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Merge branch 4.x

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This commit is contained in:
Alexander Smorkalov
2023-06-01 09:37:38 +03:00
parent d24ffe9a65 fc5d412ba7
commit 5af40a0269
565 changed files with 84396 additions and 17589 deletions
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modules/gapi/test/gapi_fluid_resize_test.cpp
+4
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@@ -829,7 +829,11 @@ TEST_P(Preproc4lpiTest, Test)
cv::cvtColor(in_mat, rgb_mat, cv::COLOR_YUV2RGB_NV12);
cv::resize(rgb_mat, out_mat_ocv, out_sz, 0, 0, interp);
#if defined(__arm__) || defined(__aarch64__)
EXPECT_GE(2, cvtest::norm(out_mat(roi), out_mat_ocv(roi), NORM_INF));
#else
EXPECT_EQ(0, cvtest::norm(out_mat(roi), out_mat_ocv(roi), NORM_INF));
#endif
}
INSTANTIATE_TEST_CASE_P(Fluid, Preproc4lpiTest,
modules/gapi/test/infer/gapi_infer_onnx_test.cpp
+59 -37
View File
@@ -332,9 +332,8 @@ public:
// Inputs Run params
std::vector<Ort::Value> in_tensors;
for(size_t i = 0; i < num_in; ++i) {
char* in_node_name_p = session.GetInputName(i, allocator);
in_node_names.emplace_back(in_node_name_p);
allocator.Free(in_node_name_p);
auto in_node_name_p = session.GetInputNameAllocated(i, allocator);
in_node_names.emplace_back(in_node_name_p.get());
in_node_dims = toORT(ins[i].size);
in_tensors.emplace_back(Ort::Value::CreateTensor<T>(memory_info,
const_cast<T*>(ins[i].ptr<T>()),
@@ -345,9 +344,8 @@ public:
// Outputs Run params
if (custom_out_names.empty()) {
for(size_t i = 0; i < num_out; ++i) {
char* out_node_name_p = session.GetOutputName(i, allocator);
out_node_names.emplace_back(out_node_name_p);
allocator.Free(out_node_name_p);
auto out_node_name_p = session.GetOutputNameAllocated(i, allocator);
out_node_names.emplace_back(out_node_name_p.get());
}
} else {
out_node_names = std::move(custom_out_names);
@@ -425,13 +423,17 @@ public:
cv::Rect(cv::Point{50, 100}, cv::Size{250, 360})
};
void preprocess(const cv::Mat& src, cv::Mat& dst) {
// FIXME(dm): There's too much "preprocess" routines in this file
// Only one must stay but better design it wisely (and later)
void preprocess(const cv::Mat& src, cv::Mat& dst, bool norm = true) {
const int new_h = 224;
const int new_w = 224;
cv::Mat tmp, cvt, rsz;
cv::resize(src, rsz, cv::Size(new_w, new_h));
rsz.convertTo(cvt, CV_32F, 1.f / 255);
tmp = (cvt - mean) / std;
rsz.convertTo(cvt, CV_32F, norm ? 1.f / 255 : 1.f);
tmp = norm
? (cvt - mean) / std
: cvt;
toCHW(tmp, dst);
dst = dst.reshape(1, {1, 3, new_h, new_w});
}
@@ -552,16 +554,16 @@ TEST_F(ONNXClassification, Infer)
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// ONNX_API code
cv::Mat processed_mat;
preprocess(in_mat, processed_mat);
preprocess(in_mat, processed_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(processed_mat, out_onnx);
// G_API code
G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
cv::GMat in;
cv::GMat out = cv::gapi::infer<SqueezNet>(in);
cv::GComputation comp(cv::GIn(in), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(in_mat),
cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net)));
@@ -575,7 +577,7 @@ TEST_F(ONNXClassification, InferTensor)
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// Create tensor
cv::Mat tensor;
preprocess(in_mat, tensor);
preprocess(in_mat, tensor, false); // NO normalization for 1.0-9, see #23597
// ONNX_API code
infer<float>(tensor, out_onnx);
// G_API code
@@ -583,7 +585,9 @@ TEST_F(ONNXClassification, InferTensor)
cv::GMat in;
cv::GMat out = cv::gapi::infer<SqueezNet>(in);
cv::GComputation comp(cv::GIn(in), cv::GOut(out));
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path };
