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fixed and generalized ocl::blendLinear
This commit is contained in:
Ilya Lavrenov
parent
529f086b62
commit
c49c3e0a91
@ -47,48 +47,61 @@
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#include "perf_precomp.hpp"
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using namespace perf;
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using namespace cv;
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using std::tr1::get;
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///////////// blend ////////////////////////
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template <typename T>
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static void blendLinearGold(const cv::Mat &img1, const cv::Mat &img2,
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const cv::Mat &weights1, const cv::Mat &weights2,
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cv::Mat &result_gold)
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static void blendLinearGold(const Mat &img1, const Mat &img2,
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const Mat &weights1, const Mat &weights2,
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Mat &result_gold)
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{
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CV_Assert(img1.size() == img2.size() && img1.type() == img2.type());
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CV_Assert(weights1.size() == weights2.size() && weights1.size() == img1.size() &&
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weights1.type() == CV_32FC1 && weights2.type() == CV_32FC1);
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result_gold.create(img1.size(), img1.type());
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int cn = img1.channels();
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int step1 = img1.cols * img1.channels();
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for (int y = 0; y < img1.rows; ++y)
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{
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const float *weights1_row = weights1.ptr<float>(y);
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const float *weights2_row = weights2.ptr<float>(y);
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const T *img1_row = img1.ptr<T>(y);
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const T *img2_row = img2.ptr<T>(y);
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T *result_gold_row = result_gold.ptr<T>(y);
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const float * const weights1_row = weights1.ptr<float>(y);
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const float * const weights2_row = weights2.ptr<float>(y);
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const T * const img1_row = img1.ptr<T>(y);
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const T * const img2_row = img2.ptr<T>(y);
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T * const result_gold_row = result_gold.ptr<T>(y);
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for (int x = 0; x < img1.cols * cn; ++x)
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for (int x = 0; x < step1; ++x)
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{
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int x1 = x * cn;
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float w1 = weights1_row[x];
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float w2 = weights2_row[x];
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result_gold_row[x] = static_cast<T>((img1_row[x1] * w1
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+ img2_row[x1] * w2) / (w1 + w2 + 1e-5f));
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int x1 = x / cn;
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float w1 = weights1_row[x1], w2 = weights2_row[x1];
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result_gold_row[x] = saturate_cast<T>(((float)img1_row[x] * w1
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+ (float)img2_row[x] * w2) / (w1 + w2 + 1e-5f));
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}
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}
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}
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typedef TestBaseWithParam<Size> blendLinearFixture;
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typedef void (*blendFunction)(const Mat &img1, const Mat &img2,
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const Mat &weights1, const Mat &weights2,
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Mat &result_gold);
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PERF_TEST_P(blendLinearFixture, blendLinear, OCL_TYPICAL_MAT_SIZES)
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typedef Size_MatType blendLinearFixture;
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PERF_TEST_P(blendLinearFixture, blendLinear, ::testing::Combine(
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OCL_TYPICAL_MAT_SIZES, testing::Values(CV_8UC1, CV_8UC3, CV_32FC1)))
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{
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const Size srcSize = GetParam();
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const int type = CV_8UC1;
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Size_MatType_t params = GetParam();
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const Size srcSize = get<0>(params);
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const int srcType = get<1>(params);
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const double eps = CV_MAT_DEPTH(srcType) <= CV_32S ? 1.0 : 0.2;
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Mat src1(srcSize, type), src2(srcSize, CV_8UC1), dst;
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Mat src1(srcSize, srcType), src2(srcSize, srcType), dst(srcSize, srcType);
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Mat weights1(srcSize, CV_32FC1), weights2(srcSize, CV_32FC1);
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declare.in(src1, src2, WARMUP_RNG);
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declare.in(src1, src2, WARMUP_RNG).out(dst);
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randu(weights1, 0.0f, 1.0f);
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randu(weights2, 0.0f, 1.0f);
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@ -97,17 +110,20 @@ PERF_TEST_P(blendLinearFixture, blendLinear, OCL_TYPICAL_MAT_SIZES)
