问如何对MNIST手写数字预测的数字进行中心/检测？

EN

img=<base64 string represeting 313*320 image of grid> 

[This is the base64 image at the top](https://i.stack.imgur.com/OrPis.jpg)

import cv2
import json
import numpy as np
import tensorflow as tf
import base64
import matplotlib.pyplot as plt

model = tf.keras.models.load_model("newmodel")
data = base64.b64decode(img)
np_data = np.fromstring(data, np.uint8)
img = cv2.imdecode(np_data, cv2.IMREAD_GRAYSCALE)
height, width = img.shape
img = cv2.resize(img, (width, width))
height, width = img.shape
print(height,width)
nums = []
for y in range(9):
    row = []
    for x in range(9):

            left = round(x*(width)/9+3)
            top = round(y*(height)/9+3)
            right = round((x+1)*(width)/9-3)
            bottom = round((y+1)*(height)/9-3)
            image = img[top:bottom, left:right]
            image = np.array(image)
            image = cv2.resize(image, (28,28))
            image = 255-image 

            #Checking for empty cells
            numofBlack = 0
            for r in image:
                for item in r:
                    if item > 127:
                        numofBlack += 1
            if numofBlack < 50:
                row.append(0)

            else:
                pred = model.predict(image.reshape(1,28, 28, 1))
                row.append(int(pred.argmax()))

    nums.append(row)
print(nums)