Enhancement: Introduce Advanced Image Processing Operators (Canny, Histogram Equalization, Brightness/Contrast, Morphology)

[khushalkks]

The current operator ecosystem in ImageLab provides a solid base for fundamental image processing operations (blurring, thresholding, geometric transforms, Sobel derivatives, etc.).
To further strengthen the pipeline for real-world computer vision and image preprocessing workflows, I would like to propose adding a small set of widely adopted advanced operators.

These additions would significantly enhance the flexibility and practical usability of the system while aligning with standard OpenCV-based processing pipelines.
Problem Statement

While the existing operator architecture is modular and extensible, the following commonly used operations are currently missing:

Robust edge detection (Canny)
Contrast enhancement via histogram equalization
Direct brightness and contrast adjustments
Morphological transformations (erosion, dilation, etc.)
These operations are frequently required in production-grade CV pipelines. Their absence may require users to preprocess images externally before using ImageLab.
Proposed Enhancements
I propose introducing the following operators:

1. Canny Edge Detection
   Purpose: Accurate multi-stage edge detection
   Common use cases: Feature extraction, object detection preprocessing
   Suggested parameters:
   threshold1
   threshold2

2. Histogram Equalization
   Purpose: Improve contrast in grayscale images
   Use cases: Low-light image enhancement, preprocessing before thresholding
   Suggested parameter:
   mode (e.g., grayscale)

3. Brightness & Contrast Adjustment
   Purpose: Controlled linear intensity transformation
   Use cases: Image enhancement and normalization
   Suggested parameters:
   brightness
   contrast

4. Morphological Operations
   Purpose: Structural image refinement
   Operations:
   erosion
   dilation
   opening
   closing
   Suggested parameters:
   operation
   kernel_size
   Design & Implementation Considerations

Each operator will inherit from BaseOperator
Registration will follow the existing registry.py pattern
Parameter validation will be implemented to ensure robustness

No breaking changes to the current pipeline execution flow
Unit tests can be added for coverage
Benefits

Enhances ImageLab’s capability for real-world CV workflows
Reduces need for external preprocessing
Keeps architecture modular and scalable
Aligns the project more closely with standard OpenCV feature sets

Increases the practical value of the visual pipeline builder
Willing to Contribute

I would be happy to implement these operators and submit a structured PR following project guidelines, if this proposal is approved.

[m-rayan-hassan]

Implementation Complete ✅

I've implemented the proposed enhancements and opened a PR: #209

New Operators Added:

Operator	Category	Parameters
Canny Edge Detection	Filtering	threshold1, threshold2
Histogram Equalization	Conversions	(none — automatic)
Brightness & Contrast	Conversions	brightness, contrast

Note: Morphological operations (erosion, dilation, opening, closing) were already present in the codebase via Erosion, Dilation, and Morphological operators, so no changes were needed there.

What's included in the PR:

• 3 new backend operator classes inheriting from BaseOperator
• Registry registration for all operators
• Frontend Blockly block definitions with appropriate input fields
• Category sidebar entries and operator documentation
• 28 unit tests (all passing) covering shape, dtype, edge cases, parameter coercion, and semantic correctness

No breaking changes to existing pipeline execution.