ANSCORE/modules/ANSOCR/ANSPaddleOCR/src/ocr_det.cpp

#include "include/ocr_det.h"

namespace PaddleOCR {

	Detector::Detector(std::string model_path)
	{
		ov::Core core;
		this->model_path = model_path;
		this->model = core.read_model(this->model_path);
		this->model->reshape({ 1, 3, ov::Dimension(32, this->limit_side_len_), ov::Dimension(1, this->limit_side_len_) });
		//core.set_property("CPU", ov::hint::performance_mode(ov::hint::PerformanceMode::THROUGHPUT));
		this->compiled_model = core.compile_model(this->model, "CPU");
		//this->compiled_model = core.compile_model(this->model, "CPU");
		this->infer_request = this->compiled_model.create_infer_request();
	}
	void Detector::SetParameters(std::string limit_type,
		std::string det_db_score_mode,
		bool is_scale,
		double det_db_thresh,
		double det_db_box_thresh,
		double det_db_unclip_ratio,
		bool use_dilation)
	{
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		this->limit_type_ = limit_type;
		this->det_db_score_mode_ = det_db_score_mode;
		this->is_scale_ = is_scale;
		this->det_db_thresh_ = det_db_thresh;
		this->det_db_box_thresh_ = det_db_box_thresh;
		this->det_db_unclip_ratio_ = det_db_unclip_ratio;
		this->use_dilation_ = use_dilation;
	}
	void Detector::GetParameters(std::string& limit_type,
		std::string& det_db_score_mode,
		bool& is_scale,
		double& det_db_thresh,
		double& det_db_box_thresh,
		double& det_db_unclip_ratio,
		bool& use_dilation)
	{
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		limit_type = this->limit_type_;
		det_db_score_mode = this->det_db_score_mode_;
		is_scale = this->is_scale_;
		det_db_thresh = this->det_db_thresh_;
		det_db_box_thresh = this->det_db_box_thresh_;
		det_db_unclip_ratio = this->det_db_unclip_ratio_;
		use_dilation = this->use_dilation_;
	}
	void Detector::Run(const cv::Mat& src_img, std::vector<OCRPredictResult>& ocr_results)
	{
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		try {
			this->src_img = src_img;
			this->resize_op_.Run(this->src_img, this->resize_img, this->limit_type_,
				this->limit_side_len_, this->ratio_h, this->ratio_w);

			this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
				this->is_scale_);

			std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
			ov::Shape intput_shape = { 1, 3, (size_t)resize_img.rows, (size_t)resize_img.cols };
			this->permute_op_.Run(&resize_img, input.data());

			std::vector<std::vector<std::vector<int>>> boxes;
			auto input_port = this->compiled_model.input();

			// -------- set input --------
			ov::Tensor input_tensor(input_port.get_element_type(), intput_shape, input.data());
			this->infer_request.set_input_tensor(input_tensor);
			// -------- start inference --------

	/*		this->infer_request.start_async();
			this->infer_request.wait();*/

			this->infer_request.infer();

			auto output = this->infer_request.get_output_tensor(0);
			const float* out_data = output.data<const float>();

			ov::Shape output_shape = output.get_shape();
			const size_t n2 = output_shape[2];
			const size_t n3 = output_shape[3];
			const int n = n2 * n3;

			std::vector<float> pred(n, 0.0);
			std::vector<unsigned char> cbuf(n, ' ');

			for (int i = 0; i < n; i++) {
				pred[i] = float(out_data[i]);
				cbuf[i] = (unsigned char)((out_data[i]) * 255);
			}

			cv::Mat cbuf_map(n2, n3, CV_8UC1, (unsigned char*)cbuf.data());
			cv::Mat pred_map(n2, n3, CV_32F, (float*)pred.data());

			const double threshold = this->det_db_thresh_ * 255;
			const double maxvalue = 255;
			cv::Mat bit_map;
			cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
			if (this->use_dilation_) {
				cv::Mat dila_ele =
					cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
				cv::dilate(bit_map, bit_map, dila_ele);
			}

			boxes = post_processor_.BoxesFromBitmap(
				pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_,
				this->det_db_score_mode_);

			boxes = post_processor_.FilterTagDetRes(boxes, this->ratio_h, this->ratio_w, this->src_img);
			for (int i = 0; i < boxes.size(); i++) {
				OCRPredictResult res;
				res.box = boxes[i];
				ocr_results.push_back(res);
			}
			// sort boex from top to bottom, from left to right
			Utility::sorted_boxes(ocr_results);
		}
		catch (const std::exception& e) {
			std::cerr << e.what() << std::endl;
		}
	}
}