Rozpoznawanie banknotów2
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<caret line="4" column="16" lean-forward="true" selection-start-line="4" selection-start-column="16" selection-end-line="4" selection-end-column="16" />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/sth.py" />
|
||||
<entry file="file://$PROJECT_DIR$/main6.py" />
|
||||
<entry file="file://$PROJECT_DIR$/Kinga/__init__.py" />
|
||||
<entry file="file://$PROJECT_DIR$/Kinga/main5.py" />
|
||||
<entry file="file://$PROJECT_DIR$/Kinga/mian7.py" />
|
||||
<entry file="file://$PROJECT_DIR$/Kinga/main8.py" />
|
||||
<entry file="file://$PROJECT_DIR$/Kinga/main1.py" />
|
||||
<entry file="file://$PROJECT_DIR$/Kinga/main2.py" />
|
||||
<entry file="file://$PROJECT_DIR$/Kinga/main3.py" />
|
||||
<entry file="file://$PROJECT_DIR$/../README.md">
|
||||
<provider selected="true" editor-type-id="split-provider[text-editor;markdown-preview-editor]">
|
||||
<state split_layout="SPLIT">
|
||||
@ -517,15 +442,45 @@
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/Sara/image_classification.py">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="154">
|
||||
<caret line="7" selection-start-line="7" selection-end-line="7" />
|
||||
<folding>
|
||||
<element signature="e#0#94#0" expanded="true" />
|
||||
</folding>
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/main.py">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="13508">
|
||||
<caret line="668" column="15" lean-forward="true" selection-start-line="668" selection-start-column="15" selection-end-line="668" selection-end-column="15" />
|
||||
<state relative-caret-position="323">
|
||||
<caret line="506" column="79" selection-start-line="506" selection-start-column="79" selection-end-line="506" selection-end-column="79" />
|
||||
<folding>
|
||||
<element signature="e#0#13#0" expanded="true" />
|
||||
</folding>
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/Kinga/sth.py">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="625">
|
||||
<caret line="90" column="62" selection-start-line="90" selection-start-column="62" selection-end-line="90" selection-end-column="62" />
|
||||
<folding>
|
||||
<element signature="e#0#94#0" expanded="true" />
|
||||
</folding>
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/Kinga/main4.py">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="504">
|
||||
<caret line="76" column="14" lean-forward="true" selection-start-line="76" selection-start-column="14" selection-end-line="76" selection-end-column="14" />
|
||||
<folding>
|
||||
<element signature="e#0#94#0" expanded="true" />
|
||||
</folding>
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
</component>
|
||||
</project>
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 269 KiB |
@ -1,186 +1,78 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.utils.data import DataLoader
|
||||
from torchvision import transforms, datasets
|
||||
import matplotlib.pyplot as plt
|
||||
from PIL import Image
|
||||
import os
|
||||
import os.path
|
||||
torch.manual_seed(42)
|
||||
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
|
||||
from keras.models import Sequential
|
||||
from keras.layers import Conv2D, MaxPooling2D
|
||||
from keras.layers import Activation, Dropout, Flatten, Dense
|
||||
from keras import backend as K
|
||||
|
||||
img_width, img_height = 256, 256
|
||||
|
||||
train_dir = 'data2/train'
|
||||
examine_dir = 'data2/validation'
|
||||
train_data_dir = 'data/train'
|
||||
examine_data_dir = 'data/examine'
|
||||
nb_train_samples = 290
|
||||
nb_examine_samples = 80
|
||||
epochs = 1
|
||||
batch_size = 16
|
||||
|
||||
|
||||
transform_img = transforms.Compose(
|
||||
[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')
|
||||
if K.image_data_format() == 'channels_first':
|
||||
input_shape = (3, img_width, img_height)
|
||||
else:
|
||||
device = torch.device('cpu')
|
||||
input_shape = (img_width, img_height, 3)
|
||||
|
||||
epochs = 35
|
||||
batch_size = 25
|
||||
learning_rate = 0.001
|
||||
model = Sequential()
|
||||
model.add(Conv2D(32, (2, 2), input_shape=input_shape))
|
||||
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):
|
||||
