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Update obj. detect sample

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This commit is contained in:
Alexander Nesterov
2019-01-22 15:01:48 -01:00
committed by Alexander Nesterov
parent eb00dce78e
commit cca2c4b314
2 changed files with 35 additions and 57 deletions
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samples/dnn/object_detection.cpp
+27 -39
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@@ -153,51 +153,39 @@ void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net)
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
if (outLayerType == "DetectionOutput")
{
// Network produces output blob with a shape 1x1xNx7 where N is a number of
// detections and an every detection is a vector of values
// [batchId, classId, confidence, left, top, right, bottom]
CV_Assert(outs.size() == 1);
float* data = (float*)outs[0].data;
for (size_t i = 0; i < outs[0].total(); i += 7)
CV_Assert(outs.size() > 0);
for (size_t k = 0; k < outs.size(); k++)
{
float confidence = data[i + 2];
if (confidence > confThreshold)
float* data = (float*)outs[k].data;
for (size_t i = 0; i < outs[k].total(); i += 7)
{
int left = (int)data[i + 3];
int top = (int)data[i + 4];
int right = (int)data[i + 5];
int bottom = (int)data[i + 6];
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 == "DetectionOutput")
{
// Network produces output blob with a shape 1x1xNx7 where N is a number of
// detections and an every detection is a vector of values
// [batchId, classId, confidence, left, top, right, bottom]
CV_Assert(outs.size() == 1);
float* data = (float*)outs[0].data;
for (size_t i = 0; i < outs[0].total(); i += 7)
{
float confidence = data[i + 2];
if (confidence > confThreshold)
{
int left = (int)(data[i + 3] * frame.cols);
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 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);
float confidence = data[i + 2];
if (confidence > confThreshold)
{
int left = (int)data[i + 3];
int top = (int)data[i + 4];
int right = (int)data[i + 5];
int bottom = (int)data[i + 6];
int width = right - left + 1;
int height = bottom - top + 1;
if (width * height <= 1)
{
left = (int)(data[i + 3] * frame.cols);
top = (int)(data[i + 4] * frame.rows);
right = (int)(data[i + 5] * frame.cols);
bottom = (int)(data[i + 6] * frame.rows);
width = right - left + 1;
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);
}
}
}
}