我试图用MNIST作为数据集& amp;来预测手写数字。蟒蛇。现在,我必须将已裁剪的图像作为程序的输入。 使用以下函数进行进一步处理以使其成为MNIST数据集格式,但如何自动裁剪作为输入的随机图像?
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 (nheigth == 0): #rare case but minimum is 1 pixel
nheigth = 1
# resize and sharpen
img = im.resize((20,nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
wtop = int(round(((28 - nheight)/2),0)) #caculate horizontal pozition
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]
return tva
答案 0 :(得分:0)
您可以使用OpenCV轮廓来定位实际图像中的潜在数字,其中一些技术将取决于您正在使用的实际数据。在http://www.pyimagesearch.com/2017/02/13/recognizing-digits-with-opencv-and-python/有一个数字候选位置示例 这可以给你一些指示。
但是,您可能会遇到一些脚本的问题,因为我相信在所有欧洲脚本中,每个数字应该是连续且不同的,我不确定这两个点是否适用于所有脚本。