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* [Docs] Pick seg fastdeploy docs from PaddleSeg * [Docs] update seg docs * [Docs] Add c&csharp examples for seg * [Docs] Add c&csharp examples for seg * [Doc] Update paddleseg README.md * Update README.md
60 lines
2.4 KiB
C#
60 lines
2.4 KiB
C#
// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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using System;
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using System.IO;
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using System.Runtime.InteropServices;
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using OpenCvSharp;
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using fastdeploy;
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namespace Test
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{
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public class TestPaddleSegModel
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{
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public static void Main(string[] args)
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{
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if (args.Length < 3) {
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Console.WriteLine(
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"Usage: infer_demo path/to/model_dir path/to/image run_option" +
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"e.g ./infer_model ./ppseg_model_dir ./test.jpeg 0"
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);
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Console.WriteLine( "The data type of run_option is int, 0: run with cpu; 1: run with gpu");
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return;
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}
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string model_dir = args[0];
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string image_path = args[1];
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string model_file = model_dir + "\\" + "model.pdmodel";
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string params_file = model_dir + "\\" + "model.pdiparams";
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string config_file = model_dir + "\\" + "deploy.yaml";
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RuntimeOption runtimeoption = new RuntimeOption();
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int device_option = Int32.Parse(args[2]);
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if(device_option==0){
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runtimeoption.UseCpu();
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}else{
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runtimeoption.UseGpu();
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}
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fastdeploy.vision.segmentation.PaddleSegModel model = new fastdeploy.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtimeoption, ModelFormat.PADDLE);
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if(!model.Initialized()){
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Console.WriteLine("Failed to initialize.\n");
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}
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Mat image = Cv2.ImRead(image_path);
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fastdeploy.vision.SegmentationResult res = model.Predict(image);
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Console.WriteLine(res.ToString());
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Mat res_img = fastdeploy.vision.Visualize.VisSegmentation(image, res, 0.5f);
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Cv2.ImShow("result.png", res_img);
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Cv2.WaitKey(0);
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}
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}
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} |