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ocl::oclMat oclSrc1(src1), oclSrc2(src2), oclDst;
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ocl::oclMat oclWeights1(weights1), oclWeights2(weights2);
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OCL_TEST_CYCLE() cv::ocl::blendLinear(oclSrc1, oclSrc2, oclWeights1, oclWeights2, oclDst);
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OCL_TEST_CYCLE() ocl::blendLinear(oclSrc1, oclSrc2, oclWeights1, oclWeights2, oclDst);
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oclDst.download(dst);
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SANITY_CHECK(dst);
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SANITY_CHECK(dst, eps);
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}
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else if (RUN_PLAIN_IMPL)
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{
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TEST_CYCLE() blendLinearGold<uchar>(src1, src2, weights1, weights2, dst);
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blendFunction funcs[] = { (blendFunction)blendLinearGold<uchar>, (blendFunction)blendLinearGold<float> };
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int funcIdx = CV_MAT_DEPTH(srcType) == CV_8UC1 ? 0 : 1;
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SANITY_CHECK(dst);
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TEST_CYCLE() (funcs[funcIdx])(src1, src2, weights1, weights2, dst);
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SANITY_CHECK(dst, eps);
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}
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else
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OCL_PERF_ELSE
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@ -49,35 +49,51 @@
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using namespace cv;
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using namespace cv::ocl;
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void cv::ocl::blendLinear(const oclMat &img1, const oclMat &img2, const oclMat &weights1, const oclMat &weights2,
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oclMat &result)
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void cv::ocl::blendLinear(const oclMat &src1, const oclMat &src2, const oclMat &weights1, const oclMat &weights2,
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oclMat &dst)
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{
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cv::ocl::Context *ctx = img1.clCxt;
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assert(ctx == img2.clCxt && ctx == weights1.clCxt && ctx == weights2.clCxt);
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int channels = img1.oclchannels();
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int depth = img1.depth();
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int rows = img1.rows;
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int cols = img1.cols;
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int istep = img1.step1();
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int wstep = weights1.step1();
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size_t globalSize[] = {cols * channels / 4, rows, 1};
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size_t localSize[] = {256, 1, 1};
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CV_Assert(src1.depth() <= CV_32F);
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CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
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CV_Assert(weights1.size() == weights2.size() && weights1.size() == src1.size() &&
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weights1.type() == CV_32FC1 && weights2.type() == CV_32FC1);
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dst.create(src1.size(), src1.type());
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size_t globalSize[] = { dst.cols, dst.rows, 1};
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size_t localSize[] = { 16, 16, 1 };
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int depth = dst.depth(), ocn = dst.oclchannels();
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int src1_step = src1.step / src1.elemSize(), src1_offset = src1.offset / src1.elemSize();
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int src2_step = src2.step / src2.elemSize(), src2_offset = src2.offset / src2.elemSize();
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int weight1_step = weights1.step / weights1.elemSize(), weight1_offset = weights1.offset / weights1.elemSize();
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int weight2_step = weights2.step / weights2.elemSize(), weight2_offset = weights2.offset / weights2.elemSize();
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int dst_step = dst.step / dst.elemSize(), dst_offset = dst.offset / dst.elemSize();
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const char * const channelMap[] = { "", "", "2", "4", "4" };
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const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
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std::string buildOptions = format("-D T=%s%s -D convertToT=convert_%s%s%s -D FT=float%s -D convertToFT=convert_float%s",
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typeMap[depth], channelMap[ocn], typeMap[depth], channelMap[ocn],
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depth >= CV_32S ? "" : "_sat_rte", channelMap[ocn], channelMap[ocn]);
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vector< pair<size_t, const void *> > args;
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result.create(img1.size(), CV_MAKE_TYPE(depth,img1.channels()));
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if(globalSize[0] != 0)
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{
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args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data ));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&img1.data ));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&img2.data ));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&weights1.data ));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&weights2.data ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&rows ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&cols ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&istep ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&wstep ));
