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C++ & OpenVINO

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This commit is contained in:
Alexander Popov 2024-08-24 15:24:01 +03:00
parent 4e84f550d4
commit 8a04448f63
Signed by: iiiypuk
GPG Key ID: E47FE0AB36CD5ED6
13 changed files with 296 additions and 232 deletions
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16
code/MicroPython/network_ap.py
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@ -6,16 +6,10 @@ def connect(essid='ESP', clients=3) -> bool:
print('Starting AP: {0}...'.format(essid)) print('Starting AP: {0}...'.format(essid))
ap = network.WLAN(network.AP_IF) ap = network.WLAN(network.AP_IF)
ap.active(True) ap.active(True)
ap.config(essid=essid) ap.config(essid=essid, max_clients=clients)
ap.config(max_clients=clients)
time.sleep(3) while ap.active() == False:
pass
if ap.isconnected(): print('AP "{0}" started'.format(essid))
print('AP "{0}" started'.format(essid)) return True
return True
else:
print('Starting AP failed!')
return False

10
projects/OpenVINO/.gitignore vendored Normal file
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@ -0,0 +1,10 @@
# models
Models/
*.onnx
*.pt
# junk
trash/
11.jpg
12.jpg
Python/

1
projects/OpenVINO/C++/.clang-format Symbolic link
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../../../code/C++/.clang-format

10
projects/OpenVINO/C++/.gitignore vendored Normal file
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@ -0,0 +1,10 @@
# xmake
.xmake/
build/
# binary
a.out
# other
infer.cpp
convert.py

10
projects/OpenVINO/C++/build.sh Executable file
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@ -0,0 +1,10 @@
#!/bin/sh
clear
CV_INCLUDE=/opt/opencv-4.8.0/include/opencv4/
CV_LIB=/opt/opencv-4.8.0/lib/
export LD_LIBRARY_PATH=${CV_LIB}:${LD_LIBRARY_PATH}
g++ -I${CV_INCLUDE} -L${CV_LIB} -o inference detect.cpp -lopenvino -lopencv_core -lopencv_imgproc -lopencv_highgui -lopencv_imgcodecs -lopencv_dnn &&
./inference

47
projects/OpenVINO/C++/i.cpp Normal file
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#include <string>
#include <vector>
#include "infer.hpp"
int main(int argc, char *argv[]) {
// Проверяет количество аргументов
if (argc != 3) {
std::cerr << "Использование: " << argv[0] << " <model_path> <image_path>" << std::endl;
return EXIT_FAILURE;
}
// Получает пути к модели и изображению из аргументов программы
const std::string model_path = argv[1];
const std::string image_path = argv[2];
// Проверяем наличие OpenVINO попыткой вывести версию
try {
std::cout << ov::get_openvino_version() << std::endl;
} catch (const std::exception &ex) {
std::cerr << ex.what() << std::endl;
return EXIT_FAILURE;
}
// Читает изображение из файла
cv::Mat image = cv::imread(image_path);
if (image.empty()) {
std::cerr << "ОШИБКА: Не удалось загрузить изображение" << std::endl;
return EXIT_FAILURE;
}
// Определение значений
const float probability = 0.5;
const float NMS = 0.5;
// Создание объекта класса распознования
Inf *i;
i = new Inf(model_path, cv::Size(640, 640), probability, NMS);
// Запуск распознования объектов
i->inference(image);
// Запись результата в файл
cv::imwrite("/tmp/cpp_openvino_result.bmp", image);
return EXIT_SUCCESS;
}

