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Merge pull request #24539 from LaurentBerger:blobrecttoimage

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Add blobrecttoimage #24539

### Pull Request Readiness Checklist

resolves https://github.com/opencv/opencv/issues/14659

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 #14659
- [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
This commit is contained in:
Laurent Berger 2023-12-19 18:00:04 +01:00 committed by GitHub
parent fa5ed62a66
commit 3e6dcdc0a4
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 183 additions and 15 deletions
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14
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -1222,6 +1222,20 @@ CV__DNN_INLINE_NS_BEGIN
CV_PROP_RW DataLayout datalayout; //!< Order of output dimensions. Choose DNN_LAYOUT_NCHW or DNN_LAYOUT_NHWC.
CV_PROP_RW ImagePaddingMode paddingmode; //!< Image padding mode. @see ImagePaddingMode.
CV_PROP_RW Scalar borderValue; //!< Value used in padding mode for padding.
/** @brief Get rectangle coordinates in original image system from rectangle in blob coordinates.
* @param rBlob rect in blob coordinates.
* @param size original input image size.
* @returns rectangle in original image coordinates.
*/
CV_WRAP Rect blobRectToImageRect(const Rect &rBlob, const Size &size);
/** @brief Get rectangle coordinates in original image system from rectangle in blob coordinates.
* @param rBlob rect in blob coordinates.
* @param rImg result rect in image coordinates.
* @param size original input image size.
*/
CV_WRAP void blobRectsToImageRects(const std::vector<Rect> &rBlob, CV_OUT std::vector<Rect>& rImg, const Size& size);
};
/** @brief Creates 4-dimensional blob from image with given params.

28
modules/dnn/misc/python/test/test_dnn.py
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@ -127,6 +127,34 @@ class dnn_test(NewOpenCVTests):
targets = cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_OPENCV)
self.assertTrue(cv.dnn.DNN_TARGET_CPU in targets)
def test_blobRectsToImageRects(self):
paramNet = cv.dnn.Image2BlobParams()
paramNet.size = (226, 226)
paramNet.ddepth = cv.CV_32F
paramNet.mean = [0.485, 0.456, 0.406]
paramNet.scalefactor = [0.229, 0.224, 0.225]
paramNet.swapRB = False
paramNet.datalayout = cv.dnn.DNN_LAYOUT_NCHW
paramNet.paddingmode = cv.dnn.DNN_PMODE_LETTERBOX
rBlob = np.zeros(shape=(20, 4), dtype=np.int32)
rImg = paramNet.blobRectsToImageRects(rBlob, (356, 356))
self.assertTrue(type(rImg[0, 0])==np.int32)
self.assertTrue(rImg.shape==(20, 4))
def test_blobRectToImageRect(self):
paramNet = cv.dnn.Image2BlobParams()
paramNet.size = (226, 226)
paramNet.ddepth = cv.CV_32F
paramNet.mean = [0.485, 0.456, 0.406]
paramNet.scalefactor = [0.229, 0.224, 0.225]
paramNet.swapRB = False
paramNet.datalayout = cv.dnn.DNN_LAYOUT_NCHW
paramNet.paddingmode = cv.dnn.DNN_PMODE_LETTERBOX
rBlob = np.zeros(shape=(20, 4), dtype=np.int32)
rImg = paramNet.blobRectToImageRect((0, 0, 0, 0), (356, 356))
self.assertTrue(type(rImg[0])==int)
def test_blobFromImage(self):
np.random.seed(324)

60
modules/dnn/src/dnn_utils.cpp
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@ -382,6 +382,66 @@ void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_)
}
}
Rect Image2BlobParams::blobRectToImageRect(const Rect &r, const Size &oriImage)
{
CV_Assert(!oriImage.empty());
std::vector<Rect> rImg, rBlob;
rBlob.push_back(Rect(r));
rImg.resize(1);
this->blobRectsToImageRects(rBlob, rImg, oriImage);
return Rect(rImg[0]);
}
void Image2BlobParams::blobRectsToImageRects(const std::vector<Rect> &rBlob, std::vector<Rect>& rImg, const Size& imgSize)
{
Size size = this->size;
rImg.resize(rBlob.size());
if (size != imgSize)
{
if (this->paddingmode == DNN_PMODE_CROP_CENTER)
{
float resizeFactor = std::max(size.width / (float)imgSize.width,
size.height / (float)imgSize.height);
for (int i = 0; i < rBlob.size(); i++)
{
rImg[i] = Rect((rBlob[i].x + 0.5 * (imgSize.width * resizeFactor - size.width)) / resizeFactor,
(rBlob[i].y + 0.5 * (imgSize.height * resizeFactor - size.height)) / resizeFactor,
rBlob[i].width / resizeFactor,
rBlob[i].height / resizeFactor);
}
}
else if (this->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);
int top = (size.height - rh) / 2;
int left = (size.width - rw) / 2;
for (int i = 0; i < rBlob.size(); i++)
{
rImg[i] = Rect((rBlob[i].x - left) / resizeFactor,
(rBlob[i].y - top) / resizeFactor,
rBlob[i].width / resizeFactor,
rBlob[i].height / resizeFactor);
}
}
else if (this->paddingmode == DNN_PMODE_NULL)
{
for (int i = 0; i < rBlob.size(); i++)
{
rImg[i] = Rect(rBlob[i].x * (float)imgSize.width / size.width,
rBlob[i].y * (float)imgSize.height / size.height,
rBlob[i].width * (float)imgSize.width / size.width,
rBlob[i].height * (float)imgSize.height / size.height);
}
}
else
CV_Error(CV_StsBadArg, "Unknown padding mode");
}
}
CV__DNN_INLINE_NS_END
}} // namespace cv::dnn

