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Notice: This documentation is archived. For the latest product features and documentation, please visit Dynamsoft Capture Vision Documentation.

User Guide - C++

In this guide, you will learn step by step on how to build a barcode reader, label recognizer and document normalizer application with Dynamsoft Capture Vision SDK using C++ language.

Read more on Dynamsoft Capture Vision Features

Table of Contents

Requirements

  • Windows:
    • Supported Versions: Windows 7 and higher, or Windows Server 2003 and higher
    • Architecture: x64 and x86
    • Development Environment: Visual Studio 2012 or higher.
  • Linux:
    • Supported Distributions: Ubuntu 14.04.4+ LTS, Debian 8+, CentOS 6+
    • Architectures: x64 (arm 32-bit and arm 64-bit coming soon)
    • Minimum GLIBC Version: GLIBC_2.18 or higher
    • Compiler: G++ 5.4 or higher

Installation

If you haven’t downloaded the SDK yet, download the C/C++ Package now and unpack the package into a directory of your choice.

For this tutorial, we unpack it to a pseudo directory [INSTALLATION FOLDER], change it to your unpacking path for the following content.

Build Your First Application

Let’s start by creating a console application which demonstrates the minimum code needed to capture content from an image file.

You can download the complete source code from here.

Create A New Project

For Windows

  1. Open Visual Studio. Go to File > New > Project, select Empty App and enter CaptureFromAnImage in the name text box.

  2. Add a new source file named CaptureFromAnImage.cpp into the project.

For Linux

  1. Create a new source file named CaptureFromAnImage.cpp and place it into the folder [INSTALLATION FOLDER]\Resources\CaptureVision\Samples\HelloWorld\CaptureFromAnImage.

  2. Create a file named Makefile and put it in the same directory as the file CaptureFromAnImage.cpp. The content of Makefile is as follows:

     CC=gcc
 CCFLAGS=-c -std=c++11

 DLRMODEL_PATH=../../../CharacterModel
 DS_LIB_PATH=../../../Lib/Linux/x64
 LDFLAGS=-L $(DS_LIB_PATH) -Wl,-rpath=$(DS_LIB_PATH) -Wl,-rpath=./
 DS_LIB=-lDynamsoftCaptureVisionRouter -lDynamsoftCore -lDynamsoftLicense -lDynamsoftUtility

 STDLIB=-lstdc++

 TARGET=CaptureFromAnImage
 OBJECT=CaptureFromAnImage.o
 SOURCE=CaptureFromAnImage.cpp

 # build rule for target.
 $(TARGET): $(OBJECT)
     $(CC) -o $(TARGET) $(OBJECT) $(STDLIB) $(DS_LIB) $(LDFLAGS)
     cp -r $(DLRMODEL_PATH) $(DS_LIB_PATH)

 # target to build an object file
 $(OBJECT): $(SOURCE)
     $(CC) $(CCFLAGS) $(SOURCE)

 # the clean target
 .PHONY : clean
 clean: 
     rm -f $(OBJECT) $(TARGET) -r $(DS_LIB_PATH)/CharacterModel

    Note: The DS_LIB_PATH variable should be set to the correct directory where the DLR library files are located. The files and character models directory can be found in [INSTALLATION FOLDER]/Lib/Linux/x64.

Include the Library

  1. Add headers and libs in CaptureFromAnImage.cpp.

     #include <iostream>
 #include <string>
 #include "[INSTALLATION FOLDER]/Include/DynamsoftCaptureVisionRouter.h"

 using namespace std;
 using namespace dynamsoft::cvr;
 using namespace dynamsoft::dlr;
 using namespace dynamsoft::dbr;
 using namespace dynamsoft::ddn;
 using namespace dynamsoft::license;
 using namespace dynamsoft::utility;

 // The following code only applies to Windows.
 #if defined(_WIN64) || defined(_WIN32)
     #ifdef _WIN64
         #pragma comment(lib, "[INSTALLATION FOLDER]/Lib/Windows/x64/DynamsoftCaptureVisionRouterx64.lib")
         #pragma comment(lib, "[INSTALLATION FOLDER]/Lib/Windows/x64/DynamsoftCorex64.lib")
         #pragma comment(lib, "[INSTALLATION FOLDER]/Lib/Windows/x64/DynamsoftLicensex64.lib")
         #pragma comment(lib, "[INSTALLATION FOLDER]/Lib/Windows/x64/DynamsoftUtilityx64.lib")
     #else
         #pragma comment(lib, "[INSTALLATION FOLDER]/Lib/Windows/x86/DynamsoftCaptureVisionRouterx86.lib")
         #pragma comment(lib, "[INSTALLATION FOLDER]/Lib/Windows/x86/DynamsoftCorex86.lib")
         #pragma comment(lib, "[INSTALLATION FOLDER]/Lib/Windows/x86/DynamsoftLicensex86.lib")
         #pragma comment(lib, "[INSTALLATION FOLDER]/Lib/Windows/x86/DynamsoftUtilityx86.lib")
     #endif
 #endif