136
projects/OpenVINO/C++/infer.cc Normal file
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@ -0,0 +1,136 @@
#include "infer.hpp"
Inf::Inf(const std::string &model_path, const float &model_probability, const float &model_NMS) {
input_shape = cv::Size(640, 640);
probability = model_probability;
NMS = model_NMS;
init(model_path);
};
Inf::Inf(const std::string &model_path, const cv::Size model_input_shape, const float &model_probability, const float &model_NMS) {
input_shape = model_input_shape;
probability = model_probability;
NMS = model_NMS;
init(model_path);
};
void Inf::init(const std::string &model_path) {
ov::Core core;
std::shared_ptr<ov::Model> model = core.read_model(model_path);
// Если модель имеет динамические формы,
// изменяем модель в соответствиии с указанной формой
if (model->is_dynamic()) {
model->reshape({1, 3, static_cast<long int>(input_shape.height), static_cast<long int>(input_shape.width)});
}
// Настройка предварительной обработки для модели
ov::preprocess::PrePostProcessor ppp = ov::preprocess::PrePostProcessor(model);
ppp.input().tensor().set_element_type(ov::element::u8).set_layout("NHWC").set_color_format(ov::preprocess::ColorFormat::BGR);
ppp.input()
.preprocess()
.convert_element_type(ov::element::f32)
.convert_color(ov::preprocess::ColorFormat::RGB)
.scale({255, 255, 255});
ppp.input().model().set_layout("NCHW");
ppp.output().tensor().set_element_type(ov::element::f32);
model = ppp.build();
compiled_model = core.compile_model(model, "AUTO");
inference_request = compiled_model.create_infer_request();
const std::vector<ov::Output<ov::Node>> inputs = model->inputs();
const ov::Shape in_shape = inputs[0].get_shape();
input_shape = cv::Size2f(in_shape[2], in_shape[1]);
const std::vector<ov::Output<ov::Node>> outputs = model->outputs();
const ov::Shape out_shape = outputs[0].get_shape();
output_shape = cv::Size(out_shape[2], out_shape[1]);
};
void Inf::pre(const cv::Mat &frame) {
cv::Mat resized_frame;
cv::resize(frame, resized_frame, input_shape, 0, 0, cv::INTER_AREA); // Resize the frame to match the model input shape
// Calculate scaling factor
scale_factor.x = static_cast<float>(frame.cols / input_shape.width);
scale_factor.y = static_cast<float>(frame.rows / input_shape.height);
float *input_data = (float *)resized_frame.data; // Get pointer to resized frame data
const ov::Tensor input_tensor =
ov::Tensor(compiled_model.input().get_element_type(), compiled_model.input().get_shape(), input_data);
inference_request.set_input_tensor(input_tensor); // Set input tensor for inference
};
void Inf::post(cv::Mat &frame) {
std::vector<int> class_list;
std::vector<float> confidence_list;
std::vector<cv::Rect> box_list;
const float *detections = inference_request.get_output_tensor().data<const float>();
const cv::Mat detection_outputs(output_shape, CV_32F, (float *)detections);
for (int i = 0; i < detection_outputs.cols; ++i) {
const cv::Mat classes_scores = detection_outputs.col(i).rowRange(4, detection_outputs.rows);
cv::Point class_id;
double score;
cv::minMaxLoc(classes_scores, nullptr, &score, nullptr, &class_id);
if (score > probability) {
class_list.push_back(class_id.y);
confidence_list.push_back(score);
const float x = detection_outputs.at<float>(0, i);
const float y = detection_outputs.at<float>(1, i);
const float w = detection_outputs.at<float>(2, i);
const float h = detection_outputs.at<float>(3, i);
cv::Rect box;
box.x = static_cast<int>(x);
box.y = static_cast<int>(y);
box.width = static_cast<int>(w);
box.height = static_cast<int>(h);
box_list.push_back(box);
}
}
std::vector<int> NMS_result;
cv::dnn::NMSBoxes(box_list, confidence_list, probability, NMS, NMS_result);
for (int i = 0; i < NMS_result.size(); ++i) {
Detection result;
const unsigned short id = NMS_result[i];
result.class_id = class_list[id];
result.probability = confidence_list[id];
result.box = GetBoundingBox(box_list[id]);
DrawDetectedObject(frame, result);
}
};
void Inf::inference(cv::Mat &frame) {
pre(frame);
inference_request.infer();
post(frame);
};
cv::Rect Inf::GetBoundingBox(const cv::Rect &src) const {
cv::Rect box = src;
box.x = (box.x - box.width / 2) * scale_factor.x;
box.y = (box.y - box.height / 2) * scale_factor.y;
box.width *= scale_factor.x;
box.height *= scale_factor.y;
return box;
}
void Inf::DrawDetectedObject(cv::Mat &frame, const Detection &detection) const {
const cv::Rect &box = detection.box;
const float &confidence = detection.probability;
const int &class_id = detection.class_id;
const cv::Scalar &color = cv::Scalar(0, 0, 180);
cv::rectangle(frame, cv::Point(box.x, box.y), cv::Point(box.x + box.width, box.y + box.height), color, 3);
}

