- Implement SingleInstance class using QSharedMemory and file locking
- Cross-platform support with fcntl on Unix/macOS and file creation on Windows
- Show warning dialog when user tries to launch second instance
- Automatic cleanup of resources on application exit
- Graceful handling of instance detection failures
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Remove "All files (*)" option from file dialog, only allow .mflow files
- Change error handling to return to login page instead of opening empty pipeline
- Update error message to be more specific about file format requirements
- Properly clean up dashboard window when file load fails
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Update all imports to use relative imports instead of cluster4npu_ui.* prefix
- Remove export configuration functionality from dashboard menu
- Remove performance analysis action from pipeline menu
- Update dependencies in pyproject.toml to include NodeGraphQt and PyQt5
- Maintain clean import structure across all modules
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Implement MultiSeriesDongleManager for parallel inference across different dongle series
- Add GOPS-based load balancing (KL720: 1425 GOPS, KL520: 345 GOPS ratio ~4:1)
- Ensure sequential result output despite heterogeneous processing speeds
- Include comprehensive threading architecture with dispatcher, per-dongle workers, and result ordering
- Add performance statistics and monitoring capabilities
- Update project configuration and documentation
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add smart path truncation for long file paths (preserves filename and parent folder)
- Set maximum width constraints on all UI components (QPushButton, QComboBox, QSpinBox, QDoubleSpinBox, QLineEdit)
- Add tooltips showing full paths for truncated file path buttons
- Disable horizontal scrollbar and optimize right panel width (320-380px)
- Improve styling for all property widgets with consistent theme
- Add better placeholder text for input fields
Key improvements:
- File paths like "C:/Very/Long/Path/.../filename.nef" → "...Long/Path/filename.nef"
- All widgets limited to 250px max width to prevent panel expansion
- Enhanced hover and focus states for better UX
- Properties panel now fits within fixed width without horizontal scroll
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add upload_fw property with enhanced UI checkbox styling
- Connect checkbox to inference pipeline process
- Enable/disable firmware upload based on user selection
- Add visual feedback and logging for firmware upload status
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Simplified language for better readability
- Added specific performance expectations (4-5 FPS)
- Clear test scenarios for QA validation
- Direct problem-to-solution mapping
- Removed technical jargon for broader audience
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Minor improvements:
- Remove duplicate logging from inference results to reduce console noise
- Update deployment dialog UI text to remove emoji for cleaner display
- Clean up commented debug statements across multiple files
- Improve user experience with more professional terminal output
- Maintain functionality while reducing visual clutter
This commit focuses on polish and user experience improvements
without changing core functionality.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Major improvements:
- Add intelligent memory management for both input and output queues
- Implement frame dropping strategy to prevent memory overflow
- Set output queue limit to 50 results with FIFO cleanup
- Add input queue management with real-time frame dropping
- Filter async results from callbacks and display to reduce noise
- Improve system stability and prevent queue-related hangs
- Add comprehensive logging for dropped frames and results
Performance enhancements:
- Maintain real-time processing by prioritizing latest frames
- Prevent memory accumulation that previously caused system freezes
- Ensure consistent queue size reporting and FPS calculations
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Rebrand README from InferencePipeline to Cluster4NPU UI Visual Pipeline Designer
- Focus documentation on PyQt5-based GUI and drag-and-drop workflow
- Update PROJECT_SUMMARY with current capabilities and focused development priorities
- Streamline DEVELOPMENT_ROADMAP with 4-phase implementation plan
- Remove redundant Chinese technical summary files (STAGE_IMPROVEMENTS_SUMMARY.md, UI_FIXES_SUMMARY.md, STATUS_BAR_FIXES_SUMMARY.md)
- Align all documentation with actual three-panel UI architecture and NodeGraphQt integration
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add pipeline activity logging every 10 results to track processing
- Add queue size monitoring in InferencePipeline coordinator
- Add camera frame capture logging every 100 frames
