学习 MNIST 后对非 MNIST 图像进行分类

Posted 2023-03-12

【中文标题】学习 MNIST 后对非 MNIST 图像进行分类

【英文标题】：Classifying non-MNIST image after learning MNIST

【发布时间】：2017-12-02 15:13:29

【问题描述】：

我的机器学习算法已经学习了 MNIST 数据库中的 70000 张图像。我想在 MNIST 数据集中未包含的图像上对其进行测试。但是，我的预测函数无法读取我的测试图像的数组表示。

如何在外部图像上测试我的算法？ 为什么我的代码失败了？

PS 我用的是python3

收到错误：

Traceback (most recent call last):
  File "hello_world2.py", line 28, in <module>
    print(sgd_clf.predict(arr))
  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py", line 336, in predict
    scores = self.decision_function(X)
  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py", line 317, in decision_function
    % (X.shape[1], n_features))
ValueError: X has 15 features per sample; expecting 784

代码：

# Common Imports
import numpy as np
from sklearn.datasets import fetch_mldata
from sklearn.linear_model import SGDClassifier
from PIL import Image
from resizeimage import resizeimage   

# loading and learning MNIST data
mnist = fetch_mldata('MNIST original')
x, y = mnist["data"], mnist["target"]
sgd_clf = SGDClassifier(random_state=42)
sgd_clf.fit(x, y)

# loading and converting to array a non-MNIST image of a "5", which is in the same folder
img = Image.open("5.png")
arr = np.array(img)

# trying to predict that the image is a "5"
img = Image.open("5.png")   
img = img.convert('L') #makes it greyscale
img = resizeimage.resize_thumbnail(img, [28,28])
arr = np.array(img)

print(sgd_clf.predict(arr)) # ERROR... why????????? How do you fix it?????

【问题讨论】：

该图像必须调整大小。 MNIST 图像为 28x28。

另外，您的图像似乎是 3 通道的。您必须对其进行灰度化。

如何调整 MNIST 图像的大小？ （注意：请参阅原始代码进行编辑。谢谢。）

这个例子可能会有所帮助：github.com/niektemme/tensorflow-mnist-predict/blob/master/…

【参考方案1】：

这不仅仅是调整大小的问题，图像需要数字居中和黑底白字等。我一直在研究这项工作的功能。这是使用 opencv 的当前版本，虽然它可以做进一步的改进，包括使用 PIL 代替 opencv，但它应该给出所需步骤的概念。

def open_as_mnist(image_path):
    """
    Assume this is a color or grey scale image of a digit which has not so far been preprocessed

    Black and White
    Resize to 20 x 20 (digit in center ideally)
    Sharpen
    Add white border to make it 28 x 28
    Convert to white on black
    """
    # open as greyscale
    image = cv2.imread(image_path, 0)

    # crop to contour with largest area
    cropped = do_cropping(image)

    # resizing the image to 20 x 20
    resized20 = cv2.resize(cropped, (20, 20), interpolation=cv2.INTER_CUBIC)

    cv2.imwrite('1_resized.jpg', resized20)

    # gaussian filtering
    blurred = cv2.GaussianBlur(resized20, (3, 3), 0)

    # white digit on black background
    ret, thresh = cv2.threshold(blurred, 127, 255, cv2.THRESH_BINARY_INV)

    padded = to20by20(thresh)


    resized28 = padded_image(padded, 28)

    # normalize the image values to fit in the range [0,1]
    norm_image = np.asarray(resized28, dtype=np.float32) / 255.

    # cv2.imshow('image', norm_image)
    # cv2.waitKey(0)

    # # Flatten the image to a 1-D vector and return
    flat = norm_image.reshape(1, 28 * 28)
    # return flat

    # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.
    tva = [(255 - x) * 1.0 / 255.0 for x in flat]
    return tva



def padded_image(image, tosize):
    """
    This method adds padding to the image and makes it to a tosize x tosize array,
    without losing the aspect ratio.
    Assumes desired image is square

    :param image: the input image as numpy array
    :param tosize: the final dimensions
    """

    # image dimensions
    image_height, image_width = image.shape


    # if not already square then pad to square
    if image_height != image_width:

        # Add padding
        # The aim is to make an image of different width and height to a sqaure image
        # For that first the biggest attribute among width and height are determined.
        max_index = np.argmax([image_height, image_width])


        # if height is the biggest one, then add padding to width until width becomes
        # equal to height
        if max_index == 0:
            #order of padding is: top, bottom, left, right
            left = int((image_height - image_width) / 2)
            right = image_height - image_width - left
            padded_img = cv2.copyMakeBorder(image, 0, 0,
                                            left,
                                            right,
                                            cv2.BORDER_CONSTANT)

        # else if width is the biggest one, then add padding to height until height becomes
        # equal to width
        else:
            top = int((image_width - image_height) / 2)
            bottom = image_width - image_height - top
            padded_img = cv2.copyMakeBorder(image, top, bottom, 0, 0,  cv2.BORDER_CONSTANT)
    else:
        padded_img = image


    # now that it's a square, add any additional padding required
    image_height, image_width = padded_img.shape
    padding = tosize - image_height

    # need to handle where padding is not divisiable by 2
    left = top = int(padding/2)
    right = bottom = padding - left
    resized = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT)


    return resized

【参考方案2】：

如果你想阅读图片然后调整它的大小，请尝试

In [1]: import PIL.Image as Image

In [2]: img = Image.open('2.jpg', mode='r')

In [3]: img.mode
Out[3]: 'RGB'

In [4]: img.size
Out[4]: (2880, 1800)

In [5]: img_new = img.resize([4000, 4000], Image.ANTIALIAS)

In [6]: img_new2 = img.resize([32, 32], Image.ANTIALIAS)

文档是here

这是2.jpg，抱歉，不是数字。

此图来自网络，不好意思，忘记出处了。

如果遇到模式是'RGBA'，建议你转成'RGB'模式，

newimg = Image.new('RGB', img.size)
newimg.paste(img, mask=img.split()[3])
return newimg

【参考方案3】：

请试试这个：

img = Image.open("5.png")
img = img.resize((28,28))
img = img.convert('L') #makes it greyscale

【讨论】：

试过了。取得了进展。但我仍然需要调整图像大小。我对如何调整 MNIST 图像的大小感到困惑。 （注意：请参阅上面的代码了解最近的改进。谢谢。）

我更新了我的答案。可能是因为您将图像转换为灰色后调整了图像大小，所以它再次向图像添加了 3 层。而且你不需要使用另一个库来调整大小。