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(tensor),
cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net)));
@@ -598,7 +602,7 @@ TEST_F(ONNXClassification, InferROI)
const auto ROI = rois.at(0);
// ONNX_API code
cv::Mat roi_mat;
preprocess(in_mat(ROI), roi_mat);
preprocess(in_mat(ROI), roi_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(roi_mat, out_onnx);
// G_API code
G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
@@ -606,9 +610,9 @@ TEST_F(ONNXClassification, InferROI)
cv::GOpaque<cv::Rect> rect;
cv::GMat out = cv::gapi::infer<SqueezNet>(rect, in);
cv::GComputation comp(cv::GIn(in, rect), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(in_mat, ROI),
cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net)));
@@ -623,7 +627,7 @@ TEST_F(ONNXClassification, InferROIList)
// ONNX_API code
for (size_t i = 0; i < rois.size(); ++i) {
cv::Mat roi_mat;
preprocess(in_mat(rois[i]), roi_mat);
preprocess(in_mat(rois[i]), roi_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(roi_mat, out_onnx);
}
// G_API code
@@ -634,7 +638,9 @@ TEST_F(ONNXClassification, InferROIList)
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(in_mat, rois),
cv::gout(out_gapi),
cv::compile_args(cv::gapi::networks(net)));
@@ -649,7 +655,7 @@ TEST_F(ONNXClassification, Infer2ROIList)
// ONNX_API code
for (size_t i = 0; i < rois.size(); ++i) {
cv::Mat roi_mat;
preprocess(in_mat(rois[i]), roi_mat);
preprocess(in_mat(rois[i]), roi_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(roi_mat, out_onnx);
}
// G_API code
@@ -660,7 +666,9 @@ TEST_F(ONNXClassification, Infer2ROIList)
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(in_mat, rois),
cv::gout(out_gapi),
cv::compile_args(cv::gapi::networks(net)));
@@ -749,7 +757,7 @@ TEST_F(ONNXMediaFrame, InferBGR)
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// ONNX_API code
cv::Mat processed_mat;
preprocess(in_mat, processed_mat);
preprocess(in_mat, processed_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(processed_mat, out_onnx);
// G_API code
auto frame = MediaFrame::Create<TestMediaBGR>(in_mat);
@@ -759,7 +767,9 @@ TEST_F(ONNXMediaFrame, InferBGR)
cv::GComputation comp(cv::GIn(in), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(frame),
cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net)));
@@ -776,7 +786,7 @@ TEST_F(ONNXMediaFrame, InferYUV)
cv::Mat pp;
cvtColorTwoPlane(m_in_y, m_in_uv, pp, cv::COLOR_YUV2BGR_NV12);
cv::Mat processed_mat;
preprocess(pp, processed_mat);
preprocess(pp, processed_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(processed_mat, out_onnx);
// G_API code
G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
@@ -785,7 +795,9 @@ TEST_F(ONNXMediaFrame, InferYUV)
cv::GComputation comp(cv::GIn(in), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(frame),
cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net)));
@@ -800,7 +812,7 @@ TEST_F(ONNXMediaFrame, InferROIBGR)
auto frame = MediaFrame::Create<TestMediaBGR>(in_mat);
// ONNX_API code
cv::Mat roi_mat;
preprocess(in_mat(rois.front()), roi_mat);
preprocess(in_mat(rois.front()), roi_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(roi_mat, out_onnx);
// G_API code
G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
@@ -810,7 +822,9 @@ TEST_F(ONNXMediaFrame, InferROIBGR)
cv::GComputation comp(cv::GIn(in, rect), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(frame, rois.front()),
cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net)));
@@ -827,7 +841,7 @@ TEST_F(ONNXMediaFrame, InferROIYUV)
cv::Mat pp;
cvtColorTwoPlane(m_in_y, m_in_uv, pp, cv::COLOR_YUV2BGR_NV12);
cv::Mat roi_mat;