212
projects/OpenVINO/C++/infer.cpp
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@ -1,212 +0,0 @@
// Copyright (C) 2018-2023 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
// https://docs.openvino.ai/2023.3/openvino_sample_hello_reshape_ssd.html
#include <memory>
#include <string>
#include <vector>
// clang-format off
#include "openvino/openvino.hpp"
#include "openvino/opsets/opset9.hpp"
#include "format_reader_ptr.h"
#include "samples/args_helper.hpp"
#include "samples/common.hpp"
#include "samples/slog.hpp"
// clang-format on
// thickness of a line (in pixels) to be used for bounding boxes
constexpr int BBOX_THICKNESS = 2;
using namespace ov::preprocess;
int main(int argc, char* argv[]) {
try {
// -------- Get OpenVINO runtime version -----------------------------
slog::info << ov::get_openvino_version() << slog::endl;
// --------------------------- Parsing and validation of input arguments
if (argc != 4) {
std::cout << "Usage : " << argv[0] << " <path_to_model> <path_to_image> <device>" << std::endl;
return EXIT_FAILURE;
}
const std::string model_path{argv[1]};
const std::string image_path{argv[2]};
const std::string device_name{argv[3]};
// -------------------------------------------------------------------
// Step 1. Initialize OpenVINO Runtime core
ov::Core core;
// -------------------------------------------------------------------
// Step 2. Read a model
slog::info << "Loading model files: " << model_path << slog::endl;
std::shared_ptr<ov::Model> model = core.read_model(model_path);
printInputAndOutputsInfo(*model);
// Step 3. Validate model inputs and outputs
OPENVINO_ASSERT(model->inputs().size() == 1, "Sample supports models with 1 input only");
OPENVINO_ASSERT(model->outputs().size() == 1, "Sample supports models with 1 output only");
// SSD has an additional post-processing DetectionOutput layer that simplifies output filtering,
// try to find it.
const ov::NodeVector ops = model->get_ops();
const auto it = std::find_if(ops.begin(), ops.end(), [](const std::shared_ptr<ov::Node>& node) {
return std::string{node->get_type_name()} ==
std::string{ov::opset9::DetectionOutput::get_type_info_static().name};
});
if (it == ops.end()) {
throw std::logic_error("model does not contain DetectionOutput layer");
}
// -------------------------------------------------------------------
// Step 4. Read input image
// Read input image without resize
FormatReader::ReaderPtr reader(image_path.c_str());
if (reader.get() == nullptr) {
std::cout << "Image " + image_path + " cannot be read!" << std::endl;
return 1;
}
std::shared_ptr<unsigned char> image_data = reader->getData();
size_t image_channels = 3;
size_t image_width = reader->width();
size_t image_height = reader->height();
// -------------------------------------------------------------------
// Step 5. Reshape model to image size and batch size
// assume model layout NCHW
const ov::Layout model_layout{"NCHW"};
ov::Shape tensor_shape = model->input().get_shape();
size_t batch_size = 1;
tensor_shape[ov::layout::batch_idx(model_layout)] = batch_size;
tensor_shape[ov::layout::channels_idx(model_layout)] = image_channels;
tensor_shape[ov::layout::height_idx(model_layout)] = image_height;
tensor_shape[ov::layout::width_idx(model_layout)] = image_width;
std::cout << "Reshape network to the image size = [" << image_height << "x" << image_width << "] " << std::endl;
model->reshape({{model->input().get_any_name(), tensor_shape}});
printInputAndOutputsInfo(*model);
// -------------------------------------------------------------------
// Step 6. Configure model preprocessing
const ov::Layout tensor_layout{"NHWC"};
// clang-format off
ov::preprocess::PrePostProcessor ppp = ov::preprocess::PrePostProcessor(model);
// 1) input() with no args assumes a model has a single input
ov::preprocess::InputInfo& input_info = ppp.input();
// 2) Set input tensor information:
// - precision of tensor is supposed to be 'u8'
