We Keep Coding

Why do we convert image in arrays for image processing?

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.

Resize CSV data using Python and Keras

I have CSV files that I need to feed to a Deep-Learning network. Currently my CSV files are of size 360*480, but the network restricts them to be of size 224*224. I am using Python and Keras for the deep-learning part. So how can I resize the matrices?
I was thinking that since aspect ratio is 3:4, so if I resize them to 224:(224*4/3) = 224:299, and then crop the width of the matrix to 224, it could serve the purpose. But I cannot find a suitable function to do that. Please suggest.

I think you're looking for cv.resize() if you're using images.
If not, try numpy.ndarray.resize()

Image processing
If you want to do nontrivial alterations to the data as images (i.e. interpolating between pixel values, assuming that they represent photographs) then you might want to use proper image processing libraries for that. You'd need to treat them not as raw matrixes (csv of numbers) but convert them to rgb images, do the transformations you desire, and convert them back to a numpy matrix.
OpenCV (https://docs.opencv.org/3.4/da/d6e/tutorial_py_geometric_transformations.html)
or Pillow (https://pillow.readthedocs.io/en/3.1.x/reference/Image.html) might be useful to do that.

I found a short and simple way to solve this. This uses the Python Image Library/Pillow.
import numpy as np
import pylab as pl
from PIL import Image
matrix = np.array(list(csv.reader(open('./path/mat.csv', "r"), delimiter=","))).astype("uint8") #read csv
imgObj = Image.fromarray(matrix) #convert matrix to Image object
resized_imgObj = img.resize((224,224)) #resize Image object
imgObj.show()
resized_imgObj.show()
resized_matrix = np.asarray(img) #convert Image object to matrix
While numpy module also has a resize function, but it is not as useful as the aforementioned way.
When I tried it, the resized matrix had lost all the intricacies and aesthetic aspect of the original matrix. This is probably due to the fact that numpy.ndarray.resize doesn't interpolate and missing entries are filled with zeros.
So, for this case Image.resize() is more useful.

You could also convert the csv file to a list, truncate the list, and then convert the list to a numpy array and then use np.reshape.

Loading 3D Model but getting 2D Array in Python

I`ve downloaded a sample .stl file from here: [https://www.thingiverse.com/thing:156207]
Then I've used this code to get a numpy array for further image processing with matplotlib:
import numpy as np
from stl import mesh
np.set_printoptions(threshold=np.nan)
# Using an existing stl file:
your_mesh = mesh.Mesh.from_file('300_polygon_sphere_100mm.stl')
data = np.array(your_mesh)
print(data.shape)
Unfortunately, this is an array with only two dimensions. I've checked the .stl file with my editor and there are three dimensions.
Can someone help me? My goal is to create a code with that i can slice 3D models to get acces to the sliced 2d images.
Thanks.
EDIT: I've tried to reshape it:
data_reshaped = np.reshape(data, (550, 3, 3))
But i guess this totally wrong. And i don't know if the pattern is (Z, X, Y).
I want to do some slicing operations on the 3d array to get XY images like this guy is very easily doing https://www.youtube.com/watch?v=5jQVQE6yfio&list=PLT66ZlnovHPYzny9TYM1mx02k5Xnw_kjw&t=215s&index=3

You won't be able to just load the .stl file into a numpy array and perform slicing as shown in the video you linked. In the video, they load a model that is stored as a 3D numpy array.
However, the model you are trying to load consists of a polygonal mesh. This means you only have the coordinate values of the vertices. You can open the .stl file in a text editor to see its contents. (By converting the loaded mesh into a numpy array you just extract those coordinate values. You can actually compare the values in the numpy array and the text file, they are the same.) The resulting numpy array has shape (550, 9). The first dimension is defined by the number of faces in the model (in this case, the model has 550 faces). As each face has three vertices, which have three coordinate values each, hence you have 9 numbers per face. So the third dimension is not lost. It's just stored in a different manner.
Simply reshaping the array won't create you a model of which you can get slices of, as shown in the video. To achieve this, you have to convert the meshed model into a rasterized one. You could do this by initializing an empty 3D array that contains the whole model and then determining for each pixel if it intersects with the geometry of the mesh you loaded.

Numpy Concatenate Images into Array

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.

Numpy array to image is blurry

I'm trying to save a numpy array as a png but the output is always blurry or the size of the image is very small. I've tried looking around SO and various other sources but I haven't been able to figure this one out.
(1) Using the following technique I get an image of the appropriate size (800x600px) but the matrix is blurry:
import matplotlib.pyplot as plt
plt.imshow(matrix)
plt.savefig(filename)
(2) Using another technique, the image is very small (60x60px) but the matrix is not blurry:
import matplotlib
matplotlib.image.imsave('name.png', array)
(3) After running my python program to produce a numpy array A, if I then run the command
matshow(A)
and then I hit the save icon to save the image, the result is an image of a larger size (800x600px) and the matrix is not blurry.
Using technique (3) wouldn't be an issue, but the thing is I have a lot of numpy arrays that I need to save as images, so it would be very time consuming to use technique (3) for each array.
Note: I can't post the images because I don't have enough reputation, so here is a link to them instead: https://www.dropbox.com/sh/mohtpm97xyy8ahi/AACgIpY5ECBDIbcnNRS9kd8La?dl=0
If anyone has had success producing clearer images of a larger size, like those in technique 3, any insight would be much appreciated.