Issue: The script iterates through each pixel in the image to determine cropping boundaries, which is computationally expensive.
Solution:
Use NumPy arrays to represent image data. Convert the image to a NumPy array and use vectorized operations to check for black and white pixels across columns.
Alternatively, use OpenCV, which offers faster image processing capabilities due to its C++ backend.
Implementation Suggestion:
Convert image to a grayscale NumPy array.
Identify columns with black or white pixels using logical operations across entire columns instead of row-by-row checks.
Child of Issue #32
Issue: The script iterates through each pixel in the image to determine cropping boundaries, which is computationally expensive.
Solution:
Use NumPy arrays to represent image data. Convert the image to a NumPy array and use vectorized operations to check for black and white pixels across columns. Alternatively, use OpenCV, which offers faster image processing capabilities due to its C++ backend. Implementation Suggestion:
Convert image to a grayscale NumPy array. Identify columns with black or white pixels using logical operations across entire columns instead of row-by-row checks.