preprocess(pp(rois.front()), roi_mat);
preprocess(pp(rois.front()), roi_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(roi_mat, out_onnx);
// G_API code
G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
@@ -837,7 +851,9 @@ TEST_F(ONNXMediaFrame, InferROIYUV)
cv::GComputation comp(cv::GIn(in, rect), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(frame, rois.front()),
cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net)));
@@ -853,7 +869,7 @@ TEST_F(ONNXMediaFrame, InferListBGR)
// ONNX_API code
for (size_t i = 0; i < rois.size(); ++i) {
cv::Mat roi_mat;
preprocess(in_mat(rois[i]), roi_mat);
preprocess(in_mat(rois[i]), roi_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(roi_mat, out_onnx);
}
// G_API code
@@ -864,7 +880,9 @@ TEST_F(ONNXMediaFrame, InferListBGR)
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(frame, rois),
cv::gout(out_gapi),
cv::compile_args(cv::gapi::networks(net)));
@@ -882,7 +900,7 @@ TEST_F(ONNXMediaFrame, InferListYUV)
cvtColorTwoPlane(m_in_y, m_in_uv, pp, cv::COLOR_YUV2BGR_NV12);
for (size_t i = 0; i < rois.size(); ++i) {
cv::Mat roi_mat;
preprocess(pp(rois[i]), roi_mat);
preprocess(pp(rois[i]), roi_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(roi_mat, out_onnx);
}
// G_API code
@@ -893,7 +911,9 @@ TEST_F(ONNXMediaFrame, InferListYUV)
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(frame, rois),
cv::gout(out_gapi),
cv::compile_args(cv::gapi::networks(net)));
@@ -935,7 +955,7 @@ TEST_F(ONNXMediaFrame, InferList2BGR)
// ONNX_API code
for (size_t i = 0; i < rois.size(); ++i) {
cv::Mat roi_mat;
preprocess(in_mat(rois[i]), roi_mat);
preprocess(in_mat(rois[i]), roi_mat, false); // NO normalization for 1.0-9, see #23597
infer<float>(roi_mat, out_onnx);
}
// G_API code
@@ -946,7 +966,9 @@ TEST_F(ONNXMediaFrame, InferList2BGR)
cv::GComputation comp(cv::GIn(in, rr), cv::GOut(out));
// NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
auto net = cv::gapi::onnx::Params<SqueezNet> {
model_path
}.cfgNormalize({false});
comp.apply(cv::gin(frame, rois),
cv::gout(out_gapi),
cv::compile_args(cv::gapi::networks(net)));
modules/gapi/test/internal/gapi_int_executor_tests.cpp
+1 -1
View File
@@ -27,7 +27,7 @@ class GMockExecutable final: public cv::gimpl::GIslandExecutable
m_priv->m_reshape_counter++;
}
virtual void handleNewStream() override { }
virtual void run(std::vector<InObj>&&, std::vector<OutObj>&&) { }
virtual void run(std::vector<InObj>&&, std::vector<OutObj>&&) override { }
virtual bool allocatesOutputs() const override
{
return true;
modules/gapi/test/streaming/gapi_streaming_tests.cpp
+4 -4
View File
@@ -190,7 +190,7 @@ public:
: cv::gapi::wip::GCaptureSource(pipeline) {
}
bool pull(cv::gapi::wip::Data& data) {
bool pull(cv::gapi::wip::Data& data) override {
if (cv::gapi::wip::GCaptureSource::pull(data)) {
data = cv::MediaFrame::Create<TestMediaBGR>(cv::util::get<cv::Mat>(data));
return true;
@@ -232,7 +232,7 @@ public:
: cv::gapi::wip::GCaptureSource(pipeline) {
}
bool pull(cv::gapi::wip::Data& data) {
bool pull(cv::gapi::wip::Data& data) override {
if (cv::gapi::wip::GCaptureSource::pull(data)) {
cv::Mat bgr = cv::util::get<cv::Mat>(data);
cv::Mat y, uv;
@@ -256,7 +256,7 @@ public:
: cv::gapi::wip::GCaptureSource(pipeline) {
}
bool pull(cv::gapi::wip::Data& data) {
bool pull(cv::gapi::wip::Data& data) override {
if (cv::gapi::wip::GCaptureSource::pull(data)) {
cv::Mat bgr = cv::util::get<cv::Mat>(data);
cv::Mat gray;
@@ -319,7 +319,7 @@ public:
return "InvalidSource sucessfuly failed!";
}
bool pull(cv::gapi::wip::Data& d) {
bool pull(cv::gapi::wip::Data& d) override {
++m_curr_frame_id;
if (m_curr_frame_id > m_num_frames) {
return false;