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Merge pull request #22750 from zihaomu:improve_blobFromImage

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DNN: Add New API blobFromImageParam #22750

The purpose of this PR:

1. Add new API `blobFromImageParam` to extend `blobFromImage` API. It can support the different data layout (NCHW or NHWC), and letter_box.
2. ~~`blobFromImage` can output `CV_16F`~~

### 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
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Zihao Mu
2023-04-22 00:10:17 +08:00
committed by GitHub
parent 810096c276
commit 601778e0e6
6 changed files with 408 additions and 152 deletions
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modules/dnn/test/test_misc.cpp
+57
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@@ -63,6 +63,63 @@ TEST(imagesFromBlob, Regression)
}
}
TEST(blobFromImageWithParams_4ch, NHWC_scalar_scale)
{
Mat img(10, 10, CV_8UC4, cv::Scalar(0,1,2,3));
std::vector<double> factorVec = {0.1, 0.2, 0.3, 0.4};
Scalar scalefactor(factorVec[0], factorVec[1], factorVec[2], factorVec[3]);
Image2BlobParams param;
param.scalefactor = scalefactor;
param.datalayout = DNN_LAYOUT_NHWC;
Mat blob = dnn::blobFromImageWithParams(img, param); // [1, 10, 10, 4]
float* blobPtr = blob.ptr<float>(0);
std::vector<float> targetVec = {(float )factorVec[0] * 0, (float )factorVec[1] * 1, (float )factorVec[2] * 2, (float )factorVec[3] * 3}; // Target Value.
for (int hi = 0; hi < 10; hi++)
{
for (int wi = 0; wi < 10; wi++)
{
float* hwPtr = blobPtr + hi * 10 * 4 + wi * 4;
// Check equal
EXPECT_NEAR(hwPtr[0], targetVec[0], 1e-5);
EXPECT_NEAR(hwPtr[1], targetVec[1], 1e-5);
EXPECT_NEAR(hwPtr[2], targetVec[2], 1e-5);
EXPECT_NEAR(hwPtr[3], targetVec[3], 1e-5);
}
}
}
TEST(blobFromImageWithParams_4ch, letter_box)
{
Mat img(40, 20, CV_8UC4, cv::Scalar(0,1,2,3));
// Construct target mat.
Mat targetCh[4];
// The letterbox will add zero at the left and right of output blob.
// After the letterbox, every row data would have same value showing as valVec.
std::vector<uint8_t> valVec = {0,0,0,0,0, 1,1,1,1,1,1,1,1,1,1, 0,0,0,0,0};
Mat rowM(1, 20, CV_8UC1, valVec.data());
for(int i = 0; i < 4; i++)
{
targetCh[i] = rowM * i;
}
Mat targetImg;
merge(targetCh, 4, targetImg);
Size targeSize(20, 20);
Image2BlobParams param;
param.size = targeSize;
param.paddingmode = DNN_PMODE_LETTERBOX;
Mat blob = dnn::blobFromImageWithParams(img, param);
Mat targetBlob = dnn::blobFromImage(targetImg, 1.0, targeSize); // only convert data from uint8 to float32.
EXPECT_EQ(0, cvtest::norm(targetBlob, blob, NORM_INF));
}
TEST(readNet, Regression)
{
Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt"),