Parameter adjustments - #30
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Bug #1: FileDirValue validates before creating directory - Fixed by creating directory first, then validating - Prevents crashes during segmentation initialization Bug #2: FileNameValue circular validation logic - Fixed by setting value first, allowing model names - Prevents TypeError when using model names vs file paths Test Results: - Segmentation works without manual directory creation - Model name resolution works correctly - Integration test: 18 fruits detected successfully Closes: segmentation analysis crashes on startup See: BUGFIXES.md for detailed documentation
- Add configurable confidence_threshold parameter (FloatValue) - Default value: 0.25 (YOLO standard) - Range: 0.0 to 1.0 - CLI usage: --confidence_threshold 0.5 - Parameter passed to YOLO model prediction Enables users to fine-tune detection sensitivity: - Lower values: more detections (may include false positives) - Higher values: fewer detections (only high-confidence ones) Tested with apple tray demo image - parameter correctly applied
spficklin
requested changes
Sep 10, 2025
| """ | ||
| {@inheritdoc} | ||
| """ | ||
| self.value = value if self.validate() else None |
| self.confidence_threshold.setMax(1.0) | ||
| self.confidence_threshold.setIsRequired(False) | ||
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|
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| self.addInParam(self.model, self.input_images, self.confidence_threshold) |
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Looks good, just test with a high threhsold that you do see some segements falling out.
- Add input validation before assigning to self.value - Validate that input is a string and not empty - Only set self.value after successful directory creation and validation - Addresses requirement to validate all values before they get set
- Add parameter_adjustments.rst with detailed confidence threshold guide - Include usage examples, recommended values, and practical scenarios - Update index.rst to include new documentation page - Provides users with clear guidance on optimizing detection quality
- Replace parameter_adjustments.rst with parameter_adjustments.md - Use Markdown format for better readability and compatibility - Contains comprehensive confidence threshold parameter documentation - Provides users with clear guidance on optimizing detection quality
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Summary
Changes
FloatValueconfidence threshold parameter toSegmentationclassBenefits
Test Plan