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-rw-r--r--source/use_case/object_detection/src/DetectorPostProcessing.cc123
1 files changed, 70 insertions, 53 deletions
diff --git a/source/use_case/object_detection/src/DetectorPostProcessing.cc b/source/use_case/object_detection/src/DetectorPostProcessing.cc
index a890c9e..fb1606a 100644
--- a/source/use_case/object_detection/src/DetectorPostProcessing.cc
+++ b/source/use_case/object_detection/src/DetectorPostProcessing.cc
@@ -21,64 +21,73 @@
namespace arm {
namespace app {
-namespace object_detection {
-
-DetectorPostprocessing::DetectorPostprocessing(
- const float threshold,
- const float nms,
- int numClasses,
- int topN)
- : m_threshold(threshold),
- m_nms(nms),
- m_numClasses(numClasses),
- m_topN(topN)
-{}
-
-void DetectorPostprocessing::RunPostProcessing(
- uint32_t imgRows,
- uint32_t imgCols,
- TfLiteTensor* modelOutput0,
- TfLiteTensor* modelOutput1,
- std::vector<DetectionResult>& resultsOut)
+
+ DetectorPostProcess::DetectorPostProcess(
+ TfLiteTensor* modelOutput0,
+ TfLiteTensor* modelOutput1,
+ std::vector<object_detection::DetectionResult>& results,
+ int inputImgRows,
+ int inputImgCols,
+ const float threshold,
+ const float nms,
+ int numClasses,
+ int topN)
+ : m_outputTensor0{modelOutput0},
+ m_outputTensor1{modelOutput1},
+ m_results{results},
+ m_inputImgRows{inputImgRows},
+ m_inputImgCols{inputImgCols},
+ m_threshold(threshold),
+ m_nms(nms),
+ m_numClasses(numClasses),
+ m_topN(topN)
{
- /* init postprocessing */
- Network net {
- .inputWidth = static_cast<int>(imgCols),
- .inputHeight = static_cast<int>(imgRows),
- .numClasses = m_numClasses,
+ /* Init PostProcessing */
+ this->m_net =
+ object_detection::Network {
+ .inputWidth = inputImgCols,
+ .inputHeight = inputImgRows,
+ .numClasses = numClasses,
.branches = {
- Branch {
- .resolution = static_cast<int>(imgCols/32),
- .numBox = 3,
- .anchor = anchor1,
- .modelOutput = modelOutput0->data.int8,
- .scale = ((TfLiteAffineQuantization*)(modelOutput0->quantization.params))->scale->data[0],
- .zeroPoint = ((TfLiteAffineQuantization*)(modelOutput0->quantization.params))->zero_point->data[0],
- .size = modelOutput0->bytes
+ object_detection::Branch {
+ .resolution = inputImgCols/32,
+ .numBox = 3,
+ .anchor = anchor1,
+ .modelOutput = this->m_outputTensor0->data.int8,
+ .scale = (static_cast<TfLiteAffineQuantization*>(
+ this->m_outputTensor0->quantization.params))->scale->data[0],
+ .zeroPoint = (static_cast<TfLiteAffineQuantization*>(
+ this->m_outputTensor0->quantization.params))->zero_point->data[0],
+ .size = this->m_outputTensor0->bytes
},
- Branch {
- .resolution = static_cast<int>(imgCols/16),
- .numBox = 3,
- .anchor = anchor2,
- .modelOutput = modelOutput1->data.int8,
- .scale = ((TfLiteAffineQuantization*)(modelOutput1->quantization.params))->scale->data[0],
- .zeroPoint = ((TfLiteAffineQuantization*)(modelOutput1->quantization.params))->zero_point->data[0],
- .size = modelOutput1->bytes
+ object_detection::Branch {
+ .resolution = inputImgCols/16,
+ .numBox = 3,
+ .anchor = anchor2,
+ .modelOutput = this->m_outputTensor1->data.int8,
+ .scale = (static_cast<TfLiteAffineQuantization*>(
+ this->m_outputTensor1->quantization.params))->scale->data[0],
+ .zeroPoint = (static_cast<TfLiteAffineQuantization*>(
+ this->m_outputTensor1->quantization.params))->zero_point->data[0],
+ .size = this->m_outputTensor1->bytes
}
},
.topN = m_topN
};
/* End init */
+}
+bool DetectorPostProcess::DoPostProcess()
+{
