- 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>
- 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 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>
- Add result callback mechanism to WorkflowOrchestrator
- Implement result_updated signal in DeploymentWorker
- Create detailed inference results display with timestamps and formatted output
- Support both tuple and dict result formats
- Add auto-scrolling results panel with history management
- Connect pipeline results to Live View tab for real-time monitoring
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix ambiguous truth value error in get_latest_inference_result method
- Fix ambiguous truth value error in postprocess function
- Replace direct array evaluation with explicit length checks
- Use proper None checks instead of truthy evaluation on numpy arrays
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix remaining array comparison error in inference result validation
- Update PyQt signal signature for proper numpy array handling
- Improve DeploymentWorker to keep running after deployment
- Enhance stop button with non-blocking UI updates and better error handling
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix ambiguous truth value error in InferencePipeline result handling
- Add stop inference button to deployment dialog with proper UI state management
- Improve error handling for tuple vs dict result types
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add detailed TODO.md with complete project roadmap and implementation priorities
- Implement CameraSource class with multi-camera support and real-time capture
- Add VideoFileSource class with batch processing and frame control capabilities
- Create foundation for complete input/output data flow integration
- Document current auto-resize preprocessing implementation status
- Establish clear development phases and key missing components
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Always store firmware paths (scpu_fw_path, ncpu_fw_path) when provided, not just when upload_fw=True
- Restore firmware upload condition to only run when upload_fw=True
- Fix 'MultiDongle' object has no attribute 'scpu_fw_path' error during pipeline initialization
- Ensure firmware paths are available for both upload and non-upload scenarios
This resolves the pipeline deployment error where firmware paths were missing
even when provided to the constructor, causing initialization failures.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Device Detection Updates:
- Update device series detection to use product_id mapping (0x100 -> KL520, 0x720 -> KL720)
- Handle JSON dict format from kp.core.scan_devices() properly
- Extract usb_port_id correctly from device descriptors
- Support multiple device descriptor formats (dict, list, object)
- Enhanced debug output shows Product ID for verification
Pipeline Deployment Fixes:
- Remove invalid preprocessor/postprocessor parameters from MultiDongle constructor
- Add max_queue_size parameter support to MultiDongle
- Fix pipeline stage initialization to match MultiDongle constructor
- Add auto_detect parameter support for pipeline stages
- Store stage processors as instance variables for future use
Example Updates:
- Update device_detection_example.py to show Product ID in output
- Enhanced error handling and format detection
Resolves pipeline deployment error: "MultiDongle.__init__() got an unexpected keyword argument 'preprocessor'"
Now properly handles real device descriptors with correct product_id to series mapping.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add scan_devices() method using kp.core.scan_devices() for device discovery
- Add connect_auto_detected_devices() for automatic device connection
- Add device series detection (KL520, KL720, KL630, KL730, KL540, etc.)
- Add auto_detect parameter to MultiDongle constructor
- Add get_device_info() and print_device_info() methods to display port IDs and series
- Update connection logic to use kp.core.connect_devices() per official docs
- Add device_detection_example.py with usage examples
- Maintain backward compatibility with manual port specification
Features display dongle series and port ID as requested for better device management.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add DataProcessor abstract base class with process method
- Add PostProcessor class for handling inference output data
- Fix PreProcessor inheritance from DataProcessor
- Resolves "name 'DataProcessor' is not defined" error during pipeline deployment
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
