3f modelPoint = morphableModel.getShapeModel().getMeanAtPoint(lm.getName());
cv::Vec2f screenPoint = fitting::projectAffine(modelPoint, affineCam, img.cols, img.rows);
cv::circle(affineCamLandmarksProjectionImage, Point2f(screenPoint), 4.0f, Scalar(0.0f, 255.0f, 0.0f));
}
// Estimate the shape coefficients:
// Detector variances: Should not be in pixels. Should be normalised by the IED. Normalise by the image dimensions is not a good idea either, it has nothing to do with it. See comment in fitShapeToLandmarksLinear().
// Let's just use the hopefully reasonably set default value for now (around 3 pixels)
vector<float> fittedCoeffs = fitting::fitShapeToLandmarksLinear(morphableModel, affineCam, landmarksClipSpace, lambda);
// Obtain the full mesh and render it using the estimated camera:
Mesh mesh = morphableModel.drawSample(fittedCoeffs, vector<float>()); // takes standard-normal (not-normalised) coefficients
render::SoftwareRenderer softwareRenderer(img.cols, img.rows);
Mat fullAffineCam = fitting::calculateAffineZDirection(affineCam);
fullAffineCam.at<float>(2, 3) = fullAffineCam.at<float>(2, 2); // Todo: Find out and document why this is necessary!
fullAffineCam.at<float>(2, 2) = 1.0f;
softwareRenderer.doBackfaceCulling = true;
auto framebuffer = softwareRenderer.render(mesh, fullAffineCam); // hmm, do we have the z-test disabled?
// Write:
// ptree .fit file: cam-params, model-file, model-params-fn(alphas)
// alphas
// texmap
ptree fittingFile;
fittingFile.put("camera", string("affine"));
fittingFile.put("camera.matrix", string("2 5.4 232.22"));
fittingFile.put("imageWidth", img.cols);
fittingFile.put("imageHeight", img.rows);
fittingFile.put("fittingParameters.lambda", lambda);