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Update dnn_utils.cpp

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CSBVision 2023-08-08 13:31:32 +02:00 committed by Alexander Smorkalov
parent 7d59db4ec4
commit 674c618471
2 changed files with 61 additions and 28 deletions
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67
modules/dnn/src/dnn_utils.cpp
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@ -5,6 +5,7 @@
#include "precomp.hpp"
#include <opencv2/imgproc.hpp>
#include <opencv2/core/utils/logger.hpp>
namespace cv {
@ -100,15 +101,29 @@ void blobFromImagesWithParams(InputArrayOfArrays images_, OutputArray blob_, con
images_.getMatVector(images);
CV_Assert(!images.empty());
int nch = images[0].channels();
Scalar scalefactor = param.scalefactor;
if (param.ddepth == CV_8U)
{
CV_Assert(scalefactor == Scalar::all(1.0) && "Scaling is not supported for CV_8U blob depth");
CV_Assert(param.scalefactor == Scalar::all(1.0) && "Scaling is not supported for CV_8U blob depth");
CV_Assert(param.mean == Scalar() && "Mean subtraction is not supported for CV_8U blob depth");
}
int nch = images[0].channels();
Scalar scalefactor = param.scalefactor;
Scalar mean = param.mean;
if (param.swapRB)
{
if (nch > 2)
{
std::swap(mean[0], mean[2]);
std::swap(scalefactor[0], scalefactor[2]);
}
else
{
CV_LOG_WARNING(NULL, "Red/blue color swapping requires at least three image channels.");
}
}
for (size_t i = 0; i < images.size(); i++)
{
Size imgSize = images[i].size();
@ -126,34 +141,26 @@ void blobFromImagesWithParams(InputArrayOfArrays images_, OutputArray blob_, con
size);
images[i] = images[i](crop);
}
else if (param.paddingmode == DNN_PMODE_LETTERBOX)
{
float resizeFactor = std::min(size.width / (float)imgSize.width,
size.height / (float)imgSize.height);
int rh = int(imgSize.height * resizeFactor);
int rw = int(imgSize.width * resizeFactor);
resize(images[i], images[i], Size(rw, rh), INTER_LINEAR);
int top = (size.height - rh)/2;
int bottom = size.height - top - rh;
int left = (size.width - rw)/2;
int right = size.width - left - rw;
copyMakeBorder(images[i], images[i], top, bottom, left, right, BORDER_CONSTANT);
}
else
{
if (param.paddingmode == DNN_PMODE_LETTERBOX)
{
float resizeFactor = std::min(size.width / (float)imgSize.width,
size.height / (float)imgSize.height);
int rh = int(imgSize.height * resizeFactor);
int rw = int(imgSize.width * resizeFactor);
resize(images[i], images[i], Size(rw, rh), INTER_LINEAR);
int top = (size.height - rh)/2;
int bottom = size.height - top - rh;
int left = (size.width - rw)/2;
int right = size.width - left - rw;
copyMakeBorder(images[i], images[i], top, bottom, left, right, BORDER_CONSTANT);
}
else
resize(images[i], images[i], size, 0, 0, INTER_LINEAR);
resize(images[i], images[i], size, 0, 0, INTER_LINEAR);
}
}
Scalar mean = param.mean;
if (param.swapRB)
{
std::swap(mean[0], mean[2]);
std::swap(scalefactor[0], scalefactor[2]);
}
if (images[i].depth() == CV_8U && param.ddepth == CV_32F)
images[i].convertTo(images[i], CV_32F);
@ -220,18 +227,22 @@ void blobFromImagesWithParams(InputArrayOfArrays images_, OutputArray blob_, con
CV_Assert(image.depth() == blob_.depth());
CV_Assert(image.channels() == image0.channels());
CV_Assert(image.size() == image0.size());
if (param.swapRB)
if (nch > 2 && param.swapRB)
{
Mat tmpRB;
cvtColor(image, tmpRB, COLOR_BGR2RGB);
tmpRB.copyTo(Mat(tmpRB.rows, tmpRB.cols, subMatType, blob.ptr((int)i, 0)));
}
else
{
image.copyTo(Mat(image.rows, image.cols, subMatType, blob.ptr((int)i, 0)));
}
}
}
else
{
CV_Error(Error::StsUnsupportedFormat, "Unsupported data layout in blobFromImagesWithParams function.");
}
}
void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_)

22
modules/dnn/test/test_misc.cpp
View File

@ -120,6 +120,28 @@ TEST(blobFromImageWithParams_4ch, letter_box)
EXPECT_EQ(0, cvtest::norm(targetBlob, blob, NORM_INF));
}
TEST(blobFromImagesWithParams_4ch, multi_image)
{
Mat img(10, 10, CV_8UC4, cv::Scalar(0, 1, 2, 3));
Scalar scalefactor(0.1, 0.2, 0.3, 0.4);
Image2BlobParams param;
param.scalefactor = scalefactor;
param.datalayout = DNN_LAYOUT_NHWC;
Mat blobs = blobFromImagesWithParams(std::vector<Mat> { img, 2*img }, param);
vector<Range> ranges;
ranges.push_back(Range(0, 1));
ranges.push_back(Range(0, blobs.size[1]));
ranges.push_back(Range(0, blobs.size[2]));
ranges.push_back(Range(0, blobs.size[3]));
Mat blob0 = blobs(ranges);
ranges[0] = Range(1, 2);
Mat blob1 = blobs(ranges);
EXPECT_EQ(0, cvtest::norm(2*blob0, blob1, NORM_INF));
}
TEST(readNet, Regression)
{
Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt"),