PIL image to array (numpy array to array) - Python

I highly recommend you use the tobytes function of the Image object. After some timing checks this is much more efficient.

def jpg_image_to_array(image_path):  """  Loads JPEG image into 3D Numpy array of shape   (width, height, channels)  """  with Image.open(image_path) as image:             im_arr = np.fromstring(image.tobytes(), dtype=np.uint8)    im_arr = im_arr.reshape((image.size[1], image.size[0], 3))                                     return im_arr

The timings I ran on my laptop show

In [76]: %timeit np.fromstring(im.tobytes(), dtype=np.uint8)1000 loops, best of 3: 230 µs per loopIn [77]: %timeit np.array(im.getdata(), dtype=np.uint8)10 loops, best of 3: 114 ms per loop

```

I think what you are looking for is:

list(im.getdata())

or, if the image is too big to load entirely into memory, so something like that:

for pixel in iter(im.getdata()):    print pixel

from PIL documentation:

getdata

im.getdata() => sequence

Returns the contents of an image as a sequence object containing pixel values. The sequence object is flattened, so that values for line one follow directly after the values of line zero, and so on.

Note that the sequence object returned by this method is an internal PIL data type, which only supports certain sequence operations, including iteration and basic sequence access. To convert it to an ordinary sequence (e.g. for printing), use list(im.getdata()).

Based on zenpoy's answer:

import Imageimport numpydef image2pixelarray(filepath):    """    Parameters    ----------    filepath : str        Path to an image file    Returns    -------    list        A list of lists which make it simple to access the greyscale value by        im[y][x]    """    im = Image.open(filepath).convert('L')    (width, height) = im.size    greyscale_map = list(im.getdata())    greyscale_map = numpy.array(greyscale_map)    greyscale_map = greyscale_map.reshape((height, width))    return greyscale_map