s452730/Projekt_KELNER
4
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

po naciśnieciu "e" sieć sie uczy, po podjeściu do prawego górnego odpalane jest prediction

Browse Source
This commit is contained in:
Michał Szuszert 2022-05-26 16:16:16 +02:00
parent 5f198cde38
commit a51f3e31dc
1 changed files with 12 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
tiles.py
View File

@ -13,6 +13,7 @@ import matplotlib.pyplot as plt
import os import os
import cv2 import cv2
from tqdm import tqdm from tqdm import tqdm
import keras
from keras.models import Sequential from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D from keras.layers import Conv2D, MaxPooling2D
@ -642,8 +643,8 @@ def prepare_img(filepath):
return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1) / 255 return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1) / 255
def predict(model): def predict(model,filepath):
return model.predict([prepare_img('directory')]) return model.predict([prepare_img(filepath)])
def result(prediction): def result(prediction):
@ -733,6 +734,11 @@ def main():
key = pygame.key.get_pressed() key = pygame.key.get_pressed()
left, middle, right = pygame.mouse.get_pressed() left, middle, right = pygame.mouse.get_pressed()
if key[pygame.K_e]:
X, y = create_training_data()
model = learn_neural_network(X,y)
if middle: if middle:
waiterGo(mouseToNum()) waiterGo(mouseToNum())
elif right: elif right:
@ -800,9 +806,12 @@ def main():
if waiter.loc == [576, 32]: if waiter.loc == [576, 32]:
if first_time: if first_time:
number = np.random.randint(20) number = np.random.randint(20)
image = pygame.image.load('test/' + str(number) + '.jpg') path = ('test/' + str(number) + '.jpg')
image = pygame.image.load(path)
first_time = False first_time = False
display_img(display, image) display_img(display, image)
prediction = predict(model, path)
result(prediction)
else: else:
first_time = True first_time = True