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std::string kernelName = "BlendLinear";
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args.push_back( make_pair( sizeof(cl_mem), (void *)&src1.data ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&src1_offset ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&src1_step ));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&src2.data ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&src2_offset ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&src2_step ));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&weights1.data ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&weight1_offset ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&weight1_step ));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&weights2.data ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&weight2_offset ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&weight2_step ));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&dst_offset ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&dst_step ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&dst.rows ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&dst.cols ));
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openCLExecuteKernel(ctx, &blend_linear, kernelName, globalSize, localSize, args, channels, depth);
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}
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openCLExecuteKernel(src1.clCxt, &blend_linear, "blendLinear", globalSize, localSize, args,
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-1, -1, buildOptions.c_str());
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}
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@ -42,99 +42,37 @@
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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__kernel void BlendLinear_C1_D0(
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__global uchar4 *dst,
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__global uchar4 *img1,
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__global uchar4 *img2,
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__global float4 *weight1,
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__global float4 *weight2,
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int rows,
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int cols,
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int istep,
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int wstep
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)
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#if defined (DOUBLE_SUPPORT)
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#ifdef cl_amd_fp64
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#pragma OPENCL EXTENSION cl_amd_fp64:enable
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#elif defined (cl_khr_fp64)
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#pragma OPENCL EXTENSION cl_khr_fp64:enable
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#endif
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#endif
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__kernel void blendLinear(__global const T * src1, int src1_offset, int src1_step,
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__global const T * src2, int src2_offset, int src2_step,
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__global const float * weight1, int weight1_offset, int weight1_step,
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__global const float * weight2, int weight2_offset, int weight2_step,
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__global T * dst, int dst_offset, int dst_step,
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int rows, int cols)
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{
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int idx = get_global_id(0);
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int idy = get_global_id(1);
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if (idx << 2 < cols && idy < rows)
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int x = get_global_id(0);
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int y = get_global_id(1);
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if (x < cols && y < rows)
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{
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int pos = mad24(idy,istep >> 2,idx);
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int wpos = mad24(idy,wstep >> 2,idx);
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float4 w1 = weight1[wpos], w2 = weight2[wpos];
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dst[pos] = convert_uchar4((convert_float4(img1[pos]) * w1 +
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convert_float4(img2[pos]) * w2) / (w1 + w2 + 1e-5f));
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}
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}
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__kernel void BlendLinear_C4_D0(
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__global uchar4 *dst,
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__global uchar4 *img1,
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__global uchar4 *img2,
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__global float *weight1,
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__global float *weight2,
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int rows,
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int cols,
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int istep,
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int wstep
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)
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{
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int idx = get_global_id(0);
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int idy = get_global_id(1);
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if (idx < cols && idy < rows)
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{
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int pos = mad24(idy,istep >> 2,idx);
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int wpos = mad24(idy,wstep, idx);
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float w1 = weight1[wpos];
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float w2 = weight2[wpos];
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dst[pos] = convert_uchar4((convert_float4(img1[pos]) * w1 +
