diff --git a/modules/dnn/src/layers/detection_output_layer.cpp b/modules/dnn/src/layers/detection_output_layer.cpp index fba7835147..6da162423f 100644 --- a/modules/dnn/src/layers/detection_output_layer.cpp +++ b/modules/dnn/src/layers/detection_output_layer.cpp @@ -55,29 +55,13 @@ namespace util { template -std::string to_string(T value) -{ - std::ostringstream stream; - stream << value; - return stream.str(); -} - -template -void make_error(const std::string& message1, const T& message2) -{ - std::string error(message1); - error += std::string(util::to_string(message2)); - CV_Error(Error::StsBadArg, error.c_str()); -} - -template -bool SortScorePairDescend(const std::pair& pair1, +static inline bool SortScorePairDescend(const std::pair& pair1, const std::pair& pair2) { return pair1.first > pair2.first; } -} +} // namespace class DetectionOutputLayerImpl : public DetectionOutputLayer { @@ -133,7 +117,7 @@ public: message += " layer parameter does not contain "; message += parameterName; message += " parameter."; - CV_Error(Error::StsBadArg, message); + CV_ErrorNoReturn(Error::StsBadArg, message); } else { @@ -209,180 +193,173 @@ public: CV_TRACE_FUNCTION(); CV_TRACE_ARG_VALUE(name, "name", name.c_str()); - const float* locationData = inputs[0]->ptr(); - const float* confidenceData = inputs[1]->ptr(); - const float* priorData = inputs[2]->ptr(); + std::vector allDecodedBBoxes; + std::vector > > allConfidenceScores; int num = inputs[0]->size[0]; - int numPriors = inputs[2]->size[2] / 4; - // Retrieve all location predictions. - std::vector allLocationPredictions; - GetLocPredictions(locationData, num, numPriors, _numLocClasses, - _shareLocation, &allLocationPredictions); + // extract predictions from input layers + { + int numPriors = inputs[2]->size[2] / 4; - // Retrieve all confidences. - std::vector > > allConfidenceScores; - GetConfidenceScores(confidenceData, num, numPriors, _numClasses, - &allConfidenceScores); + const float* locationData = inputs[0]->ptr(); + const float* confidenceData = inputs[1]->ptr(); + const float* priorData = inputs[2]->ptr(); - // Retrieve all prior bboxes. It is same within a batch since we assume all - // images in a batch are of same dimension. - std::vector priorBBoxes; - std::vector > priorVariances; - GetPriorBBoxes(priorData, numPriors, &priorBBoxes, &priorVariances); + // Retrieve all location predictions + std::vector allLocationPredictions; + GetLocPredictions(locationData, num, numPriors, _numLocClasses, + _shareLocation, allLocationPredictions); - const bool clip_bbox = false; - // Decode all loc predictions to bboxes. - std::vector allDecodedBBoxes; - DecodeBBoxesAll(allLocationPredictions, priorBBoxes, priorVariances, num, - _shareLocation, _numLocClasses, _backgroundLabelId, - _codeType, _varianceEncodedInTarget, clip_bbox, &allDecodedBBoxes); + // Retrieve all confidences + GetConfidenceScores(confidenceData, num, numPriors, _numClasses, allConfidenceScores); - int numKept = 0; + // Retrieve all prior bboxes + std::vector priorBBoxes; + std::vector > priorVariances; + GetPriorBBoxes(priorData, numPriors, priorBBoxes, priorVariances); + + // Decode all loc predictions to bboxes + DecodeBBoxesAll(allLocationPredictions, priorBBoxes, priorVariances, num, + _shareLocation, _numLocClasses, _backgroundLabelId, + _codeType, _varianceEncodedInTarget, false, allDecodedBBoxes); + } + + size_t numKept = 0; std::vector > > allIndices; for (int i = 0; i < num; ++i) { - const LabelBBox& decodeBBoxes = allDecodedBBoxes[i]; - const std::vector >& confidenceScores = - allConfidenceScores[i]; - std::map > indices; - int numDetections = 0; - for (int c = 0; c < (int)_numClasses; ++c) - { - if (c == _backgroundLabelId) - { - // Ignore background class. - continue; - } - if (confidenceScores.size() <= c) - { - // Something bad happened if there are no predictions for current label. - util::make_error("Could not find confidence predictions for label ", c); - } - - const std::vector& scores = confidenceScores[c]; - int