/* // Creation feature pyramid with nullable border // // API // featurePyramid* createFeaturePyramidWithBorder(const IplImage *image, int maxXBorder, int maxYBorder); // INPUT // image - initial image // maxXBorder - the largest root filter size (X-direction) // maxYBorder - the largest root filter size (Y-direction) // OUTPUT // RESULT // Feature pyramid with nullable border */ CvLSVMFeaturePyramid* createFeaturePyramidWithBorder(IplImage *image, int maxXBorder, int maxYBorder) { int opResult; int bx, by; int level; CvLSVMFeaturePyramid *H; // Obtaining feature pyramid opResult = getFeaturePyramid(image, LAMBDA, SIDE_LENGTH, 0, 0, image->width, image->height, &H); if (opResult != LATENT_SVM_OK) { freeFeaturePyramidObject(&H); return NULL; } /* if (opResult != LATENT_SVM_OK) */ // Addition nullable border for each feature map // the size of the border for root filters computeBorderSize(maxXBorder, maxYBorder, &bx, &by); for (level = 0; level < H->countLevel; level++) { addNullableBorder(H->pyramid[level], bx, by); } return H; }
/* // find rectangular regions in the given image that are likely // to contain objects and corresponding confidence levels // // API // CvSeq* cvLatentSvmDetectObjects(const IplImage* image, // CvLatentSvmDetector* detector, // CvMemStorage* storage, // float overlap_threshold = 0.5f, int numThreads = -1); // INPUT // image - image to detect objects in // detector - Latent SVM detector in internal representation // storage - memory storage to store the resultant sequence // of the object candidate rectangles // overlap_threshold - threshold for the non-maximum suppression algorithm [here will be the reference to original paper] // OUTPUT // sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures) */ CvSeq* cvLatentSvmDetectObjects(IplImage* image, CvLatentSvmDetector* detector, CvMemStorage* storage, float overlap_threshold, int numThreads) { CvLSVMFeaturePyramid *H = 0; CvPoint *points = 0, *oppPoints = 0; int kPoints = 0; float *score = 0; unsigned int maxXBorder = 0, maxYBorder = 0; int numBoxesOut = 0; CvPoint *pointsOut = 0; CvPoint *oppPointsOut = 0; float *scoreOut = 0; CvSeq* result_seq = 0; int error = 0; if(image->nChannels == 3) cvCvtColor(image, image, CV_BGR2RGB); // Getting maximum filter dimensions getMaxFilterDims((const CvLSVMFilterObject**)(detector->filters), detector->num_components, detector->num_part_filters, &maxXBorder, &maxYBorder); // Create feature pyramid with nullable border H = createFeaturePyramidWithBorder(image, maxXBorder, maxYBorder); // Search object error = searchObjectThresholdSomeComponents(H, (const CvLSVMFilterObject**)(detector->filters), detector->num_components, detector->num_part_filters, detector->b, detector->score_threshold, &points, &oppPoints, &score, &kPoints, numThreads); if (error != LATENT_SVM_OK) { return NULL; } // Clipping boxes clippingBoxes(image->width, image->height, points, kPoints); clippingBoxes(image->width, image->height, oppPoints, kPoints); // NMS procedure nonMaximumSuppression(kPoints, points, oppPoints, score, overlap_threshold, &numBoxesOut, &pointsOut, &oppPointsOut, &scoreOut); result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvObjectDetection), storage ); for (int i = 0; i < numBoxesOut; i++) { CvObjectDetection detection = {{0, 0, 0, 0}, 0}; detection.score = scoreOut[i]; CvRect bounding_box = {0, 0, 0, 0}; bounding_box.x = pointsOut[i].x; bounding_box.y = pointsOut[i].y; bounding_box.width = oppPointsOut[i].x - pointsOut[i].x; bounding_box.height = oppPointsOut[i].y - pointsOut[i].y; detection.rect = bounding_box; cvSeqPush(result_seq, &detection); } if(image->nChannels == 3) cvCvtColor(image, image, CV_RGB2BGR); freeFeaturePyramidObject(&H); free(points); free(oppPoints); free(score); return result_seq; }