Python opencv 机器学习 2.knn k近邻 ocr识别数字 使用digits.png（opencv自带）

import cv2
import numpy as np
from matplotlib import pyplot as plt
# 识别数字OCR

img = cv2.imread('digits.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

cells = [np.hsplit(row, 100) for row in np .vsplit(gray, 50)]

x = np.array(cells)

train = x[:, :50].reshape(-1, 400).astype(np.float32)
test = x[:, 50:100].reshape(-1, 400).astype(np.float32)

k = np.arange(10)
train_labels = np.repeat(k, 250)[:, np.newaxis]
test_labels = train_labels.copy()

knn = cv2.ml.KNearest_create()
knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
ret, result, neighbours, dist = knn.findNearest(test, k=5)

'''
# 保存训练数据
np.savez('knn_data.npz', train=train, train_labels=train_labels)
# 导入训练数据，需取代前面的训练过程
with np.load('knn_data.npz') as data:
    print(data.files)
    train = data['train']
    train_labels = data['train_labels']
'''

matches = result == test_labels
correct = np.count_nonzero(matches)
accuracy = correct*100.0/result.size
print(accuracy)