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Xiao Ling
Xiao Ling

Posted on • Originally published at dynamsoft.com

Building a Multi-Modal Computer Vision Desktop App with AI-Assisted Development

#ai #python #barcode #programming

AI agents are transforming software development by empowering developers to build complex applications through iterative development, debugging, and optimization. These agents can analyze requirements, propose architectures, generate code, and even troubleshoot issues—dramatically accelerating the development lifecycle.

In this tutorial, we'll explore how to leverage Claude Sonnect 4 to build a sophisticated desktop GUI application from scratch using the Dynamsoft Capture Vision SDK. The result will be a multi-modal computer vision application capable of detecting barcodes/QR codes, normalizing documents, and extracting Machine Readable Zones (MRZ) from passports and ID cards.

Demo: Scan Barcodes, MRZ, and Documents with a Python Desktop App

This demo showcases how AI-assisted development can deliver professional-grade applications that rival commercial solutions.

Prerequisites

  • 30-day trial license for Dynamsoft Capture Vision

  • Python dependencies:

    dynamsoft-capture-vision-bundle>=2.0.20 PySide6>=6.5.0 opencv-python>=4.8.0 Pillow>=10.0.0 numpy>=1.24.0 facenet-pytorch>=2.5.0 torch>=1.11.0 torchvision>=0.12.0 psutil>=5.9.0

Project Overview

What We Built

A comprehensive desktop application featuring:

  • Dual-mode interface: Picture processing and real-time camera capture
  • Multi-detection capabilities: Barcodes/QR codes, document normalization, and MRZ reading
  • Advanced UI: Tabbed interface with zoom controls, annotation overlays, and export functionality
  • Face detection: Integrated MTCNN for passport/ID processing

Technology Stack 

- Python 3.11+ - PySide6 (Qt6) for modern GUI - Dynamsoft Capture Vision SDK (The powerhouse behind all detection) - facenet-pytorch for face detection
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Why Dynamsoft Capture Vision SDK?

The Dynamsoft Capture Vision SDK is the cornerstone of our application, providing enterprise-grade computer vision capabilities that would be extremely difficult to implement from scratch. Here's why it was the perfect choice:

  • Barcode/QR Code Reading: Supports 1D, 2D, and postal codes
  • Document Detection: Advanced edge detection and perspective correction
  • MRZ Processing: Specialized OCR for Machine Readable Zones with field validation
  • Unified API: Single SDK handles multiple detection types seamlessly
  • Cross-platform: Consistent performance across Windows, Linux, and macOS
  • Flexible Templates: Pre-configured detection templates for common scenarios
  • Intermediate Results: Access to detection pipeline stages for custom processing
  • Extensive Customization: Fine-tune detection parameters for specific use cases

AI-Assisted Development Workflow

The Iterative Approach

Our development process followed a systematic AI-assisted methodology:

graph TD A[Initial Requirements] --> B[AI Analysis & Planning] B --> C[Code Generation] C --> D[Testing & Validation] D --> E[Issue Identification] E --> F[AI Debugging & Fix] F --> G[Verification] G --> H{More Issues?} H -->|Yes| E H -->|No| I[Feature Enhancement] I --> J[Optimization] J --> K[Final Validation]
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AI Agent Collaboration Pattern

  1. Requirement Analysis: AI breaks down complex requirements into manageable components
  2. Architecture Design: AI suggests optimal design patterns and project structure
  3. Code Generation: AI writes initial implementations with proper error handling
  4. Debugging Partnership: Human identifies issues, AI diagnoses and provides solutions
  5. Optimization Cycles: AI suggests performance improvements and best practices

Initial Requirements and Architecture

User Requirements 

"I want a desktop application that can: - Detect barcodes and QR codes from images and camera - Process documents (scan and normalize) - Read passport/ID card information (MRZ) - Have a modern, user-friendly interface - Support both file upload and real-time camera processing"
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AI's Initial Architecture Analysis

The AI agent analyzed these requirements and proposed:

