Halcon Deep Learning Object Detection 和 C# 客户端调用模型进行识别

分类识别样例程序

工具

• Halcon 开发工具
  

• Halcon 标注工具
  

• Vs2022 社区版

标注图片

样例图片

利用了demo的图片

C:\Users\Public\Documents\MVTec\HALCON-20.11-Steady\examples\images\food

导入图片并标注

在 DL（Halcon 标注工具）工具中新建一个分类项目，如下图导入图片并且标注

样例工程地址

C:\Users\Administrator\AppData\Roaming\MVTec\DeepLearningTool\examples\projects\Example_ObjDetection_PillBags.dltp

Halcon代码

* 
* Deep learning object detection workflow:
* 
* This example demonstrates the overall workflow for
* object detection based on deep learning, using axis-aligned bounding boxes.
* (instance_type = 'rectangle1')
* 
* PLEASE NOTE:
* - This is a bare-bones example.
* - For this, default parameters are used as much as possible.
*   Hence, the results may differ from those obtained in other examples.
* - For more detailed steps, please refer to the respective examples from the series,
*   e.g. detect_pills_deep_learning_1_prepare.hdev etc.
* 
dev_close_window ()
dev_update_off ()
set_system ('seed_rand', 42)
* 
* ***   0) SET INPUT/OUTPUT PATHS   ***
* 
* get_system ('example_dir', HalconExampleDir)
* PillBagJsonFile := HalconExampleDir + '/hdevelop/Deep-Learning/Detection/pill_bag.json'
* InputImageDir := HalconExampleDir + '/images/'
* 
OutputDir := 'detect_pills_data'
* Set to true, if the results should be deleted after running this program.
RemoveResults := false
* 
* ***   1.) PREPARE   ***
* 
* Read in a DLDataset.
* Here, we read the data from a COCO file.
* Alternatively, you can read a DLDataset dictionary
* as created by e.g., the MVTec Deep Learning Tool using read_dict().
* read_dl_dataset_from_coco (PillBagJsonFile, InputImageDir, [], DLDataset)
read_dict ('D:/test/halcon/images/pill_bag/药片检测.hdict', [], [], DLDataset)
* 
* Create the detection model with parameters suitable for
* the dataset.
create_dict (DLModelDetectionParam)
set_dict_tuple (DLModelDetectionParam, 'image_dimensions', [512,320,3])
set_dict_tuple (DLModelDetectionParam, 'max_level', 4)
get_dict_tuple (DLDataset, 'class_ids', ClassIDs)
set_dict_tuple (DLModelDetectionParam, 'class_ids', ClassIDs)
create_dl_model_detection ('pretrained_dl_classifier_compact.hdl', |ClassIDs|, DLModelDetectionParam, DLModelHandle)
* 
* Preprocess the data in DLDataset.
split_dl_dataset (DLDataset, 60, 20, [])
create_dict (PreprocessSettings)
* Here, existing preprocessed data will be overwritten.
set_dict_tuple (PreprocessSettings, 'overwrite_files', true)
create_dl_preprocess_param_from_model (DLModelHandle, 'none', 'full_domain', [], [], [], DLPreprocessParam)
preprocess_dl_dataset (DLDataset, OutputDir, DLPreprocessParam, PreprocessSettings, DLDatasetFileName)
write_dict (DLPreprocessParam, 'D:/test/halcon/目标检测-药片/detect_pills_data/dl_preprocess_param.hdict', [], [])
* 
* Inspect 10 randomly selected preprocessed DLSamples visually.
* create_dict (WindowDict)
* get_dict_tuple (DLDataset, 'samples', DatasetSamples)
* for Index := 0 to 9 by 1
*     SampleIndex := round(rand(1) * (|DatasetSamples| - 1))
*     read_dl_samples (DLDataset, SampleIndex, DLSample)
*     dev_display_dl_data (DLSample, [], DLDataset, 'bbox_ground_truth', [], WindowDict)
*     dev_disp_text ('Press F5 to continue', 'window', 'bottom', 'right', 'black', [], [])
*     stop ()
* endfor
* dev_close_window_dict (WindowDict)

stop()


