Question

我是tensorflow的新手。我使用MNIST的列车数据训练了数字预测模型。然后我用我自己的图像测试模型。它无法预测实际结果。

问题是：

MNIST的图像需要黑白
图像尺寸标准化为适合20x20像素的盒子，并且使用质心以28x28图像为中心。
我不想使用OpenCV

问题是如何将我自己的手写数字图像移动到28x28图像的中心。自己的图像可以是任何颜色和图像来改变黑白MNIST的图像

Answer 1

我会像这样使用numpy食谱 - https://www.kaggle.com/c/digit-recognizer/forums/t/6366/normalization-and-centering-of-images-in-mnist

您可以将其重新映射到纯TensorFlow管道，但鉴于它的小图像，我不确定它是否必要。

如果你采用另一种方式，你会得到更好的准确性 - 而不是规范输入数据，通过训练随机移位/重新缩放的MNIST数字的更大数据集，使你的网络缺乏规范化。

Answer 2

from PIL import Image, ImageFilter


def imageprepare(argv):
    """
    This function returns the pixel values.
    The imput is a png file location.
    """
    im = Image.open(argv).convert('L')
    width = float(im.size[0])
    height = float(im.size[1])
    newImage = Image.new('L', (28, 28), (255))  # creates white canvas of 28x28 pixels

    if width > height:  # check which dimension is bigger
        # Width is bigger. Width becomes 20 pixels.
        nheight = int(round((20.0 / width * height), 0))  # resize height according to ratio width
        if (nheight == 0):  # rare case but minimum is 1 pixel
            nheight = 1
            # resize and sharpen
        img = im.resize((20, nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
        wtop = int(round(((28 - nheight) / 2), 0))  # calculate horizontal position
        newImage.paste(img, (4, wtop))  # paste resized image on white canvas
    else:
        # Height is bigger. Heigth becomes 20 pixels.
        nwidth = int(round((20.0 / height * width), 0))  # resize width according to ratio height
        if (nwidth == 0):  # rare case but minimum is 1 pixel
            nwidth = 1
            # resize and sharpen
        img = im.resize((nwidth, 20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
        wleft = int(round(((28 - nwidth) / 2), 0))  # caculate vertical pozition
        newImage.paste(img, (wleft, 4))  # paste resized image on white canvas

    # newImage.save("sample.png

    tv = list(newImage.getdata())  # get pixel values

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

x=imageprepare('./image.png')#file path here
print(len(x))# mnist IMAGES are 28x28=784 pixels

将自己的图像转换为MNIST的图像

2 个答案: