CODEWITHSUNDEEP
deep-learningcomputer-visionobject-detection
FenryrMKIII
FenryrMKIII

Reputation: 1198

Resizing big images for object detection

I need to perform object detection using deep learning on "huge" images, say 10000x10000 pixels.

At some point in the workflow, I need to resize the images down to something more manageable say 640x640. At the moment, I am achieving this using opencv : 

import cv2
img = cv2.imread("some/path/to/my/img")
h, w = 640, 640
img = cv2.resize("some/path/to/my/img_resized", (w,h))

Now, when I am trying to look at some of these pictures (e.g. to check my bounding boxes are well-defined) with my human eye, I "can't see anything" in the sense that the resize is so aggressive that the image is heavily pixelated.

Does this cause an issue for the training of the algorithm ? Because in the end, I can get back the bounding boxes output by the model back to the original image (100000x10000px) using some transform. That is not an issue. But I can't tell if working on such pixelated images during training causes something to go wrong ?

Upvotes: 0

Views: 1628

Answers (1)

Nopileos
Nopileos

Reputation: 2117

It really depends what information is lost during the resizing. From 10000x10000 to 640x640 I would assume almost everything relevant is lost making the problem a lot harder if even solvable at all. If you can't solve the problem (seeing the objects in the resized image) it is a very bad starting point to solve the problem with a neural network. I would still try and see if the network does anything.

It probably won't work good. An easy approach trying to solve this is splitting up the initial image in patches and do the detection on them and combine the results. This can work but depending on the problem might not be sufficient.

If this is not sufficient for your problem you might wanna do some state of the art research and try to find someone with a similar problem. I know that medical images also can be quite big. Also people dealing with satellite images might have the same problem of very big input images and maybe came up with ways to solve this.

Upvotes: 1

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