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Merge pull request opencv#23894 from kallaballa:blobFromImagesWithParams
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Pertaining Issue: opencv#5697

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
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@kallaballa
kallaballa authored Oct 20, 2023
1 parent d142a79 commit c2f909f
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Showing 2 changed files with 155 additions and 36 deletions.
168 changes: 140 additions & 28 deletions modules/dnn/src/dnn_utils.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,48 @@ Image2BlobParams::Image2BlobParams(const Scalar& scalefactor_, const Size& size_
datalayout(datalayout_), paddingmode(mode_)
{}

void getVector(InputArrayOfArrays images_, std::vector<Mat>& images) {
images_.getMatVector(images);
}

void getVector(InputArrayOfArrays images_, std::vector<UMat>& images) {
images_.getUMatVector(images);
}

void getMat(UMat& blob, InputArray blob_, AccessFlag flag) {
if(blob_.kind() == _InputArray::UMAT)
blob = blob_.getUMat();
else if(blob_.kind() == _InputArray::MAT) {
blob = blob_.getUMat();
}
}

void getMat(Mat& blob, InputArray blob_, AccessFlag flag) {
if(blob_.kind() == _InputArray::UMAT)
blob = blob_.getMat();
else if(blob_.kind() == _InputArray::MAT) {
blob = blob_.getMat();
}
}

void getChannelFromBlob(Mat& m, InputArray blob, int i, int j, int rows, int cols, int type) {
m = Mat(rows, cols, type, blob.getMat().ptr(i, j));
}

void getChannelFromBlob(UMat& m, InputArray blob, int i, int j, int rows, int cols, int type) {
UMat ublob = blob.getUMat();
int offset = (i * ublob.step.p[0] + j * ublob.step.p[1]) / ublob.elemSize();
int length = 1;
for(int i = 0; i < ublob.dims; ++i) {
length *= ublob.size[i];
}

const int newShape[1] { length };
UMat reshaped = ublob.reshape(1, 1, newShape);
UMat roi = reshaped(Rect(0, offset, 1, rows * cols));
m = roi.reshape(CV_MAT_CN(type), rows);
}

Mat blobFromImage(InputArray image, const double scalefactor, const Size& size,
const Scalar& mean, bool swapRB, bool crop, int ddepth)
{
Expand All @@ -35,8 +77,13 @@ void blobFromImage(InputArray image, OutputArray blob, double scalefactor,
const Size& size, const Scalar& mean, bool swapRB, bool crop, int ddepth)
{
CV_TRACE_FUNCTION();
std::vector<Mat> images(1, image.getMat());
blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop, ddepth);
if (image.kind() == _InputArray::UMAT) {
std::vector<UMat> images(1, image.getUMat());
blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop, ddepth);
} else {
std::vector<Mat> images(1, image.getMat());
blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop, ddepth);
}
}

Mat blobFromImages(InputArrayOfArrays images, double scalefactor, Size size,
Expand All @@ -52,9 +99,9 @@ void blobFromImages(InputArrayOfArrays images_, OutputArray blob_, double scalef
Size size, const Scalar& mean_, bool swapRB, bool crop, int ddepth)
{
CV_TRACE_FUNCTION();
if (images_.kind() != _InputArray::STD_VECTOR_MAT && images_.kind() != _InputArray::STD_ARRAY_MAT &&
if (images_.kind() != _InputArray::STD_VECTOR_UMAT && images_.kind() != _InputArray::STD_VECTOR_MAT && images_.kind() != _InputArray::STD_ARRAY_MAT &&
images_.kind() != _InputArray::STD_VECTOR_VECTOR) {
String error_message = "The data is expected as vectors of vectors or vectors of matrices.";
String error_message = "The data is expected as vectors of vectors, vectors of Mats or vectors of UMats.";
CV_Error(Error::StsBadArg, error_message);
}
Image2BlobParams param(Scalar::all(scalefactor), size, mean_, swapRB, ddepth);
Expand All @@ -71,13 +118,6 @@ Mat blobFromImageWithParams(InputArray image, const Image2BlobParams& param)
return blob;
}

void blobFromImageWithParams(InputArray image, OutputArray blob, const Image2BlobParams& param)
{
CV_TRACE_FUNCTION();
std::vector<Mat> images(1, image.getMat());
blobFromImagesWithParams(images, blob, param);
}

