import cv2
import numpy as np
img = cv2.imread('digits.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Now we split the image to 5000 cells, each 20x20 size
cells = [np.hsplit(row, 100) for row in np.vsplit(gray, 50)]
# Make it into a numpy array: its size will be (50, 100, 20, 20)
x = np.array(cells)
# Now we prepare the training data and test data
train = x[:,:50].reshape(-1,400).astype(np.float32) # Size = (2500,400)
test = x[:,50:100].reshape(-1,400).astype(np.float32) # Size = (2500,400)
# Create labels for train and test data
k = np.arange(10)
train_labels = np.repeat(k, 250)[:, np.newaxis]
test_labels = train_labels.copy()
# Initiate kNN, train it on the training data, then test it with the test data with k=1
knn = cv2.ml.KNearest_create()
knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
ret, result, neighbours, dist = knn.findNearest(test, k=5)
# Now we check the accuracy of classification
# For that, compare the result with test_labels and check which are wrong
matches = result==test_labels
correct = np.count_nonzero(matches)
accuracy = correct * 100.0/result.size
print( accuracy )  # 91.76

# Save the data
np.savez('knn_dight_data.npz', train=train, train_labels=train_labels)
# Now load the data
whit np.load('knn_data.npz') as data:
    print(data.files)
    train = data['train']
    train_labels = data['train_labels']

train_uint8 = train.astype(np.uint8)
train_labels_uint8 = train_labels.astype(np.uint8)
np.savez('knn_dight_data_int8.npz', train=train_uint8, train_labels=train_labels_uint8)

# 取測試集中的一個(gè)元素
single_data = testData[0].reshape(-1, 400)
single_label = labels[0]
ret, result, neighbours, dist = knn.findNearest(data, k=5)
print(result)  # [[0]]
print(label)   # [[0.]]
print(result==label)  # True

import numpy as np
import cv2
#  Load the data and convert the letters to numbers
data = np.loadtxt('letter-recognition.data', dtype='float32', delimiter=',', converters={0: lambda ch: ord(ch)-ord('A')})
# Split the dataset in two, with 10000 samples each for training and test sets
train, test = np.vsplit(data, 2)
# Split trainData and testData into features and responses
responses, trainData = np.hsplit(train, [1])
labels, testData = np.hsplit(test, [1])
# Initiate the kNN, classify, measure accuracy
knn = cv2.ml.KNearest_create()
knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)
ret, result, neighbours, dist = knn.findNearest(testData, k=5)
correct = np.count_nonzero(result==labels)
accuracy = correct * 100 / result.size
print(accuracy)  # 93.06