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Rozpoznawanie banknotów2

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Kinga 2020-05-18 14:17:23 +02:00
parent 2e97c014fe
commit 9c106e0980
4 changed files with 261 additions and 355 deletions
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287
Restaurant/.idea/workspace.xml
View File

@ -2,9 +2,9 @@
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@ -15,22 +15,49 @@
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@ -62,14 +89,15 @@
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@ -96,31 +124,6 @@
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@ -149,7 +152,28 @@
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@ -192,27 +216,6 @@
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@ -234,7 +237,7 @@
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@ -242,11 +245,11 @@
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@ -257,11 +260,11 @@
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Restaurant/Kinga/data/examine/20/examine_013.png.jpg
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Restaurant/Kinga/main4.py
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@ -1,186 +1,78 @@
import torch from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import torch.nn as nn from keras.models import Sequential
from torch.utils.data import DataLoader from keras.layers import Conv2D, MaxPooling2D
from torchvision import transforms, datasets from keras.layers import Activation, Dropout, Flatten, Dense
import matplotlib.pyplot as plt from keras import backend as K
from PIL import Image
import os
import os.path
torch.manual_seed(42)
img_width, img_height = 256, 256
train_dir = 'data2/train' train_data_dir = 'data/train'
examine_dir = 'data2/validation' examine_data_dir = 'data/examine'
nb_train_samples = 290
nb_examine_samples = 80
epochs = 1
batch_size = 16
if K.image_data_format() == 'channels_first':
transform_img = transforms.Compose( input_shape = (3, img_width, img_height)
[transforms.Resize(255),
#transforms.RandomHorizontalFlip(),
#transforms.ToTensor()().unsqueeze_(0),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_data = datasets.ImageFolder(train_dir, transform=transform_img)
examine_data = datasets.ImageFolder(examine_dir, transform=transform_img)
train_loader = DataLoader(dataset = train_data, batch_size = 255, shuffle=True, num_workers=0, collate_fn=None)
examine_loader = DataLoader(dataset = examine_data, batch_size = 255, shuffle=False, num_workers=0, collate_fn=None)
if torch.cuda.is_available():
device = torch.device('cuda')
else: else:
device = torch.device('cpu') input_shape = (img_width, img_height, 3)
epochs = 35 model = Sequential()
batch_size = 25 model.add(Conv2D(32, (2, 2), input_shape=input_shape))
learning_rate = 0.001 model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
import torch.nn.functional as F model.add(Conv2D(32, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
class CNN(nn.Module): model.add(Conv2D(64, (2, 2)))
def __init__(self): model.add(Activation('relu'))
super(CNN, self).__init__() model.add(MaxPooling2D(pool_size=(2, 2)))
self.conv1 = nn.Conv2d(in_channels=3, out_channels=10, kernel_size=3)
self.conv2 = nn.Conv2d(10, 20, kernel_size=3)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(720, 1024)
self.fc2 = nn.Linear(1024, 2)
def forward(self, x): model.add(Flatten())
x = F.relu(F.max_pool2d(self.conv1(x), 2)) model.add(Dense(64))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) model.add(Activation('relu'))
x = x.view(x.shape[0],-1) model.add(Dropout(0.5))
x = F.relu(self.fc1(x)) model.add(Dense(7))
x = F.dropout(x, training=self.training) model.add(Activation('sigmoid'))
x = self.fc2(x)
return x
model = CNN() model.compile(loss='categorical_crossentropy',
print(model) optimizer='rmsprop',
metrics=['accuracy'])
train_datagen = ImageDataGenerator(
rotation_range=45,
width_shift_range=0.3,
height_shift_range=0.3,
rescale=1. / 255,
shear_range=0.25,
zoom_range=0.1,
horizontal_flip=True)
examine_datagen = ImageDataGenerator(rescale=1. / 255)
model = CNN().to(device) train_generator = train_datagen.flow_from_directory(
criterion = nn.CrossEntropyLoss() train_data_dir,
optimizer = torch.optim.Adam(model.parameters(),lr = learning_rate) target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
examine_generator = examine_datagen.flow_from_directory(
examine_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
