我是tensorflow的新手。我用MNIST的训练数据训练了数字预测模型。然后我用我自己的形象来测试模型。它无法预测实际结果。
问题是:
OpenCV
问题是如何将我自己的手写数字图像转移到28x28图像的中心。自己的图像可以是任何颜色,而该图像可以改变黑白MNIST的图像
发布于 2019-05-03 11:00:51
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
发布于 2016-03-08 02:04:02
我会用像这张一样的小秘方-- https://www.kaggle.com/c/digit-recognizer/forums/t/6366/normalization-and-centering-of-images-in-mnist
您可能可以将其重新映射到纯TensorFlow管道,但考虑到它是很小的图像,我不确定它是否必要。
另外,如果采用另一种方式,您将获得更高的准确性--而不是标准化输入数据,而是通过在更大的数据集上进行随机移位/重标的MNIST数字的培训,使您的网络对缺乏规范化的情况更加健壮。
https://stackoverflow.com/questions/35842274
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