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I am working with this large data set of around 70,000 images. I find that reading the images in OpenCV takes too much time. I have access to an NVidia GTX 1080, and would prefer to run the imread on the GPU instead as I believe it would be faster. I have installed CUDA 9.0, CuDNN 8.0, and libcupti, which is necessary for Tensorflow-GPU, and also want a means to use GPU with OpenCV commands like imread. I noticed that some Github links offer instructions for OpenCV-GPU with CUDA 8.0, and since I have CUDA 9.0, I don't believe those will suit my case.

Any help/advice would be greatly appreciated.

So, I wrote a code for a SIFT/SURF+BOVW+SVM Classifier for 20 kinds of texture in Python. In method train(), I extract SIFT/SURF feature descriptors for every image in my training set, and I have created a BOWKMeansTrainer as follows:

dictionarySize = 20

BOW = cv.BOWKMeansTrainer(dictionarySize)

I have 80 training images.

So, I add the descriptors of each image to BOW like: kp,desc=surf(img) BOW.add(desc)

All of these form a dictionary with size (20,64). What does this 64 mean?

And, how does the BOW trainer know that some image x belongs to class c? What data do I feed into the SVM?

Help in understanding this would be greatly appreciated. Thanks

Okay. Thanks

How to configure opencv 3 with GPU Support, Cuda 9.0, Ubuntu 16.04? I am working with this large data set of around 70,0

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Thanks for the detailed description.

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