I have an RGB image. I want to convert it to numpy array. I did the following

im = cv.LoadImage("abc.tiff")
a = numpy.asarray(im)

It creates an array with no shape. I assume it is a iplimage object.

You can use newer OpenCV python interface (if I’m not mistaken it is available since OpenCV 2.2). It natively uses numpy arrays:

import cv2
im = cv2.imread("abc.tiff",mode='RGB')
print type(im)

result:

<type 'numpy.ndarray'>

PIL (Python Imaging Library) and Numpy work well together.

I use the following functions.

from PIL import Image
import numpy as np

def load_image( infilename ) :
    img = Image.open( infilename )
    img.load()
    data = np.asarray( img, dtype="int32" )
    return data

def save_image( npdata, outfilename ) :
    img = Image.fromarray( np.asarray( np.clip(npdata,0,255), dtype="uint8"), "L" )
    img.save( outfilename )

The ‘Image.fromarray’ is a little ugly because I clip incoming data to [0,255], convert to bytes, then create a grayscale image. I mostly work in gray.

An RGB image would be something like:

 outimg = Image.fromarray( ycc_uint8, "RGB" )
 outimg.save( "ycc.tif" )

You can also use matplotlib for this.

from matplotlib.image import imread

img = imread('abc.tiff')
print(type(img))

output: <class 'numpy.ndarray'>

As of today, your best bet is to use:

img = cv2.imread(image_path)   # reads an image in the BGR format
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)   # BGR -> RGB

You’ll see img will be a numpy array of type:

<class 'numpy.ndarray'>

Late answer, but I’ve come to prefer the imageio module to the other alternatives

import imageio
im = imageio.imread('abc.tiff')

Similar to cv2.imread(), it produces a numpy array by default, but in RGB form.

You need to use cv.LoadImageM instead of cv.LoadImage:

In [1]: import cv
In [2]: import numpy as np
In [3]: x = cv.LoadImageM('im.tif')
In [4]: im = np.asarray(x)
In [5]: im.shape
Out[5]: (487, 650, 3)

When using the answer from David Poole I get a SystemError with gray scale PNGs and maybe other files. My solution is:

import numpy as np
from PIL import Image

img = Image.open( filename )
try:
    data = np.asarray( img, dtype='uint8' )
except SystemError:
    data = np.asarray( img.getdata(), dtype='uint8' )

Actually img.getdata() would work for all files, but it’s slower, so I use it only when the other method fails.

OpenCV image format supports the numpy array interface. A helper function can be made to support either grayscale or color images. This means the BGR -> RGB conversion can be conveniently done with a numpy slice, not a full copy of image data.

Note: this is a stride trick, so modifying the output array will also change the OpenCV image data. If you want a copy, use .copy() method on the array!

import numpy as np

def img_as_array(im):
    """OpenCV's native format to a numpy array view"""
    w, h, n = im.width, im.height, im.channels
    modes = {1: "L", 3: "RGB", 4: "RGBA"}
    if n not in modes:
        raise Exception('unsupported number of channels: {0}'.format(n))
    out = np.asarray(im)
    if n != 1:
        out = out[:, :, ::-1]  # BGR -> RGB conversion
    return out

I also adopted imageio, but I found the following machinery useful for pre- and post-processing:

import imageio
import numpy as np

def imload(*a, **k):
    i = imageio.imread(*a, **k)
    i = i.transpose((1, 0, 2))  # x and y are mixed up for some reason...
    i = np.flip(i, 1)  # make coordinate system right-handed!!!!!!
    return i/255


def imsave(i, url, *a, **k):
    # Original order of arguments was counterintuitive. It should
    # read verbally "Save the image to the URL" — not "Save to the
    # URL the image."

    i = np.flip(i, 1)
    i = i.transpose((1, 0, 2))
    i *= 255

    i = i.round()
    i = np.maximum(i, 0)
    i = np.minimum(i, 255)

    i = np.asarray(i, dtype=np.uint8)

    imageio.imwrite(url, i, *a, **k)

The rationale is that I am using numpy for image processing, not just image displaying. For this purpose, uint8s are awkward, so I convert to floating point values ranging from 0 to 1.

When saving images, I noticed I had to cut the out-of-range values myself, or else I ended up with a really gray output. (The gray output was the result of imageio compressing the full range, which was outside of [0, 256), to values that were inside the range.)

There were a couple other oddities, too, which I mentioned in the comments.

You can get numpy array of rgb image easily by using numpy and Image from PIL

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

im = Image.open('*image_name*') #These two lines
im_arr = np.array(im) #are all you need
plt.imshow(im_arr) #Just to verify that image array has been constructed properly

load the image by using following syntax:-

from keras.preprocessing import image

X_test=image.load_img('four.png',target_size=(28,28),color_mode="grayscale"); #loading image and then convert it into grayscale and with it's target size 
X_test=image.img_to_array(X_test); #convert image into array