Initialize a Capture Vision Router Instance

  1. Initialize the license key

     char error[512];
    
 CLicenseManager::InitLicense("DLS2eyJvcmdhbml6YXRpb25JRCI6IjIwMDAwMSJ9", error, 512);
 cout << "License initialization: " << error << endl;

    Note:

    • An internet connection is required for the license to work.
    • “DLS2***” is a default free public trial license used in the sample.
    • If the license has expired, please request an extension through the customer portal.

  2. Create an instance of Dynamsoft Capture Vision Router

     CCaptureVisionRouter* router = new CCaptureVisionRouter();

Capture and Output Captured Results

  1. Capture content from an image file

     string imageFile = "[INSTALLATION FOLDER]/Resources/CaptureVision/Images/dcv-sample-image.png";
 CCapturedResult* result = router->Capture(imageFile.c_str(), CPresetTemplate::PT_DEFAULT);

    Note:

    Please change all [INSTALLATION FOLDER] in above code snippet to your unpacking path.

  2. Output the captured results

     cout << "File: " << imageFile << endl;

 // 4.Outputs the result.
 if (result->GetErrorCode() != 0) {
     cout << "Error: " << result->GetErrorCode() << "," << result->GetErrorString() << endl;
 }

 /*
 * There can be multiple types of result items per image.
 */
 int count = result->GetCount();
 cout << "Captured " << count << " items" << endl;
 for (int i = 0; i < count; i++) {
     const CCapturedResultItem* item = result->GetItem(i);

     CapturedResultItemType type = item->GetType();
     if (type == CapturedResultItemType::CRIT_BARCODE) {
         const CBarcodeResultItem* barcode = dynamic_cast<const CBarcodeResultItem*>(item);

         // Output the decoded barcode text.
         cout << ">>Item " << i << ": " << "Barcode," << barcode->GetText() << endl;
     }
     else if (type == CapturedResultItemType::CRIT_TEXT_LINE) {
         const CTextLineResultItem* textLine = dynamic_cast<const CTextLineResultItem*>(item);

         // Output the recognized text line.
         cout << ">>Item " << i << ": " << "TextLine," << textLine->GetText() << endl;
     }
     else if (type == CapturedResultItemType::CRIT_NORMALIZED_IMAGE) {
         const CNormalizedImageResultItem* normalizedImage = dynamic_cast<const CNormalizedImageResultItem*>(item);

         string outPath = "normalizedResult_";
         outPath += to_string(i) + ".png";

         CImageManager manager;

         // Save normalized iamge to file.
         errorcode = manager.SaveToFile(normalizedImage->GetImageData(), outPath.c_str());
         if (errorcode == 0) {
             cout << ">>Item " << i << ": " << "NormalizedImage," << outPath << endl;
         }
     }
 }

Release the Allocated Memory

delete router, router = NULL;
delete result, result = NULL;

Build and Run the Project

On windows

  1. Build the application through Visual Studio.

  2. Copy the related DLL files to the same folder as the EXE file. The DLL files can be found in [INSTALLATION FOLDER]\Lib\Windows\[platforms]

    Note: Select the corresponding folder (x86 or x64) based on your project’s platform setting.

  3. Copy the [INSTALLATION FOLDER]\Resources\LabelRecognizer\CharacterModel directory to the same folder as the EXE file.

  4. Run the program CaptureFromAnImage.exe.

On Linux

  1. Open a terminal and change to the target directory where Makefile located in. Build the sample:

     >make

  2. Run the program CaptureFromAnImage.

     >./CaptureFromAnImage

Process Multiple Images

If you need to process multiple images at once instead of one image, you can follow these steps:

Preparation Steps

  1. Create a new project named CaptureFromMultipleImages.
  2. Initialize a Capture Vision Router Instance.
  3. Include the Library.

You can download the complete source code from here.

