Обнаружение квантованного объекта SsD на объекте Raspberry Pi

Обнаружение объектов не работает, как ожидалось для меня на Raspberry Pi с tenorflow Lite с использованием C ++. Мой код компилируется и запускается, но кажется, что вывод никогда не заполняется должным образом. Могу ли я пропустить какие-либо зависимости или получить неправильный доступ к результатам?

Я следовал за учебником:
https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193

И есть detect.tflite модель от:
https://storage.googleapis.com/download.tensorflow.org/models/tflite/pets_ssd_mobilenet_v1_0.75_quant_2018_06_29.zip

Я скомпилировал tenorflow lite и opencv для raspberry pi и изменил minimal.cc для чтения изображения и выполнения вывода следующим образом:

/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <cstdio>
#include "tensorflow/contrib/lite/interpreter.h"#include "tensorflow/contrib/lite/kernels/register.h"#include "tensorflow/contrib/lite/model.h"#include "tensorflow/contrib/lite/optional_debug_tools.h"
#include <iostream>
#include "opencv2/core.hpp"#include "opencv2/imgcodecs.hpp"
// This is an example that is minimal to read a model
// from disk and perform inference.
//
// Usage: detect <tflite model> <image filepath>

using namespace tflite;

#define TFLITE_MINIMAL_CHECK(x)                              \
if (!(x)) {                                                \
fprintf(stderr, "Error at %s:%d\n", __FILE__, __LINE__); \
exit(1);                                                 \
}

int main(int argc, char* argv[]) {
if(argc != 3) {
fprintf(stderr, "minimal <tflite model filepath> <image filepath>\n");
return 1;
} else {
fprintf(stdout, "Reading model from %s\n", argv[1]);
fprintf(stdout, "Reading image from %s\n", argv[2]);
}
const char* model_filename = argv[1];
const char* image_filename = argv[2];

// Load model
std::unique_ptr<tflite::FlatBufferModel> model =
tflite::FlatBufferModel::BuildFromFile(model_filename);
TFLITE_MINIMAL_CHECK(model != nullptr);

// Build the interpreter
tflite::ops::builtin::BuiltinOpResolver resolver;
InterpreterBuilder builder(*model.get(), resolver);
std::unique_ptr<Interpreter> interpreter;
builder(&interpreter);
TFLITE_MINIMAL_CHECK(interpreter != nullptr);

// Allocate tensor buffers.
TFLITE_MINIMAL_CHECK(interpreter->AllocateTensors() == kTfLiteOk);
printf("=== Pre-invoke Interpreter State ===\n");
tflite::PrintInterpreterState(interpreter.get());

// Fill input buffers
// TODO(user): Insert code to fill input tensors
cv::Mat img = cv::imread(image_filename);
//std::cout << "before: " << interpreter->typed_input_tensor<uchar>(0) << std::endl;
//std::cout << "image: " << img.data << std::endl;
memcpy(interpreter->typed_input_tensor<uchar>(0), img.data, img.total() * img.elemSize());
//std::cout << "after: " << interpreter->typed_input_tensor<uchar>(0) << std::endl;

// Run inference
TFLITE_MINIMAL_CHECK(interpreter->Invoke() == kTfLiteOk);
printf("\n\n=== Post-invoke Interpreter State ===\n");
tflite::PrintInterpreterState(interpreter.get());

// Read output buffers
// TODO(user): Insert getting data out code.
cv::Mat results0(10, 4, CV_8U);
cv::Mat results1(1, 10, CV_8U);
cv::Mat results2(1, 10, CV_8U);
cv::Mat results3(1, 1, CV_8U);

results0.data = interpreter->typed_output_tensor<uchar>(0);
results1.data = interpreter->typed_output_tensor<uchar>(1);
results2.data = interpreter->typed_output_tensor<uchar>(2);
results3.data = interpreter->typed_output_tensor<uchar>(3);

std::cout << "results 0: " << results0 << std::endl;
std::cout << "results 1: " << results1 << std::endl;
std::cout << "results 2: " << results2 << std::endl;
std::cout << "results 3: " << results3 << std::endl;

return 0;
}

Результаты, которые я вижу:

results 0: []
results 1: []
results 2: []
results 3: []