Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #24539 from LaurentBerger:blobrecttoimage

Browse Source 
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
pull/24723/head
Laurent Berger 1 year ago committed by GitHub
parent fa5ed62a66
commit 3e6dcdc0a4
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 183 additions and 15 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 14
      modules/dnn/include/opencv2/dnn/dnn.hpp
  2. 28
      modules/dnn/misc/python/test/test_dnn.py
  3. 60
      modules/dnn/src/dnn_utils.cpp
  4. 90
      modules/dnn/test/test_misc.cpp

14
modules/dnn/include/opencv2/dnn/dnn.hpp
Unescape View File

@ -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
Unescape View File

@ -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
Unescape View File

@ -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

90
modules/dnn/test/test_misc.cpp
Unescape View File

@ -13,17 +13,83 @@
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];
for(int i = 0; i < 4; i++)
ch[i] = Mat::ones(10, 10, CV_8U)*i;
for (int i = 0; i < 4; i++)
ch[i] = Mat::ones(10, 10, CV_8U) * i;
Mat img;
merge(ch, 4, img);
Mat blob = dnn::blobFromImage(img, 1., Size(), Scalar(), false, false);
for(int i = 0; i < 4; i++)
for (int i = 0; i < 4; i++)
{
ch[i] = Mat(img.rows, img.cols, CV_32F, blob.ptr(0, i));
ASSERT_DOUBLE_EQ(cvtest::norm(ch[i], cv::NORM_INF), i);
@ -32,7 +98,7 @@ TEST(blobFromImage_4ch, Regression)
TEST(blobFromImage, allocated)
{
int size[] = {1, 3, 4, 5};
int size[] = { 1, 3, 4, 5 };
Mat img(size[2], size[3], CV_32FC(size[1]));
Mat blob(4, size, CV_32F);
void* blobData = blob.data;
@ -66,8 +132,8 @@ 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};
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]);
@ -77,7 +143,7 @@ TEST(blobFromImageWithParams_4ch, NHWC_scalar_scale)
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.
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++)
@ -128,16 +194,16 @@ TEST(blobFromImageWithParams_CustomPadding, letter_box)
TEST(blobFromImageWithParams_4ch, letter_box)
{
Mat img(40, 20, CV_8UC4, cv::Scalar(0,1,2,3));
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};
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++)
for (int i = 0; i < 4; i++)
{
targetCh[i] = rowM * i;
}
@ -163,7 +229,7 @@ TEST(blobFromImagesWithParams_4ch, multi_image)
param.scalefactor = scalefactor;
param.datalayout = DNN_LAYOUT_NHWC;
Mat blobs = blobFromImagesWithParams(std::vector<Mat> { img, 2*img }, param);
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]));
@ -173,7 +239,7 @@ TEST(blobFromImagesWithParams_4ch, multi_image)
ranges[0] = Range(1, 2);
Mat blob1 = blobs(ranges);
EXPECT_EQ(0, cvtest::norm(2*blob0, blob1, NORM_INF));
EXPECT_EQ(0, cvtest::norm(2 * blob0, blob1, NORM_INF));
}
TEST(readNet, Regression)

Write Preview
Loading…
Cancel
Save