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convert_float4(img2[pos]) * w2) / (w1 + w2 + 1e-5f));
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}
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}
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__kernel void BlendLinear_C1_D5(
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__global float4 *dst,
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__global float4 *img1,
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__global float4 *img2,
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__global float4 *weight1,
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__global float4 *weight2,
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int rows,
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int cols,
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int istep,
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int wstep
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)
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{
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int idx = get_global_id(0);
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int idy = get_global_id(1);
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if (idx << 2 < cols && idy < rows)
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{
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int pos = mad24(idy,istep >> 2,idx);
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int wpos = mad24(idy,wstep >> 2,idx);
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float4 w1 = weight1[wpos], w2 = weight2[wpos];
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dst[pos] = (img1[pos] * w1 + img2[pos] * w2) / (w1 + w2 + 1e-5f);
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}
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}
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__kernel void BlendLinear_C4_D5(
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__global float4 *dst,
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__global float4 *img1,
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__global float4 *img2,
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__global float *weight1,
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__global float *weight2,
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int rows,
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int cols,
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int istep,
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int wstep
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)
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{
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int idx = get_global_id(0);
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int idy = get_global_id(1);
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if (idx < cols && idy < rows)
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{
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int pos = mad24(idy,istep >> 2,idx);
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int wpos = mad24(idy,wstep, idx);
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float w1 = weight1[wpos];
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float w2 = weight2[wpos];
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dst[pos] = (img1[pos] * w1 + img2[pos] * w2) / (w1 + w2 + 1e-5f);
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int src1_index = mad24(y, src1_step, src1_offset + x);
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int src2_index = mad24(y, src2_step, src2_offset + x);
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int weight1_index = mad24(y, weight1_step, weight1_offset + x);
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int weight2_index = mad24(y, weight2_step, weight2_offset + x);
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int dst_index = mad24(y, dst_step, dst_offset + x);
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FT w1 = (FT)(weight1[weight1_index]), w2 = (FT)(weight2[weight2_index]);
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FT den = w1 + w2 + (FT)(1e-5f);
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FT num = w1 * convertToFT(src1[src1_index]) + w2 * convertToFT(src2[src2_index]);
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dst[dst_index] = convertToT(num / den);
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}
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}
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@ -47,73 +47,124 @@
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using namespace cv;
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using namespace cv::ocl;
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using namespace cvtest;
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using namespace testing;
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using namespace std;
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#ifdef HAVE_OPENCL
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template <typename T>
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void blendLinearGold(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &weights1, const cv::Mat &weights2, cv::Mat &result_gold)
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static void blendLinearGold(const Mat &img1, const Mat &img2,
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const Mat &weights1, const Mat &weights2,
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Mat &result_gold)
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{
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CV_Assert(img1.size() == img2.size() && img1.type() == img2.type());
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CV_Assert(weights1.size() == weights2.size() && weights1.size() == img1.size() &&
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weights1.type() == CV_32FC1 && weights2.type() == CV_32FC1);
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result_gold.create(img1.size(), img1.type());
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int cn = img1.channels();
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int step1 = img1.cols * img1.channels();
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for (int y = 0; y < img1.rows; ++y)
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{
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const float *weights1_row = weights1.ptr<float>(y);
