Optimize conversion of numpy ndarray to string

Question

I am currently doing a python program to convert from image to hex string and the other way around. I need two functions, one that takes an image and returns a hex string that corresponds to the RGB values of each pixel, and another function that takes a hex string, two ints, and generates a visible image of that size corresponding to that hex string.

I currently use imageio to get an RGB matrix from the image and then convert that to hex. I'm trying to optimize the Image to bytes part, as it takes around 2.5 seconds for a 442KB image of 918 x 575 pixels.

How could I make it quicker?

Here's the code:

def rgb2hex(rgb):

    """
    convert a list or tuple of RGB values
    to a string in hex
    """

    r,g,b = rgb
    return '{:02x}{:02x}{:02x}'.format(r, g, b)


def arrayToString(array):
    """
    convert an array to a string
    """

    string = ""
    for element in array:
        string += str(element)

    return string


def sliceStr(string,sliceLenght):
    """
    slice a string in chunks of sliceLenght lenght
    """

    string = str(string)
    array = np.array([string[i:i+sliceLenght] for i in range(0,len(string),sliceLenght)])
    return array



def hexToRGB(hexadecimal):
    """
    convert a hex string to an array of RGB values
    """
    h = hexadecimal.lstrip('#')
    if len(h)!=6:
        return
    return [int(h[i:i+2], 16) for i in (0, 2, 4)]

def ImageToBytes(image):
    """
    Image to convert from image to bytes
    """
    dataToEncrypt =imageio.imread(image)

    if dataToEncrypt.shape[2] ==4:
        dataToEncrypt = np.delete(dataToEncrypt,3,2)

    originalRows, originalColumns,_ = dataToEncrypt.shape


    #converting rgb to hex
    hexVal = np.apply_along_axis(rgb2hex, 2, dataToEncrypt)
    hexVal = np.apply_along_axis(arrayToString, 1, hexVal)
    hexVal = str(np.apply_along_axis(arrayToString, 0, hexVal))

    byteImage = bytes.fromhex(hexVal)

    return (byteImage, [originalRows,originalColumns])

score 3 · Accepted Answer · answered Nov 21 '22 at 02:53

3

One simple approach is to use tobytes on the numpy array. E.g.,

image = imageio.imread(filename)
# Drop the alpha channel.
if image.shape[2] == 4:
    image = image[..., :3]
# Convert to bytes directly.
byte_image = image.tobytes()

On my machine, this gives a 250x speed up compared with converting to strings first. Note: this will only work if the dtype of the array is uint8. But that's luckily the default provided by imread.

answered Nov 21 '22 at 02:53

Till Hoffmann

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If you only read common formats (JPEG, PNG, GIF, WEBP), you can also use `iio.imread(filename, mode="RGB")`. This will always return an RGB image and covers edge cases like grayscale or binary images where `shape[2]` will break (PNG supports these for example). – FirefoxMetzger Dec 08 '22 at 11:24

score 1 · Answer 2 · answered Nov 21 '22 at 02:47

This should be more faster due to less conversions

def ImageToBytes2(image):
    """
    Image to convert from image to bytes
    """
    dataToEncrypt =imageio.imread(image)

    if dataToEncrypt.shape[2] ==4:
        dataToEncrypt = np.delete(dataToEncrypt,3,2)

    originalRows, originalColumns,_ = dataToEncrypt.shape

    dataToEncrypt = dataToEncrypt.reshape(1,originalRows*originalColumns*3)

    #converting rgb to hex
    byteImage = bytes(dataToEncrypt)

    return (byteImage, [originalRows,originalColumns])

Optimize conversion of numpy ndarray to string

2 Answers2