Fixed some of the conversion warnings by adding appropriate casts

examples/fit_model.cpp

@@ -179,7 +179,7 @@ int main(int argc, char *argv[])
 	// Draw the mean-face landmarks projected using the estimated camera:
 	for (auto&& vertex : modelPoints) {
 		Vec2f screenPoint = fitting::project_affine(vertex, affineCam, image.cols, image.rows);
-		cv::circle(outimg, cv::Point2f(screenPoint), 5.0f, { 0.0f, 255.0f, 0.0f });
+		cv::circle(outimg, cv::Point2f(screenPoint), 5, { 0.0f, 255.0f, 0.0f });
 	}
 
 	// Estimate the shape coefficients by fitting the shape to the landmarks:
@@ -194,7 +194,7 @@ int main(int argc, char *argv[])
 	for (auto&& idx : vertexIndices) {
 		Vec4f modelPoint(mesh.vertices[idx][0], mesh.vertices[idx][1], mesh.vertices[idx][2], mesh.vertices[idx][3]);
 		Vec2f screenPoint = fitting::project_affine(modelPoint, affineCam, image.cols, image.rows);
-		cv::circle(outimg, cv::Point2f(screenPoint), 3.0f, { 0.0f, 0.0f, 255.0f });
+		cv::circle(outimg, cv::Point2f(screenPoint), 3, { 0.0f, 0.0f, 255.0f });
 	}
 
 	// Save the output image:

include/eos/fitting/affine_camera_estimation.hpp

@@ -51,7 +51,7 @@ cv::Mat estimate_affine_camera(std::vector<cv::Vec2f> image_points, std::vector<
 	assert(image_points.size() == model_points.size());
 	assert(image_points.size() >= 4); // Number of correspondence points given needs to be equal to or larger than 4
 
-	const auto numCorrespondences = image_points.size();
+	const int numCorrespondences = static_cast<int>(image_points.size());
 
 	Mat matImagePoints; // will be numCorrespondences x 2, CV_32FC1
 	Mat matModelPoints; // will be numCorrespondences x 3, CV_32FC1
@@ -76,13 +76,13 @@ cv::Mat estimate_affine_camera(std::vector<cv::Vec2f> image_points, std::vector<
 	matImagePoints = matImagePoints - imagePointsMean;
 	// scale the image points such that the RMS distance from the origin is sqrt(2):
 	// 1) calculate the average norm (root mean squared distance) of all vectors
-	float averageNorm = 0.0f; // TODO change to double!
+	float average_norm = 0.0f;
 	for (int row = 0; row < matImagePoints.rows; ++row) {
-		averageNorm += cv::norm(matImagePoints.row(row), cv::NORM_L2);
+		average_norm += static_cast<float>(cv::norm(matImagePoints.row(row), cv::NORM_L2));
 	}
-	averageNorm /= matImagePoints.rows;
-	// 2) multiply every vectors coordinate by sqrt(2)/avgnorm
-	float scaleFactor = std::sqrt(2) / averageNorm;
+	average_norm /= matImagePoints.rows;
+	// 2) multiply every vectors coordinate by sqrt(2)/average_norm
+	float scaleFactor = static_cast<float>(std::sqrt(2)) / average_norm;
 	matImagePoints *= scaleFactor; // add unit homogeneous component here
 	// The points in matImagePoints now have a RMS distance from the origin of sqrt(2).
 	// The normalisation matrix so that the 2D points are mean-free and their norm is as described above.
@@ -102,13 +102,13 @@ cv::Mat estimate_affine_camera(std::vector<cv::Vec2f> image_points, std::vector<
 	matModelPoints = matModelPoints - modelPointsMean;
 	// scale the model points such that the RMS distance from the origin is sqrt(3):
 	// 1) calculate the average norm (root mean squared distance) of all vectors
-	averageNorm = 0.0f;
+	average_norm = 0.0f;
 	for (int row = 0; row < matModelPoints.rows; ++row) {
-		averageNorm += cv::norm(matModelPoints.row(row), cv::NORM_L2);
+		average_norm += static_cast<float>(cv::norm(matModelPoints.row(row), cv::NORM_L2));
 	}
-	averageNorm /= matModelPoints.rows;
+	average_norm /= matModelPoints.rows;
 	// 2) multiply every vectors coordinate by sqrt(3)/avgnorm
-	scaleFactor = std::sqrt(3) / averageNorm;
+	scaleFactor = static_cast<float>(std::sqrt(3)) / average_norm;
 	matModelPoints *= scaleFactor; // add unit homogeneous component here
 	// The points in matModelPoints now have a RMS distance from the origin of sqrt(3).
 	// The normalisation matrix so that the 3D points are mean-free and their norm is as described above.

include/eos/fitting/linear_shape_fitting.hpp

@@ -123,7 +123,7 @@ inline std::vector<float> fit_shape_to_landmarks_linear(morphablemodel::Morphabl
 	using RowMajorMatrixXf = Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
 	Eigen::Map<RowMajorMatrixXf> AtOmegaAReg_Eigen(AtOmegaAReg.ptr<float>(), AtOmegaAReg.rows, AtOmegaAReg.cols);
 	Eigen::FullPivLU<RowMajorMatrixXf> luOfAtOmegaAReg(AtOmegaAReg_Eigen); // Calculate the full-pivoting LU decomposition of the regularized AtA. Note: We could also try FullPivHouseholderQR if our system is non-minimal (i.e. there are more constraints than unknowns).
-	float rankOfAtOmegaAReg = luOfAtOmegaAReg.rank();
+	auto rankOfAtOmegaAReg = luOfAtOmegaAReg.rank();
 	bool isAtOmegaARegInvertible = luOfAtOmegaAReg.isInvertible();
 	float threshold = /*2 * */ std::abs(luOfAtOmegaAReg.maxPivot()) * luOfAtOmegaAReg.threshold();
 	RowMajorMatrixXf AtARegInv_EigenFullLU = luOfAtOmegaAReg.inverse(); // Note: We should use ::solve() instead