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Set output layers names and types for models in DLDT's intermediate representation

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Dmitry Kurtaev
2018-06-28 09:09:11 +03:00
parent e4b51fa8ad
commit 346871e27f
4 changed files with 81 additions and 49 deletions
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samples/dnn/object_detection.cpp
+26 -17
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@@ -22,6 +22,7 @@ const char* keys =
"{ height | -1 | Preprocess input image by resizing to a specific height. }"
"{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
"{ thr | .5 | Confidence threshold. }"
"{ thr | .4 | Non-maximum suppression threshold. }"
"{ backend | 0 | Choose one of computation backends: "
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
@@ -37,7 +38,7 @@ const char* keys =
using namespace cv;
using namespace dnn;
float confThreshold;
float confThreshold, nmsThreshold;
std::vector<std::string> classes;
void postprocess(Mat& frame, const std::vector<Mat>& out, Net& net);
@@ -59,6 +60,7 @@ int main(int argc, char** argv)
}
confThreshold = parser.get<float>("thr");
nmsThreshold = parser.get<float>("nms");
float scale = parser.get<float>("scale");
Scalar mean = parser.get<Scalar>("mean");
bool swapRB = parser.get<bool>("rgb");
@@ -144,6 +146,9 @@ void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net)
static std::vector<int> outLayers = net.getUnconnectedOutLayers();
static std::string outLayerType = net.getLayer(outLayers[0])->type;
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect> boxes;
if (net.getLayer(0)->outputNameToIndex("im_info") != -1) // Faster-RCNN or R-FCN
{
// Network produces output blob with a shape 1x1xNx7 where N is a number of
@@ -160,8 +165,11 @@ void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net)
int top = (int)data[i + 4];
int right = (int)data[i + 5];
int bottom = (int)data[i + 6];
int classId = (int)(data[i + 1]) - 1; // Skip 0th background class id.
drawPred(classId, confidence, left, top, right, bottom, frame);
int width = right - left + 1;
int height = bottom - top + 1;
classIds.push_back((int)(data[i + 1]) - 1); // Skip 0th background class id.
boxes.push_back(Rect(left, top, width, height));
confidences.push_back(confidence);
}
}
}
@@ -181,16 +189,16 @@ void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net)
int top = (int)(data[i + 4] * frame.rows);
int right = (int)(data[i + 5] * frame.cols);
int bottom = (int)(data[i + 6] * frame.rows);
int classId = (int)(data[i + 1]) - 1; // Skip 0th background class id.
drawPred(classId, confidence, left, top, right, bottom, frame);
int width = right - left + 1;
int height = bottom - top + 1;
classIds.push_back((int)(data[i + 1]) - 1); // Skip 0th background class id.
boxes.push_back(Rect(left, top, width, height));
confidences.push_back(confidence);
}
}
}
else if (outLayerType == "Region")
{
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect> boxes;
for (size_t i = 0; i < outs.size(); ++i)
{
// Network produces output blob with a shape NxC where N is a number of
@@ -218,18 +226,19 @@ void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net)
}
}
}
std::vector<int> indices;
NMSBoxes(boxes, confidences, confThreshold, 0.4f, indices);
for (size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
Rect box = boxes[idx];
drawPred(classIds[idx], confidences[idx], box.x, box.y,
box.x + box.width, box.y + box.height, frame);
}
}
else
CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
std::vector<int> indices;
NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
for (size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
Rect box = boxes[idx];
drawPred(classIds[idx], confidences[idx], box.x, box.y,
box.x + box.width, box.y + box.height, frame);
}
}
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)