Mat blobFromImagesWithParams(InputArrayOfArrays images, const Image2BlobParams& param)
{
CV_TRACE_FUNCTION();
Expand All @@ -86,19 +126,22 @@ Mat blobFromImagesWithParams(InputArrayOfArrays images, const Image2BlobParams&
return blob;
}

void blobFromImagesWithParams(InputArrayOfArrays images_, OutputArray blob_, const Image2BlobParams& param)
template<class Tmat>
void blobFromImagesWithParamsImpl(InputArrayOfArrays images_, Tmat& blob_, const Image2BlobParams& param)
{
CV_TRACE_FUNCTION();
if (images_.kind() != _InputArray::STD_VECTOR_MAT && images_.kind() != _InputArray::STD_ARRAY_MAT &&
images_.kind() != _InputArray::STD_VECTOR_VECTOR) {
String error_message = "The data is expected as vectors of vectors or vectors of matrices.";
if(!std::is_same<Tmat, UMat>::value && !std::is_same<Tmat, Mat>::value) {
String error_message = "The template parameter is expected to be either a cv::Mat or a cv::UMat";
CV_Error(Error::StsBadArg, error_message);
}

CV_CheckType(param.ddepth, param.ddepth == CV_32F || param.ddepth == CV_8U,
"Blob depth should be CV_32F or CV_8U");
Size size = param.size;
std::vector<Mat> images;
images_.getMatVector(images);

std::vector<Tmat> images;
getVector(images_, images);

CV_Assert(!images.empty());

if (param.ddepth == CV_8U)
Expand Down Expand Up @@ -164,12 +207,12 @@ void blobFromImagesWithParams(InputArrayOfArrays images_, OutputArray blob_, con
if (images[i].depth() == CV_8U && param.ddepth == CV_32F)
images[i].convertTo(images[i], CV_32F);

images[i] -= mean;
subtract(images[i], mean, images[i]);
multiply(images[i], scalefactor, images[i]);
}

size_t nimages = images.size();
Mat image0 = images[0];
Tmat image0 = images[0];
CV_Assert(image0.dims == 2);

if (param.datalayout == DNN_LAYOUT_NCHW)
Expand All @@ -178,21 +221,22 @@ void blobFromImagesWithParams(InputArrayOfArrays images_, OutputArray blob_, con
{
int sz[] = { (int)nimages, nch, image0.rows, image0.cols };
blob_.create(4, sz, param.ddepth);
Mat blob = blob_.getMat();
Mat ch[4];
std::vector<Tmat> ch(4);

for (size_t i = 0; i < nimages; i++)
{
const Mat& image = images[i];
const Tmat& image = images[i];
CV_Assert(image.depth() == blob_.depth());
nch = image.channels();
CV_Assert(image.dims == 2 && (nch == 3 || nch == 4));
CV_Assert(image.size() == image0.size());

for (int j = 0; j < nch; j++)
ch[j] = Mat(image.rows, image.cols, param.ddepth, blob.ptr((int)i, j));
for (int j = 0; j < nch; j++) {
getChannelFromBlob(ch[j], blob_, i, j ,image.rows, image.cols, param.ddepth);
}
if (param.swapRB)
std::swap(ch[0], ch[2]);

split(image, ch);
}
}
Expand All @@ -201,11 +245,12 @@ void blobFromImagesWithParams(InputArrayOfArrays images_, OutputArray blob_, con
CV_Assert(nch == 1);
int sz[] = { (int)nimages, 1, image0.rows, image0.cols };
blob_.create(4, sz, param.ddepth);
Mat blob = blob_.getMat();
Mat blob;
getMat(blob, blob_, ACCESS_RW);

for (size_t i = 0; i < nimages; i++)
{
const Mat& image = images[i];
const Tmat& image = images[i];
CV_Assert(image.depth() == blob_.depth());
nch = image.channels();
CV_Assert(image.dims == 2 && (nch == 1));
Expand All @@ -219,11 +264,12 @@ void blobFromImagesWithParams(InputArrayOfArrays images_, OutputArray blob_, con
{
int sz[] = { (int)nimages, image0.rows, image0.cols, nch};
blob_.create(4, sz, param.ddepth);
Mat blob = blob_.getMat();
Mat blob;
getMat(blob, blob_, ACCESS_RW);
int subMatType = CV_MAKETYPE(param.ddepth, nch);
for (size_t i = 0; i < nimages; i++)
{
const Mat& image = images[i];
const Tmat& image = images[i];
CV_Assert(image.depth() == blob_.depth());
CV_Assert(image.channels() == image0.channels());
CV_Assert(image.size() == image0.size());
Expand All @@ -243,6 +289,72 @@ void blobFromImagesWithParams(InputArrayOfArrays images_, OutputArray blob_, con
{
CV_Error(Error::StsUnsupportedFormat, "Unsupported data layout in blobFromImagesWithParams function.");
}
CV_Assert(blob_.total());
}

