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Add parameters in TensorNet creation function to configure input/output blob #43

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Add parameters in TensorNet creation function to configure input/output blob #43

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11 changes: 10 additions & 1 deletion src/node_detectnet.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -152,6 +152,15 @@ int main(int argc, char **argv)
private_nh.param<float>("mean_pixel_value", mean_pixel, mean_pixel);
private_nh.param<float>("threshold", threshold, threshold);

// input and output blob for DetectNet model
std::string input_blob = DETECTNET_DEFAULT_INPUT;
std::string output_cvg = DETECTNET_DEFAULT_COVERAGE;
std::string output_bbox = DETECTNET_DEFAULT_BBOX;

private_nh.param<std::string>("input_blob", input_blob, input_blob);
private_nh.param<std::string>("output_cvg", output_cvg, output_cvg);
private_nh.param<std::string>("output_bbox", output_bbox, output_bbox);


/*
* load object detection network
Expand All @@ -176,7 +185,7 @@ int main(int argc, char **argv)
private_nh.getParam("class_labels_path", class_labels_path);

// create network using custom model paths
net = detectNet::Create(prototxt_path.c_str(), model_path.c_str(), mean_pixel, class_labels_path.c_str(), threshold);
net = detectNet::Create(prototxt_path.c_str(), model_path.c_str(), mean_pixel, class_labels_path.c_str(), threshold, input_blob.c_str(), output_cvg.c_str(), output_bbox.c_str());
}

if( !net )
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11 changes: 9 additions & 2 deletions src/node_imagenet.cpp
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Expand Up @@ -119,7 +119,14 @@ int main(int argc, char **argv)
use_model_name = true;
}


// input and output blob for ImageNet model
std::string input_blob = IMAGENET_DEFAULT_INPUT;
std::string output_blob = IMAGENET_DEFAULT_OUTPUT;

private_nh.param<std::string>("input_blob", input_blob, input_blob);
private_nh.param<std::string>("output_blob", output_blob, output_blob);


/*
* load image recognition network
*/
Expand All @@ -140,7 +147,7 @@ int main(int argc, char **argv)
else
{
// create network using custom model paths
net = imageNet::Create(prototxt_path.c_str(), model_path.c_str(), NULL, class_labels_path.c_str());
net = imageNet::Create(prototxt_path.c_str(), model_path.c_str(), NULL, class_labels_path.c_str(), input_blob.c_str(), output_blob.c_str());
}

if( !net )
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9 changes: 8 additions & 1 deletion src/node_segnet.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -201,6 +201,13 @@ int main(int argc, char **argv)
overlay_filter = segNet::FilterModeFromStr(overlay_filter_str.c_str(), segNet::FILTER_LINEAR);
mask_filter = segNet::FilterModeFromStr(mask_filter_str.c_str(), segNet::FILTER_LINEAR);

// input and output blob for SegNet model
std::string input_blob = SEGNET_DEFAULT_INPUT;
std::string output_blob = SEGNET_DEFAULT_OUTPUT;

private_nh.param<std::string>("input_blob", input_blob, input_blob);
private_nh.param<std::string>("output_blob", output_blob, output_blob);


/*
* load segmentation network
Expand All @@ -225,7 +232,7 @@ int main(int argc, char **argv)
private_nh.getParam("class_labels_path", class_labels_path);

// create network using custom model paths
net = segNet::Create(prototxt_path.c_str(), model_path.c_str(), class_labels_path.c_str(), class_colors_path.c_str());
net = segNet::Create(prototxt_path.c_str(), model_path.c_str(), class_labels_path.c_str(), class_colors_path.c_str(), input_blob.c_str(), output_blob.c_str());
}

if( !net )
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