120 lines
5.3 KiB
C++
120 lines
5.3 KiB
C++
/* Copyright 2019 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 "tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h"
|
|
|
|
#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
|
|
#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
|
|
#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
|
|
#include "tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h"
|
|
#include "tensorflow/lite/micro/micro_error_reporter.h"
|
|
#include "tensorflow/lite/micro/micro_interpreter.h"
|
|
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
|
|
#include "tensorflow/lite/schema/schema_generated.h"
|
|
#include "tensorflow/lite/version.h"
|
|
|
|
// Globals, used for compatibility with Arduino-style sketches.
|
|
namespace {
|
|
tflite::ErrorReporter* error_reporter = nullptr;
|
|
const tflite::Model* model = nullptr;
|
|
tflite::MicroInterpreter* interpreter = nullptr;
|
|
TfLiteTensor* input = nullptr;
|
|
|
|
// In order to use optimized tensorflow lite kernels, a signed int8_t quantized
|
|
// model is preferred over the legacy unsigned model format. This means that
|
|
// throughout this project, input images must be converted from unisgned to
|
|
// signed format. The easiest and quickest way to convert from unsigned to
|
|
// signed 8-bit integers is to subtract 128 from the unsigned value to get a
|
|
// signed value.
|
|
|
|
// An area of memory to use for input, output, and intermediate arrays.
|
|
constexpr int kTensorArenaSize = 115 * 1024;
|
|
static uint8_t tensor_arena[kTensorArenaSize];
|
|
} // namespace
|
|
|
|
// The name of this function is important for Arduino compatibility.
|
|
void person_detect_init() {
|
|
// Set up logging. Google style is to avoid globals or statics because of
|
|
// lifetime uncertainty, but since this has a trivial destructor it's okay.
|
|
// NOLINTNEXTLINE(runtime-global-variables)
|
|
static tflite::MicroErrorReporter micro_error_reporter;
|
|
error_reporter = µ_error_reporter;
|
|
|
|
// Map the model into a usable data structure. This doesn't involve any
|
|
// copying or parsing, it's a very lightweight operation.
|
|
model = tflite::GetModel(g_person_detect_model_data);
|
|
if (model->version() != TFLITE_SCHEMA_VERSION) {
|
|
TF_LITE_REPORT_ERROR(error_reporter,
|
|
"Model provided is schema version %d not equal "
|
|
"to supported version %d.",
|
|
model->version(), TFLITE_SCHEMA_VERSION);
|
|
return;
|
|
}
|
|
|
|
// Pull in only the operation implementations we need.
|
|
// This relies on a complete list of all the ops needed by this graph.
|
|
// An easier approach is to just use the AllOpsResolver, but this will
|
|
// incur some penalty in code space for op implementations that are not
|
|
// needed by this graph.
|
|
//
|
|
// tflite::AllOpsResolver resolver;
|
|
// NOLINTNEXTLINE(runtime-global-variables)
|
|
static tflite::MicroMutableOpResolver<5> micro_op_resolver;
|
|
micro_op_resolver.AddAveragePool2D();
|
|
micro_op_resolver.AddConv2D();
|
|
micro_op_resolver.AddDepthwiseConv2D();
|
|
micro_op_resolver.AddReshape();
|
|
micro_op_resolver.AddSoftmax();
|
|
|
|
// Build an interpreter to run the model with.
|
|
// NOLINTNEXTLINE(runtime-global-variables)
|
|
static tflite::MicroInterpreter static_interpreter(
|
|
model, micro_op_resolver, tensor_arena, kTensorArenaSize, error_reporter);
|
|
interpreter = &static_interpreter;
|
|
|
|
// Allocate memory from the tensor_arena for the model's tensors.
|
|
TfLiteStatus allocate_status = interpreter->AllocateTensors();
|
|
if (allocate_status != kTfLiteOk) {
|
|
TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
|
|
return;
|
|
}
|
|
|
|
// Get information about the memory area to use for the model's input.
|
|
input = interpreter->input(0);
|
|
}
|
|
|
|
// The name of this function is important for Arduino compatibility.
|
|
int person_detect(uint8_t * hardware_input) {
|
|
// Get image from provider.
|
|
if (kTfLiteOk != GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
|
|
input->data.int8, hardware_input)) {
|
|
TF_LITE_REPORT_ERROR(error_reporter, "Image capture failed.");
|
|
}
|
|
|
|
// Run the model on this input and make sure it succeeds.
|
|
if (kTfLiteOk != interpreter->Invoke()) {
|
|
TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed.");
|
|
}
|
|
|
|
TfLiteTensor* output = interpreter->output(0);
|
|
|
|
// Process the inference results.
|
|
int8_t person_score = output->data.uint8[kPersonIndex];
|
|
int8_t no_person_score = output->data.uint8[kNotAPersonIndex];
|
|
RespondToDetection(error_reporter, person_score, no_person_score);
|
|
if(person_score >= no_person_score + 50) return 1;
|
|
else return 0;
|
|
}
|