I'm new to Media/Image Framework in python. I have a use case, like below,
I have multiple 2D Images (For ex., i have .jpg images in various angle of furniture)
generate the 3D images .glb from 2D images.
Finally, I need to see .glb image in WebAR
Thanks,
Related
I'm working with satellite imagery (from Sentinel-2), in particular with cloud detection and cloud cleaning.
I got a batch of images of the same area, but in different periods:
From these images, you can see that the position of the clouds is always different.
I also have the mask for each image, where the black areas represent clouds:
These masks are not perfect, but this is not a problem.
What I want to do is to use the mask to cut all the white portions (so get the land and exclude the clouds), and then fill these cuts with a black portion of another image (fill the "hole" in the image with a part of another image without clouds).
Imagery is in TIFF format, while masks are in JPG format.
I'm using Python with libraries like Rasterio, numpy and scikit-image, so a Pythonic solution would be appreciated.
I was reading the source of the random walker algorithm in scikit-image library, and it's written there that:
Parameters
data : array_like
Image to be segmented in phases. Gray-level `data` can be two- or
three-dimensional
My question is: what do they mean by 3D gray-level image?
A 2D image is an image that is indexed by (x,y). A 3D image is an image that is indexed by (x,y,z).
A digital image samples the real world in some way. Photography produces a 2D projection of the 3D world, the digital photograph is a sampling of that projection. But other imaging modalities do not project, and can sample all three dimensions of the 3D world. For example:
Confocal microscopy
Computed tomography
Magnetic resonance imaging
Besides these, a 2D time-series (a movie) is sometimes also treated as a 3D image, applying algorithms that work on 3D images.
I have a folder(training dataset) of 4500 png files labeled 1.png (lego block images) and so on. I also have a CSV file that has data on which png belongs to which class(there are 16 classes ie 16 different types of blocks and images are taken from 400 different angles).
I have to further classify another 1600 images based on this data in the 16 classes. I am trying to implement the VGG16 model for this purpose. Is it necessary to have 16 different folders with images belonging to that class? If so, how do I move the png files from my current folder which has all the images together using python? Also, is there a better way to go about this task?
I have around 200,000 images that need to be rotated correctly.
Also, 30 images with their corresponding rotated images, how will I train opencv to achieve what I want? Some tips would be appreciated.
I'm using this library for opencv
Thanks!
Open each file using the readImage method as per their examples and the Matrix your callback receives has a rotate function you can use.
My friend and I are making a game, and when I upload his 2D images into Unity, the colors are quite under-saturated. Is this possible to remedy?
The filter is Point, and format is Compressed. I tried 16 bits and True Color, but it didn't change anything.