* 
* ***   2.) TRAIN   ***
* 
* Training can be performed on a GPU or CPU.
* See the respective system requirements in the Installation Guide.
* If possible a GPU is used in this example.
* In case you explicitely wish to run this example on the CPU,
* choose the CPU device instead.
query_available_dl_devices (['runtime','runtime'], ['gpu','cpu'], DLDeviceHandles)
if (|DLDeviceHandles| == 0)
    throw ('No supported device found to continue this example.')
endif
* Due to the filter used in query_available_dl_devices, the first device is a GPU, if available.
DLDevice := DLDeviceHandles[1]
get_dl_device_param (DLDevice, 'type', DLDeviceType)
if (DLDeviceType == 'cpu')
    * The number of used threads may have an impact
    * on the training duration.
    NumThreadsTraining := 4
    set_system ('thread_num', NumThreadsTraining)
endif
* 
* For details see the documentation of set_dl_model_param () and get_dl_model_param ().
set_dl_model_param (DLModelHandle, 'batch_size', 1)
set_dl_model_param (DLModelHandle, 'learning_rate', 0.001)
set_dl_model_param (DLModelHandle, 'device', DLDevice)
* 
* Here, we run a short training of 10 epochs.
* For better model performance increase the number of epochs,
* from 10 to e.g. 60.
create_dl_train_param (DLModelHandle, 10, 1, 'true', 42, [], [], TrainParam)
* The training and thus the call of train_dl_model_batch ()
* is done using the following procedure.
train_dl_model (DLDataset, DLModelHandle, TrainParam, 0, TrainResults, TrainInfos, EvaluationInfos)
* 
* Read the best model, which is written to file by train_dl_model.
read_dl_model ('model_best.hdl', DLModelHandle)
dev_disp_text ('Press F5 to continue', 'window', 'bottom', 'left', 'black', [], [])
stop ()
* 
dev_close_window ()
dev_close_window ()

stop ()

* 
* ***   3.) EVALUATE   ***
* 
create_dict (GenParamEval)
set_dict_tuple (GenParamEval, 'detailed_evaluation', true)
set_dict_tuple (GenParamEval, 'show_progress', true)
*
*设置评估时使用cpu，因为当前笔记本的mx320 gpu 不够用
*
set_dl_model_param(DLModelHandle,'runtime','cpu')
evaluate_dl_model (DLDataset, DLModelHandle, 'split', 'test', GenParamEval, EvaluationResult, EvalParams)
* 
create_dict (DisplayMode)
set_dict_tuple (DisplayMode, 'display_mode', ['pie_charts_precision','pie_charts_recall'])
dev_display_detection_detailed_evaluation (EvaluationResult, EvalParams, DisplayMode, WindowDict)
dev_disp_text ('Press F5 to continue', 'window', 'bottom', 'right', 'black', [], [])
stop ()
dev_close_window_dict (WindowDict)
* 
* Optimize the model for inference,
* meaning, reduce its memory consumption.
set_dl_model_param (DLModelHandle, 'optimize_for_inference', 'true')
set_dl_model_param (DLModelHandle, 'batch_size', 1)
* Save the model in this optimized state.
write_dl_model (DLModelHandle, 'model_best.hdl')

stop ()
* 
* ***   4.) INFER   ***
* 
* To demonstrate the inference steps, we apply
* the trained model to some randomly chosen example images.
list_image_files ('C:/Users/Administrator/AppData/Roaming/MVTec/DeepLearningTool/examples/images/pill_bag', 'default', 'recursive', ImageFiles)
tuple_shuffle (ImageFiles, ImageFilesShuffled)
* 
create_dict (WindowDict)
for IndexInference := 0 to 9 by 1
    read_image (Image, ImageFilesShuffled[IndexInference])
    gen_dl_samples_from_images (Image, DLSampleInference)
    preprocess_dl_samples (DLSampleInference, DLPreprocessParam)
    apply_dl_model (DLModelHandle, DLSampleInference, [], DLResult)
    * 
    dev_display_dl_data (DLSampleInference, DLResult, DLDataset, 'bbox_result', [], WindowDict)
    dev_disp_text ('Press F5 to continue', 'window', 'bottom', 'right', 'black', [], [])
    stop ()
endfor
dev_close_window_dict (WindowDict)