- Add MultiDongle send/receive thread logging every 100 operations
- Add error handling for repeated callback failures in camera source
This will help identify where the pipeline stops processing:
- Camera capture stopping
- MultiDongle threads blocking
- Pipeline coordinator hanging
- Queue capacity issues
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Remove all emojis from terminal output formatting for cleaner display
- Add debug print statement to track pipeline.get_current_fps() values
- Change FPS display to "Pipeline FPS (Output Queue)" for clarity
- Simplify output formatting by removing emoji decorations
- This will help identify why FPS calculation isn't working as expected
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Remove qRegisterMetaType import that is not available in all PyQt5 versions
- Remove QTextCursor import and registration that was causing import error
- Simplify deployment dialog initialization to avoid PyQt5 compatibility issues
- The QTextCursor warning was not critical and the registration was unnecessary
This fixes the "cannot import name 'qRegisterMetaType' from 'PyQt5.QtCore'" error
that prevented deployment dialog from opening.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Remove kp.core.set_timeout() call that causes crashes when camera is connected
- Add explanatory message indicating timeout is skipped for stability
- This prevents the system crash that occurs during camera initialization
- Trade-off: Removes USB timeout but ensures stable camera operation
The timeout setting was conflicting with camera connection process,
causing the entire system to crash during device initialization.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add qRegisterMetaType(QTextCursor) to prevent Qt threading warning
- Import QTextCursor and qRegisterMetaType from PyQt5
- Resolves "Cannot queue arguments of type 'QTextCursor'" warning
- Ensures thread-safe GUI updates for terminal display
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Re-enable kp.core.set_timeout() which is required for proper device communication
- Fix GUI terminal truncation issue by using append() instead of setPlainText()
- Remove aggressive line limiting that was causing log display to stop midway
- Implement gentler memory management (trim only after 1000+ lines)
- This should resolve pipeline timeout issues and complete log display
The previous USB timeout disable was causing stage timeouts without inference results.
The terminal display issue was due to frequent text replacement causing display corruption.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add time-window based FPS calculation using output queue timestamps
- Replace misleading "Theoretical FPS" (based on processing time) with real "Pipeline FPS"
- Track actual inference output generation rate over 10-second sliding window
- Add thread-safe FPS calculation with proper timestamp management
- Display realistic FPS values (4-9 FPS) instead of inflated values (90+ FPS)
Key improvements:
- _record_output_timestamp(): Records when each output is generated
- get_current_fps(): Calculates FPS based on actual throughput over time window
- Thread-safe implementation with fps_lock for concurrent access
- Automatic cleanup of old timestamps outside the time window
- Integration with GUI display to show meaningful FPS metrics
This provides users with accurate inference throughput measurements that reflect
real-world performance, especially important for multi-dongle setups where
understanding actual scaling is crucial.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Comment out print() statements in InferencePipeline that duplicate GUI callback output
- Prevents each inference result from appearing multiple times in terminal
- Keeps logging system clean while maintaining GUI formatted display
- This was causing terminal output to show each result 2-3 times due to:
1. InferencePipeline print() statements captured by StdoutCapture
2. Same results formatted and sent via terminal_output callback
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add StdoutCapture context manager to capture all print() statements
- Connect captured output to GUI terminal display via stdout_captured signal
- Fix logging issue where pipeline initialization and operation logs were not shown in app
- Prevent infinite recursion with _emitting flag in TeeWriter
- Ensure both console and GUI receive all log messages during deployment
- Comment out USB timeout setting that was causing device timeout issues
This resolves the issue where logs would stop showing partially in the app,
ensuring complete visibility of MultiDongle and InferencePipeline operations.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fixed NameError where 'processed_result' was referenced but not defined.
Should use 'inference_result' which contains the actual inference output
from MultiDongle.get_latest_inference_result().
