How to Rectify Convexity Defects in Sudoku Square Boundary Detection with OpenCV?

Convexity Defects in Sudoku Squares

In the process of utilizing OpenCV for Sudoku puzzle solving, irregularities may arise when identifying the accurate boundary of the Sudoku square. The issue pertains to convexity defects resulting from the approximate nature of the contour approximation method.

Question: How can one rectify these distortions or alleviate the disparity between the original and approximated contour in OpenCV?

Answer:

The response proposes a solution that requires translation into OpenCV from Mathematica. The strategy employs several image processing techniques:

1. Brightness Adjustment:

Normalize pixel values by dividing grayscale pixel values by the closing operation output, enhancing contrast.

2. Grid Extraction:

Identify the Sudoku area using connected component analysis, isolating the component with the largest convex area.

3. Mask Creation:

Generate a mask image for the Sudoku grid by filling the identified component.

4. Grid Line Detection:

Distinguish vertical and horizontal grid lines using a second-order derivative filter on the masked image. Binarization and connected component analysis are subsequently utilized to extract grid lines, with calipers used to filter out non-grid components.

5. Grid Line Intersection:

Create 2x10 mask images for each set of vertical/horizontal grid lines. Dilate the intersection of these lines to calculate their center of gravity, representing grid line intersections.

6. Image Transformation:

Define interpolation functions for X/Y coordinates using the grid line intersections. Perform an image transformation based on these functions to achieve a rectangular Sudoku grid.

While the provided solution requires adaptation to OpenCV, the fundamental image processing techniques employed offer a framework for resolving convexity defects in Sudoku square identification.

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