// - layout of data is 'NHWC'
input_info.tensor().
set_element_type(ov::element::u8).
set_layout(tensor_layout);
// 3) Adding explicit preprocessing steps:
// - convert u8 to f32
// - convert layout to 'NCHW' (from 'NHWC' specified above at tensor layout)
ppp.input().preprocess().
convert_element_type(ov::element::f32).
convert_layout("NCHW");
// 4) Here we suppose model has 'NCHW' layout for input
input_info.model().set_layout("NCHW");
// 5) output () with no args assumes a model has a single output
ov::preprocess::OutputInfo& output_info = ppp.output();
// 6) declare output element type as FP32
output_info.tensor().set_element_type(ov::element::f32);
// 7) Apply preprocessing modifing the original 'model'
model = ppp.build();
// clang-format on
// -------------------------------------------------------------------
// Step 7. Loading a model to the device
ov::CompiledModel compiled_model = core.compile_model(model, device_name);
// -------------------------------------------------------------------
// Step 8. Create an infer request
ov::InferRequest infer_request = compiled_model.create_infer_request();
// Step 9. Fill model with input data
ov::Tensor input_tensor = infer_request.get_input_tensor();
// copy NHWC data from image to tensor with batch
unsigned char* image_data_ptr = image_data.get();
unsigned char* tensor_data_ptr = input_tensor.data<unsigned char>();
size_t image_size = image_width * image_height * image_channels;
for (size_t i = 0; i < image_size; i++) {
tensor_data_ptr[i] = image_data_ptr[i];
}
// -------------------------------------------------------------------
// Step 10. Do inference synchronously
infer_request.infer();
// Step 11. Get output data from the model
ov::Tensor output_tensor = infer_request.get_output_tensor();
ov::Shape output_shape = model->output().get_shape();
const size_t ssd_object_count = output_shape[2];
const size_t ssd_object_size = output_shape[3];
const float* detections = output_tensor.data<const float>();
// -------------------------------------------------------------------
std::vector<int> boxes;
std::vector<int> classes;
// Step 12. Parse SSD output
for (size_t object = 0; object < ssd_object_count; object++) {
int image_id = static_cast<int>(detections[object * ssd_object_size + 0]);
if (image_id < 0) {
break;
}
// detection, has the format: [image_id, label, conf, x_min, y_min, x_max, y_max]
int label = static_cast<int>(detections[object * ssd_object_size + 1]);
float confidence = detections[object * ssd_object_size + 2];
int xmin = static_cast<int>(detections[object * ssd_object_size + 3] * image_width);
int ymin = static_cast<int>(detections[object * ssd_object_size + 4] * image_height);
int xmax = static_cast<int>(detections[object * ssd_object_size + 5] * image_width);
int ymax = static_cast<int>(detections[object * ssd_object_size + 6] * image_height);
if (confidence > 0.5f) {
// collect only objects with >50% probability
classes.push_back(label);
boxes.push_back(xmin);
boxes.push_back(ymin);
boxes.push_back(xmax - xmin);
boxes.push_back(ymax - ymin);
std::cout << "[" << object << "," << label << "] element, prob = " << confidence << ", (" << xmin
<< "," << ymin << ")-(" << xmax << "," << ymax << ")" << std::endl;
}
}
// draw bounding boxes on the image
addRectangles(image_data.get(), image_height, image_width, boxes, classes, BBOX_THICKNESS);
const std::string image_name = "hello_reshape_ssd_output.bmp";
if (writeOutputBmp(image_name, image_data.get(), image_height, image_width)) {
std::cout << "The resulting image was saved in the file: " + image_name << std::endl;
} else {
throw std::logic_error(std::string("Can't create a file: ") + image_name);
}
} catch (const std::exception& ex) {
std::cerr << ex.what() << std::endl;
return EXIT_FAILURE;
}
std::cout << std::endl
<< "This sample is an API example, for any performance measurements "
"please use the dedicated benchmark_app tool"
<< std::endl;
return EXIT_SUCCESS;
}

