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dnn: handle 4-channel images in blobFromImage (#9944)

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Vladislav Sovrasov 2017-10-27 14:06:53 +03:00 committed by Vadim Pisarevsky
parent e5766513a4
commit 5bf39ceb5d
3 changed files with 34 additions and 4 deletions
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4
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -688,7 +688,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
CV_EXPORTS_W Mat readTorchBlob(const String &filename, bool isBinary = true); CV_EXPORTS_W Mat readTorchBlob(const String &filename, bool isBinary = true);
/** @brief Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center, /** @brief Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center,
* subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels. * subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels.
* @param image input image (with 1- or 3-channels). * @param image input image (with 1-, 3- or 4-channels).
* @param size spatial size for output image * @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended * @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true. * to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true.
@ -706,7 +706,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
/** @brief Creates 4-dimensional blob from series of images. Optionally resizes and /** @brief Creates 4-dimensional blob from series of images. Optionally resizes and
* crops @p images from center, subtract @p mean values, scales values by @p scalefactor, * crops @p images from center, subtract @p mean values, scales values by @p scalefactor,
* swap Blue and Red channels. * swap Blue and Red channels.
* @param images input images (all with 1- or 3-channels). * @param images input images (all with 1-, 3- or 4-channels).
* @param size spatial size for output image * @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended * @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true. * to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true.

4
modules/dnn/src/dnn.cpp
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@ -136,7 +136,7 @@ Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size siz
Mat blob, image; Mat blob, image;
if (nch == 3 || nch == 4) if (nch == 3 || nch == 4)
{ {
int sz[] = { (int)nimages, 3, image0.rows, image0.cols }; int sz[] = { (int)nimages, nch, image0.rows, image0.cols };
blob = Mat(4, sz, CV_32F); blob = Mat(4, sz, CV_32F);
Mat ch[4]; Mat ch[4];
@ -148,7 +148,7 @@ Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size siz
CV_Assert(image.dims == 2 && (nch == 3 || nch == 4)); CV_Assert(image.dims == 2 && (nch == 3 || nch == 4));
CV_Assert(image.size() == image0.size()); CV_Assert(image.size() == image0.size());
for( int j = 0; j < 3; j++ ) for( int j = 0; j < nch; j++ )
ch[j] = Mat(image.rows, image.cols, CV_32F, blob.ptr((int)i, j)); ch[j] = Mat(image.rows, image.cols, CV_32F, blob.ptr((int)i, j));
if(swapRB) if(swapRB)
std::swap(ch[0], ch[2]); std::swap(ch[0], ch[2]);

30
modules/dnn/test/test_misc.cpp Normal file
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@ -0,0 +1,30 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "test_precomp.hpp"
namespace cvtest
{
TEST(blobFromImage_4ch, Regression)
{
Mat ch[4];
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++)
{
ch[i] = Mat(img.rows, img.cols, CV_32F, blob.ptr(0, i));
ASSERT_DOUBLE_EQ(cvtest::norm(ch[i], cv::NORM_INF), i);
}
}
}