/* Start postprocessing */
int originalImageWidth = originalImageSize;
int originalImageHeight = originalImageSize;
std::forward_list<image::Detection> detections;
- GetNetworkBoxes(net, originalImageWidth, originalImageHeight, m_threshold, detections);
+ GetNetworkBoxes(this->m_net, originalImageWidth, originalImageHeight, m_threshold, detections);
/* Do nms */
- CalculateNMS(detections, net.numClasses, m_nms);
+ CalculateNMS(detections, this->m_net.numClasses, m_nms);
for (auto& it: detections) {
float xMin = it.bbox.x - it.bbox.w / 2.0f;
@@ -104,24 +113,24 @@ void DetectorPostprocessing::RunPostProcessing(
float boxWidth = xMax - xMin;
float boxHeight = yMax - yMin;
- for (int j = 0; j < net.numClasses; ++j) {
+ for (int j = 0; j < this->m_net.numClasses; ++j) {
if (it.prob[j] > 0) {
- DetectionResult tmpResult = {};
+ object_detection::DetectionResult tmpResult = {};
tmpResult.m_normalisedVal = it.prob[j];
tmpResult.m_x0 = boxX;
tmpResult.m_y0 = boxY;
tmpResult.m_w = boxWidth;
tmpResult.m_h = boxHeight;
- resultsOut.push_back(tmpResult);
+ this->m_results.push_back(tmpResult);
}
}
}
+ return true;
}
-
-void DetectorPostprocessing::InsertTopNDetections(std::forward_list<image::Detection>& detections, image::Detection& det)
+void DetectorPostProcess::InsertTopNDetections(std::forward_list<image::Detection>& detections, image::Detection& det)
{
std::forward_list<image::Detection>::iterator it;
std::forward_list<image::Detection>::iterator last_it;
@@ -136,7 +145,12 @@ void DetectorPostprocessing::InsertTopNDetections(std::forward_list<image::Detec
}
}
-void DetectorPostprocessing::GetNetworkBoxes(Network& net, int imageWidth, int imageHeight, float threshold, std::forward_list<image::Detection>& detections)
+void DetectorPostProcess::GetNetworkBoxes(
+ object_detection::Network& net,
+ int imageWidth,
+ int imageHeight,
+ float threshold,
+ std::forward_list<image::Detection>& detections)
{
int numClasses = net.numClasses;
int num = 0;
@@ -169,10 +183,14 @@ void DetectorPostprocessing::GetNetworkBoxes(Network& net, int imageWidth, int i
int bbox_h_offset = bbox_x_offset + 3;
int bbox_scores_offset = bbox_x_offset + 5;
- det.bbox.x = (static_cast<float>(net.branches[i].modelOutput[bbox_x_offset]) - net.branches[i].zeroPoint) * net.branches[i].scale;
- det.bbox.y = (static_cast<float>(net.branches[i].modelOutput[bbox_y_offset]) - net.branches[i].zeroPoint) * net.branches[i].scale;
- det.bbox.w = (static_cast<float>(net.branches[i].modelOutput[bbox_w_offset]) - net.branches[i].zeroPoint) * net.branches[i].scale;
- det.bbox.h = (static_cast<float>(net.branches[i].modelOutput[bbox_h_offset]) - net.branches[i].zeroPoint) * net.branches[i].scale;
+ det.bbox.x = (static_cast<float>(net.branches[i].modelOutput[bbox_x_offset])
+ - net.branches[i].zeroPoint) * net.branches[i].scale;
+ det.bbox.y = (static_cast<float>(net.branches[i].modelOutput[bbox_y_offset])
+ - net.branches[i].zeroPoint) * net.branches[i].scale;
+ det.bbox.w = (static_cast<float>(net.branches[i].modelOutput[bbox_w_offset])
+ - net.branches[i].zeroPoint) * net.branches[i].scale;
+ det.bbox.h = (static_cast<float>(net.branches[i].modelOutput[bbox_h_offset])
+ - net.branches[i].zeroPoint) * net.branches[i].scale;
float bbox_x, bbox_y;
@@ -218,6 +236,5 @@ void DetectorPostprocessing::GetNetworkBoxes(Network& net, int imageWidth, int i
num -=1;
}
-} /* namespace object_detection */
} /* namespace app */
} /* namespace arm */
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-28 20:41:47 +0000