Question: 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

```

Answer: 
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 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))

```