46
projects/OpenVINO/C++/infer.hpp Normal file
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@ -0,0 +1,46 @@
#ifndef INFER_HPP_
#define INFER_HPP_
#include <opencv2/dnn/dnn.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include <openvino/openvino.hpp>
// Структура обнаружения
struct Detection {
short class_id; // Идентификатор класс
float probability; // Вероятность обнаружения
cv::Rect box; // Размеры объекта
};
// Класс обнаружения
class Inf {
public:
Inf() {};
Inf(const std::string &model_path, const float &model_probability, const float &model_NMS);
Inf(const std::string &model_path, const cv::Size model_input_shape, const float &model_probability, const float &model_NMS);
~Inf() {};
void inference(cv::Mat &frame);
private:
void init(const std::string &model_path);
void pre(const cv::Mat &frame);
void post( cv::Mat &frame);
cv::Rect GetBoundingBox(const cv::Rect &src) const;
void DrawDetectedObject(cv::Mat &frame, const Detection &detections) const;
cv::Point2f scale_factor;
cv::Size2f input_shape;
cv::Size output_shape;
ov::InferRequest inference_request;
ov::CompiledModel compiled_model;
float probability;
float NMS;
};
#endif // INFER_HPP_

1
projects/OpenVINO/C++/result.bmp Symbolic link
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@ -0,0 +1 @@
/tmp/cpp_openvino_result.bmp

26
projects/OpenVINO/C++/xmake.lua Normal file
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@ -0,0 +1,26 @@
set_project("inf")
set_languages("cxx17")
add_rules("mode.debug", "mode.release")
if is_mode("debug") then
set_symbols("debug")
set_optimize("none")
end
add_includedirs(
"/opt/opencv-4.8.0/include/opencv4/" -- OpenCV
)
add_linkdirs(
"/opt/opencv-4.8.0/lib/" -- OpenCV
)
target("inf")
set_kind("binary")
add_syslinks(
"openvino",
"opencv_core", "opencv_imgproc", "opencv_highgui",
"opencv_imgcodecs", "opencv_dnn"
)
add_files("i.cpp", "infer.cc")
add_runenvs("LD_LIBRARY_PATH", "/opt/opencv-4.8.0/lib/")

3
projects/OpenVINO/Python/.gitignore vendored
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@ -1,3 +0,0 @@
# models
intel/
*.onnx

10
projects/OpenVINO/Python/detect.py
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@ -4,10 +4,8 @@ import cv2
import numpy as np import numpy as np
import openvino as ov import openvino as ov
# model_path = './yolov8/yolov8n/yolov8n.xml' model_path = '../Models/yolov8n_openvino_model/yolov8n.xml'
model_path = './yolov8/yolov8s/yolov8s.xml' image_path = '../../../assets/bus.jpg'
# model_path = './intel/person-detection-retail-0013/FP16/person-detection-retail-0013.xml'
image_path = 'cat_dog.jpg'
device_name = 'CPU' device_name = 'CPU'
@ -30,7 +28,7 @@ def main():
ppp = ov.preprocess.PrePostProcessor(model) ppp = ov.preprocess.PrePostProcessor(model)
ppp.input().tensor().set_element_type(ov.Type.u8).set_layout(ov.Layout('NHWC')) ppp.input().tensor().set_element_type(ov.Type.u8).set_layout(ov.Layout('NHWC'))
ppp.input().model().set_layout(ov.Layout('NCHW')) ppp.input().model().set_layout(ov.Layout('NCHW'))
ppp.output().tensor().set_element_type(ov.Type.f32) # f16 ppp.output().tensor().set_element_type(ov.Type.f32)
model = ppp.build() model = ppp.build()
compiled_model = core.compile_model(model, device_name) compiled_model = core.compile_model(model, device_name)
@ -43,7 +41,7 @@ def main():
for detection in detections: for detection in detections:
confidence = detection[2] confidence = detection[2]
if confidence > 0.7: if confidence > 0.25:
class_id = int(detection[1]) class_id = int(detection[1])
xmin = int(detection[3] * w) xmin = int(detection[3] * w)