Strengthen the types

[agfianf]

User description

• Replace Any types with TypedDict in device_info.py for device specs
• Add ColorDiffMetrics and ColorDiffResult TypedDicts for calc_color_diff_patches
• Add DetectionParams and CorrectionParams TypedDicts for analyzer
• Update loose dict and list types with proper type parameters
• Add CorrectionModel type alias in _factory.py
• Update docstrings to reflect new type annotations
• Add noqa comments for intentional Any usage in **kwargs

---

PR Type

Enhancement

---

Description

• Replace loose dict and list types with specific type parameters

• Add TypedDict definitions for structured data (ColorDiffMetrics, ColorDiffResult, DetectionParams, CorrectionParams, DeviceSpecsDict)

• Add CorrectionModel type alias for correction model union type

• Improve error handling in CorrectionModelFactory with validation

• Update docstrings to reflect new type annotations and add noqa comments for intentional Any usage

---

Diagram Walkthrough

flowchart LR
  A["Loose dict/list types"] -->|"Replace with"| B["Specific type parameters"]
  C["Unstructured data"] -->|"Define as"| D["TypedDict definitions"]
  E["Union return types"] -->|"Create"| F["Type aliases"]
  G["Factory method"] -->|"Add"| H["Error handling & validation"]

Loading

File Walkthrough

Relevant files

Enhancement

det_yv8_onnx.py Strengthen type annotations in YOLOv8 detector                      color_correction/core/card_detection/det_yv8_onnx.py Update docstring type annotations from list to list[str] for input/output names Update input_shape type from tuple to tuple[int, ...] for better specificity +3/-3      _factory.py Add type alias and improve factory error handling                color_correction/core/correction/_factory.py Add CorrectionModel type alias for union of correction model classes Replace str parameter with LiteralModelCorrection for type safety Replace dict with Any for **kwargs with noqa comment Add comprehensive docstring with parameter and return type documentation Implement error handling with KeyError for invalid model names Update return type annotation to use new CorrectionModel alias +37/-5    polynomial.py Strengthen polynomial model type annotations                          color_correction/core/correction/polynomial.py Import Pipeline type from sklearn for proper type annotation Replace dict with Any for **kwargs with noqa comments Update self.model type from untyped to Pipeline | None Update self.degree type annotation to int Change fit() return type from np.ndarray to Pipeline Update docstrings to reflect new type annotations +12/-11  color_correction.py Add TypedDicts for color difference metrics                            color_correction/services/color_correction.py Add ColorDiffMetrics TypedDict for color difference metrics (min, max, mean, std) Add ColorDiffResult TypedDict for structured color difference results Replace dict with Any for **kwargs with noqa comment Update calc_color_diff_patches() return type from dict to ColorDiffResult Simplify and clarify docstring for return type using TypedDict +29/-15  correction_analyzer.py Add TypedDicts for detection and correction parameters      color_correction/services/correction_analyzer.py Add DetectionParams TypedDict with optional detection_conf_th parameter Add CorrectionParams TypedDict with optional degree parameter Replace loose dict types with specific TypedDict types in method signatures Update parameter types from np.ndarray to ImageBGR for clarity Update return type from dict to dict[str, Any] for specificity Update docstrings to reference new TypedDict types +36/-23  device_info.py Add DeviceSpecsDict TypedDict for hardware detection          color_correction/utils/device_info.py Add DeviceSpecsDict TypedDict for device specifications with optional fields Replace dict[str, Any] parameters with DeviceSpecsDict in detect_darwin, detect_linux, detect_windows functions Update detector_map type annotation to dict[str, Callable[[DeviceSpecsDict], DeviceSpecsDict]] Initialize specs variable with explicit DeviceSpecsDict type annotation Remove unused Any import and add Callable import +22/-12  report_generator.py Strengthen report generator type annotations                          color_correction/utils/report_generator.py Update generate_table() method signature with specific type parameters Change headers parameter type from list to list[Any] Change rows parameter type from list to list[str] Update docstring to clarify parameter types +5/-4

---

[qodo-code-review]

PR Compliance Guide 🔍

Below is a summary of compliance checks for this PR:

Security Compliance
🟢	No security concerns identified No security vulnerabilities detected by AI analysis. Human verification advised for critical code.
Ticket Compliance
⚪	🎫 No ticket provided Create ticket/issue
Codebase Duplication Compliance
⚪	Codebase context is not defined Follow the guide to enable codebase context checks.
Custom Compliance
🟢	Generic: Comprehensive Audit Trails Objective: To create a detailed and reliable record of critical system actions for security analysis and compliance. Status: Passed Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Meaningful Naming and Self-Documenting Code Objective: Ensure all identifiers clearly express their purpose and intent, making code self-documenting Status: Passed Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Robust Error Handling and Edge Case Management Objective: Ensure comprehensive error handling that provides meaningful context and graceful degradation Status: Passed Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Secure Error Handling Objective: To prevent the leakage of sensitive system information through error messages while providing sufficient detail for internal debugging. Status: Passed Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Secure Logging Practices Objective: To ensure logs are useful for debugging and auditing without exposing sensitive information like PII, PHI, or cardholder data. Status: Passed Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Security-First Input Validation and Data Handling Objective: Ensure all data inputs are validated, sanitized, and handled securely to prevent vulnerabilities Status: Passed Learn more about managing compliance generic rules or creating your own custom rules

Compliance status legend

🟢 - Fully Compliant
🟡 - Partial Compliant
🔴 - Not Compliant
⚪ - Requires Further Human Verification
🏷️ - Compliance label

[qodo-code-review]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Fix incorrect model factory instantiation **Refactor the model factory to instantiate only the selected model class on demand. This fixes a bug where all models were instantiated on every call and kwargs were incorrectly passed. color_correction/core/correction/_factory.py [43-53] -model_registry: dict[str, CorrectionModel] = { - "least_squares": LeastSquaresRegression(), - "polynomial": Polynomial(**kwargs), - "linear_reg": LinearRegression(), - "affine_reg": AffineRegression(), +model_map = { + "least_squares": LeastSquaresRegression, + "polynomial": Polynomial, + "linear_reg": LinearRegression, + "affine_reg": AffineRegression, } -model = model_registry.get(model_name) -if model is None: - valid_models = list(model_registry.keys()) +model_class = model_map.get(model_name) + +if model_class is None: + valid_models = list(model_map.keys()) raise KeyError(f"Unknown model '{model_name}'. Valid options: {valid_models}") -return model +if model_class is Polynomial: + return model_class(**kwargs) + +return model_class() + Apply / Chat Suggestion importance[1-10]: 9 __ Why: This suggestion correctly identifies a bug where Polynomial(**kwargs) is executed on every call, potentially causing crashes if kwargs are passed for other models. The proposed fix correctly applies the factory pattern, improving efficiency and preventing runtime errors.	High
General	Use explicit degree parameter **In the Polynomial class constructor, replace the generic kwargs with an explicit degree: int = 2 parameter to make the API clearer and prevent unsupported arguments. color_correction/core/correction/polynomial.py [29-46] -def __init__(self, **kwargs: Any) -> None: # noqa: ANN401 +def __init__(self, degree: int = 2) -> None: self.model: Pipeline | None = None - self.degree: int = kwargs.get("degree", 2) + self.degree: int = degree Apply / Chat Suggestion importance[1-10]: 7 __ Why: The suggestion correctly proposes changing **kwargs to an explicit degree parameter in the __init__ method, which improves API clarity and enables type checking. This is a valuable improvement for code maintainability and robustness.	Medium
More