导出语言文件

C# 客户端使用模型进行识别

新建项目

• 新建一个桌面项目
• 将导出的.cs文件添加到项目中
• 创建页面
  页面如下
  

代码

using HalconDotNet;
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;

namespace 目标检测
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
            this.Load += Form1_Load;
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            openFileDialog1.FileOk += OpenFileDialog1_FileOk;
        }
        private void OpenFileDialog1_FileOk(object sender, CancelEventArgs e)
        {
            pictureBox1.Image = new Bitmap(openFileDialog1.FileName);
        }
        private void button1_Click(object sender, EventArgs e)
        {
            openFileDialog1.ShowDialog();
        }

        private void button2_Click(object sender, EventArgs e)
        {
            if (!string.IsNullOrEmpty(openFileDialog1.FileName))
                Action(openFileDialog1.FileName);
        } 

        private void Action(string filename)
        {
            HDevelopExport developExport = new HDevelopExport();

            // Local iconic variables 

            HObject ho_ImageBatch;

            // Local control variables 

            HTuple hv_DLDeviceHandles = new HTuple(), hv_DLDevice = new HTuple();
            HTuple hv_ClassNames = new HTuple(), hv_ClassIDs = new HTuple();
            HTuple hv_DLModelHandle = new HTuple(), hv_DLPreprocessParam = new HTuple();
            HTuple hv_WindowHandleDict = new HTuple(), hv_DLDataInfo = new HTuple();
            HTuple hv_DLSample = new HTuple(), hv_DLResult = new HTuple();
            HTuple hv_txt = new HTuple(), hv_DetectedClassIDs = new HTuple();
            HTuple hv_DetectedConfidences = new HTuple(), hv_Rows1 = new HTuple();
            HTuple hv_Rows2 = new HTuple(), hv_Cols1 = new HTuple();
            HTuple hv_Cols2 = new HTuple(), hv_Index = new HTuple();
            // Initialize local and output iconic variables 
            HOperatorSet.GenEmptyObj(out ho_ImageBatch);
            try
            {
                //
                //This example is part of a series of examples, which summarizes
                //the workflow for DL object detection. It uses the MVTec pill bag dataset.
                //
                //The four parts are:
                //1. Creation of the model and dataset preprocessing.
                //2. Training of the model.
                //3. Evaluation of the trained model.
                //4. Inference on new images.
                //
                //This examples covers part 4: 'Inference on new images'
                //
                //It explains how to apply a trained model on new images and shows
                //an application based on the pill bag dataset.
                //
                //Please note: This script uses a pretrained model. To use the output
                //of part 1 and part 2 of this example series, set UsePretrainedModel
                //to false below.
                //
               

                //
                //Inference can be done on a GPU or CPU.
                //See the respective system requirements in the Installation Guide.
                //If possible a GPU is used in this example.
                //In case you explicitely wish to run this example on the CPU,
                //choose the CPU device instead.
                hv_DLDeviceHandles.Dispose();
                HOperatorSet.QueryAvailableDlDevices((new HTuple("runtime")).TupleConcat("runtime"),
                    (new HTuple("gpu")).TupleConcat("cpu"), out hv_DLDeviceHandles);
                if ((int)(new HTuple((new HTuple(hv_DLDeviceHandles.TupleLength())).TupleEqual(
                    0))) != 0)
                {
                    throw new HalconException("No supported device found to continue this example.");
                }
                //Due to the filter used in query_available_dl_devices, the first device is a GPU, if available.
                hv_DLDevice.Dispose();
                using (HDevDisposeHelper dh = new HDevDisposeHelper())
                {
                    hv_DLDevice = hv_DLDeviceHandles.TupleSelect(
                        0);
                }

