diff options
Diffstat (limited to 'intern/libmv/libmv/simple_pipeline/detect.cc')
| -rw-r--r-- | intern/libmv/libmv/simple_pipeline/detect.cc | 172 |
1 files changed, 91 insertions, 81 deletions
diff --git a/intern/libmv/libmv/simple_pipeline/detect.cc b/intern/libmv/libmv/simple_pipeline/detect.cc index 46599a4c49e..0897c5598c6 100644 --- a/intern/libmv/libmv/simple_pipeline/detect.cc +++ b/intern/libmv/libmv/simple_pipeline/detect.cc @@ -22,14 +22,14 @@ ** ****************************************************************************/ -#include <stdlib.h> #include <memory.h> +#include <stdlib.h> #include <queue> #include "libmv/base/scoped_ptr.h" #include "libmv/image/array_nd.h" -#include "libmv/image/image_converter.h" #include "libmv/image/convolve.h" +#include "libmv/image/image_converter.h" #include "libmv/logging/logging.h" #include "libmv/simple_pipeline/detect.h" @@ -55,7 +55,7 @@ double kDefaultHarrisThreshold = 1e-5; class FeatureComparison { public: - bool operator() (const Feature &left, const Feature &right) const { + bool operator()(const Feature& left, const Feature& right) const { return right.score > left.score; } }; @@ -63,18 +63,17 @@ class FeatureComparison { // Filter the features so there are no features closer than // minimal distance to each other. // This is a naive implementation with O(n^2) asymptotic. -void FilterFeaturesByDistance(const vector<Feature> &all_features, +void FilterFeaturesByDistance(const vector<Feature>& all_features, int min_distance, - vector<Feature> *detected_features) { + vector<Feature>* detected_features) { const int min_distance_squared = min_distance * min_distance; // Use priority queue to sort the features by their score. // // Do this on copy of the input features to prevent possible // distortion in callee function behavior. - std::priority_queue<Feature, - std::vector<Feature>, - FeatureComparison> priority_features; + std::priority_queue<Feature, std::vector<Feature>, FeatureComparison> + priority_features; for (int i = 0; i < all_features.size(); i++) { priority_features.push(all_features.at(i)); @@ -85,7 +84,7 @@ void FilterFeaturesByDistance(const vector<Feature> &all_features, Feature a = priority_features.top(); for (int i = 0; i < detected_features->size(); i++) { - Feature &b = detected_features->at(i); + Feature& b = detected_features->at(i); if (Square(a.x - b.x) + Square(a.y - b.y) < min_distance_squared) { ok = false; break; @@ -100,9 +99,9 @@ void FilterFeaturesByDistance(const vector<Feature> &all_features, } } -void DetectFAST(const FloatImage &grayscale_image, - const DetectOptions &options, - vector<Feature> *detected_features) { +void DetectFAST(const FloatImage& grayscale_image, + const DetectOptions& options, + vector<Feature>* detected_features) { #ifndef LIBMV_NO_FAST_DETECTOR const int min_distance = options.min_distance; const int min_trackness = options.fast_min_trackness; @@ -111,12 +110,14 @@ void DetectFAST(const FloatImage &grayscale_image, const int height = grayscale_image.Width() - 2 * margin; const int stride = grayscale_image.Width(); - scoped_array<unsigned char> byte_image(FloatImageToUCharArray(grayscale_image)); + scoped_array<unsigned char> byte_image( + FloatImageToUCharArray(grayscale_image)); const int byte_image_offset = margin * stride + margin; - // TODO(MatthiasF): Support targetting a feature count (binary search trackness) + // TODO(MatthiasF): Support targetting a feature count (binary search + // trackness) int num_features; - xy *all = fast9_detect(byte_image.get() + byte_image_offset, + xy* all = fast9_detect(byte_image.get() + byte_image_offset, width, height, stride, @@ -126,13 +127,13 @@ void DetectFAST(const FloatImage &grayscale_image, free(all); return; } - int *scores = fast9_score(byte_image.get() + byte_image_offset, + int* scores = fast9_score(byte_image.get() + byte_image_offset, stride, all, num_features, min_trackness); // TODO(MatthiasF): merge