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如何将 RGB 图像转换为 numpy 数组？

发布于 2024-12-10 06:36:31 字数 164 浏览 3 评论 0原文

我有一个 RGB 图像。我想将它转换为 numpy 数组。我做了以下操作

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

它创建了一个没有形状的数组。我假设它是一个 iplimage 对象。

原文

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.

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评论（15）

万水千山粽是情ミ 2024-12-17 06:36:31

您可以使用更新的 OpenCV python 接口（如果我没记错的话，它自 OpenCV 2.2 起就可用）。它本身使用 numpy 数组：

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

结果：

<type 'numpy.ndarray'>

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'>
回复 原文
坏尐絯 2024-12-17 06:36:31

PIL（Python 图像库）和 Numpy 可以很好地协同工作。

我使用以下功能。

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 )

“Image.fromarray”有点难看，因为我将传入数据剪辑为 [0,255]，转换为字节，然后创建灰度图像。我大部分时间都穿灰色的衣服。

RGB 图像类似于：

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

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" )
回复 原文
眼泪淡了忧伤 2024-12-17 06:36:31

您还可以使用 matplotlib 来实现此目的。

from matplotlib.image import imread

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

输出：
<类'numpy.ndarray'>

You can also use matplotlib for this.

from matplotlib.image import imread

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

output:
<class 'numpy.ndarray'>

回复 原文
星 2024-12-17 06:36:31

截至今天，您最好的选择是使用：

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

您将看到 img 将是类型为 numpy 的数组：

<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'>
回复 原文
三五鸿雁 2024-12-17 06:36:31

迟到的答案，但与其他替代方案相比，我更喜欢 imageio 模块

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

与 cv2.imread() 类似，它默认生成一个 numpy 数组，但采用 RGB形式。

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.

回复 原文
甜扑 2024-12-17 06:36:31

您可以使用 numpy 和 Image from PIL 轻松获取 rgb 图像的 numpy 数组

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

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
回复 原文
写给空气的情书 2024-12-17 06:36:31

您需要使用 cv.LoadImageM 而不是 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)

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)
回复 原文
许你一世情深 2024-12-17 06:36:31

使用以下语法加载图像：-

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

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
回复 原文
梦行七里 2024-12-17 06:36:31

当使用 David Poole 的答案时，我收到一个包含灰度 PNG 和其他文件的系统错误。我的解决方案是：

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' )

实际上 img.getdata() 适用于所有文件，但速度较慢，因此我仅在其他方法失败时才使用它。

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.

回复 原文
若有似无的小暗淡 2024-12-17 06:36:31

OpenCV 图像格式支持 numpy 数组接口。可以创建辅助函数来支持灰度或彩色图像。这意味着 BGR -> RGB 转换可以使用 numpy 切片方便地完成，而不是图像数据的完整副本。

注意：这是一个跨步技巧，因此修改输出数组也会更改 OpenCV 图像数据。如果你想要一个副本，请在数组上使用 .copy() 方法！

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

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
回复 原文
旧城空念 2024-12-17 06:36:31

我还采用了 imageio，但我发现以下机制对于预处理和后处理很有用：

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)

理由是我使用 numpy 进行图像处理，而不仅仅是图像显示。为此，uint8 很尴尬，所以我转换为 0 到 1 范围内的浮点值。

保存图像时，我注意到我必须自己剪切超出范围的值，否则我最终会得到真正的灰色输出。 （灰色输出是 imageio 将 [0, 256) 之外的整个范围压缩到该范围内的值的结果。）

还有一些其他奇怪的地方，我在评论中提到过。

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.

回复 原文
月隐月明月朦胧 2024-12-17 06:36:31

使用 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])

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])
回复 原文
依 靠 2024-12-17 06:36:31

我们可以使用open CV2的以下函数来转换BGR 2 RGB格式。

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

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

RBG_Image = cv2.cvtColor(Image, cv.COLOR_BGR2RGB)
回复 原文
何必那么矫情 2024-12-17 06:36:31

尝试计时将图像加载到 numpy 数组的选项，它们非常相似。为了简单和快速，请使用 plt.imread。

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)

结果：

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

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
回复 原文
天涯沦落人 2024-12-17 06:36:31

您可以尝试以下方法。以下是文档的链接。

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)

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)
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