train_losses = [] model.fit_generator(
valid_losses = [] train_generator,
num_epochs = 25 steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=examine_generator,
validation_steps=nb_examine_samples // batch_size)
for epoch in range(1, num_epochs + 1): model.save_weights('model_paymenttttt.h5')
# keep-track-of-training-and-validation-loss results = model.evaluate(train_generator)
train_loss = 0.0 print(results)
valid_loss = 0.0 print((train_generator.class_indices))
# training-the-model
model.train()
for data, target in train_loader:
# move-tensors-to-GPU
data = data.to(device)
target = target.to(device)
# clear-the-gradients-of-all-optimized-variables
optimizer.zero_grad()
# forward-pass: compute-predicted-outputs-by-passing-inputs-to-the-model
output = model(data)
# calculate-the-batch-loss
loss = criterion(output, target)
# backward-pass: compute-gradient-of-the-loss-wrt-model-parameters
loss.backward()
# perform-a-ingle-optimization-step (parameter-update)
optimizer.step()
# update-training-loss
train_loss += loss.item() * data.size(0)
# validate-the-model
model.eval()
for data, target in examine_loader:
data = data.to(device)
target = target.to(device)
output = model(data)
loss = criterion(output, target)
# update-average-validation-loss
valid_loss += loss.item() * data.size(0)
# calculate-average-losses
train_loss = train_loss / len(train_loader)
valid_loss = valid_loss / len(examine_loader)
train_losses.append(train_loss)
valid_losses.append(valid_loss)
# print-training/validation-statistics
print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
epoch, train_loss, valid_loss))
# test-the-model
model.eval() # it-disables-dropout
with torch.no_grad():
correct = 0
total = 0
for images, labels in examine_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Test Accuracy of the model: {} %'.format(100 * correct / total))
# Save
torch.save(model.state_dict(), 'model.ckpt')
'''''
def train(model, dataset, n_iter=100, batch_size=256):
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = nn.NLLLoss()
dl = DataLoader(dataset, batch_size=batch_size)
model.train()
for epoch in range(n_iter):
for images, targets in dl:
optimizer.zero_grad()
out = model(images.to(device))
loss = criterion(out, targets.to(device))
loss.backward()
optimizer.step()
if epoch % 10 == 0:
print('epoch: %3d loss: %.4f' % (epoch, loss))
def accuracy(model, dataset):
model.eval()
correct = sum([(model(images.to(device)).argmax(dim=1) == targets).sum()
for images, targets in DataLoader(dataset, batch_size=256)])
return correct.float()/len(dataset)
model = nn.Sequential(
nn.Linear(28*28, 10),
nn.LogSoftmax(dim=-1)
).to(device)
#print(train(model, train_data))
#print(accuracy(model, examine_data))
hidden_size = 300
model_2 = nn.Sequential(
nn.Linear(28*28, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, 10),
nn.LogSoftmax(dim=-1)
).to(device)
#print(train(model_2, train_data))
#print(accuracy(model_2, examine_data))
#torch.save(model, 'model.pth')
'''''

95
Restaurant/main.py
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@ -479,6 +479,41 @@ def evaluate(b):
pass pass
def decide(client, image, dish):
img_width, img_height = 256, 256
client.pay(image)
test_image = load_img(image, target_size=(img_width, img_height))
test_image = img_to_array(test_image)
test_image = test_image.reshape((1,) + test_image.shape)
result = pln_model.predict(test_image)
print(result)
if result.tolist() == [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]:
messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(10 - dish.price) + " zł.")
return 0
elif result.tolist() == [[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]]:
messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(100 - dish.price) + " zł.")
return 0
elif result.tolist() == [[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]]:
messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(20 - dish.price) + " zł.")
return 0
elif result.tolist() == [[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]]:
messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(200 - dish.price) + " zł.")
return 0
elif result.tolist() == [[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]]:
messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(50 - dish.price) + " zł.")
return 0
elif result.tolist() == [[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]]:
messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(500 - dish.price) + " zł.")