# Core Architecture Components class BarcodeReaderMainWindow(QMainWindow): """Main application window with tabbed interface""" class CameraWidget(QWidget): """Real-time camera capture and processing""" class ImageDisplayWidget(QLabel): """Image display with zoom and annotation capabilities""" class ProcessingWorker(QThread): """Background processing to keep UI responsive""" class MyIntermediateResultReceiver(IntermediateResultReceiver): """SDK integration for advanced processing"""
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Iterative Development Process

Phase 1: Basic GUI Structure

AI's First Implementation:

def setup_ui(self): """Setup the main user interface with tabbed layout.""" central_widget = QWidget() self.setCentralWidget(central_widget) # Main layout  main_layout = QVBoxLayout(central_widget) # Create tab widget  self.tab_widget = QTabWidget() main_layout.addWidget(self.tab_widget) # Create tabs  self.picture_tab = self.create_picture_mode_tab() self.camera_tab = self.create_camera_mode_tab() self.tab_widget.addTab(self.picture_tab, "📁 Picture Mode") self.tab_widget.addTab(self.camera_tab, "📷 Camera Mode")
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Key AI Decisions:

  • Tabbed interface for clear mode separation
  • Responsive layout with proper widget sizing
  • Consistent styling and iconography

Phase 2: SDK Integration

Challenge: Complex Dynamsoft SDK integration

AI Solution: Proper license management and error handling

def initialize_license_once(): """Initialize Dynamsoft license globally, only once.""" global _LICENSE_INITIALIZED if not _LICENSE_INITIALIZED: try: error_code, error_message = LicenseManager.init_license(LICENSE_KEY) if error_code == EnumErrorCode.EC_OK or error_code == EnumErrorCode.EC_LICENSE_CACHE_USED: _LICENSE_INITIALIZED = True print("✅ Dynamsoft license initialized successfully!") return True else: print(f"❌ License initialization failed: {error_code}, {error_message}") return False except Exception as e: print(f"❌ Error initializing license: {e}") return False return True
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Phase 3: Camera Integration

Challenge: Real-time camera processing with Qt integration

AI Approach: Hybrid OpenCV/Qt solution

def update_frame(self): """Update camera frame display with real-time results fetching.""" if not self.camera_running or not self.opencv_capture: return try: ret, frame = self.opencv_capture.read() if not ret: return # Store the raw frame for detection processing  with QMutexLocker(self.frame_mutex): self.current_frame = frame.copy() # Send frame for detection if enabled  if self.detection_enabled and self.frame_fetcher: try: image_data = convertMat2ImageData(frame) self.frame_fetcher.add_frame(image_data) except Exception as e: pass # Silently ignore frame processing errors  # Process and display results  self.display_annotated_frame(frame) except Exception as e: pass # Silently ignore frame update errors
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Key Technical Challenges and Solutions

Challenge 1: Directory Tracking for User Experience

Problem: File dialogs always opening in current directory

AI Enhancement: Persistent directory tracking

def update_last_used_directory(self, file_path): """Update the last used directory from a file path.""" if file_path: directory = os.path.dirname(os.path.abspath(file_path)) self.last_used_directory = directory print(f"📁 Updated last used directory to: {directory}") def get_last_used_directory(self): """Get the last used directory, or current directory if none.""" return self.last_used_directory if self.last_used_directory else os.getcwd()
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Challenge 2: Multi-Threading for UI Responsiveness

Problem: Heavy processing blocking the UI

AI Solution: QThread-based background processing

class ProcessingWorker(QThread): """Worker thread for detection processing to keep UI responsive.""" # Define signals  finished = Signal(object) # Processing results  error = Signal(str) # Error message  progress = Signal(str) # Progress message  def run(self): """Run detection in background thread.""" try: mode_name = self.detection_mode.split(" - ")[0] if " - " in self.detection_mode else self.detection_mode self.progress.emit(f"🔍 Starting {mode_name} detection...") # Get the appropriate template for the detection mode  template = DETECTION_MODES[mode_name]["template"] results = self.cvr_instance.capture_multi_pages(self.file_path, template) self.finished.emit(results) except Exception as e: self.error.emit(str(e))
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Challenge 3: Complex Result Handling