with close feature suppression - xy *nonmax = nonmax_suppression(all, scores, num_features, &num_features); + xy* nonmax = nonmax_suppression(all, scores, num_features, &num_features); free(all); // Remove too close features // TODO(MatthiasF): A resolution independent parameter would be better than @@ -152,89 +153,104 @@ void DetectFAST(const FloatImage &grayscale_image, free(scores); free(nonmax); #else - (void) grayscale_image; // Ignored. - (void) options; // Ignored. - (void) detected_features; // Ignored. + (void)grayscale_image; // Ignored. + (void)options; // Ignored. + (void)detected_features; // Ignored. LOG(FATAL) << "FAST detector is disabled in this build."; #endif } #ifdef __SSE2__ -static unsigned int SAD(const ubyte* imageA, const ubyte* imageB, - int strideA, int strideB) { +static unsigned int SAD(const ubyte* imageA, + const ubyte* imageB, + int strideA, + int strideB) { __m128i a = _mm_setzero_si128(); for (int i = 0; i < 16; i++) { - a = _mm_adds_epu16(a, - _mm_sad_epu8(_mm_loadu_si128((__m128i*)(imageA+i*strideA)), - _mm_loadu_si128((__m128i*)(imageB+i*strideB)))); + a = _mm_adds_epu16( + a, + _mm_sad_epu8(_mm_loadu_si128((__m128i*)(imageA + i * strideA)), + _mm_loadu_si128((__m128i*)(imageB + i * strideB)))); } return _mm_extract_epi16(a, 0) + _mm_extract_epi16(a, 4); } #else -static unsigned int SAD(const ubyte* imageA, const ubyte* imageB, - int strideA, int strideB) { +static unsigned int SAD(const ubyte* imageA, + const ubyte* imageB, + int strideA, + int strideB) { unsigned int sad = 0; for (int i = 0; i < 16; i++) { for (int j = 0; j < 16; j++) { - sad += abs((int)imageA[i*strideA+j] - imageB[i*strideB+j]); + sad += abs((int)imageA[i * strideA + j] - imageB[i * strideB + j]); } } return sad; } #endif -void DetectMORAVEC(const FloatImage &grayscale_image, - const DetectOptions &options, - vector<Feature> *detected_features) { +void DetectMORAVEC(const FloatImage& grayscale_image, + const DetectOptions& options, + vector<Feature>* detected_features) { const int distance = options.min_distance; const int margin = options.margin; - const unsigned char *pattern = options.moravec_pattern; + const unsigned char* pattern = options.moravec_pattern; const int count = options.moravec_max_count; const int width = grayscale_image.Width() - 2 * margin; const int height = grayscale_image.Width() - 2 * margin; const int stride = grayscale_image.Width(); - scoped_array<unsigned char> byte_image(FloatImageToUCharArray(grayscale_image)); + scoped_array<unsigned char> byte_image( + FloatImageToUCharArray(grayscale_image)); unsigned short histogram[256]; memset(histogram, 0, sizeof(histogram)); - scoped_array<ubyte> scores(new ubyte[width*height]); - memset(scores.get(), 0, width*height); + scoped_array<ubyte> scores(new ubyte[width * height]); + memset(scores.get(), 0, width * height); const int r = 1; // radius for self similarity comparison - for (int y = distance; y < height-distance; y++) { - for (int x = distance; x < width-distance; x++) { - const ubyte* s = &byte_image[y*stride+x]; - int score = // low self-similarity with overlapping patterns - // OPTI: load pattern once + for (int y = distance; y < height - distance; y++) { + for (int x = distance; x < width - distance; x++) { + const ubyte* s = &byte_image[y * stride + x]; + // low self-similarity with overlapping patterns + // OPTI: load pattern once + // clang-format off + int score = SAD(s, s-r*stride-r, stride, stride)+SAD(s, s-r*stride, stride, stride)+SAD(s, s-r*stride+r, stride, stride)+ SAD(s, s -r, stride, stride)+ SAD(s, s +r, stride, stride)+ SAD(s, s+r*stride-r, stride, stride)+SAD(s, s+r*stride, stride, stride)+SAD(s, s+r*stride+r, stride, stride); + // clang-format on + score /= 256; // normalize - if (pattern) // find only features similar to pattern + if (pattern) // find only features similar to pattern score -= SAD(s, pattern, stride, 