How can OpenCV be used to remove convexity defects in Sudoku square boundaries?

Removing Convexity Defects in Sudoku Squares with OpenCV

Problem Statement:

The task involves solving Sudoku puzzles from input images using OpenCV. After detecting and warping the Sudoku square, a discrepancy persists between the original and approximated contours representing the square's boundary, leading to distortion.

Solution:

The provided solution in Mathematica involves several image processing operations to accurately warp the image within the correct Sudoku square boundary.

Step 1: Brightness Adjustment

To enhance the image's contrast, the pixel values are divided by the result of a closing operation.

Step 2: Sudoku Area Identification

Connected component analysis identifies the Sudoku component with the largest convex area, creating a mask to exclude the background.

Step 3: Grid Lines Detection

A 2nd order derivative filter is applied to the masked image to find vertical and horizontal lines, which are extracted using another round of connected component analysis.

Step 4: Grid Intersection Points

Intersections of the grid lines are determined by dilating and calculating pixel-by-pixel intersections. The centers of these intersections represent the grid points.

Step 5: Interpolation and Transformation

Interpolation functions are defined to map the XY pixel coordinates through the grid points. The image is then transformed using these functions to obtain the rectified Sudoku square.

OpenCV Implementation:

While the provided solution is in Mathematica, it can be translated to OpenCV using its extensive image processing capabilities. The following steps could be taken in OpenCV:

1. Brightness adjustment can be achieved using the GaussianBlur and divide functions.
2. Connected component analysis is supported by the connectedComponentsWithStats function.
3. The GaussianBlur and threshold functions can be used for grid line detection.
4. Grid intersection points can be calculated using dilate and bitwise_and operations.
5. The getPerspectiveTransform function can be utilized for image transformation based on the detected grid points.

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