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.
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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.
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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.
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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.
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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 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>
