Develop Reference

Accessing each pixel value from a numpy array in an efficient manner

I have a pixel array of shape (height, width, channels) of an image and I want to process each pixel efficiently (I would be processing each pixel about 60 times per second)
Given a numpy pixel array of the sort: pix = np.arange(60).reshape(4, 5, 3)
I want to do something like:
for i in pix:
for j in i:
some_func(j)
but in a more efficient manner. I tried using np.nditer, but I could get it to access only the channel values and not the pixel as a whole

Subtract 2D array from 4D array

I have a greyscale image, represented by a 2D array of integers, shape (1000, 1000).
I then use sklearn.feature_extraction.image.extract_patches_2d() to generate an array of 3x3 'patches' from this image, resulting in an array of shape (1000000, 3, 3), as there are 1 million 3x3 arrays for each pixel value in the original image.
I reshape this to (1000, 1000, 3, 3), which is a 1000x1000 array of 3x3 arrays, one 3x3 array for each pixel in the original image.
I now want to effectively subtract the 2D array from the 4D array. I have already found a method to do this, but I would like to make one using vectorisation.
I currently iterate through each pixel and subtract the value there from the 3x3 array at the same index. This is a little bit slow.
This is what currently loads images, formats the arrays before hand, and then performs this subtraction.
from PIL import Image, ImageOps
from skimage import io
from sklearn.feature_extraction import image
import numpy
jitter = 1
patchsize = (jitter*2)+1
#load image as greyscale image using PIL
original = load_image_greyscale(filename)
#create a padded version of the image so that 1000x1000 patches are made
#instead of 998x998
padded = numpy.asarray(ImageOps.expand(original,jitter))
#extract these 3x3 patches using sklearn
patches = image.extract_patches_2d(padded,(patchsize,patchsize))
#convert image to numpy array
pixel_array = numpy.asarray(original)
#then reshape the array of patches so it matches array_image
patch_array = numpy.reshape(patches, (pixel_array.shape[0],pixel_array.shape[1],patchsize,patchsize))
#create a copy for results
patch_array_copy = numpy.copy(patch_array)
#iterate over each 3x3 array in the patch array and subtract the pixel value
#at the same index in the pixel array
for x in range(pixel_array.shape[0]):
for y in range(pixel_array.shape[1]):
patch_array_copy[x,y] = patch_array[x,y] - pixel_array[x,y]
I would like a way to perform the final step in the for loop using matrix operations.
I would also like to extend this at some point to work with RGB images, effectively making it a subtraction of an array with shape(1000,1000,3) from an array with shape(1000,1000,3,3,3). But i'm trying to go one step at a time here.
Any help or tips or suggestions or links to helpful resources would be greatly appreciated.

Unique colours in stack of images (4d numpy array)

I have a stack of RGB images in a 4d numpy array, so the shape is
(n_images, height, width, n_channels) where n_channels is 3.
How can I get a list of unique RGB values across all the images.
I found this question numpy: unique list of colors in the image for a single image but want to apply it to my stack of images without a for-loop.

You can use np.unique and set the axis, to look across the images. so basically it will look for unique pixel values.
i am reusing the answer from link you shared for your scenario.
np.unique(img.reshape(-1, img.shape[3]), axis=0)
The above code will result in an array of shape (unique_pixels_len,3)
The original answer had img.shape[2] i.e. the channels and in your case it is img.shape[3] which represents the channels.

how to change pixel value in a numpy array

I have a big RGB image as a numpy array,i want to set all pixel that has R=0,G=0,B=0 to R=255,G=0,B=0.
what is the fastest way?
i tried:
for pix in result:
if np.all(np.logical_and(pix[0]==pix[1],pix[2]==0,pix[2]==pix[1])):
pix [0] = 255
but in this way i don't have a single pixel. there is a similar way that it is not to iterate the index?

Here is a vectorized solution. Your image is basically an w by h by 3(colors) array. We can make use of the broadcasting rules that are not easy to grasp but are very powerful.
Basically, we compare the whole array to a 3 vector with the values that you are looking for. Due to the broadcasting rules Numpy will then compare each pixel to that three vector and tell you if it matched (so in this specific case, if the red, green and blue matched). You will end up with an boolean array of trues and falses of the same size as the image.
now we only want to find the pixels where all three colors matched. For that we use the "all" method, which is true, if all values of an array are true. If we apply that to a certain axis -- in this case the color axis -- we get an w by h array that is true, wherever all the colors matched.
Now we can apply this 2D boolean mask back to our original w by h by 3 array and get the pixels that match our color. we can now reassign them -- again with broadcasting.
Here is the example code
import numpy as np
#create a 2x2x3 image with ones
img = np.ones( (2,2,3) )
#make the off diagonal pixels into zeros
img[0,1] = [0,0,0]
img[1,0] = [0,0,0]
#find the only zeros pixels with the mask
#(of course any other color combination would work just as well)
#... and apply "all" along the color axis
mask = (img == [0.,0.,0.]).all(axis=2)
#apply the mask to overwrite the pixels
img[ mask ] = [255,0,0]

Since all values are positive or null, a simple and efficient way is:
img[img.sum(axis=2)==0,0]=255
img.sum(axis=2)==0 select good pixels in the two first dimensions, 0 the red canal in the third.

Python - get white pixels of image

I'm would like to go from an image filename to a list of coordinates of the white pixels in the image.
I know it involves PIL. I have tried using Image.load() but this doesn't help because the output is not indexable (to use in a for loop).

You can dump an image as a numpy array and manipulate the pixel values that way.
from PIL import Image
import numpy as np
im=Image.open("someimage.png")
pixels=np.asarray(im.getdata())
npixels,bpp=pixels.shape
This will give you an array whose dimensions will depend on how many bands you have per pixel (bpp above) and the number of rows times the number of columns in the image -- shape will give you the size of the resulting array. Once you have the pixel values, it ought to be straightforward to filter out those whose values are 255
To convert a numpy array back to an image use:
im=Image.fromarray(pixels)