Add an Image Source as the Input

The class CDirectoryFetcher is capable of converting a local directory to an image source. We will use it to connect multiple images to the image-processing engine.

  1. Setting up a directory fetcher to retrieve image data sources from a directory.

     CDirectoryFetcher *dirFetcher = new CDirectoryFetcher;
 dirFetcher->SetDirectory("[Your Image Path]");

 router->SetInput(dirFetcher);

  2. Create a class MyImageSourceStateListener to implement the CImageSourceStateListenter interface, and call StopCapturing in the callback function.

     class MyImageSourceStateListener : public CImageSourceStateListener
 {
 private:
     CCaptureVisionRouter* m_router;

 public:
     MyImageSourceStateListener(CCaptureVisionRouter* router) {
         m_router = router;
     }

     virtual void OnImageSourceStateReceived(ImageSourceState state)
     {
         if(state == ISS_EXHAUSTED)
             m_router->StopCapturing();
     }
 };

  3. Register the MyImageSourceStateListener object to monitor the status of the image source.

     CImageSourceStateListener *listener = new MyImageSourceStateListener(router);
 router->AddImageSourceStateListener(listener);

Add Captured Result Receiver

  1. Create a class MyResultReceiver to implement the CCapturedResultReceiver interface, and get the captured results in OnCapturedResultReceived callback function.

     class MyResultReceiver : public CCapturedResultReceiver
 {
 public:
     virtual void OnCapturedResultReceived(const CCapturedResult* pResult)
     {
         const CFileImageTag *tag = dynamic_cast<const CFileImageTag*>(pResult->GetSourceImageTag());

         cout << "File: " << tag->GetFilePath() << endl;
         cout << "Page: " << tag->GetPageNumber() << endl;

         if (pResult->GetErrorCode() != EC_OK)
         {
             cout << "Error: " << pResult->GetErrorString() << endl;
         }
         else
         {
             int lCount = pResult->GetCount();
             cout << "Captured " << lCount << " items" << endl;
             for (int i = 0; i < lCount; i++) {
                 const CCapturedResultItem* item = pResult->GetItem(i);

                 CapturedResultItemType type = item->GetType();
                 if (type == CapturedResultItemType::CRIT_BARCODE) {
                     const CBarcodeResultItem* barcode = dynamic_cast<const CBarcodeResultItem*>(item);

                     // Output the decoded barcode text.
                     cout << ">>Item " << i << ": " << "Barcode," << barcode->GetText() << endl;
                 }
                 else if (type == CapturedResultItemType::CRIT_TEXT_LINE) {
                     const CTextLineResultItem* textLine = dynamic_cast<const CTextLineResultItem*>(item);

                     // Output the recognized text line.
                     cout << ">>Item " << i << ": " << "TextLine," << textLine->GetText() << endl;
                 }
                 else if (type == CapturedResultItemType::CRIT_NORMALIZED_IMAGE) {
                     const CNormalizedImageResultItem* normalizedImage = dynamic_cast<const CNormalizedImageResultItem*>(item);

                     string outPath = "normalizedResult_";
                     outPath += to_string(i) + ".png";

                     CImageManager manager;

                     // Save normalized iamge to file.
                     int errorcode = manager.SaveToFile(normalizedImage->GetImageData(), outPath.c_str());
                     if (errorcode == 0) {
                         cout << ">>Item " << i << ": " << "NormalizedImage," << outPath << endl;
                     }
                 }
             }
         }

         cout << endl;
     }
 };

    For the error handling mechanism, the SDK returns Error Code in the CCapturedResult object. You can add error handling code as needed. See Error Code for a full list of supported error codes.

  2. Register the MyResultReceiver object to monitor the captured results of the router.

     CCapturedResultReceiver* recv = new MyResultReceiver();
 router->AddResultReceiver(recv);

Start Capturing

  1. Start Capturing with the default Capture Vision Template.

     router->StartCapturing(CPresetTemplate::PT_DEFAULT, true);

    Note:

    • During the process, the callback function OnCapturedResultReceived() is triggered each time an image finishes processing. After all images are processed, the listener function OnImageSourceStateReceived() will return the image source state as ISS_EXHAUSTED and the process is stopped with the method StopCapturing().

Release Allocated Memory

delete router, router = NULL;
delete dirFetcher, dirFetcher = NULL;
delete listener, listener = NULL;
delete recv, recv = NULL;

Build and Run the Project Again

Please refer to Build and Run the Project.

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