label = _shareLocation ? -1 : c; - if (decodeBBoxes.find(label) == decodeBBoxes.end()) - { - // Something bad happened if there are no predictions for current label. - util::make_error("Could not find location predictions for label ", label); - continue; - } - const std::vector& bboxes = - decodeBBoxes.find(label)->second; - ApplyNMSFast(bboxes, scores, _confidenceThreshold, _nmsThreshold, 1.0, - _topK, &(indices[c])); - numDetections += indices[c].size(); - } - if (_keepTopK > -1 && numDetections > _keepTopK) - { - std::vector > > scoreIndexPairs; - for (std::map >::iterator it = indices.begin(); - it != indices.end(); ++it) - { - int label = it->first; - const std::vector& labelIndices = it->second; - if (confidenceScores.size() <= label) - { - // Something bad happened for current label. - util::make_error("Could not find location predictions for label ", label); - continue; - } - const std::vector& scores = confidenceScores[label]; - for (size_t j = 0; j < labelIndices.size(); ++j) - { - size_t idx = labelIndices[j]; - CV_Assert(idx < scores.size()); - scoreIndexPairs.push_back( - std::make_pair(scores[idx], std::make_pair(label, idx))); - } - } - // Keep outputs k results per image. - std::sort(scoreIndexPairs.begin(), scoreIndexPairs.end(), - util::SortScorePairDescend >); - scoreIndexPairs.resize(_keepTopK); - // Store the new indices. - std::map > newIndices; - for (size_t j = 0; j < scoreIndexPairs.size(); ++j) - { - int label = scoreIndexPairs[j].second.first; - int idx = scoreIndexPairs[j].second.second; - newIndices[label].push_back(idx); - } - allIndices.push_back(newIndices); - numKept += _keepTopK; - } - else - { - allIndices.push_back(indices); - numKept += numDetections; - } + numKept += processDetections_(allDecodedBBoxes[i], allConfidenceScores[i], allIndices); } if (numKept == 0) { CV_ErrorNoReturn(Error::StsError, "Couldn't find any detections"); - return; } - int outputShape[] = {1, 1, numKept, 7}; + int outputShape[] = {1, 1, (int)numKept, 7}; outputs[0].create(4, outputShape, CV_32F); float* outputsData = outputs[0].ptr(); - int count = 0; + size_t count = 0; for (int i = 0; i < num; ++i) { - const std::vector >& confidenceScores = - allConfidenceScores[i]; - const LabelBBox& decodeBBoxes = allDecodedBBoxes[i]; - for (std::map >::iterator it = allIndices[i].begin(); - it != allIndices[i].end(); ++it) + count += outputDetections_(i, &outputsData[count * 7], + allDecodedBBoxes[i], allConfidenceScores[i], + allIndices[i]); + } + CV_Assert(count == numKept); + } + + size_t outputDetections_( + const int i, float* outputsData, + const LabelBBox& decodeBBoxes, const std::vector >& confidenceScores, + const std::map >& indicesMap + ) + { + size_t count = 0; + for (std::map >::const_iterator it = indicesMap.begin(); it != indicesMap.end(); ++it) + { + int label = it->first; + if (confidenceScores.size() <= label) + CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find confidence predictions for label %d", label)); + const std::vector& scores = confidenceScores[label]; + int locLabel = _shareLocation ? -1 : label; + LabelBBox::const_iterator label_bboxes = decodeBBoxes.find(locLabel); + if (label_bboxes == decodeBBoxes.end()) + CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find location predictions for label %d", locLabel)); + const std::vector& indices = it->second; + + for (size_t j = 0; j < indices.size(); ++j, ++count) + { + int idx = indices[j]; + const caffe::NormalizedBBox& decode_bbox = label_bboxes->second[idx]; + outputsData[count * 7] = i; + outputsData[count * 7 + 1] = label; + outputsData[count * 7 + 2] = scores[idx]; + outputsData[count * 7 + 3] = decode_bbox.xmin(); + outputsData[count * 7 + 4] = decode_bbox.ymin(); + outputsData[count * 7 + 5] = decode_bbox.xmax(); + outputsData[count * 7 + 6] = decode_bbox.ymax(); + } + } + return count; + } + + size_t processDetections_( + const LabelBBox& decodeBBoxes, const std::vector >& confidenceScores, + std::vector > >& allIndices + ) + { + std::map > indices; + size_t numDetections = 0; + for (int c = 0; c < (int)_numClasses; ++c) + { + if (c == _backgroundLabelId) + continue; // Ignore background class. + if (c >= confidenceScores.size()) + CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find confidence predictions for label %d", c)); + + const std::vector& scores = confidenceScores[c]; + int label = _shareLocation ? -1 : c; + + LabelBBox::const_iterator label_bboxes = decodeBBoxes.find(label); + if (label_bboxes == decodeBBoxes.end()) + CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find location predictions for label %d", label)); + ApplyNMSFast(label_bboxes->second, scores, _confidenceThreshold, _nmsThreshold, 1.0, _topK, indices[c]); + numDetections += indices[c].size(); + } + if (_keepTopK > -1 && numDetections > (size_t)_keepTopK) + { + std::vector > > scoreIndexPairs; + for (std::map >::iterator it = indices.begin(); + it != indices.end(); ++it) { int label = it->first; - if (confidenceScores.size() <= label) - { - // Something bad happened if there are no predictions for current label. - util::make_error("Could not find confidence predictions for label ", label); - continue; - } + const std::vector& labelIndices = it->second; + if (label >= confidenceScores.size()) + CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find location predictions for label %d", label)); const std::vector& scores = confidenceScores[label]; - int locLabel = _shareLocation ? -1 : label; - if (decodeBBoxes.find(locLabel) == decodeBBoxes.end()) + for (size_t j = 0; j < labelIndices.size(); ++j) { - // Something bad happened if there are no predictions for current label. - util::make_error("Could not find location predictions for label ", locLabel); - continue; + size_t idx = labelIndices[j]; + CV_Assert(idx < scores.size()); + scoreIndexPairs.push_back(std::make_pair(scores[idx], std::make_pair(label, idx))); } - const std::vector& bboxes = - decodeBBoxes.find(locLabel)->second; - std::vector& indices = it->second; + } + // Keep outputs k results per image. + std::sort(scoreIndexPairs.begin(), scoreIndexPairs.end(), + util::SortScorePairDescend >); + scoreIndexPairs.resize(_keepTopK); - for (size_t j = 0; j < indices.size(); ++j) - { - int idx = indices[j]; - outputsData[count * 7] = i; - outputsData[count * 7 + 1] = label; - outputsData[count * 7 + 2] = scores[idx]; - caffe::NormalizedBBox clipBBox = bboxes[idx]; - outputsData[count * 7 + 3] = clipBBox.xmin(); - outputsData[count * 7 + 4] = clipBBox.ymin(); - outputsData[count * 7 + 5] = clipBBox.xmax(); - outputsData[count * 7 + 6] = clipBBox.ymax(); - - ++count; - } + std::map > newIndices; + for (size_t j = 0; j < scoreIndexPairs.size(); ++j) + { + int label = scoreIndexPairs[j].second.first; + int idx = scoreIndexPairs[j].second.second; + newIndices[label].push_back(idx); } + allIndices.push_back(newIndices); + return (size_t)_keepTopK; + } + else + { + allIndices.push_back(indices); + return numDetections; } } - // Compute bbox size. - float BBoxSize(const caffe::NormalizedBBox& bbox, - const bool normalized=true) + + // ************************************************************** + // Utility functions + // ************************************************************** + + // Compute bbox size + template + static float BBoxSize(const caffe::NormalizedBBox& bbox) { if (bbox.xmax() < bbox.xmin() || bbox.ymax() < bbox.ymin()) { - // If bbox is invalid (e.g. xmax < xmin or ymax < ymin), return 0. - return 0; + return 0; // If bbox is invalid (e.g. xmax < xmin or ymax < ymin), return 0. } else { @@ -407,193 +384,155 @@ public: } } - // Clip the caffe::NormalizedBBox such that the range for each corner is [0, 1]. - void ClipBBox(const caffe::NormalizedBBox& bbox, - caffe::NormalizedBBox* clipBBox) - { - clipBBox->set_xmin(std::max(std::min(bbox.xmin(), 1.f), 0.f)); - clipBBox->set_ymin(std::max(std::min(bbox.ymin(), 1.f), 0.f)); - clipBBox->set_xmax(std::max(std::min(bbox.xmax(), 1.f), 0.f)); - clipBBox->set_ymax(std::max(std::min(bbox.ymax(), 