Convert an Image to 2D Array in Python
I Want to Convert an Image to 2D Array with 5 Columns Where Each Row Is of the Form [R, G, B, X, Y]. X, Y Is the Position of the Pixel and R, G, B Are the...
I want to convert an image to 2D array with 5 columns where each row is of the form [r, g, b, x, y]. x, y is the position of the pixel and r,g,b are the pixel values. (I will be using this array as input to a machine learning model). Is there a more efficient implementation than this in python?
import Image
import numpy as np
im = Image.open("farm.jpg")
col,row = im.size
data = np.zeros((row*col, 5))
pixels = im.load()
for i in range(row):
for j in range(col):
r,g,b = pixels[i,j]
data[i*col + j,:] = r,g,b,i,j
4 Answers
I had to write this recently and ended up with
indices = np.dstack(np.indices(im.shape[:2]))
data = np.concatenate((im, indices), axis=-1)
Where im is a numpy array. You are probably better off reading the images straight into numpy arrays with
from scipy.misc import imread
im = imread("farm.jpg")
Or, better still if you have Scikit Image installed
from skimage.io import imread
im = imread("farm.jpg")
I am not sure if this is the very efficient. But here you go, say arr = np.array(im); then you can do something like this.
>>> arr = np.arange(150).reshape(5, 10, 3)
>>> x, y, z = arr.shape
>>> indices = np.vstack(np.unravel_index(np.arange(x*y), (y, x))).T
#or indices = np.hstack((np.repeat(np.arange(y), x)[:,np.newaxis], np.tile(np.arange(x), y)[:,np.newaxis]))
>>> np.hstack((arr.reshape(x*y, z), indices))
array([[ 0, 1, 2, 0, 0],
[ 3, 4, 5, 0, 1],
[ 6, 7, 8, 0, 2],
[ 9, 10, 11, 0, 3],
[ 12, 13, 14, 0, 4],
[ 15, 16, 17, 1, 0],
[ 18, 19, 20, 1, 1],
[ 21, 22, 23, 1, 2],
[ 24, 25, 26, 1, 3],
[ 27, 28, 29, 1, 4],
[ 30, 31, 32, 2, 0],
[ 33, 34, 35, 2, 1],
[ 36, 37, 38, 2, 2],
...
[129, 130, 131, 8, 3],
[132, 133, 134, 8, 4],
[135, 136, 137, 9, 0],
[138, 139, 140, 9, 1],
[141, 142, 143, 9, 2],
[144, 145, 146, 9, 3],
[147, 148, 149, 9, 4]])
I used "+" to combine two tuple, and use .append() to make "data" list.No need to use Numpy here.
row,col = im.size
data=[] #r,g,b,i,j
pixels=im.load()
for i in range(row):
for j in range(col):
data.append(pixels[i,j]+(i,j))
steps are :
convert images to grayscale (opencv)
convert grayscale to binary image (opencv)
convert to binary 2D matrix (scipy , pillow, numpy)
from scipy.ndimage import zoom
from PIL import Image
import numpy as np
srcImage = Image.open("image_in_binary_color.jpg")
grayImage = srcImage.convert('L')
array = np.array(grayImage)
array = zoom(array, 310/174)
np.savetxt("binarized.txt", array<128, fmt="%d")
print("\n\n Output Stored to binarized.txt.......#")
- store it in a file named binarized.txt
This is how i did it :
Maya is a hardware enthusiast who tests and reviews smart home devices, smartphones, wearables, and audio gear. She focuses on practical consumer value and build quality.