66
modules/dnn/test/test_misc.cpp
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@ -13,6 +13,72 @@
namespace opencv_test { namespace {
TEST(blobRectToImageRect, DNN_PMODE_NULL)
{
Size inputSize(50 + (rand() % 100) / 4 * 4, 50 + (rand() % 100) / 4 * 4);
Size imgSize(200 + (rand() % 100) / 4 * 4, 200 + (rand() % 100) / 4 * 4);
Rect rBlob(inputSize.width / 2 - inputSize.width / 4, inputSize.height / 2 - inputSize.height / 4, inputSize.width / 2, inputSize.height / 2);
Image2BlobParams paramNet;
paramNet.scalefactor = Scalar::all(1.f);
paramNet.size = inputSize;
paramNet.ddepth = CV_32F;
paramNet.mean = Scalar();
paramNet.swapRB = false;
paramNet.datalayout = DNN_LAYOUT_NHWC;
paramNet.paddingmode = DNN_PMODE_NULL;
Rect rOri = paramNet.blobRectToImageRect(rBlob, imgSize);
Rect rImg = Rect(rBlob.x * (float)imgSize.width / inputSize.width, rBlob.y * (float)imgSize.height / inputSize.height,
rBlob.width * (float)imgSize.width / inputSize.width, rBlob.height * (float)imgSize.height / inputSize.height);
ASSERT_EQ(rImg, rOri);
}
TEST(blobRectToImageRect, DNN_PMODE_CROP_CENTER)
{
Size inputSize(50 + (rand() % 100) / 4 * 4, 50 + (rand() % 100) / 4 * 4);
Size imgSize(200 + (rand() % 100) / 4 * 4, 200 + (rand() % 100) / 4 * 4);
Rect rBlob(inputSize.width / 2 - inputSize.width / 4, inputSize.height / 2 - inputSize.height / 4, inputSize.width / 2, inputSize.height / 2);
Image2BlobParams paramNet;
paramNet.scalefactor = Scalar::all(1.f);
paramNet.size = inputSize;
paramNet.ddepth = CV_32F;
paramNet.mean = Scalar();
paramNet.swapRB = false;
paramNet.datalayout = DNN_LAYOUT_NHWC;
paramNet.paddingmode = DNN_PMODE_CROP_CENTER;
Rect rOri = paramNet.blobRectToImageRect(rBlob, imgSize);
float resizeFactor = std::max(inputSize.width / (float)imgSize.width,
inputSize.height / (float)imgSize.height);
Rect rImg = Rect((rBlob.x + 0.5 * (imgSize.width * resizeFactor - inputSize.width)) / resizeFactor, (rBlob.y + 0.5 * (imgSize.height * resizeFactor - inputSize.height)) / resizeFactor,
rBlob.width / resizeFactor, rBlob.height / resizeFactor);
ASSERT_EQ(rImg, rOri);
}
TEST(blobRectToImageRect, DNN_PMODE_LETTERBOX)
{
Size inputSize(50 + (rand() % 100) / 4 * 4, 50 + (rand() % 100) / 4 * 4);
Size imgSize(200 + (rand() % 100) / 4 * 4, 200 + (rand() % 100) / 4 * 4);
Rect rBlob(inputSize.width / 2 - inputSize.width / 4, inputSize.height / 2 - inputSize.height / 4, inputSize.width / 2, inputSize.height / 2);
Image2BlobParams paramNet;
paramNet.scalefactor = Scalar::all(1.f);
paramNet.size = inputSize;
paramNet.ddepth = CV_32F;
paramNet.mean = Scalar();
paramNet.swapRB = false;
paramNet.datalayout = DNN_LAYOUT_NHWC;
paramNet.paddingmode = DNN_PMODE_LETTERBOX;
Rect rOri = paramNet.blobRectToImageRect(rBlob, imgSize);
float resizeFactor = std::min(inputSize.width / (float)imgSize.width,
inputSize.height / (float)imgSize.height);
int rh = int(imgSize.height * resizeFactor);
int rw = int(imgSize.width * resizeFactor);
int top = (inputSize.height - rh) / 2;
int left = (inputSize.width - rw) / 2;
Rect rImg = Rect((rBlob.x - left) / resizeFactor, (rBlob.y - top) / resizeFactor, rBlob.width / resizeFactor, rBlob.height / resizeFactor);
ASSERT_EQ(rImg, rOri);
}
TEST(blobFromImage_4ch, Regression)
{
Mat ch[4];