1.f), 0.f)); - clipBBox->clear_size(); - clipBBox->set_size(BBoxSize(*clipBBox)); - clipBBox->set_difficult(bbox.difficult()); - } - // Decode a bbox according to a prior bbox. - void DecodeBBox( + // Decode a bbox according to a prior bbox + template + static void DecodeBBox( const caffe::NormalizedBBox& prior_bbox, const std::vector& prior_variance, - const CodeType code_type, const bool variance_encoded_in_target, + const CodeType code_type, const bool clip_bbox, const caffe::NormalizedBBox& bbox, - caffe::NormalizedBBox* decode_bbox) { - if (code_type == caffe::PriorBoxParameter_CodeType_CORNER) { - if (variance_encoded_in_target) { - // variance is encoded in target, we simply need to add the offset - // predictions. - decode_bbox->set_xmin(prior_bbox.xmin() + bbox.xmin()); - decode_bbox->set_ymin(prior_bbox.ymin() + bbox.ymin()); - decode_bbox->set_xmax(prior_bbox.xmax() + bbox.xmax()); - decode_bbox->set_ymax(prior_bbox.ymax() + bbox.ymax()); - } else { - // variance is encoded in bbox, we need to scale the offset accordingly. - decode_bbox->set_xmin( - prior_bbox.xmin() + prior_variance[0] * bbox.xmin()); - decode_bbox->set_ymin( - prior_bbox.ymin() + prior_variance[1] * bbox.ymin()); - decode_bbox->set_xmax( - prior_bbox.xmax() + prior_variance[2] * bbox.xmax()); - decode_bbox->set_ymax( - prior_bbox.ymax() + prior_variance[3] * bbox.ymax()); - } - } else if (code_type == caffe::PriorBoxParameter_CodeType_CENTER_SIZE) { - float prior_width = prior_bbox.xmax() - prior_bbox.xmin(); - CV_Assert(prior_width > 0); - float prior_height = prior_bbox.ymax() - prior_bbox.ymin(); - CV_Assert(prior_height > 0); - float prior_center_x = (prior_bbox.xmin() + prior_bbox.xmax()) / 2.; - float prior_center_y = (prior_bbox.ymin() + prior_bbox.ymax()) / 2.; - - float decode_bbox_center_x, decode_bbox_center_y; - float decode_bbox_width, decode_bbox_height; - if (variance_encoded_in_target) { - // variance is encoded in target, we simply need to retore the offset - // predictions. - decode_bbox_center_x = bbox.xmin() * prior_width + prior_center_x; - decode_bbox_center_y = bbox.ymin() * prior_height + prior_center_y; - decode_bbox_width = exp(bbox.xmax()) * prior_width; - decode_bbox_height = exp(bbox.ymax()) * prior_height; - } else { - // variance is encoded in bbox, we need to scale the offset accordingly. - decode_bbox_center_x = - prior_variance[0] * bbox.xmin() * prior_width + prior_center_x; - decode_bbox_center_y = - prior_variance[1] * bbox.ymin() * prior_height + prior_center_y; - decode_bbox_width = - exp(prior_variance[2] * bbox.xmax()) * prior_width; - decode_bbox_height = - exp(prior_variance[3] * bbox.ymax()) * prior_height; + caffe::NormalizedBBox& decode_bbox) + { + float bbox_xmin = variance_encoded_in_target ? bbox.xmin() : prior_variance[0] * bbox.xmin(); + float bbox_ymin = variance_encoded_in_target ? bbox.ymin() : prior_variance[1] * bbox.ymin(); + float bbox_xmax = variance_encoded_in_target ? bbox.xmax() : prior_variance[2] * bbox.xmax(); + float bbox_ymax = variance_encoded_in_target ? bbox.ymax() : prior_variance[3] * bbox.ymax(); + switch(code_type) + { + case caffe::PriorBoxParameter_CodeType_CORNER: + decode_bbox.set_xmin(prior_bbox.xmin() + bbox_xmin); + decode_bbox.set_ymin(prior_bbox.ymin() + bbox_ymin); + decode_bbox.set_xmax(prior_bbox.xmax() + bbox_xmax); + decode_bbox.set_ymax(prior_bbox.ymax() + bbox_ymax); + break; + case caffe::PriorBoxParameter_CodeType_CENTER_SIZE: + { + float prior_width = prior_bbox.xmax() - prior_bbox.xmin(); + CV_Assert(prior_width > 0); + float prior_height = prior_bbox.ymax() - prior_bbox.ymin(); + CV_Assert(prior_height > 0); + float prior_center_x = (prior_bbox.xmin() + prior_bbox.xmax()) * .5; + float prior_center_y = (prior_bbox.ymin() + prior_bbox.ymax()) * .5; + + float decode_bbox_center_x, decode_bbox_center_y; + float decode_bbox_width, decode_bbox_height; + decode_bbox_center_x = bbox_xmin * prior_width + prior_center_x; + decode_bbox_center_y = bbox_ymin * prior_height + prior_center_y; + decode_bbox_width = exp(bbox_xmax) * prior_width; + decode_bbox_height = exp(bbox_ymax) * prior_height; + decode_bbox.set_xmin(decode_bbox_center_x - decode_bbox_width * .5); + decode_bbox.set_ymin(decode_bbox_center_y - decode_bbox_height * .5); + decode_bbox.set_xmax(decode_bbox_center_x + decode_bbox_width * .5); + decode_bbox.set_ymax(decode_bbox_center_y + decode_bbox_height * .5); + break; + } + default: + CV_ErrorNoReturn(Error::StsBadArg, "Unknown type."); + }; + if (clip_bbox) + { + // Clip the caffe::NormalizedBBox such that the range for each corner is [0, 1] + decode_bbox.set_xmin(std::max(std::min(decode_bbox.xmin(), 1.f), 0.f)); + decode_bbox.set_ymin(std::max(std::min(decode_bbox.ymin(), 1.f), 0.f)); + decode_bbox.set_xmax(std::max(std::min(decode_bbox.xmax(), 1.f), 0.f)); + decode_bbox.set_ymax(std::max(std::min(decode_bbox.ymax(), 1.f), 0.f)); } - - decode_bbox->set_xmin(decode_bbox_center_x - decode_bbox_width / 2.); - decode_bbox->set_ymin(decode_bbox_center_y - decode_bbox_height / 2.); - decode_bbox->set_xmax(decode_bbox_center_x + decode_bbox_width / 2.); - decode_bbox->set_ymax(decode_bbox_center_y + decode_bbox_height / 2.); - } else { - CV_Error(Error::StsBadArg, "Unknown LocLossType."); - } - float bbox_size = BBoxSize(*decode_bbox); - decode_bbox->set_size(bbox_size); - if (clip_bbox) { - ClipBBox(*decode_bbox, decode_bbox); - } + decode_bbox.clear_size(); + decode_bbox.set_size(BBoxSize(decode_bbox)); } - // Decode a set of bboxes according to a set of prior bboxes. - void DecodeBBoxes( + // Decode a set of bboxes according to a set of prior bboxes + static void DecodeBBoxes( const std::vector& prior_bboxes, const std::vector >& prior_variances, const CodeType code_type, const bool variance_encoded_in_target, const bool clip_bbox, const std::vector& bboxes, - std::vector* decode_bboxes) { - CV_Assert(prior_bboxes.size() == prior_variances.size()); - CV_Assert(prior_bboxes.size() == bboxes.size()); - int num_bboxes = prior_bboxes.size(); - if (num_bboxes >= 1) { - CV_Assert(prior_variances[0].size() == 4); - } - decode_bboxes->clear(); - for (int i = 0; i < num_bboxes; ++i) { - caffe::NormalizedBBox decode_bbox; - DecodeBBox(prior_bboxes[i], prior_variances[i], code_type, - variance_encoded_in_target, clip_bbox, bboxes[i], &decode_bbox); - decode_bboxes->push_back(decode_bbox); - } + std::vector& decode_bboxes) + { + CV_Assert(prior_bboxes.size() == prior_variances.size()); + CV_Assert(prior_bboxes.size() == bboxes.size()); + size_t num_bboxes = prior_bboxes.size(); + CV_Assert(num_bboxes == 0 || prior_variances[0].size() == 4); + decode_bboxes.clear(); decode_bboxes.resize(num_bboxes); + if(variance_encoded_in_target) + { + for (int i = 0; i < num_bboxes; ++i) + DecodeBBox(prior_bboxes[i], prior_variances[i], code_type, + clip_bbox, bboxes[i], decode_bboxes[i]); + } + else + { + for (int i = 0; i < num_bboxes; ++i) + DecodeBBox(prior_bboxes[i], prior_variances[i], code_type, + clip_bbox, bboxes[i], decode_bboxes[i]); + } } - // Decode all bboxes in a batch. - void DecodeBBoxesAll(const std::vector& all_loc_preds, + // Decode all bboxes in a batch + static void DecodeBBoxesAll(const std::vector& all_loc_preds, const std::vector& prior_bboxes, const std::vector >& prior_variances, const int num, const bool share_location, const int num_loc_classes, const int background_label_id, const CodeType code_type, const bool variance_encoded_in_target, - const bool clip, std::vector* all_decode_bboxes) { - CV_Assert(all_loc_preds.size() == num); - all_decode_bboxes->clear(); - all_decode_bboxes->resize(num); - for (int i = 0; i < num; ++i) { - // Decode predictions into bboxes. - LabelBBox& decode_bboxes = (*all_decode_bboxes)[i]; - for (int c = 0; c < num_loc_classes; ++c) { - int label = share_location ? -1 : c; - if (label == background_label_id) { - // Ignore background class. - continue; - } - if (all_loc_preds[i].find(label) == all_loc_preds[i].end()) { - // Something bad happened if there