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const float *weights2_row = weights2.ptr<float>(y);
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const T *img1_row = img1.ptr<T>(y);
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const T *img2_row = img2.ptr<T>(y);
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T *result_gold_row = result_gold.ptr<T>(y);
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const float * const weights1_row = weights1.ptr<float>(y);
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const float * const weights2_row = weights2.ptr<float>(y);
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const T * const img1_row = img1.ptr<T>(y);
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const T * const img2_row = img2.ptr<T>(y);
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T * const result_gold_row = result_gold.ptr<T>(y);
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for (int x = 0; x < img1.cols * cn; ++x)
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for (int x = 0; x < step1; ++x)
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{
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float w1 = weights1_row[x / cn];
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float w2 = weights2_row[x / cn];
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result_gold_row[x] = static_cast<T>((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f));
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int x1 = x / cn;
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float w1 = weights1_row[x1], w2 = weights2_row[x1];
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result_gold_row[x] = saturate_cast<T>(((float)img1_row[x] * w1
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+ (float)img2_row[x] * w2) / (w1 + w2 + 1e-5f));
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}
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}
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}
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PARAM_TEST_CASE(Blend, cv::Size, MatType/*, UseRoi*/)
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PARAM_TEST_CASE(Blend, MatDepth, int, bool)
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{
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cv::Size size;
|
||||
int type;
|
||||
int depth, channels;
|
||||
bool useRoi;
|
||||
|
||||
Mat src1, src2, weights1, weights2, dst;
|
||||
Mat src1_roi, src2_roi, weights1_roi, weights2_roi, dst_roi;
|
||||
oclMat gsrc1, gsrc2, gweights1, gweights2, gdst, gst;
|
||||
oclMat gsrc1_roi, gsrc2_roi, gweights1_roi, gweights2_roi, gdst_roi;
|
||||
|
||||
virtual void SetUp()
|
||||
{
|
||||
size = GET_PARAM(0);
|
||||
type = GET_PARAM(1);
|
||||
depth = GET_PARAM(0);
|
||||
channels = GET_PARAM(1);
|
||||
useRoi = GET_PARAM(2);
|
||||
}
|
||||
|
||||
void random_roi()
|
||||
{
|
||||
const int type = CV_MAKE_TYPE(depth, channels);
|
||||
|
||||
const double upValue = 1200;
|
||||
|
||||
Size roiSize = randomSize(1, 20);
|
||||
Border src1Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
|
||||
randomSubMat(src1, src1_roi, roiSize, src1Border, type, -upValue, upValue);
|
||||
|
||||
Border src2Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
|
||||
randomSubMat(src2, src2_roi, roiSize, src2Border, type, -upValue, upValue);
|
||||
|
||||
Border weights1Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
|
||||
randomSubMat(weights1, weights1_roi, roiSize, weights1Border, CV_32FC1, -upValue, upValue);
|
||||
|
||||
Border weights2Border = randomBorder(0, useRoi ? MAX_VALUE : 0);
|
||||
randomSubMat(weights2, weights2_roi, roiSize, weights2Border, CV_32FC1, -upValue, upValue);
|
||||
|
||||
Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
|
||||
randomSubMat(dst, dst_roi, roiSize, dstBorder, type, 5, 16);
|
||||
|
||||
generateOclMat(gsrc1, gsrc1_roi, src1, roiSize, src1Border);
|
||||
generateOclMat(gsrc2, gsrc2_roi, src2, roiSize, src2Border);
|
||||
generateOclMat(gweights1, gweights1_roi, weights1, roiSize, weights1Border);
|
||||
generateOclMat(gweights2, gweights2_roi, weights2, roiSize, weights2Border);
|
||||
generateOclMat(gdst, gdst_roi, dst, roiSize, dstBorder);
|
||||
}
|
||||
|
||||
void Near(double eps = 0.0)
|
||||
{
|
||||
Mat whole, roi;
|
||||
gdst.download(whole);
|
||||
gdst_roi.download(roi);
|
||||
|
||||
EXPECT_MAT_NEAR(dst, whole, eps);
|
||||
EXPECT_MAT_NEAR(dst_roi, roi, eps);
|
||||
}
|
||||
};
|
||||
|
||||
typedef void (*blendLinearFunc)(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &weights1, const cv::Mat &weights2, cv::Mat &result_gold);
|
||||
|
||||
OCL_TEST_P(Blend, Accuracy)
|
||||
{
|
||||
int depth = CV_MAT_DEPTH(type);
|
||||
for (int i = 0; i < LOOP_TIMES; ++i)
|
||||
{
|
||||
random_roi();
|
||||
|
||||
cv::Mat img1 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0);
|
||||
cv::Mat img2 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0);
|
||||
cv::Mat weights1 = randomMat(size, CV_32F, 0, 1);
|
||||
cv::Mat weights2 = randomMat(size, CV_32F, 0, 1);
|
||||
cv::ocl::blendLinear(gsrc1_roi, gsrc2_roi, gweights1_roi, gweights2_roi, gdst_roi);
|
||||
|
||||
cv::ocl::oclMat gimg1(img1), gimg2(img2), gweights1(weights1), gweights2(weights2);
|
||||
cv::ocl::oclMat dst;
|
||||
static blendLinearFunc funcs[] = {
|
||||
blendLinearGold<uchar>,
|
||||
blendLinearGold<schar>,
|
||||
blendLinearGold<ushort>,
|
||||
blendLinearGold<short>,
|
||||
blendLinearGold<int>,
|
||||
blendLinearGold<float>,
|
||||
};
|
||||
|
||||
cv::ocl::blendLinear(gimg1, gimg2, gweights1, gweights2, dst);
|
||||
cv::Mat result;
|
||||
cv::Mat result_gold;
|
||||
dst.download(result);
|
||||
if (depth == CV_8U)
|
||||
blendLinearGold<uchar>(img1, img2, weights1, weights2, result_gold);
|
||||
else
|
||||
blendLinearGold<float>(img1, img2, weights1, weights2, result_gold);
|
||||
blendLinearFunc func = funcs[depth];
|
||||
func(src1_roi, src2_roi, weights1_roi, weights2_roi, dst_roi);
|
||||
|
||||
EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.f : 1e-5f);
|
||||
Near(depth <= CV_32S ? 1.0 : 0.2);
|
||||
}
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, Blend, Combine(
|
||||
DIFFERENT_SIZES,
|
||||
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC4))
|
||||
));
|
||||
#endif
|
||||
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, Blend,
|
||||
Combine(testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F),
|
||||
testing::Range(1, 5), Bool()));
|
||||
|
||||
Loading…
Reference in New Issue
Block a user