Delete model_infer.cpp

examples/C#_deploy/model_infer.cpp

@@ -1,163 +0,0 @@
-// Copyright (c) 2021 PaddlePaddle 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 <gflags/gflags.h>
-#include <string>
-#include <vector>
-
-#include "model_deploy/common/include/paddle_deploy.h"
-
-PaddleDeploy::Model* model;
-
-extern "C" __declspec(dllexport) void InitModel(const char* model_type, const char* model_filename, const char* params_filename, const char* cfg_file)
-{
-	bool use_gpu = false;
-	int gpu_id = 0;
-
-	// create model
-	model = PaddleDeploy::CreateModel(model_type);  //FLAGS_model_type
-
-	// model init
-	model->Init(cfg_file);
-
-	// inference engine init
-	PaddleDeploy::PaddleEngineConfig engine_config;
-	engine_config.model_filename = model_filename;
-	engine_config.params_filename = params_filename;
-	engine_config.use_gpu = use_gpu;
-	engine_config.gpu_id = gpu_id;
-	bool init = model->PaddleEngineInit(engine_config);
-	if (init)
-	{
-		std::cout << "init model success" << std::endl;
-	}
-}
-/*
-* img: input for predicting.
-*
-* nWidth: width of img.
-*
-* nHeight: height of img.
-*
-* nChannel: channel of img.
-*
-* output: result of pridict ,include category_id£¬score£¬coordinate¡£
-*
-* nBoxesNum£º number of box
-*
-* LabelList: label list of result
-*/
-extern "C" __declspec(dllexport) void ModelPredict(const unsigned char* img, int nWidth, int nHeight,int nChannel, float* output, int* nBoxesNum, char* LabelList)
-{
-	// prepare data
-	std::vector<cv::Mat> imgs;
-
-	int nType = 0;
-	if (nChannel==1)
-	{
-		nType = CV_8UC1;
-	}
-	else if (nChannel == 2)
-	{
-		nType = CV_8UC2;
-	}
-	else if (nChannel == 3)
-	{
-		nType = CV_8UC3;
-	}
-	else if (nChannel == 4)
-	{
-		nType = CV_8UC4;
-	}
-
-	cv::Mat input = cv::Mat::zeros(cv::Size(nWidth, nHeight), nType);
-	memcpy(input.data, img, nHeight * nWidth * nChannel * sizeof(uchar));
-	//cv::imwrite("./1.png", input);
-	imgs.push_back(std::move(input));
-
-	// predict
-	std::vector<PaddleDeploy::Result> results;
-	bool pre = model->Predict(imgs, &results, 1);
-	if (pre)
-	{
-		std::cout << "model predict success" << std::endl;
-	}
-	nBoxesNum[0] = results.size();
-	std::string label ="";
-	for (int num = 0; num < results.size(); num++)
-	{
-		//std::cout << "res: " << results[num] << std::endl;
-		for (int i = 0; i < results[num].det_result->boxes.size(); i++)
-		{
-			//std::cout << "category: " << results[num].det_result->boxes[i].category << std::endl;
-			label = label + results[num].det_result->boxes[i].category+ " ";
-			// labelindex
-			output[num * 6 + 0] = results[num].det_result->boxes[i].category_id;
-			// score
-			output[num * 6 + 1] = results[num].det_result->boxes[i].score;
-			//// box
-			output[num * 6 + 2] = results[num].det_result->boxes[i].coordinate[0];
-			output[num * 6 + 3] = results[num].det_result->boxes[i].coordinate[1];
-			output[num * 6 + 4] = results[num].det_result->boxes[i].coordinate[2];
-			output[num * 6 + 5] = results[num].det_result->boxes[i].coordinate[3];
-		}
-	}
-	memcpy(LabelList, label.c_str(), strlen(label.c_str()));
-}
-
-extern "C" __declspec(dllexport) void DestructModel()
-{
-	delete model;
-	std::cout << "destruct model success" << std::endl;
-
-}
-
-//DEFINE_string(model_filename, "", "Path of det inference model");
-//DEFINE_string(params_filename, "", "Path of det inference params");
-//DEFINE_string(cfg_file, "", "Path of yaml file");
-//DEFINE_string(model_type, "", "model type");
-//DEFINE_string(image, "", "Path of test image file");
-//DEFINE_bool(use_gpu, false, "Infering with GPU or CPU");
-//DEFINE_int32(gpu_id, 0, "GPU card id");
-//
-//int main(int argc, char** argv) {
-//  // Parsing command-line
-//  google::ParseCommandLineFlags(&argc, &argv, true);
-//
-//  // create model
-//  PaddleDeploy::Model* model = PaddleDeploy::CreateModel(FLAGS_model_type);
-//
-//  // model init
-//  model->Init(FLAGS_cfg_file);
-//
-//  // inference engine init
-//  PaddleDeploy::PaddleEngineConfig engine_config;
-//  engine_config.model_filename = FLAGS_model_filename;
-//  engine_config.params_filename = FLAGS_params_filename;
-//  engine_config.use_gpu = FLAGS_use_gpu;
-//  engine_config.gpu_id = FLAGS_gpu_id;
-//  model->PaddleEngineInit(engine_config);
-//
-//  // prepare data
-//  std::vector<cv::Mat> imgs;
-//  imgs.push_back(std::move(cv::imread(FLAGS_image)));
-//
-//  // predict
-//  std::vector<PaddleDeploy::Result> results;
-//  model->Predict(imgs, &results, 1);
-//
-//  std::cout << results[0] << std::endl;
-//  delete model;
-//  return 0;
-//}