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How does it do the blur detection? I assume it's also just a convolutional kernel applied on the image in some way. So it would do just exactly the same as a ti

How does it do the blur detection? I assume it's also just a convolutional kernel applied on the image in some way. So it would do just exactly the same as a tiny neural network with one convolutional layer. Thus, they would be exactly the same speed, if they would use the same underlying native kernels. However, the native kernels in TF/PyTorch/etc are more heavily optimized than OpenCV, thus I assume that OpenCV would actually be slower here. And also more complicated, as you need to mix two big frameworks.

filterfiber 3y ago
Realistically I think you'll often want both depending on what you're doing. Especially for things like blur detection, what's your acceptable specificity, acceptable/scale of performance, where you're running the algorithm (on device vs cloud). I'm not an expert at all but most image processing network I've seen generally involve at least a few plus a few other layers. I don't think you can get away with a single convolution, at least not that well. OpenCV you could use Laplacian variance which looks like it's just a single line of code. > cv2.Laplacian(image, cv2.CV_64F).var() Many of the NN implementations look like their finetuned off google's ViT checkpoints. I really can't imagine these are faster (at least not without spending extra on GPU/TPU's) than Laplacian variance but I could be wrong. And I assume you might be able to get better evaluation performance from a finetuned NN but depending on what you're doing, that's a ton of work compared to opencv. https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/ https://pyimagesearch.com/2015/09/07/blur-detection-with-ope... https://sh-tsang.medium.com/review-bdnet-blur-detection-convolutional-neural-network-blur-detection-9150bedd0546 https://sh-tsang.medium.com/review-bdnet-blur-detection-conv...

filterfiber 3y ago

Realistically I think you'll often want both depending on what you're doing. Especially for things like blur detection, what's your acceptable specificity, acceptable/scale of performance, where you're running the algorithm (on device vs cloud). I'm not an expert at all but most image processing network I've seen generally involve at least a few plus a few other layers. I don't think you can get away with a single convolution, at least not that well. OpenCV you could use Laplacian variance which looks like it's just a single line of code. > cv2.Laplacian(image, cv2.CV_64F).var() Many of the NN implementations look like their finetuned off google's ViT checkpoints. I really can't imagine these are faster (at least not without spending extra on GPU/TPU's) than Laplacian variance but I could be wrong. And I assume you might be able to get better evaluation performance from a finetuned NN but depending on what you're doing, that's a ton of work compared to opencv. https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/ https://pyimagesearch.com/2015/09/07/blur-detection-with-ope... https://sh-tsang.medium.com/review-bdnet-blur-detection-convolutional-neural-network-blur-detection-9150bedd0546 https://sh-tsang.medium.com/review-bdnet-blur-detection-conv...