thebears/deepstream_yolo
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Fix logger error in DeepStream 6.0 / 6.0.1 + Change output classes format + Fixes

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This commit is contained in:
Marcos Luciano
2023-06-08 13:47:43 -03:00
parent 9fd80c5248
commit 64fa573f72
23 changed files with 233 additions and 258 deletions
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56
nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp
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@@ -73,14 +73,14 @@ addBBoxProposal(const float bx1, const float by1, const float bx2, const float b
}
static std::vector<NvDsInferParseObjectInfo>
decodeTensorYolo(const float* boxes, const float* scores, const int* classes, const uint& outputSize, const uint& netW,
decodeTensorYolo(const float* boxes, const float* scores, const float* classes, const uint& outputSize, const uint& netW,
const uint& netH, const std::vector<float>& preclusterThreshold)
{
std::vector<NvDsInferParseObjectInfo> binfo;
for (uint b = 0; b < outputSize; ++b) {
float maxProb = scores[b];
int maxIndex = classes[b];
int maxIndex = (int) classes[b];
if (maxProb < preclusterThreshold[maxIndex])
continue;
@@ -102,14 +102,14 @@ decodeTensorYolo(const float* boxes, const float* scores, const int* classes, co
}
static std::vector<NvDsInferParseObjectInfo>
decodeTensorYoloE(const float* boxes, const float* scores, const int* classes, const uint& outputSize, const uint& netW,
decodeTensorYoloE(const float* boxes, const float* scores, const float* classes, const uint& outputSize, const uint& netW,
const uint& netH, const std::vector<float>& preclusterThreshold)
{
std::vector<NvDsInferParseObjectInfo> binfo;
for (uint b = 0; b < outputSize; ++b) {
float maxProb = scores[b];
int maxIndex = classes[b];
int maxIndex = (int) classes[b];
if (maxProb < preclusterThreshold[maxIndex])
continue;
@@ -136,26 +136,14 @@ NvDsInferParseCustomYolo(std::vector<NvDsInferLayerInfo> const& outputLayersInfo
std::vector<NvDsInferParseObjectInfo> objects;
NvDsInferLayerInfo* boxes;
NvDsInferLayerInfo* scores;
NvDsInferLayerInfo* classes;
const NvDsInferLayerInfo& boxes = outputLayersInfo[0];
const NvDsInferLayerInfo& scores = outputLayersInfo[1];
const NvDsInferLayerInfo& classes = outputLayersInfo[2];
for (uint i = 0; i < 3; ++i) {
if (outputLayersInfo[i].dataType == NvDsInferDataType::INT32) {
classes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
}
else if (outputLayersInfo[i].inferDims.d[1] == 4) {
boxes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
}
else {
scores = (NvDsInferLayerInfo*) &outputLayersInfo[i];
}
}
const uint outputSize = boxes.inferDims.d[0];
const uint outputSize = boxes->inferDims.d[0];
std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYolo((const float*) (boxes->buffer),
(const float*) (scores->buffer), (const int*) (classes->buffer), outputSize, networkInfo.width, networkInfo.height,
std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYolo((const float*) (boxes.buffer),
(const float*) (scores.buffer), (const float*) (classes.buffer), outputSize, networkInfo.width, networkInfo.height,
detectionParams.perClassPreclusterThreshold);
objects.insert(objects.end(), outObjs.begin(), outObjs.end());
@@ -176,26 +164,14 @@ NvDsInferParseCustomYoloE(std::vector<NvDsInferLayerInfo> const& outputLayersInf
std::vector<NvDsInferParseObjectInfo> objects;
NvDsInferLayerInfo* boxes;
NvDsInferLayerInfo* scores;
NvDsInferLayerInfo* classes;
const NvDsInferLayerInfo& boxes = outputLayersInfo[0];
const NvDsInferLayerInfo& scores = outputLayersInfo[1];
const NvDsInferLayerInfo& classes = outputLayersInfo[2];
for (uint i = 0; i < 3; ++i) {
if (outputLayersInfo[i].dataType == NvDsInferDataType::INT32) {
classes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
}
else if (outputLayersInfo[i].inferDims.d[1] == 4) {
boxes = (NvDsInferLayerInfo*) &outputLayersInfo[i];
}
else {
scores = (NvDsInferLayerInfo*) &outputLayersInfo[i];
}
}
const uint outputSize = boxes.inferDims.d[0];
const uint outputSize = boxes->inferDims.d[0];
std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYoloE((const float*) (boxes->buffer),
(const float*) (scores->buffer), (const int*) (classes->buffer), outputSize, networkInfo.width, networkInfo.height,
std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYoloE((const float*) (boxes.buffer),
(const float*) (scores.buffer), (const float*) (classes.buffer), outputSize, networkInfo.width, networkInfo.height,
detectionParams.perClassPreclusterThreshold);
objects.insert(objects.end(), outObjs.begin(), outObjs.end());