16); - if (score <= 16) continue; // filter very self-similar features + if (score <= 16) + continue; // filter very self-similar features score -= 16; // translate to score/histogram values - if (score>255) score=255; // clip - ubyte* c = &scores[y*width+x]; + if (score > 255) + score = 255; // clip + ubyte* c = &scores[y * width + x]; for (int i = -distance; i < 0; i++) { for (int j = -distance; j < distance; j++) { - int s = c[i*width+j]; - if (s == 0) continue; - if (s >= score) goto nonmax; - c[i*width+j] = 0; + int s = c[i * width + j]; + if (s == 0) + continue; + if (s >= score) + goto nonmax; + c[i * width + j] = 0; histogram[s]--; } } for (int i = 0, j = -distance; j < 0; j++) { - int s = c[i*width+j]; - if (s == 0) continue; - if (s >= score) goto nonmax; - c[i*width+j] = 0; + int s = c[i * width + j]; + if (s == 0) + continue; + if (s >= score) + goto nonmax; + c[i * width + j] = 0; histogram[s]--; } c[0] = score, histogram[score]++; - nonmax: - { } // Do nothing. + nonmax : {} // Do nothing. } } int min = 255, total = 0; @@ -254,18 +270,16 @@ void DetectMORAVEC(const FloatImage &grayscale_image, // Score calculation above uses top left corner of the // patch as the origin, here we need to convert this value // to a pattrn center by adding 8 pixels. - detected_features->push_back(Feature((float) x + 8.0f, - (float) y + 8.0f, - (float) s, - 16.0f)); + detected_features->push_back( + Feature((float)x + 8.0f, (float)y + 8.0f, (float)s, 16.0f)); } } } } -void DetectHarris(const FloatImage &grayscale_image, - const DetectOptions &options, - vector<Feature> *detected_features) { +void DetectHarris(const FloatImage& grayscale_image, + const DetectOptions& options, + vector<Feature>* detected_features) { const double alpha = 0.06; const double sigma = 0.9; @@ -281,9 +295,7 @@ void DetectHarris(const FloatImage &grayscale_image, MultiplyElements(gradient_y, gradient_y, &gradient_yy); MultiplyElements(gradient_x, gradient_y, &gradient_xy); - FloatImage gradient_xx_blurred, - gradient_yy_blurred, - gradient_xy_blurred; + FloatImage gradient_xx_blurred, gradient_yy_blurred, gradient_xy_blurred; ConvolveGaussian(gradient_xx, sigma, &gradient_xx_blurred); ConvolveGaussian(gradient_yy, sigma, &gradient_yy_blurred); ConvolveGaussian(gradient_xy, sigma, &gradient_xy_blurred); @@ -304,10 +316,8 @@ void DetectHarris(const FloatImage &grayscale_image, double traceA = A.trace(); double harris_function = detA - alpha * traceA * traceA; if (harris_function > threshold) { - all_features.push_back(Feature((float) x, - (float) y, - (float) harris_function, - 5.0f)); + all_features.push_back( + Feature((float)x, (float)y, (float)harris_function, 5.0f)); } } } @@ -318,17 +328,18 @@ void DetectHarris(const FloatImage &grayscale_image, } // namespace DetectOptions::DetectOptions() - : type(DetectOptions::HARRIS), - margin(0), - min_distance(120), - fast_min_trackness(kDefaultFastMinTrackness), - moravec_max_count(0), - moravec_pattern(NULL), - harris_threshold(kDefaultHarrisThreshold) {} - -void Detect(const FloatImage &image, - const DetectOptions &options, - vector<Feature> *detected_features) { + : type(DetectOptions::HARRIS), + margin(0), + min_distance(120), + fast_min_trackness(kDefaultFastMinTrackness), + moravec_max_count(0), + moravec_pattern(NULL), + harris_threshold(kDefaultHarrisThreshold) { +} + +void Detect(const FloatImage& image, + const DetectOptions& options, + vector<Feature>* detected_features) { // Currently all the detectors requires image to be grayscale. // Do it here to avoid code duplication. FloatImage grayscale_image; @@ -350,8 +361,7 @@ void Detect(const FloatImage &image, } } -std::ostream& operator <<(std::ostream &os, - const Feature &feature) { +std::ostream& operator<<(std::ostream& os, const Feature& feature) { os << "x: " << feature.x << ", y: " << feature.y; os << ", score: " << feature.score; os << ", size: " << feature.size; |