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Improve preprocessing

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-06-29 15:11:52 +02:00
parent d25803d438
commit 3bf9547502
1 changed files with 24 additions and 22 deletions
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include/tkDNN/SegmentationNN.h
+24 -22
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@@ -13,8 +13,6 @@
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/hal/interface.h>
#include "tkdnn.h"
#include "NetworkViz.h"
#include "kernelsThrust.h"
@@ -38,6 +36,8 @@ class SegmentationNN {
float *tmpOutData_d;
float *tmpOutData_h;
float *mean_d, *stddev_d;
cublasHandle_t cublasHandle;
/**
@@ -47,23 +47,10 @@ class SegmentationNN {
* @param bi batch index
*/
void preprocess(cv::Mat &frame, const int bi=0) {
frame.convertTo(frame, CV_32FC3, 1 / 255.0, 0);
cv::split(frame, bgr);
float mean[] = {0.485, 0.456, 0.406};
float stddev[] = {0.229, 0.224, 0.225};
for(int i=0; i<3; i++){
bgr[2-i] -= mean[i];
bgr[2-i] /= stddev[i];
}
cv::merge(bgr, 3, frame);
int crop_size = netRT->input_dim.w;
int H = frame.rows;
int W = frame.cols;
cv::Mat frame_cropped;
cv::Mat mask(frame.size(), CV_8UC3, cv::Scalar(255,255,255));
if(H != W){
@@ -81,17 +68,22 @@ class SegmentationNN {
}
}
resize(frame_cropped, frame_cropped, cv::Size(netRT->input_dim.w, netRT->input_dim.h));
resize(mask, mask, cv::Size(netRT->input_dim.w, netRT->input_dim.h));
masks[bi] = mask.clone();
tk::dnn::dataDim_t idim = netRT->input_dim;
resize(frame_cropped, frame_cropped, cv::Size(idim.w, idim.h));
resize(mask, mask, cv::Size(idim.w, idim.h));
masks[bi] = mask;
cv::split(frame_cropped, bgr);
for (int i = 0; i < netRT->input_dim.c; i++){
for (int i = 0; i < idim.c; i++){
int idx = i * frame_cropped.rows * frame_cropped.cols;
int ch = netRT->input_dim.c-1 -i;
memcpy((void *)&input[idx + netRT->input_dim.tot()*bi], (void *)bgr[ch].data, frame_cropped.rows * frame_cropped.cols * sizeof(dnnType));
int ch = idim.c-1 -i;
memcpy((void *)&input[idx + idim.tot()*bi], (void *)bgr[ch].data, frame_cropped.rows * frame_cropped.cols * sizeof(dnnType));
}
checkCuda(cudaMemcpyAsync(input_d+ netRT->input_dim.tot()*bi, input + netRT->input_dim.tot()*bi, netRT->input_dim.tot() * sizeof(dnnType), cudaMemcpyHostToDevice, netRT->stream));
checkCuda(cudaMemcpyAsync(input_d+ idim.tot()*bi, input + idim.tot()*bi, idim.tot() * sizeof(dnnType), cudaMemcpyHostToDevice, netRT->stream));
normalize(input_d + idim.tot()*bi, idim.c, idim.h, idim.w, mean_d, stddev_d);
}
/**
@@ -157,6 +149,16 @@ class SegmentationNN {
segmented.resize(nBatches);
masks.resize(nBatches);
std::vector<float> mean = {0.485, 0.456, 0.406};
std::vector<float> stddev = {0.229, 0.224, 0.225};
checkCuda(cudaMalloc(&mean_d, sizeof(float) * mean.size()));
checkCuda(cudaMalloc(&stddev_d, sizeof(float) * stddev.size()));
checkCuda(cudaMemcpyAsync(mean_d, mean.data(), mean.size() * sizeof(float), cudaMemcpyHostToDevice, netRT->stream));
checkCuda(cudaMemcpyAsync(stddev_d, stddev.data(), stddev.size() * sizeof(float), cudaMemcpyHostToDevice, netRT->stream));
}
/**