Problem: Different result types for different detection modes

AI Approach: Unified result processing pipeline

def on_processing_finished(self, results): """Handle completion of detection processing.""" try: # Get current detection mode  current_mode_text = self.picture_detection_mode_combo.currentText() mode_name = current_mode_text.split(" - ")[0] result_list = results.get_results() # Build the page mapping from results to maintain correct order  for i, result in enumerate(result_list): if result.get_error_code() == EnumErrorCode.EC_OK: # Extract items based on detection mode  items = [] if mode_name == "Barcode": items = result.get_items() elif mode_name == "Document": processed_doc_result = result.get_processed_document_result() if processed_doc_result: items = processed_doc_result.get_deskewed_image_result_items() elif mode_name == "MRZ": # Handle both text lines and parsed results  line_result = result.get_recognized_text_lines_result() if line_result: items.extend(line_result.get_items()) parsed_result = result.get_parsed_result() if parsed_result: items.extend(parsed_result.get_items()) # Store results for display  self.page_results[i] = items
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Code Architecture and Design Patterns

1. Model-View-Controller (MVC) Pattern 

# Model: Data handling and SDK integration class DataManager: def __init__(self): self.cvr_instance = CaptureVisionRouter() self.current_pages = {} self.detection_results = {} # View: UI components class BarcodeReaderMainWindow(QMainWindow): # Main view class CameraWidget(QWidget): # Camera view class ImageDisplayWidget(QLabel): # Image display view  # Controller: Business logic and event handling def process_current_file(self): # File processing controller def on_detection_mode_changed(self): # Mode switching controller
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2. Observer Pattern for Real-time Updates 

class CameraWidget(QWidget): # Signals for loose coupling  barcodes_detected = Signal(list) frame_processed = Signal(object) error_occurred = Signal(str) def update_frame(self): # Emit signals for observers  if latest_items: self.barcodes_detected.emit(latest_items) self.frame_processed.emit(display_frame)
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3. Factory Pattern for Detection Modes 

DETECTION_MODES = { "Barcode": { "template": EnumPresetTemplate.PT_READ_BARCODES.value, "description": "Detect barcodes and QR codes" }, "Document": { "template": EnumPresetTemplate.PT_DETECT_AND_NORMALIZE_DOCUMENT.value, "description": "Detect and normalize documents" }, "MRZ": { "template": "ReadPassportAndId", "description": "Read passport and ID cards (MRZ)" } }
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4. Strategy Pattern for Export Formats 

class ExportStrategy: def export(self, data, file_path): raise NotImplementedError class TextExporter(ExportStrategy): def export(self, data, file_path): # Text export implementation  pass class CSVExporter(ExportStrategy): def export(self, data, file_path): # CSV export implementation  pass class JSONExporter(ExportStrategy): def export(self, data, file_path): # JSON export implementation  pass
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Performance Optimization

Memory Management 

def cleanup_old_barcode_colors(): """Remove barcode colors for barcodes not seen recently.""" current_time = time.time() expired_barcodes = [] for barcode_text, last_seen in BARCODE_LAST_SEEN.items(): if current_time - last_seen > 10: # Remove after 10 seconds  expired_barcodes.append(barcode_text) for barcode_text in expired_barcodes: BARCODE_COLORS.pop(barcode_text, None) BARCODE_LAST_SEEN.pop(barcode_text, None)
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Efficient Frame Processing 

def update_frame(self): """Optimized frame processing with minimal allocations.""" if not self.camera_running or not self.opencv_capture: return try: ret, frame = self.opencv_capture.read() if not ret: return # Efficient frame copying with mutex protection  with QMutexLocker(self.frame_mutex): self.current_frame = frame.copy() # Non-blocking detection processing  if self.detection_enabled and self.frame_fetcher: try: image_data = convertMat2ImageData(frame) self.frame_fetcher.add_frame(image_data) except Exception: pass # Continue processing even if detection fails
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Running the Application 

# Install dependencies pip install -r requirements.txt # Run the application python main.py
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Python desktop application for barcode, MRZ and document detection

Source Code

https://github.com/yushulx/python-barcode-qrcode-sdk/tree/main/examples/official/dcv

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