day/examples/YOLOv8-OpenVINO-CPP-Inference/inference.cc

#include "inference.h"

#include <memory>
#include <opencv2/dnn.hpp>
#include <random>

namespace yolo {

// Constructor to initialize the model with default input shape
Inference::Inference(const std::string &model_path, const float &model_confidence_threshold, const float &model_NMS_threshold) {
	model_input_shape_ = cv::Size(640, 640); // Set the default size for models with dynamic shapes to prevent errors.
	model_confidence_threshold_ = model_confidence_threshold;
	model_NMS_threshold_ = model_NMS_threshold;
	InitializeModel(model_path);
}

// Constructor to initialize the model with specified input shape
Inference::Inference(const std::string &model_path, const cv::Size model_input_shape, const float &model_confidence_threshold, const float &model_NMS_threshold) {
	model_input_shape_ = model_input_shape;
	model_confidence_threshold_ = model_confidence_threshold;
	model_NMS_threshold_ = model_NMS_threshold;
	InitializeModel(model_path);
}

void Inference::InitializeModel(const std::string &model_path) {
	ov::Core core; // OpenVINO core object
	std::shared_ptr<ov::Model> model = core.read_model(model_path); // Read the model from file

	// If the model has dynamic shapes, reshape it to the specified input shape
	if (model->is_dynamic()) {
		model->reshape({1, 3, static_cast<long int>(model_input_shape_.height), static_cast<long int>(model_input_shape_.width)});
	}

	// Preprocessing setup for the model
	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(); // Build the preprocessed model

	// Compile the model for inference
	compiled_model_ = core.compile_model(model, "AUTO");
	inference_request_ = compiled_model_.create_infer_request(); // Create inference request

	short width, height;

	// Get input shape from the model
	const std::vector<ov::Output<ov::Node>> inputs = model->inputs();
	const ov::Shape input_shape = inputs[0].get_shape();
	height = input_shape[1];
	width = input_shape[2];
	model_input_shape_ = cv::Size2f(width, height);

	// Get output shape from the model
	const std::vector<ov::Output<ov::Node>> outputs = model->outputs();
	const ov::Shape output_shape = outputs[0].get_shape();
	height = output_shape[1];
	width = output_shape[2];
	model_output_shape_ = cv::Size(width, height);
}

// Method to run inference on an input frame
void Inference::RunInference(cv::Mat &frame) {
	Preprocessing(frame); // Preprocess the input frame
	inference_request_.infer(); // Run inference
	PostProcessing(frame); // Postprocess the inference results
}

// Method to preprocess the input frame
void Inference::Preprocessing(const cv::Mat &frame) {
	cv::Mat resized_frame;
	cv::resize(frame, resized_frame, model_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 / model_input_shape_.width);
	scale_factor_.y = static_cast<float>(frame.rows / model_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); // Create input tensor
	inference_request_.set_input_tensor(input_tensor); // Set input tensor for inference
}

// Method to postprocess the inference results
void Inference::PostProcessing(cv::Mat &frame) {
	std::vector<int> class_list;
	std::vector<float> confidence_list;
	std::vector<cv::Rect> box_list;

	// Get the output tensor from the inference request
	const float *detections = inference_request_.get_output_tensor().data<const float>();
	const cv::Mat detection_outputs(model_output_shape_, CV_32F, (float *)detections); // Create OpenCV matrix from output tensor

	// Iterate over detections and collect class IDs, confidence scores, and bounding boxes
	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); // Find the class with the highest score

		// Check if the detection meets the confidence threshold
		if (score > model_confidence_threshold_) {
			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);
		}
	}

	// Apply Non-Maximum Suppression (NMS) to filter overlapping bounding boxes
	std::vector<int> NMS_result;
	cv::dnn::NMSBoxes(box_list, confidence_list, model_confidence_threshold_, model_NMS_threshold_, NMS_result);

	// Collect final detections after NMS
	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.confidence = confidence_list[id];
		result.box = GetBoundingBox(box_list[id]);

		DrawDetectedObject(frame, result);
	}
}

// Method to get the bounding box in the correct scale
cv::Rect Inference::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 Inference::DrawDetectedObject(cv::Mat &frame, const Detection &detection) const {
	const cv::Rect &box = detection.box;
	const float &confidence = detection.confidence;
	const int &class_id = detection.class_id;
	
	// Generate a random color for the bounding box
	std::random_device rd;
	std::mt19937 gen(rd());
	std::uniform_int_distribution<int> dis(120, 255);
	const cv::Scalar &color = cv::Scalar(dis(gen), dis(gen), dis(gen));
	
	// Draw the bounding box around the detected object
	cv::rectangle(frame, cv::Point(box.x, box.y), cv::Point(box.x + box.width, box.y + box.height), color, 3);
	
	// Prepare the class label and confidence text
	std::string classString = classes_[class_id] + std::to_string(confidence).substr(0, 4);
	
	// Get the size of the text box
	cv::Size textSize = cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 0.75, 2, 0);
	cv::Rect textBox(box.x, box.y - 40, textSize.width + 10, textSize.height + 20);
	
	// Draw the text box
	cv::rectangle(frame, textBox, color, cv::FILLED);
	
	// Put the class label and confidence text above the bounding box
	cv::putText(frame, classString, cv::Point(box.x + 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 0.75, cv::Scalar(0, 0, 0), 2, 0);
}
} // namespace yolo