Faster pre-processing for gray image input

[cher-liang]

Using opencv cvtColor function is significantly faster than np.tile method to enforce 3 channel input

%%timeit -r 7 -n 100
im_cv_3ch=cv.cvtColor(im,cv.COLOR_GRAY2BGR)
13.2 ms ± 250 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%%timeit -r 7 -n 100
im_np_3ch=np.tile(im[..., None], 3)
119 ms ± 2.62 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)

(Test done on Windows machine with Intel i5 CPU)

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Improved image preprocessing for grayscale images in YOLOv5.

📊 Key Changes

• Modified the method to enforce 3-channel input for grayscale images.
• Replaced np.tile() with OpenCV's cv2.cvtColor() for converting single-channel images to 3-channel BGR format.

🎯 Purpose & Impact

• The change ensures better handling of grayscale images during preprocessing, leading to potentially improved model training and inference performance with such data.
• Users working with grayscale datasets can expect their images to be correctly processed without manual intervention.
• The use of OpenCV's function may result in more efficient and standardized conversion compared to NumPy's tiling method. 🚀

[github-actions]

👋 Hello @cher-liang, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:

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[glenn-jocher]

@cher-liang 10x speedup, very nice!

PR is merged. Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