void blobFromImagesWithParams(InputArrayOfArrays images, OutputArray blob, const Image2BlobParams& param) {
CV_TRACE_FUNCTION();

if (images.kind() == _InputArray::STD_VECTOR_UMAT) {
if(blob.kind() == _InputArray::UMAT) {
UMat& u = blob.getUMatRef();
blobFromImagesWithParamsImpl<cv::UMat>(images, u, param);
return;
} else if(blob.kind() == _InputArray::MAT) {
UMat u = blob.getMatRef().getUMat(ACCESS_WRITE);
blobFromImagesWithParamsImpl<cv::UMat>(images, u, param);
u.copyTo(blob);
return;
}
} else if (images.kind() == _InputArray::STD_VECTOR_MAT) {
if(blob.kind() == _InputArray::UMAT) {
Mat m = blob.getUMatRef().getMat(ACCESS_WRITE);
blobFromImagesWithParamsImpl<cv::Mat>(images, m, param);
m.copyTo(blob);
return;
} else if(blob.kind() == _InputArray::MAT) {
Mat& m = blob.getMatRef();
blobFromImagesWithParamsImpl<cv::Mat>(images, m, param);
return;
}
}

CV_Error(Error::StsBadArg, "Images are expected to be a vector of either a Mat or UMat and Blob is expected to be either a Mat or UMat");
}

void blobFromImageWithParams(InputArray image, OutputArray blob, const Image2BlobParams& param)
{
CV_TRACE_FUNCTION();

if (image.kind() == _InputArray::UMAT) {
if(blob.kind() == _InputArray::UMAT) {
UMat& u = blob.getUMatRef();
std::vector<UMat> images(1, image.getUMat());
blobFromImagesWithParamsImpl<cv::UMat>(images, u, param);
return;
} else if(blob.kind() == _InputArray::MAT) {
UMat u = blob.getMatRef().getUMat(ACCESS_RW);
std::vector<UMat> images(1, image.getUMat());
blobFromImagesWithParamsImpl<cv::UMat>(images, u, param);
u.copyTo(blob);
return;
}
} else if (image.kind() == _InputArray::MAT) {
if(blob.kind() == _InputArray::UMAT) {
Mat m = blob.getUMatRef().getMat(ACCESS_RW);
std::vector<Mat> images(1, image.getMat());
blobFromImagesWithParamsImpl<cv::Mat>(images, m, param);
m.copyTo(blob);
return;
} else if(blob.kind() == _InputArray::MAT) {
Mat& m = blob.getMatRef();
std::vector<Mat> images(1, image.getMat());
blobFromImagesWithParamsImpl<cv::Mat>(images, m, param);
return;
}
}

CV_Error(Error::StsBadArg, "Image an Blob are expected to be either a Mat or UMat");
}

void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_)
Expand Down
23 changes: 15 additions & 8 deletions modules/objdetect/src/face_detect.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -102,12 +102,21 @@ class FaceDetectorYNImpl : public FaceDetectorYN
return 0;
}
CV_CheckEQ(input_image.size(), Size(inputW, inputH), "Size does not match. Call setInputSize(size) if input size does not match the preset size");
// Pad input_image with divisor 32
Mat pad_image = padWithDivisor(input_image);

// Build blob from input image
Mat input_blob = dnn::blobFromImage(pad_image);

Mat input_blob;
if(input_image.kind() == _InputArray::UMAT) {
// Pad input_image with divisor 32
UMat pad_image;
padWithDivisor(input_image, pad_image);
// Build blob from input image
input_blob = dnn::blobFromImage(pad_image);
} else {
// Pad input_image with divisor 32
Mat pad_image;
padWithDivisor(input_image, pad_image);
// Build blob from input image
input_blob = dnn::blobFromImage(pad_image);
}
// Forward
std::vector<String> output_names = { "cls_8", "cls_16", "cls_32", "obj_8", "obj_16", "obj_32", "bbox_8", "bbox_16", "bbox_32", "kps_8", "kps_16", "kps_32" };
std::vector<Mat> output_blobs;
Expand Down Expand Up @@ -217,13 +226,11 @@ class FaceDetectorYNImpl : public FaceDetectorYN
}
}

Mat padWithDivisor(InputArray& input_image)
void padWithDivisor(InputArray input_image, OutputArray pad_image)
{
int bottom = padH - inputH;
int right = padW - inputW;
Mat pad_image;
copyMakeBorder(input_image, pad_image, 0, bottom, 0, right, BORDER_CONSTANT, 0);
return pad_image;
}
private:
dnn::Net net;
Expand Down

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