Whenever we perform any operations with the images, we convert them into Arrays, what's the specific reason?
OpenCV images are stored as three-dimensional Numpy arrays. When you read in images using the library, they are represented as Numpy arrays.
With Numpy you can make blazing fast operations on numerical arrays, no matter which dimension, shape they are.
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I am working on Gan networks, and it happens that I have a numpy array of images with size (26600,256,256,3). is there any method that can resize the images inside that numpy array to have in the output a numpy array of (26600,64,64,3).
PS. don't take in consideration the real sizes I provided I just wanted to know the method without resizing the images manually. thank you.
I have three different isotropic MRI DICOM volumes of the same object, each with a different direction (orthogonal sagittal, coronal and transverse acquisitions of same object).
I would like to convert them to numpy arrays and plot them, in such a way that their indexing matches. Let's say that if I have three numpy arrays issued from sitk images:
sag_array = sitk.GetArrayFromImage( sag_sitk )
dors_array = sitk.GetArrayFromImage( dors_sitk )
trans_array = sitk.GetArrayFromImage( trans_sitk )
I would like to be able to plot them using the same indexing, so that the slices
sag_array[:,:,index]
dors_array[:,:,index]
trans_array[:,:,index]
correspond to the same view, with no flipping or inversion of the axes.
I guess this info is contained in the Direction of the SimpleITK images, is there a way to transfer it to the numpy arrays after the conversion?
Does the Direction property in general have any effect on the numpy conversion, or is it lost?
I solved it by pre-processing all the images with the sitk.Resample() function to a common Origin and Direction. In this way, when converting to numpy arrays, since they occupied the same physical space, they're sliced coherently among each other.
I have unsigned 16-bit grayscale tiff images as numpy arrays. I want to do some image processing on these images using OpenCV. I am converting the numpy array to Mat format using cv2.cvtColor(src, code). As far as the documentation goes, I am having a hard time finding the right code argument to correctly convert 16-bit grayscale images without losing any information.
Previously, I read the images directly using cv2.imread(src, cv2.IMREAD_UNCHANGED). However, I don't have the original image files now, only the pickled numpy array. I am looking for the code in cvtColor which does a similar thing as cv2.IMREAD_UNCHANGED
Your question is tough to follow. All you appear to have are some files containing pickled Numpy arrays, correct?
If so, you don't need any imread(), you just need to unpickle the files and you will have Numpy arrays of the type that OpenCV uses to hold images. Check their dtype is np.uint16 and their shape appears correct.
I have an 3D numpy array, start_array, I do some processing to it by applying some random translations and rotations to get transform_arry. I have access to both the arrays only. I want to identify the random transforms done to the arrays. What would be a quick and easy way to do it using python?
I have a bunch of images that I want to store into an array.
The problem is that all my images are different sizes and I don't want to necessarily change their size, because some will be square and some aren't.
I tried using np.concatenate but someone online said it was better to construct a zero matrix and fill it.
However, using
image = misc.imread(filename)
from the scipy library. The image is returned as a 3 dimensional array. How should I construct my numpy ndarray if I want to store all the images in it?
If I'm understanding the question correctly, you are trying to store a bunch of images of different sizes that are each stored as separate numpy arrays. If your images are gray scale (meaning 2D, as opposed to RGB which are 3D - a channel for R, G and B), you could store the images as the third dimension, filling in the absent pixels with 0s. But the best way would be to just use a python list (or tupple maybe) that stores a list of your numpy array images. That way they can be different sizes. i.e.: img_list = img1, img2, img3, etc.
storing them in a list may be easier, the list will store them as array() objects and size wont matter, when you do operations on them, just reference the list elements.