                //Provide the class names and IDs.
                //Class names.
                //ClassNames := ['Omega-3','KMW','Stomach tablet','Ginko','Ginseng','Glucosamine','Cognivia','Capsularum I','Iron tablet','Vitamin-B']
                //Respective class ids.
                //ClassIDs := [1,2,3,4,5,6,7,8,9,10]
                //
                //Batch Size used during inference.
                //BatchSizeInference := 1
                //
                //Postprocessing parameters for the detection model.
                //MinConfidence := 0.6
                //MaxOverlap := 0.2
                //MaxOverlapClassAgnostic := 0.7
                //
                //********************
                //**   Inference   ***
                //********************
                //Read in the retrained model.
                hv_DLModelHandle.Dispose();
                HOperatorSet.ReadDlModel("D:/test/halcon/目标检测-药片/model_best.hdl",
                    out hv_DLModelHandle);
                // stop(...); only in hdevelop
                hv_ClassIDs.Dispose();
                HOperatorSet.GetDlModelParam(hv_DLModelHandle, "class_ids", out hv_ClassIDs);
                //get_dl_model_param (DLModelHandle, 'class_names', ClassNames)
                hv_ClassNames.Dispose();
                hv_ClassNames = new HTuple();
                hv_ClassNames[0] = "Omega-3";
                hv_ClassNames[1] = "KMW";
                hv_ClassNames[2] = "Stomach tablet";
                hv_ClassNames[3] = "Ginko";
                hv_ClassNames[4] = "Ginseng";
                hv_ClassNames[5] = "Glucosamine";
                hv_ClassNames[6] = "Cognivia";
                hv_ClassNames[7] = "Capsularum I";
                hv_ClassNames[8] = "Iron tablet";
                hv_ClassNames[9] = "Vitamin-B";
                //
                //Set the batch size.
                HOperatorSet.SetDlModelParam(hv_DLModelHandle, "batch_size", 1);
                //
                //Initialize the model for inference.
                HOperatorSet.SetDlModelParam(hv_DLModelHandle, "device", hv_DLDevice);
                //
                //Set postprocessing parameters for model.
                //set_dl_model_param (DLModelHandle, 'min_confidence', MinConfidence)
                //set_dl_model_param (DLModelHandle, 'max_overlap', MaxOverlap)
                //set_dl_model_param (DLModelHandle, 'max_overlap_class_agnostic', MaxOverlapClassAgnostic)
                //
                //Get the parameters used for preprocessing.
                hv_DLPreprocessParam.Dispose();
                HOperatorSet.ReadDict("D:/test/halcon/目标检测-药片/detect_pills_data/dl_preprocess_param.hdict",
                    new HTuple(), new HTuple(), out hv_DLPreprocessParam);

                //
                //Create window dictionary for displaying results.
                hv_WindowHandleDict.Dispose();
                HOperatorSet.CreateDict(out hv_WindowHandleDict);
                //Create dictionary with dataset parameters necessary for displaying.
                hv_DLDataInfo.Dispose();
                HOperatorSet.CreateDict(out hv_DLDataInfo);
                HOperatorSet.SetDictTuple(hv_DLDataInfo, "class_names", hv_ClassNames);
                HOperatorSet.SetDictTuple(hv_DLDataInfo, "class_ids", hv_ClassIDs);