def __init__(self):
|
||||
super(CNN, self).__init__()
|
||||
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)
|
||||
model.add(Conv2D(64, (2, 2)))
|
||||
model.add(Activation('relu'))
|
||||
model.add(MaxPooling2D(pool_size=(2, 2)))
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(F.max_pool2d(self.conv1(x), 2))
|
||||
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
|
||||
x = x.view(x.shape[0],-1)
|
||||
x = F.relu(self.fc1(x))
|
||||
x = F.dropout(x, training=self.training)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
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 = CNN()
|
||||
print(model)
|
||||
model.compile(loss='categorical_crossentropy',
|
||||
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)
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = torch.optim.Adam(model.parameters(),lr = learning_rate)
|
||||
train_generator = train_datagen.flow_from_directory(
|
||||
train_data_dir,
|
||||
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 = []
|
||||
valid_losses = []
|
||||
num_epochs = 25
|
||||
model.fit_generator(
|
||||
train_generator,
|
||||
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):
|
||||
# keep-track-of-training-and-validation-loss
|
||||
train_loss = 0.0
|
||||
valid_loss = 0.0
|
||||
|
||||
# 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')
|
||||
'''''
|
||||
model.save_weights('model_paymenttttt.h5')
|
||||
results = model.evaluate(train_generator)
|
||||
print(results)
|
||||
print((train_generator.class_indices))
|
||||
@ -479,6 +479,41 @@ def evaluate(b):
|
||||
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):
|
||||
if c == 1:
|
||||
|
||||
@ -504,32 +539,23 @@ def payment(c):
|
||||
|
||||
bot.goByAStar((tables[1].pos[0] + 1, tables[1].pos[1]))
|
||||
messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_1.price) + " zł.")
|
||||
client_1.pay('Kinga/data/examine/200/examine_056.png')
|
||||
#classify image#
|
||||
#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.")
|
||||
client1_payment = ('Kinga/data/examine/200/examine_056.png')
|
||||
decide(client_1, client1_payment, dish_1)
|
||||
|
||||
bot.goByAStar((tables[4].pos[0] + 1, tables[4].pos[1]))
|
||||
messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_2.price) + " zł.")
|
||||
client_2.pay("Kinga/data/examine/100/examine_036.png")
|
||||
#tak samo
|
||||
#messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(client_2.budget - dish_2.price) + " zł.")
|
||||
|
||||
client2_payment = ("Kinga/data/examine/100/examine_036.png")
|
||||
decide(client_2, client2_payment, dish_2)
|
||||
|
||||
bot.goByAStar((tables[5].pos[0] + 1, tables[5].pos[1]))
|
||||
messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_3.price) + " zł.")
|
||||
client_3.pay("Kinga/data/examine/50/examine_029.png")
|
||||
#messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(client_3.budget - dish_3.price) + " zł.")
|
||||
|
||||
client3_payment = ("Kinga/data/examine/50/examine_029.png")
|
||||
decide(client_3, client3_payment, dish_3)
|
||||
|
||||
bot.goByAStar((tables[8].pos[0] + 1, tables[8].pos[1]))
|
||||
messagebox.showinfo("Rachunek", "Proszę o zapłatę " + str(dish_4.price) + " zł.")
|
||||
client_4.pay("Kinga/data/examine/cards/card_008.png")
|
||||
#messagebox.showinfo("Rachunek", "Dziękuję, należy się reszta: " + str(client_4.budget - dish_4.price) + " zł.")
|
||||
messagebox.showinfo("Uwaga", "Niestety, nie przyjmujemy zapłaty kartą.")
|
||||
|
||||
client4_payment = ("Kinga/data/examine/cards/card_008.png")
|
||||
decide(client_4, client4_payment, dish_4)
|
||||
|
||||
messagebox.showinfo("Informacja końcowa", "Zakończono trasę.")
|
||||
|
||||
@ -619,10 +645,39 @@ def makeImgClassificator(model):
|
||||
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():
|
||||
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
|
||||
light_beige = (255, 240, 205)
|
||||
beige = (255, 205, 178)
|
||||
@ -633,6 +688,10 @@ def main():
|
||||
makeImgClassificator(img_classify)
|
||||
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
|
||||
rows = 15
|
||||
sizeBetween = width // rows #wielkość pojedynczej kratki
|
||||
|
||||
Loading…
Reference in New Issue
Block a user