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build: support OpenCV 5 and TensorRT 10

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OpenCV: distros (Arch/CachyOS) now ship OpenCV 5 as default. The config
package rejects a 5.x install when find_package requests 4, so probe for 5
first and fall back to 4. All components used here (core, imgproc, imgcodecs,
videoio, dnn, objdetect, highgui) exist in both.

TensorRT: nvinfer1::Dims5 was removed in TRT 10 (Dims2..Dims4 remain in
NvInferLegacyDims.h). Build the TransNetV2 rank-5 input shape via the generic
nvinfer1::Dims, which is valid on both 8.x and 10.x.
This commit is contained in:
dtourolle
2026-07-30 13:07:42 +02:00
parent e5885977df
commit 5d2f673a81
2 changed files with 21 additions and 3 deletions
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CMakeLists.txt
+10 -1
View File
@@ -18,8 +18,17 @@ endif()
add_subdirectory(external/KPN)
# OpenCV (video decode, image ops, DNN inference, face detection)
find_package(OpenCV 4 REQUIRED COMPONENTS
# Accept 4 or 5: the APIs used here are stable across both, and distros have
# begun shipping 5.x as the default (Arch/CachyOS). find_package's version
# argument is a minimum, but OpenCV's config rejects a 5.x install when 4 is
# requested, so probe for 5 first and fall back to 4.
find_package(OpenCV 5 QUIET COMPONENTS
core imgproc imgcodecs videoio dnn objdetect highgui)
if(NOT OpenCV_FOUND)
find_package(OpenCV 4 REQUIRED COMPONENTS
core imgproc imgcodecs videoio dnn objdetect highgui)
endif()
message(STATUS "OpenCV: ${OpenCV_VERSION}")
# ── Model paths ───────────────────────────────────────────────────────────────
# Defined early so the backend object libraries below can embed it.
src/backends/trt_backend.cpp
+11 -2
View File
@@ -511,8 +511,17 @@ public:
}
std::lock_guard<std::mutex> lk(mu_);
context_->setInputShape(input_name_.c_str(),
nvinfer1::Dims5{1, kWindow, kFrameH, kFrameW, 3});
// TensorRT 10 removed the fixed-rank Dims5 helper (Dims2..Dims4 remain
// in NvInferLegacyDims.h). Build the rank-5 shape via the generic Dims,
// which works on both 8.x and 10.x.
nvinfer1::Dims shape{};
shape.nbDims = 5;
shape.d[0] = 1;
shape.d[1] = kWindow;
shape.d[2] = kFrameH;
shape.d[3] = kFrameW;
shape.d[4] = 3;
context_->setInputShape(input_name_.c_str(), shape);
check_cuda(cudaMemcpyAsync(d_input_, buf.data(), buf.size() * 4,
cudaMemcpyHostToDevice, stream_), "H2D input");