                //Read the images of the batch.
                ho_ImageBatch.Dispose();
                HOperatorSet.ReadImage(out ho_ImageBatch, filename);
                //
                //Generate the DLSampleBatch.
                hv_DLSample.Dispose();
                developExport.gen_dl_samples_from_images(ho_ImageBatch, out hv_DLSample);
                //
                //Preprocess the DLSampleBatch.
                developExport.preprocess_dl_samples(hv_DLSample, hv_DLPreprocessParam);
                //
                //Apply the DL model on the DLSampleBatch.
                hv_DLResult.Dispose();
                HOperatorSet.ApplyDlModel(hv_DLModelHandle, hv_DLSample, new HTuple(), out hv_DLResult);
                hv_txt.Dispose();
                hv_txt = "";
                hv_DetectedClassIDs.Dispose();
                HOperatorSet.GetDictTuple(hv_DLResult, "bbox_class_id", out hv_DetectedClassIDs);
                hv_DetectedConfidences.Dispose();
                HOperatorSet.GetDictTuple(hv_DLResult, "bbox_confidence", out hv_DetectedConfidences);
                hv_Rows1.Dispose();
                HOperatorSet.GetDictTuple(hv_DLResult, "bbox_row1", out hv_Rows1);
                hv_Rows2.Dispose();
                HOperatorSet.GetDictTuple(hv_DLResult, "bbox_row2", out hv_Rows2);
                hv_Cols1.Dispose();
                HOperatorSet.GetDictTuple(hv_DLResult, "bbox_col1", out hv_Cols1);
                hv_Cols2.Dispose();
                HOperatorSet.GetDictTuple(hv_DLResult, "bbox_col2", out hv_Cols2);
                for (hv_Index = 0; (int)hv_Index <= (int)((new HTuple(hv_DetectedClassIDs.TupleLength()
                    )) - 1); hv_Index = (int)hv_Index + 1)
                {
                    using (HDevDisposeHelper dh = new HDevDisposeHelper())
                    {
                        {
                            HTuple
                              ExpTmpLocalVar_txt = ((((((((((((hv_txt + "检测结果:") + (hv_ClassNames.TupleSelect(
                                hv_DetectedClassIDs.TupleSelect(hv_Index)))) + new HTuple(",得分:")) + (hv_DetectedConfidences.TupleSelect(
                                hv_Index))) + new HTuple(",坐标:([")) + (hv_Rows1.TupleSelect(hv_Index))) + new HTuple(",")) + (hv_Cols1.TupleSelect(
                                hv_Index))) + new HTuple("],[")) + (hv_Rows2.TupleSelect(hv_Index))) + new HTuple(",")) + (hv_Cols2.TupleSelect(
                                hv_Index))) + "])  ;\n";
                            hv_txt.Dispose();
                            hv_txt = ExpTmpLocalVar_txt;
                        }
                    }
                }

                richTextBox1.AppendText(hv_txt.S);

            }
            catch (HalconException HDevExpDefaultException)
            {
                ho_ImageBatch.Dispose();

                hv_DLDeviceHandles.Dispose();
                hv_DLDevice.Dispose();
                hv_ClassNames.Dispose();
                hv_ClassIDs.Dispose();
                hv_DLModelHandle.Dispose();
                hv_DLPreprocessParam.Dispose();
                hv_WindowHandleDict.Dispose();
                hv_DLDataInfo.Dispose();
                hv_DLSample.Dispose();
                hv_DLResult.Dispose();
                hv_txt.Dispose();
                hv_DetectedClassIDs.Dispose();
                hv_DetectedConfidences.Dispose();
                hv_Rows1.Dispose();
                hv_Rows2.Dispose();
                hv_Cols1.Dispose();
                hv_Cols2.Dispose();
                hv_Index.Dispose();

                throw HDevExpDefaultException;
            }
            ho_ImageBatch.Dispose();

            hv_DLDeviceHandles.Dispose();
            hv_DLDevice.Dispose();
            hv_ClassNames.Dispose();
            hv_ClassIDs.Dispose();
            hv_DLModelHandle.Dispose();
            hv_DLPreprocessParam.Dispose();
            hv_WindowHandleDict.Dispose();
            hv_DLDataInfo.Dispose();
            hv_DLSample.Dispose();
            hv_DLResult.Dispose();
            hv_txt.Dispose();
            hv_DetectedClassIDs.Dispose();
            hv_DetectedConfidences.Dispose();
            hv_Rows1.Dispose();
            hv_Rows2.Dispose();
            hv_Cols1.Dispose();
            hv_Cols2.Dispose();
            hv_Index.Dispose();

        }

    }
}

总结

以上内容简简单单实现一个调用功能，实际业务中还有很多功能需要完善，实际检测过程中也需要对算法的更多参数进行修改。