CODEWITHSUNDEEP
pythonpython-3.xnumpynumpy-ndarray
Tigran Fahradyan
Tigran Fahradyan

Reputation: 409

How do I use `np.where()` to compare the arrays not individual values

I have an image(2D array) with 3 color channels. Something like this:

[[[128 197 254]
  [128 197 254]
  [128 197 254]
  ...
  [182 244 255]
  [182 244 255]
  [182 244 255]]

 [[128 197 254]
  [128 197 254]
  [128 197 254]
  ...
  [182 244 255]
  [182 244 255]
  [182 244 255]]

 [[128 197 254]
  [128 197 254]
  [128 197 254]
  ...
  [182 244 255]
  [182 244 255]
  [182 244 255]]

 ...

 [[128 197 254]
  [128 197 254]
  [128 197 254]
  ...
  [182 244 255]
  [182 244 255]
  [182 244 255]]

 [[128 197 254]
  [128 197 254]
  [128 197 254]
  ...
  [182 244 255]
  [182 244 255]
  [182 244 255]]

 [[128 197 254]
  [128 197 254]
  [128 197 254]
  ...
  [182 244 255]
  [182 244 255]
  [182 244 255]]]

I want to get the indexes of the colors that are [255, 255, 255] for example. I tried using np.where() or np.argwhere(), but it compared values not arrays. What is the fastest and most efficient way to do it?

Upvotes: 2

Views: 405

Answers (2)

mrk
mrk

Reputation: 10366

A numpy way to do this with np.where would be

import numpy as np

# Generating an example array
width = 100 
height = 100
channels = 3
img = np.random.rand(width, height, channels) * 255

# Defining the three value channels
r=0 
g=1 
b=2

# Defining the query values for the channels, here [255, 255, 255]
r_query = 255
g_query = 255
b_query = 255

# Print a 2D array with the coordinates of the white pixels
print(np.where((img[:,:,r] == r_query) & (img[:,:,g] == g_query) & (img[:,:,b] == b_query)))

This gives you a 2D-Array with the coordinates of the white pixels [255, 255, 255] in your original array (image).

Note: Another way would be using OpenCV

mask = cv2.inRange(img, [255, 255, 255], [255, 255, 255])
output = cv2.bitwise_and(img, img, mask = mask)

Upvotes: -1

rafaelc
rafaelc

Reputation: 59264

IIUC, you may use np.nonzero

np.nonzero((arr==255).all(axis=2))

That will return a tuple of arrays, which represent the indexes. If you do

arr[ind]

where ind is the return from the first expr, you may access/modify all rows with all 255.

Upvotes: 3

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