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How to slice Torch images as numpy images


How to slice Torch images as numpy images

By : user2172001
Date : October 25 2020, 09:10 AM
may help you . If I understand your question correctly, then you can do it just the same way as in numpy.
Here is a short example:
code :
import torch
t = torch.rand(5, 5)
# original matrix
print(t)
h = 2
w = 2
x = 1
y = 1
# cropped out matrix
print(t[x:x+h, y:y+w])
tensor([[ 0.5402,  0.4106,  0.9904,  0.9556,  0.2217],
        [ 0.4533,  0.6300,  0.5352,  0.2710,  0.4307],
        [ 0.6389,  0.5660,  0.1582,  0.5701,  0.1614],
        [ 0.1717,  0.4071,  0.4960,  0.2127,  0.5587],
        [ 0.9529,  0.2865,  0.6667,  0.7401,  0.3372]])
tensor([[ 0.6300,  0.5352],
        [ 0.5660,  0.1582]])


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What is imapact of caching images on overall sites performance ? (small 200x150 size images, total 50 such images)

What is imapact of caching images on overall sites performance ? (small 200x150 size images, total 50 such images)


By : Tone
Date : March 29 2020, 07:55 AM
help you fix your problem Generally, the first step to higher performance would be to move images out of the DB, and simply have urls in the DB. You can then have your site serve those images as static files, very quickly. The OS/server should automatically cache according to available memory. This also has a number of other advantages:
How to load images and labels in Torch for a Convolutional Neural Network

How to load images and labels in Torch for a Convolutional Neural Network


By : Paul Rankin
Date : March 29 2020, 07:55 AM
hop of those help? Check this link The data.lua file in that is very simple to understand. They have used torch tensors, and the code is self explanatory. I had achieved a clear understanding of loading the data, post reading this link; hope it helps you too.
Visualize images in intermediate layers in torch (lua)

Visualize images in intermediate layers in torch (lua)


By : ruby rao
Date : March 29 2020, 07:55 AM
seems to work fine In the conv-nets model, I know how to visualize the filters, we can do itorch.image(model:get(1).weight) , Similarly to weight, you can use:
code :
itorch.image(model:get(1).output)
Numpy 8-bits images conversion to 16/32-bits images before cvtColor() in opencv

Numpy 8-bits images conversion to 16/32-bits images before cvtColor() in opencv


By : Jaskirat Singh
Date : March 29 2020, 07:55 AM
With these it helps I have to disagree with the answer to the question you link. Format uint8 can represent 256 values of integers i.e., integers from 0 to 255 (2^8 - 1). 256 cannot be represented as uint8. uint16 will range from 0 to (2^16 - 1) = 65535.
Therefore, if you want a 16-bit, images with 65535 color levels and if the intensity values of the images are in the range [0, 1] when you read the image and in format float, you need to multiply the values by 65535 and to and then to convert the array to uint16.
when i click a images then show 3 images on other html page but these 3 images is different for every clicked images

when i click a images then show 3 images on other html page but these 3 images is different for every clicked images


By : user3313995
Date : March 29 2020, 07:55 AM
I wish did fix the issue. get() returns the single object so you need to use filter() here like this:
code :
def detail(request,id):
    det = get_object_or_404(value,pk=id)
    u_id = related.objects.filter(u_id=det)
    return render(request,'rel/detail.html',{'det':det,'u_id':u_id})
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