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Merge pull request #26614 from KangJialiang:fix-multi-channel-mean-scale-sample-dnn-yolo

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Fix normalization parameters in YOLO example to support multi-channel mean and scale factors
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Alexander Smorkalov 2024-12-16 15:29:43 +03:00 committed by GitHub
commit 03f90aaf85
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1 changed files with 3 additions and 3 deletions
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6
samples/dnn/yolo_detector.cpp
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@ -44,8 +44,8 @@ std::string keys =
"{ nc | 80 | Number of classes. Default is 80 (coming from COCO dataset). }"
"{ thr | .5 | Confidence threshold. }"
"{ nms | .4 | Non-maximum suppression threshold. }"
"{ mean | 0.0 | Normalization constant. }"
"{ scale | 1.0 | Preprocess input image by multiplying on a scale factor. }"
"{ mean | 0.0 0.0 0.0 | Normalization constant. }"
"{ scale | 1.0 1.0 1.0 | Preprocess input image by multiplying on a scale factor. }"
"{ width | 640 | Preprocess input image by resizing to a specific width. }"
"{ height | 640 | Preprocess input image by resizing to a specific height. }"
"{ rgb | 1 | Indicate that model works with RGB input images instead BGR ones. }"
@ -221,7 +221,7 @@ int main(int argc, char** argv)
bool swapRB = parser.get<bool>("rgb");
int inpWidth = parser.get<int>("width");
int inpHeight = parser.get<int>("height");
Scalar scale = parser.get<float>("scale");
Scalar scale = parser.get<Scalar>("scale");
Scalar mean = parser.get<Scalar>("mean");
ImagePaddingMode paddingMode = static_cast<ImagePaddingMode>(parser.get<int>("paddingmode"));
//![preprocess_params]