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)) x_train = x[:1000000] y_train = y[:1000000] x_test = x[1000:] y_test = y[1000:] 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.GaussianBlur(img, (5, 5), 0) # 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 == 255: 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])