112 lines
4.6 KiB
Markdown
112 lines
4.6 KiB
Markdown
# VisionFlow Automator - Performance Optimizations
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## Overview
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This document outlines the performance and memory optimizations implemented to prevent the application from being killed or stalled during scenario execution.
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## Key Optimizations Implemented
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### 1. Memory Management
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- **Template Caching**: Added `TemplateCache` class to cache loaded CV2 templates instead of reloading from disk every loop iteration
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- **Screenshot Caching**: Implemented screenshot caching with 50ms duration to reduce memory allocations
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- **Resource Cleanup**: Added proper cleanup in `cleanup_resources()` method and `closeEvent()`
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- **Garbage Collection**: Strategic `gc.collect()` calls to force memory cleanup at appropriate times
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- **Pixmap Memory Management**: Optimized image preview handling to prevent large pixmap memory leaks
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### 2. Performance Improvements
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- **Optimized Loop Structure**: Redesigned automation loop to be more efficient
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- **Step Cooldown System**: Prevents rapid re-execution of the same step (1-second cooldown)
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- **Priority Execution**: Only executes one step per loop iteration to prevent blocking
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- **Dynamic Sleep**: Adjusts sleep time based on loop performance (0.05-0.2s)
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- **Performance Monitoring**: Added loop time tracking and warnings for slow performance
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### 3. Threading Improvements
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- **Proper Thread Management**: Added timeout for thread joining in `stop_automation()`
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- **Background Processing**: Improved worker thread handling with better error recovery
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- **Non-blocking Operations**: Reduced blocking operations in the main thread
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### 4. UI Optimizations
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- **Reduced Logging**: Changed default log level from DEBUG to INFO to reduce I/O overhead
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- **Efficient Image Previews**: Optimized image scaling and memory usage in dialogs
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- **State Update Throttling**: Reduced frequency of UI state updates
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### 5. Resource Monitoring
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- **Performance Timer**: Added 10-second monitoring timer to track application health
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- **Memory Usage Tracking**: Optional psutil integration for detailed memory monitoring
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- **Cache Size Monitoring**: Tracks template cache size and warns when it grows large
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### 6. Error Handling
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- **Graceful Degradation**: Better error handling in screenshot capture and template matching
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- **Resource Recovery**: Automatic cleanup on errors to prevent resource leaks
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- **Hotkey Error Handling**: Continues operation even if hotkey setup fails
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## Configuration Changes
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### PyAutoGUI Optimizations
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- Set `pyautogui.PAUSE = 0.01` (reduced from default 0.1s)
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- Set `pyautogui.MINIMUM_DURATION = 0` for faster actions
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- Kept `pyautogui.FAILSAFE = False` for automation reliability
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### Screenshot Optimization
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- Added minimum size validation (10x10 pixels)
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- Improved tkinter screenshot tool with better UX
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- Memory-efficient PIL image handling with proper cleanup
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### Template Matching
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- LRU cache with automatic cleanup of old entries
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- Maximum cache size of 50 templates
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- Force reload option for template updates
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## Performance Targets
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### Memory Usage
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- Template cache limited to 50 entries
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- Screenshot cache duration: 50ms
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- Automatic cleanup every 100 loop iterations
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- Garbage collection after major operations
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### Timing
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- Target loop time: 50-200ms
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- Step cooldown: 1 second
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- Performance warning threshold: 1 second average loop time
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- Monitoring interval: 10 seconds
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### Thread Safety
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- Worker thread timeout: 2 seconds
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- Daemon threads for automatic cleanup
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- Proper resource locking where needed
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## Usage Recommendations
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1. **Monitor Performance**: Check logs for performance warnings
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2. **Resource Management**: Regularly restart long-running sessions
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3. **Template Optimization**: Use appropriately sized template images
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4. **Step Design**: Avoid too many simultaneous image detections
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5. **System Resources**: Ensure adequate RAM for screenshot operations
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## Future Improvements
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1. **Multi-threading**: Consider separate threads for image processing
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2. **Image Compression**: Compress cached templates to save memory
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3. **Region Optimization**: Use smaller detection regions when possible
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4. **GPU Acceleration**: Consider OpenCV GPU operations for template matching
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5. **Background Processing**: Process non-critical operations in background
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## Troubleshooting
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### High Memory Usage
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- Check template cache size in logs
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- Reduce number of simultaneous steps
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- Restart application periodically
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### Slow Performance
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- Check average loop times in logs
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- Reduce image template sizes
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- Simplify detection regions
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- Consider fewer simultaneous detections
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### Application Crashes
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- Check log files for error patterns
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- Monitor system memory usage
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- Verify image file integrity
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- Check for corrupted templates
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