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dwidenoise: Cleanups suggested by clang-tidy
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@Lestropie
Lestropie committed Nov 6, 2024
1 parent e3bb26b commit 750cfd9
Showing 1 changed file with 24 additions and 15 deletions.
39 changes: 24 additions & 15 deletions cmd/dwidenoise.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -187,14 +187,20 @@ using voxel_type = Eigen::Array<int, 3, 1>;

class KernelVoxel {
public:
KernelVoxel(const voxel_type offset, const default_type sq_distance) : offset(offset), sq_distance(sq_distance) {}
KernelVoxel(const voxel_type &offset, const default_type sq_distance) : offset(offset), sq_distance(sq_distance) {}
KernelVoxel(const KernelVoxel &) = default;
KernelVoxel(KernelVoxel &&) = default;
~KernelVoxel() {}
KernelVoxel &operator=(const KernelVoxel &that) {
offset = that.offset;
sq_distance = that.sq_distance;
return *this;
}
KernelVoxel &operator=(KernelVoxel &&that) {
offset = that.offset;
sq_distance = that.sq_distance;
return *this;
}
bool operator<(const KernelVoxel &that) const { return sq_distance < that.sq_distance; }
default_type distance() const { return std::sqrt(sq_distance); }
voxel_type offset;
Expand Down Expand Up @@ -229,7 +235,7 @@ class KernelCube : public KernelBase {
KernelCube(const Header &header, const std::vector<uint32_t> &extent)
: KernelBase(header),
half_extent({int(extent[0] / 2), int(extent[1] / 2), int(extent[2] / 2)}),
size(extent[0] * extent[1] * extent[2]),
size(size_t(extent[0]) * size_t(extent[1]) * size_t(extent[2])),
centre_index(size / 2) {
for (auto e : extent) {
if (!(e % 2))
Expand Down Expand Up @@ -281,6 +287,7 @@ class KernelSphereBase : public KernelBase {
public:
KernelSphereBase(const Header &voxel_grid, const default_type max_radius)
: KernelBase(voxel_grid), shared(new Shared(voxel_grid, max_radius)) {}
virtual ~KernelSphereBase() {}

protected:
class Shared {
Expand All @@ -292,7 +299,7 @@ class KernelSphereBase : public KernelBase {
int(std::ceil(max_radius / voxel_grid.spacing(1))), //
int(std::ceil(max_radius / voxel_grid.spacing(2)))}); //
// Build the searchlight
data.reserve((2 * half_extents[0] + 1) * (2 * half_extents[1] + 1) * (2 * half_extents[2] + 1));
data.reserve(size_t((2 * half_extents[0] + 1) * (2 * half_extents[1] + 1) * (2 * half_extents[2] + 1)));
voxel_type offset;
for (offset[2] = -half_extents[2]; offset[2] <= half_extents[2]; ++offset[2]) {
for (offset[1] = -half_extents[1]; offset[1] <= half_extents[1]; ++offset[1]) {
Expand Down Expand Up @@ -321,6 +328,7 @@ class KernelSphereRatio : public KernelSphereBase {
KernelSphereRatio(const Header &voxel_grid, const default_type min_ratio)
: KernelSphereBase(voxel_grid, compute_max_radius(voxel_grid, min_ratio)),
min_size(std::ceil(voxel_grid.size(3) * min_ratio)) {}
~KernelSphereRatio() {}
KernelData operator()(const voxel_type &pos) const override {
KernelData result(0);
auto table_it = shared->begin();
Expand Down Expand Up @@ -376,6 +384,7 @@ class KernelSphereFixedRadius : public KernelSphereBase {
maximum_size(std::distance(shared->begin(), shared->end())) { //
INFO("Maximum number of voxels in " + str(radius) + "mm fixed-radius kernel is " + str(maximum_size));
}
~KernelSphereFixedRadius() {}
KernelData operator()(const voxel_type &pos) const {
KernelData result(0);
result.voxels.reserve(maximum_size);
Expand Down Expand Up @@ -477,9 +486,9 @@ template <typename F> class DenoisingFunctor {
ssize_t cutoff_p = 0;
for (ssize_t p = 0; p < r; ++p) // p+1 is the number of noise components
{ // (as opposed to the paper where p is defined as the number of signal components)
double lam = std::max(s[p], 0.0) / q;
const double lam = std::max(s[p], 0.0) / q;
clam += lam;
double denominator;
double denominator = std::numeric_limits<double>::signaling_NaN();
switch (estimator) {
case estimator_type::EXP1:
denominator = q;
Expand All @@ -490,9 +499,9 @@ template <typename F> class DenoisingFunctor {
default:
assert(false);
}
double gam = double(p + 1) / denominator;
double sigsq1 = clam / double(p + 1);
double sigsq2 = (lam - lam_r) / (4.0 * std::sqrt(gam));
const double gam = double(p + 1) / denominator;
const double sigsq1 = clam / double(p + 1);
const double sigsq2 = (lam - lam_r) / (4.0 * std::sqrt(gam));
// sigsq2 > sigsq1 if signal else noise
if (sigsq2 < sigsq1) {
sigma2 = sigsq1;
Expand Down Expand Up @@ -529,7 +538,7 @@ template <typename F> class DenoisingFunctor {
}
if (maxdistmap.valid()) {
assign_pos_of(dwi, 0, 3).to(maxdistmap);
maxdistmap.value() = neighbourhood.max_distance;
maxdistmap.value() = float(neighbourhood.max_distance);
}
if (voxelsmap.valid()) {
assign_pos_of(dwi, 0, 3).to(voxelsmap);
Expand Down Expand Up @@ -597,7 +606,7 @@ void run() {

estimator_type estimator = estimator_type::EXP2; // default: Exp2 (unbiased estimator)
opt = get_options("estimator");
if (opt.size())
if (!opt.empty())
estimator = estimator_type(int(opt[0][0]));

Image<real_type> noise;
Expand Down Expand Up @@ -650,10 +659,10 @@ void run() {
if (!get_options("extent").empty())
throw Exception("-extent option does not apply to spherical kernel");
opt = get_options("radius_mm");
if (opt.size())
kernel.reset(new KernelSphereFixedRadius(dwi, opt[0][0]));
if (opt.empty())
kernel = std::make_shared<KernelSphereRatio>(dwi, get_option_value("-radius_ratio", sphere_multiplier_default));
else
kernel.reset(new KernelSphereRatio(dwi, get_option_value("-radius_ratio", sphere_multiplier_default)));
kernel = std::make_shared<KernelSphereFixedRadius>(dwi, opt[0][0]);
} break;
case shape_type::CUBOID: {
if (!get_options("radius_mm").empty() || !get_options("radius_ratio").empty())
Expand All @@ -667,7 +676,7 @@ void run() {
if (extent.size() != 3)
throw Exception("-extent must be either a scalar or a list of length 3");
for (int i = 0; i < 3; i++) {
if (!(extent[i] & 1))
if ((extent[i] & 1) == 0)
throw Exception("-extent must be a (list of) odd numbers");
if (extent[i] > dwi.size(i))
throw Exception("-extent must not exceed the image dimensions");
Expand All @@ -688,7 +697,7 @@ void run() {
"and cause inconsistent denoising between adjacent voxels.");
}

kernel.reset(new KernelCube(dwi, extent));
kernel = std::make_shared<KernelCube>(dwi, extent);
} break;
default:
assert(false);
Expand Down

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