Is there a way in SciPy to import 3D images of .nrrd format and rotate the image to a specific view(to some given specific coordinates) and then display the image?
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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,
I am trying to build a machine learning algorithm where I need to convert pictures to their binaries. I am using Pillow library to get the data from images. Since the performance of the algorithm is not great, I need extra parameters to thoroughly train the network and one of the extra parameters might be hue.
So is there a method in Python that gives me hue value of an image?
I am not sure what you are really trying to do, as Christoph says in the comments, but you can find the mean hue of all the pixels in an image with PIL like this:
from PIL import Image, ImageStat
# Load image, convert to HSV and split the channels, retain H, discard S and V
H, _, _ = Image.open('image.png').convert('RGB').convert('HSV').split()
# Print the mean Hue across all pixels
print(ImageStat.Stat(H).mean)
Note that I converted to RGB first to avoid problems that may arise if you try this with palette images, CMYK images, images with transparency and greyscale images. See here.
I have to construct a 3D image of spherical particles using python array operations. The data I have is center points and radius of spherical particles in (x,y,z,r) format. Where x,y,z,r are in the form of arrays of length 55000. When I do 3D plotting of these coordinates using mpl_toolkits.mplot3d the structure looks like as one shown in figure.
Can you suggest a good way to make 3D image using numpy or scipy.ndimage image processing tools. If not possible is there any alternative method to solve this issue? Thanks in advance.
I'm struggling to find the (x,y) coordinate of certain RGB values in an image. Lets say I edit an image and put a single pixel of (0,255,5), how can I find the 2D coordinate?
My current approach is using numpy.where, but it doesn't seem to work. I'm aware of opencv storing images in BGR. I don't really want to use the HSV approach and inRange because I just want single pixels of very specific values, so that would be overkill.
import numpy as np
import cv2
im = cv2.imread(image)
point = np.where(im == 5,255,0))
I've set of vtk polygonal data files for segmented vessels:-
How to (voxelize) convert it to itk image with specific (size, origin and spacing)?
This is not a trivial problem. It is not possible to do that given your raw contours. If you can convert your contours to a closed surface, then you can use vtkVoxelModeller to create a vtkImage. Then you can create an itk image using vtkITKImageFilter.
Alternatively, you can fit a closed geometry to your contours and create voxels based on the parameterization of your geometry:
http://www.mit.edu/~adalca/files/papers/nerve_segmentation.pdf