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Key fixes:
1. Remove 'block' parameter from put_input() call - not supported in standalone code
2. Remove 'timeout' parameter from get_latest_inference_result() call
3. Improve _has_inference_result() logic to properly detect real inference results
- Don't count "Processing" or "async" status as valid results
- Only count actual tuple (prob, result_str) or meaningful dict results
- Match standalone code behavior for FPS calculation
This should resolve the "unexpected keyword argument" errors and
provide accurate FPS counting like the standalone baseline.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Key changes:
1. FPS Calculation: Only count when stage receives actual inference results
- Add _has_inference_result() method to check for valid results
- Only increment processed_count when real inference result is available
- This measures "inferences per second" not "frames per second"
2. Reduced Log Spam: Remove excessive preprocessing debug logs
- Remove shape/dtype logs for every frame
- Only log successful inference results
- Keep essential error logs
3. Maintain Async Pattern: Keep non-blocking processing
- Still use timeout=0.001 for get_latest_inference_result
- Still use block=False for put_input
- No blocking while loops
Expected result: ~4 FPS (1 dongle) vs ~9 FPS (2 dongles)
matching standalone code behavior.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Key fixes:
1. FPS Calculation: Only count actual inference results, not frame processing
- Previous: counted every frame processed (~90 FPS, incorrect)
- Now: only counts when actual inference results are received (~9 FPS, correct)
- Return None from _process_data when no inference result available
- Skip FPS counting for iterations without real results
2. Log Reduction: Significantly reduced verbose logging
- Removed excessive debug prints for preprocessing steps
- Removed "No inference result" spam messages
- Only log actual successful inference results
3. Async Processing: Maintain proper async pattern
- Still use non-blocking get_latest_inference_result(timeout=0.001)
- Still use non-blocking put_input(block=False)
- But only count real inference throughput for FPS
This should now match standalone code behavior: ~4 FPS (1 dongle) vs ~9 FPS (2 dongles)
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
The key issue was in InferencePipeline._process_data() where a 5-second
while loop was blocking waiting for inference results. This completely
serialized processing and prevented multiple dongles from working in parallel.
Changes:
- Replace blocking while loop with single non-blocking call
- Use timeout=0.001 for get_latest_inference_result (async pattern)
- Use block=False for put_input to prevent queue blocking
- Increase worker queue timeout from 0.1s to 1.0s
- Handle async processing status properly
This matches the pattern from the standalone code that achieved
4.xx FPS (1 dongle) vs 9.xx FPS (2 dongles) scaling.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Enable USB timeout (5000ms) for stable communication
- Fix send thread timeout from 0.01s to 1.0s for better blocking
- Update WebcamInferenceRunner to use async pattern (non-blocking)
- Add non-blocking put_input option to prevent frame drops
- Improve thread stopping mechanism with better cleanup
These changes follow Kneron official example pattern and should
enable proper parallel processing across multiple dongles.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fixed DeviceDescriptorList object attribute error by properly accessing
the device_descriptor_list attribute instead of treating the result as
a direct list of devices.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit integrates the dongle model detection logic into .
It refactors the method to:
- Handle in list or object format.
- Extract and for each device.
- Use to identify dongle models.
- Return a more detailed device information structure.
The previously deleted files were moved to the directory.
- Remove all debug print statements from deployment dialog
- Remove debug output from workflow orchestrator and inference pipeline
- Remove test signal emissions and unused imports
- Code is now clean and production-ready
- Results are successfully flowing from inference to GUI display
- Remove dependency on result_handler for setting pipeline result callback
- Always call result_callback when handle_result is triggered
- This fixes the issue where GUI callbacks weren't being called because
output type 'display' wasn't supported, causing result_handler to be None
- Add more debug output to trace callback flow
- Add debug output in InferencePipeline result callback to see if it's called
- Add debug output in WorkflowOrchestrator handle_result to trace callback flow
- This will help identify exactly where the callback chain is breaking
- Previous test showed GUI can receive signals but callbacks aren't triggered
- Add time import for test result generation
- Add test signal emissions to verify GUI connection works
- Add debug prints for signal establishment
- Test both result_updated and terminal_output signals
- This will help identify if the issue is signal connection or data flow
- Add debug prints in combined_result_callback to see received data
- Add debug prints in update_inference_results to track GUI updates
- Fix tuple order in terminal formatting to match actual (probability, result) format
- This will help identify why results show in terminal but not in GUI
- Add upload_fw property to ExactModelNode for firmware upload control
- Display all model node properties in right panel (model_path, scpu_fw_path, ncpu_fw_path, dongle_series, num_dongles, port_id, upload_fw)
- Replace console terminal output with GUI terminal display in deployment dialog
- Add Terminal Output section to deployment tab with proper formatting
- Terminal results now appear in app view instead of console for packaged apps
- Maintain backward compatibility with existing pipeline configurations
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
