46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
import os
|
|
import cv2
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import tensorflow as tf
|
|
|
|
def recognition():
|
|
mnist = tf.keras.datasets.mnist
|
|
(x_train, y_train), (x_test, y_test) = mnist.load_data()
|
|
|
|
x_train = tf.keras.utils.normalize(x_train, axis=1)
|
|
x_test = tf.keras.utils.normalize(x_test, axis=1)
|
|
|
|
model = tf.keras.models.Sequential()
|
|
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
|
|
model.add(tf.keras.layers.Dense(128, activation='relu'))
|
|
model.add(tf.keras.layers.Dense(128, activation='relu'))
|
|
model.add(tf.keras.layers.Dense(10, activation='softmax'))
|
|
|
|
model.compile(optimizer ='adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
|
|
|
|
model.fit(x_train, y_train, epochs = 3)
|
|
model.save('handwritten.model')
|
|
|
|
model = tf.keras.models.load_model('handwritten.model')
|
|
|
|
image_number = 1
|
|
while os.path.isfile(f"digits/digit{image_number}.png"):
|
|
try:
|
|
img = cv2.imread(f"digits/digit{image_number}.png")[:,:,0]
|
|
img = np.invert(np.array([img]))
|
|
prediction = model.predict(img)
|
|
print(f"This digit is probably a {np.argmax(prediction)}")
|
|
plt.imshow(img[0], cmap = plt.cm.binary)
|
|
plt.show()
|
|
except:
|
|
print("Error!")
|
|
finally:
|
|
image_number +=1
|
|
|
|
loss, accuracy = model.evaluate(x_test, y_test)
|
|
|
|
print(loss)
|
|
print(accuracy)
|
|
|
|
recognition() |