import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.neural_network import MLPClassifier
import cv2


# training
# recznie napisane cyfry
digits = datasets.load_digits()

y = digits.target
x = digits.images.reshape((len(digits.images), -1))
#ogarnac zbior, zwiekszyc warstwy

x_train = x[:1000000]
y_train = y[:1000000]
x_test = x[1000000:]
y_test = y[1000000:]

mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
                    solver='sgd', tol=1e-4, random_state=1,
                    learning_rate_init=.1, verbose=True)

mlp.fit(x_train, y_train)

predictions = mlp.predict(x_test)
print(accuracy_score(y_test, predictions))


# image

img = cv2.cvtColor(cv2.imread('test3.png'), cv2.COLOR_BGR2GRAY)
img = cv2.blur(img, (9, 9)) # poprawia jakosc
img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_AREA)

print(type(img))
print(img.shape)
print(img)
plt.imshow(img ,cmap='binary')
plt.show()

data = []

rows, cols = img.shape
for i in range(rows):
    for j in range(cols):
        k = img[i, j]
        if k > 225:
            k = 0 # brak czarnego
        else:
            k = 1

        data.append(k)

data = np.asarray(data, dtype=np.float32)
print(data)

predictions = mlp.predict([data])

print("Liczba to:", predictions[0])