Делать вывод для модели NVIDIA ONNX (PeopleNet) ⇐ C++

[Anonymous]

Я внедряю модель Peoplenet Onnx в моем приложении CPP.
Ниже приведены функции предварительной обработки и постобработки. < /p>
void preprocessGpuBatch(const std::vector& gpu_frames,
cv::Mat& blob,
cv::cuda::Stream& stream) {
std::vector cpu_processed_frames;

for (size_t i = 0; i < gpu_frames.size(); ++i) {
cv::cuda::GpuMat resized, float_img;

// Resize on GPU (keep this optimization)
cv::cuda::resize(gpu_frames, resized, cv::Size(INPUT_WIDTH, INPUT_HEIGHT),
0, 0, cv::INTER_LINEAR, stream);

// Convert to float32 and normalize to [0,1] range
resized.convertTo(float_img, CV_32F, 1.0/255.0, stream);

// Download to CPU
cv::Mat cpu_frame;
float_img.download(cpu_frame, stream);
stream.waitForCompletion();

// Manual normalization to [-1,1] using mean=0.5, std=0.5
cpu_frame = (cpu_frame - 0.5f) / 0.5f;

cpu_processed_frames.push_back(cpu_frame);
}

// Create blob (NCHW format)
blob = cv::dnn::blobFromImages(
cpu_processed_frames,
1.0, // No additional scaling (already normalized)
cv::Size(), // Empty size (images already resized)
cv::Scalar(), // No mean subtraction (already done)
false, // swapRB = false (assuming already RGB)
false // crop = false
);
}

std::vector postprocess(const std::vector outputs, const std::vector& original_sizes, const int actual_batch_size)
{
std::cout

Подробнее здесь: https://stackoverflow.com/questions/797 ... -peoplenet