how to convert an RGB image to numpy array?

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 crea开发者_如何学编程tes an array with no shape. I assume it is a iplimage object.

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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'>

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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:

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

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You can also use matplotlib for this.

from matplotlib.image import imread

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

output: <class 'numpy.ndarray'>

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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'>

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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.

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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)

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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

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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.

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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

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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

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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.

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We can use following function of open CV2 to convert BGR 2 RGB format.

RBG_Image = cv2.cvtColor(Image, cv.COLOR_BGR2RGB)

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Using Keras:

from keras.preprocessing import image
  
img = image.load_img('path_to_image', target_size=(300, 300))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])

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Try timing the options to load an image to numpy array, they are quite similar. Go for plt.imread for simplicity and speed.

def time_this(function, times=100):
    cum_time = 0
    for t in range(times):
        st = time.time()
        function()
        cum_time += time.time() - st
    return cum_time / times

import matplotlib.pyplot as plt
def load_img_matplotlib(img_path):
    return plt.imread(img_path)

import cv2
def load_img_cv2(img_path):
    return cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)

from PIL import Image
import numpy as np
def load_img_pil(img_path):
    img = Image.open(img_path)
    img.load()
    return np.asarray( img, dtype="int32" )

if __name__=='__main__':
    img_path = 'your_image_path'
    for load_fn in [load_img_pil, load_img_cv2, load_img_matplotlib]:
        print('-'*20)
        print(time_this(lambda: load_fn(img_path)), 10000)

Result:

--------------------
0.0065201687812805175 10000  PIL, as in [the second answer][1]https://stackoverflow.com/a/7769424/16083419)
--------------------
0.0053211402893066405 10000  CV2
--------------------
0.005320906639099121 10000  matplotlib

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You can try the following method. Here is a link to the docs.

tf.keras.preprocessing.image.img_to_array(img, data_format=None, dtype=None)
from PIL import Image
img_data = np.random.random(size=(100, 100, 3))
img = tf.keras.preprocessing.image.array_to_img(img_data)
array = tf.keras.preprocessing.image.img_to_array(img)