are no predictions for current label. - util::make_error("Could not find location predictions for label ", label); - } - const std::vector& label_loc_preds = - all_loc_preds[i].find(label)->second; - DecodeBBoxes(prior_bboxes, prior_variances, - code_type, variance_encoded_in_target, clip, - label_loc_preds, &(decode_bboxes[label])); + const bool clip, std::vector& all_decode_bboxes) + { + CV_Assert(all_loc_preds.size() == num); + all_decode_bboxes.clear(); + all_decode_bboxes.resize(num); + for (int i = 0; i < num; ++i) + { + // Decode predictions into bboxes. + const LabelBBox& loc_preds = all_loc_preds[i]; + LabelBBox& decode_bboxes = all_decode_bboxes[i]; + for (int c = 0; c < num_loc_classes; ++c) + { + int label = share_location ? -1 : c; + if (label == background_label_id) + continue; // Ignore background class. + LabelBBox::const_iterator label_loc_preds = loc_preds.find(label); + if (label_loc_preds == loc_preds.end()) + CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find location predictions for label %d", label)); + DecodeBBoxes(prior_bboxes, prior_variances, + code_type, variance_encoded_in_target, clip, + label_loc_preds->second, decode_bboxes[label]); + } } - } } - // Get prior bounding boxes from prior_data. + // Get prior bounding boxes from prior_data // prior_data: 1 x 2 x num_priors * 4 x 1 blob. // num_priors: number of priors. // prior_bboxes: stores all the prior bboxes in the format of caffe::NormalizedBBox. // prior_variances: stores all the variances needed by prior bboxes. - void GetPriorBBoxes(const float* priorData, const int& numPriors, - std::vector* priorBBoxes, - std::vector >* priorVariances) + static void GetPriorBBoxes(const float* priorData, const int& numPriors, + std::vector& priorBBoxes, + std::vector >& priorVariances) { - priorBBoxes->clear(); - priorVariances->clear(); + priorBBoxes.clear(); priorBBoxes.resize(numPriors); + priorVariances.clear(); priorVariances.resize(numPriors); for (int i = 0; i < numPriors; ++i) { int startIdx = i * 4; - caffe::NormalizedBBox bbox; + caffe::NormalizedBBox& bbox = priorBBoxes[i]; bbox.set_xmin(priorData[startIdx]); bbox.set_ymin(priorData[startIdx + 1]); bbox.set_xmax(priorData[startIdx + 2]); bbox.set_ymax(priorData[startIdx + 3]); - float bboxSize = BBoxSize(bbox); - bbox.set_size(bboxSize); - priorBBoxes->push_back(bbox); + bbox.set_size(BBoxSize(bbox)); } for (int i = 0; i < numPriors; ++i) { int startIdx = (numPriors + i) * 4; - std::vector var; + // not needed here: priorVariances[i].clear(); for (int j = 0; j < 4; ++j) { - var.push_back(priorData[startIdx + j]); + priorVariances[i].push_back(priorData[startIdx + j]); } - priorVariances->push_back(var); } } - // Scale the caffe::NormalizedBBox w.r.t. height and width. - void ScaleBBox(const caffe::NormalizedBBox& bbox, - const int height, const int width, - caffe::NormalizedBBox* scaleBBox) - { - scaleBBox->set_xmin(bbox.xmin() * width); - scaleBBox->set_ymin(bbox.ymin() * height); - scaleBBox->set_xmax(bbox.xmax() * width); - scaleBBox->set_ymax(bbox.ymax() * height); - scaleBBox->clear_size(); - bool normalized = !(width > 1 || height > 1); - scaleBBox->set_size(BBoxSize(*scaleBBox, normalized)); - scaleBBox->set_difficult(bbox.difficult()); - } - // Get location predictions from loc_data. // loc_data: num x num_preds_per_class * num_loc_classes * 4 blob. // num: the number of images. @@ -603,19 +542,19 @@ public: // share_location: if true, all classes share the same location prediction. // loc_preds: stores the location prediction, where each item contains // location prediction for an image. - void GetLocPredictions(const float* locData, const int num, + static void GetLocPredictions(const float* locData, const int num, const int numPredsPerClass, const int numLocClasses, - const bool shareLocation, std::vector* locPreds) + const bool shareLocation, std::vector& locPreds) { - locPreds->clear(); + locPreds.clear(); if (shareLocation) { CV_Assert(numLocClasses == 1); } - locPreds->resize(num); - for (int i = 0; i < num; ++i) + locPreds.resize(num); + for (int i = 0; i < num; ++i, locData += numPredsPerClass * numLocClasses * 4) { - LabelBBox& labelBBox = (*locPreds)[i]; + LabelBBox& labelBBox = locPreds[i]; for (int p = 0; p < numPredsPerClass; ++p) { int startIdx = p * numLocClasses * 4; @@ -626,13 +565,13 @@ public: { labelBBox[label].resize(numPredsPerClass); } - labelBBox[label][p].set_xmin(locData[startIdx + c * 4]); - labelBBox[label][p].set_ymin(locData[startIdx + c * 4 + 1]); - labelBBox[label][p].set_xmax(locData[startIdx + c * 4 + 2]); - labelBBox[label][p].set_ymax(locData[startIdx + c * 4 + 3]); + caffe::NormalizedBBox& bbox = labelBBox[label][p]; + bbox.set_xmin(locData[startIdx + c * 4]); + bbox.set_ymin(locData[startIdx + c * 4 + 1]); + bbox.set_xmax(locData[startIdx + c * 4 + 2]); + bbox.set_ymax(locData[startIdx + c * 4 + 3]); } } - locData += numPredsPerClass * numLocClasses * 4; } } @@ -643,25 +582,24 @@ public: // num_classes: number of classes. // conf_preds: stores the confidence prediction, where each item contains // confidence prediction for an image. - void GetConfidenceScores(const float* confData, const int num, + static void GetConfidenceScores(const float* confData, const int num, const int numPredsPerClass, const int numClasses, - std::vector > >* confPreds) + std::vector > >& confPreds) { - confPreds->clear(); - confPreds->resize(num); - for (int i = 0; i < num; ++i) + confPreds.clear(); confPreds.resize(num); + for (int i = 0; i < num; ++i, confData += numPredsPerClass * numClasses) { - std::vector >& labelScores = (*confPreds)[i]; + std::vector >& labelScores = confPreds[i]; labelScores.resize(numClasses); - for (int p = 0; p < numPredsPerClass; ++p) + for (int c = 0; c < numClasses; ++c) { - int startIdx = p * numClasses; - for (int c = 0; c < numClasses; ++c) + std::vector& classLabelScores = labelScores[c]; + classLabelScores.resize(numPredsPerClass); + for (int p = 0; p < numPredsPerClass; ++p) { - labelScores[c].push_back(confData[startIdx + c]); + classLabelScores[p] = confData[p * numClasses + c]; } } - confData += numPredsPerClass * numClasses; } } @@ -674,40 +612,35 @@ public: // nms_threshold: a threshold used in non maximum suppression. // top_k: if not -1, keep at most top_k picked indices. // indices: the kept indices of bboxes after nms. - void ApplyNMSFast(const std::vector& bboxes, + static void ApplyNMSFast(const std::vector& bboxes, const std::vector& scores, const float score_threshold, const float nms_threshold, const float eta, const int top_k, - std::vector* indices) { - // Sanity check. - CV_Assert(bboxes.size() == scores.size()); - - // Get top_k scores (with corresponding indices). - std::vector > score_index_vec; - GetMaxScoreIndex(scores, score_threshold, top_k, &score_index_vec); - - // Do nms. - float adaptive_threshold = nms_threshold; - indices->clear(); - while (score_index_vec.size() != 0) { - const int idx = score_index_vec.front().second; - bool keep = true; - for (int k = 0; k < indices->size(); ++k) { - if (keep) { - const int kept_idx = (*indices)[k]; - float overlap = JaccardOverlap(bboxes[idx], bboxes[kept_idx]); - keep = overlap <= adaptive_threshold; - } else { - break; - } - } - if (keep) { - indices->push_back(idx); - } - score_index_vec.erase(score_index_vec.begin()); - if (keep && eta < 1 && adaptive_threshold > 0.5) { - adaptive_threshold *= eta; + std::vector& indices) + { + CV_Assert(bboxes.size() == scores.size()); + + // Get top_k scores (with corresponding indices). + std::vector > score_index_vec; + GetMaxScoreIndex(scores, score_threshold, top_k, score_index_vec); + + // Do nms. + float adaptive_threshold = nms_threshold; + indices.clear(); + while (score_index_vec.size() != 0) { + const int idx = score_index_vec.front().second; + bool keep = true; + for (int k = 0; k < (int)indices.size() && keep; ++k) { + const int kept_idx = indices[k]; + float overlap = JaccardOverlap(bboxes[idx], bboxes[kept_idx]); + keep = overlap <= adaptive_threshold; + } + if (keep) + indices.push_back(idx); + score_index_vec.erase(score_index_vec.begin()); + if (keep && eta < 1 && adaptive_threshold > 0.5) { + adaptive_threshold *= eta; + } } - } } // Get max scores with corresponding indices. @@ -715,74 +648,66 @@ public: // threshold: only consider scores higher than the threshold. // top_k: if -1, keep all; otherwise, keep at most top_k. // score_index_vec: store the sorted (score, index) pair. - void GetMaxScoreIndex(const std::vector& scores, const float threshold,const int top_k, - std::vector >* score_index_vec) + static void GetMaxScoreIndex(const std::vector& scores, const float threshold, const int top_k, + std::vector >& score_index_vec) { + CV_DbgAssert(score_index_vec.empty()); // Generate index score pairs. for (size_t i = 0; i < scores.size(); ++i) { if (scores[i] > threshold) { - score_index_vec->push_back(std::make_pair(scores[i], i)); + score_index_vec.push_back(std::make_pair(scores[i], i)); } } // Sort the score pair according to the scores in descending order - std::stable_sort(score_index_vec->begin(), score_index_vec->end(), + std::stable_sort(score_index_vec.begin(), score_index_vec.end(), util::SortScorePairDescend); // Keep top_k scores if needed. - if (top_k > -1 && top_k < (int)score_index_vec->size()) + if (top_k > -1 && top_k < (int)score_index_vec.size()) { - score_index_vec->resize(top_k); + score_index_vec.resize(top_k); } } - // Compute the intersection between two bboxes. - void IntersectBBox(const caffe::NormalizedBBox& bbox1, - const caffe::NormalizedBBox& bbox2, - caffe::NormalizedBBox* intersect_bbox) { + // Compute the jaccard (intersection over union IoU) overlap between two bboxes. + template + static float JaccardOverlap(const caffe::NormalizedBBox& bbox1, + const caffe::NormalizedBBox& bbox2) + { + caffe::NormalizedBBox intersect_bbox; if (bbox2.xmin() > bbox1.xmax() || bbox2.xmax() < bbox1.xmin() || bbox2.ymin() > bbox1.ymax() || bbox2.ymax() < bbox1.ymin()) { // Return [0, 0, 0, 0] if there is no intersection. - intersect_bbox->set_xmin(0); - intersect_bbox->set_ymin(0); - intersect_bbox->set_xmax(0); - intersect_bbox->set_ymax(0); + intersect_bbox.set_xmin(0); + intersect_bbox.set_ymin(0); + intersect_bbox.set_xmax(0); + intersect_bbox.set_ymax(0); } else { - intersect_bbox->set_xmin(std::max(bbox1.xmin(), bbox2.xmin())); - intersect_bbox->set_ymin(std::max(bbox1.ymin(), bbox2.ymin())); - intersect_bbox->set_xmax(std::min(bbox1.xmax(), bbox2.xmax())); - intersect_bbox->set_ymax(std::min(bbox1.ymax(), bbox2.ymax())); + intersect_bbox.set_xmin(std::max(bbox1.xmin(), bbox2.xmin())); + intersect_bbox.set_ymin(std::max(bbox1.ymin(), bbox2.ymin())); + intersect_bbox.set_xmax(std::min(bbox1.xmax(), bbox2.xmax())); + intersect_bbox.set_ymax(std::min(bbox1.ymax(), bbox2.ymax())); } - } - // Compute the jaccard (intersection over union IoU) overlap between two bboxes. - float JaccardOverlap(const caffe::NormalizedBBox& bbox1, - const caffe::NormalizedBBox& bbox2, - const bool normalized=true) - { - caffe::NormalizedBBox intersect_bbox; - IntersectBBox(bbox1, bbox2, &intersect_bbox); float intersect_width, intersect_height; - if (normalized) - { - intersect_width = intersect_bbox.xmax() - intersect_bbox.xmin(); - intersect_height = intersect_bbox.ymax() - intersect_bbox.ymin(); - } - else - { - intersect_width = intersect_bbox.xmax() - intersect_bbox.xmin() + 1; - intersect_height = intersect_bbox.ymax() - intersect_bbox.ymin() + 1; - } + intersect_width = intersect_bbox.xmax() - intersect_bbox.xmin(); + intersect_height = intersect_bbox.ymax() - intersect_bbox.ymin(); if (intersect_width > 0 && intersect_height > 0) { + if (!normalized) + { + intersect_width++; + intersect_height++; + } float intersect_size = intersect_width * intersect_height; - float bbox1_size = BBoxSize(bbox1); - float bbox2_size = BBoxSize(bbox2); + float bbox1_size = BBoxSize(bbox1); + float bbox2_size = BBoxSize(bbox2); return intersect_size / (bbox1_size + bbox2_size - intersect_size); } else