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Change CenterNet input dimension.

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This commit changes the image input dimension, it updates the
CenterNet detection class.

Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
This commit is contained in:
Davide Sapienza
2020-02-06 18:08:06 +01:00
parent c695d8c5d7
commit 70eb5214cc
5 changed files with 22 additions and 14 deletions
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src/CenternetDetection.cpp
+11 -6
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@@ -18,7 +18,7 @@ bool CenternetDetection::init(std::string tensor_path) {
std::cout<<(tensor_path).c_str()<<"\n";
netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
dim = tk::dnn::dataDim_t(1, 3, 224, 224, 1);
dim = tk::dnn::dataDim_t(1, 3, 512, 512, 1);
const char *coco_class_name_[] = {
"person", "bicycle", "car", "motorcycle", "airplane",
"bus", "train", "truck", "boat", "traffic light", "fire hydrant",
@@ -43,9 +43,12 @@ bool CenternetDetection::init(std::string tensor_path) {
checkCuda(cudaMallocHost(&input, sizeof(dnnType)*netRT->input_dim.tot()));
checkCuda(cudaMalloc(&input_d, sizeof(dnnType)*netRT->input_dim.tot()));
dim_hm = tk::dnn::dataDim_t(1, 80, 56, 56, 1);
dim_wh = tk::dnn::dataDim_t(1, 2, 56, 56, 1);
dim_reg = tk::dnn::dataDim_t(1, 2, 56, 56, 1);
// dim_hm = tk::dnn::dataDim_t(1, 80, 56, 56, 1);
// dim_wh = tk::dnn::dataDim_t(1, 2, 56, 56, 1);
// dim_reg = tk::dnn::dataDim_t(1, 2, 56, 56, 1);
dim_hm = tk::dnn::dataDim_t(1, 80, 128, 128, 1);
dim_wh = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
dim_reg = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
checkCuda( cudaMalloc(&topk_scores, dim_hm.c * K *sizeof(float)) );
checkCuda( cudaMalloc(&topk_inds_, dim_hm.c * K *sizeof(int)) );
@@ -93,6 +96,8 @@ bool CenternetDetection::init(std::string tensor_path) {
mean << 0.408, 0.447, 0.47;
stddev << 0.289, 0.274, 0.278;
// mean << 0.485, 0.456, 0.406;
// stddev << 0.229, 0.224, 0.225;
}
void CenternetDetection::testdog() {
@@ -104,8 +109,8 @@ void CenternetDetection::testdog() {
imageORIG.convertTo(imageF, CV_32FC3, 1/255.0);
sz = imageF.size();
std::cout<<"image: "<<sz.width<<", "<<sz.height<<std::endl;
resize(imageF, imageF, cv::Size(256, 256));
const int cropSize = 224;
resize(imageF, imageF, cv::Size(512, 512));
const int cropSize = 512;
const int offsetW = (imageF.cols - cropSize) / 2;
const int offsetH = (imageF.rows - cropSize) / 2;
const cv::Rect roi(offsetW, offsetH, cropSize, cropSize);