I reshaped an image (included below) as a list of pixels, and now I want to remove the black ones (with value
[255,255,255]). What is an efficient way to do it?
I tried using
IM[IM != [255,255,255]] and I got a list of values, instead of a list of value triplets. Here is the code I’m using:
import cv2 import numpy as np IM = cv2.imread('Test_image.png') image = cv2.cvtColor(IM, cv2.COLOR_BGR2RGB) # reshape the image to be a list of pixels image_vec = np.array(image.reshape((image.shape * image.shape, 3))) image_clean = image_vec[image_vec != [255,255,255]] print(image_clean)
The issue is that numpy automatically does array-boradcasting, so using
IM != [255,255,255] will compare each element to
[255,255,255] and return a boolean array with the same shape as the one with the image data. Using this as a mask will return the values as 1D array.
An easy way to fix this is to use np.all:
image_vec[~ np.all(image_vec == 255, axis=-1)]