return 0
else:
messagebox.showinfo("Uwaga", "Niestety, nie przyjmujemy zapłaty kartą.")
return 1
def payment(c): def payment(c):
if c == 1: if c == 1:
@ -504,32 +539,23 @@ def payment(c):
bot.goByAStar((tables[1].pos[0] + 1, tables[1].pos[1])) bot.goByAStar((tables[1].pos[0] + 1, tables[1].pos[1]))
messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_1.price) + " zł.") messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_1.price) + " zł.")
client_1.pay('Kinga/data/examine/200/examine_056.png') client1_payment = ('Kinga/data/examine/200/examine_056.png')
#classify image# decide(client_1, client1_payment, dish_1)
#if img == 1:
# messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(client_1.budget - dish_1.price) + " zł.")
#else: #karta
# messagebox.showinfo("Uwaga", "Niestety, nie przyjmujemy zapłaty kartą.")
bot.goByAStar((tables[4].pos[0] + 1, tables[4].pos[1])) bot.goByAStar((tables[4].pos[0] + 1, tables[4].pos[1]))
messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_2.price) + " zł.") messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_2.price) + " zł.")
client_2.pay("Kinga/data/examine/100/examine_036.png") client2_payment = ("Kinga/data/examine/100/examine_036.png")
#tak samo decide(client_2, client2_payment, dish_2)
#messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(client_2.budget - dish_2.price) + " zł.")
bot.goByAStar((tables[5].pos[0] + 1, tables[5].pos[1])) bot.goByAStar((tables[5].pos[0] + 1, tables[5].pos[1]))
messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_3.price) + " zł.") messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_3.price) + " zł.")
client_3.pay("Kinga/data/examine/50/examine_029.png") client3_payment = ("Kinga/data/examine/50/examine_029.png")
#messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(client_3.budget - dish_3.price) + " zł.") decide(client_3, client3_payment, dish_3)
bot.goByAStar((tables[8].pos[0] + 1, tables[8].pos[1])) bot.goByAStar((tables[8].pos[0] + 1, tables[8].pos[1]))
messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_4.price) + " zł.") messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_4.price) + " zł.")
client_4.pay("Kinga/data/examine/cards/card_008.png") client4_payment = ("Kinga/data/examine/cards/card_008.png")
#messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(client_4.budget - dish_4.price) + " zł.") decide(client_4, client4_payment, dish_4)
messagebox.showinfo("Uwaga", "Niestety, nie przyjmujemy zapłaty kartą.")
messagebox.showinfo("Informacja końcowa", "Zakończono trasę.") messagebox.showinfo("Informacja końcowa", "Zakończono trasę.")
@ -619,10 +645,39 @@ def makeImgClassificator(model):
metrics=['accuracy']) metrics=['accuracy'])
def recognizeCash(model):
img_width, img_height = 256, 256
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model.add(Conv2D(32, (2, 2), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(7))
model.add(Activation('sigmoid'))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
def main(): def main():
global width, rows, bot, light_beige, beige, white, black, sizeBetween, tables, kitchen, cantwalk, puddles, window, clock, \ global width, rows, bot, light_beige, beige, white, black, sizeBetween, tables, kitchen, cantwalk, puddles, window, clock, \
img_classify img_classify, pln_model
# skróty do kolorów # skróty do kolorów
light_beige = (255, 240, 205) light_beige = (255, 240, 205)
beige = (255, 205, 178) beige = (255, 205, 178)
@ -633,6 +688,10 @@ def main():
makeImgClassificator(img_classify) makeImgClassificator(img_classify)
img_classify.load_weights('Sara/model_food_dirty.h5') img_classify.load_weights('Sara/model_food_dirty.h5')
pln_model = Sequential()
recognizeCash(pln_model)
pln_model.load_weights('Kinga/model_payment.h5')
width = 600 width = 600
rows = 15 rows = 15
sizeBetween = width // rows #wielkość pojedynczej kratki sizeBetween = width // rows #wielkość pojedynczej kratki