Neural_network #4

Merged
s481894 merged 12 commits from Neural_network into master 2024-06-04 16:59:03 +02:00
4847 changed files with 216 additions and 24 deletions

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@ -0,0 +1,27 @@
import torch.nn as nn
import torch
import torch.nn.functional as F
class Conv_Neural_Network_Model(nn.Module):
def __init__(self, num_classes=5,hidden_layer1 = 512,hidden_layer2 = 256):
super(Conv_Neural_Network_Model, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(64*25*25,hidden_layer1)
self.fc2 = nn.Linear(hidden_layer1,hidden_layer2)
self.out = nn.Linear(hidden_layer2,num_classes)
def forward(self, x):
x = self.pool1(F.relu(self.conv1(x)))
x = self.pool1(F.relu(self.conv2(x)))
x = x.view(-1, 64*25*25) #<----flattening the image
x = self.fc1(x)
x = torch.relu(x)
x = self.fc2(x)
x = torch.relu(x)
x = self.out(x)
return F.log_softmax(x, dim=-1)

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import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import datasets, transforms, utils
from torchvision.transforms import Compose, Lambda, ToTensor
import matplotlib.pyplot as plt
from NN.model import *
from PIL import Image
import pygame
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
#data transform to tensors:
data_transformer = transforms.Compose([
transforms.Resize((100, 100)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5 ), (0.5, 0.5, 0.5))
])
#loading data:
train_set = datasets.ImageFolder(root='resources/train', transform=data_transformer)
test_set = datasets.ImageFolder(root='resources/test', transform=data_transformer)
#to mozna nawet przerzucic do funkcji train:
# train_loader = DataLoader(train_set, batch_size=64, shuffle=True)
#test_loader = DataLoader(test_set, batch_size=32, shuffle=True)
#function for training model
def train(model, dataset, iter=100, batch_size=64):
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = nn.NLLLoss()
train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
model.train()
for epoch in range(iter):
for inputs, labels in train_loader:
optimizer.zero_grad()
output = model(inputs.to(device))
loss = criterion(output, labels.to(device))
loss.backward()
optimizer.step()
if epoch % 10 == 0:
print('epoch: %3d loss: %.4f' % (epoch, loss))
#function for getting accuracy
def accuracy(model, dataset):
model.eval()
with torch.no_grad():
correct = sum([
(model(inputs.to(device)).argmax(dim=1) == labels.to(device)).sum()
for inputs, labels in DataLoader(dataset, batch_size=64, shuffle=True)
])
return correct.float() / len(dataset)
# model = Conv_Neural_Network_Model()
# model.to(device)
#loading the already saved model:
# model.load_state_dict(torch.load('CNN_model.pth'))
# model.eval()
# #training the model:
# train(model, train_set)
# print(f"Accuracy of the network is: {100*accuracy(model, test_set)}%")
# torch.save(model.state_dict(), 'CNN_model.pth')
def load_model():
model = Conv_Neural_Network_Model()
model.load_state_dict(torch.load('CNN_model.pth', map_location=torch.device('cpu')))
model.eval()
return model
def load_image(image_path):
testImage = Image.open(image_path).convert('RGB')
testImage = data_transformer(testImage)
testImage = testImage.unsqueeze(0)
return testImage
def display_image(screen, image_path, position):
image = pygame.image.load(image_path)
image = pygame.transform.scale(image, (250, 250))
screen.blit(image, position)
def display_result(screen, position, predicted_class):
font = pygame.font.Font(None, 30)
displayed_text = font.render("The predicted image is: "+str(predicted_class), 1, (255,255,255))
screen.blit(displayed_text, position)
def guess_image(model, image_tensor):
with torch.no_grad():
testOutput = model(image_tensor)
_, predicted = torch.max(testOutput, 1)
predicted_class = train_set.classes[predicted.item()]
return predicted_class
#TEST - loading the image and getting results:
# testImage_path = 'resources/images/plant_photos/1c76aa4d-11f4-47d1-8bdd-2cb78deeeccf.jpg'
# testImage = Image.open(testImage_path)
# testImage = data_transformer(testImage)
# testImage = testImage.unsqueeze(0)
# testImage = testImage.to(device)
# model.load_state_dict(torch.load('CNN_model.pth'))
# model.to(device)
# model.eval()
# testOutput = model(testImage)
# _, predicted = torch.max(testOutput, 1)
# predicted_class = train_set.classes[predicted.item()]
# print(f'The predicted class is: {predicted_class}')

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@ -53,3 +53,10 @@ def get_tile_coordinates(index):
return tile.x, tile.y return tile.x, tile.y
else: else:
return None return None
def get_tile_index():
valid_indices = []
for index, tile in enumerate(tiles):
if tile.image=="resources/images/sampling.png":
valid_indices.append(index)
return random.choice(valid_indices)

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@ -3,17 +3,21 @@ import time
import random import random
import pandas as pd import pandas as pd
import joblib import joblib
from area.constants import WIDTH, HEIGHT, TILE_SIZE from area.constants import WIDTH, HEIGHT, TILE_SIZE
from area.field import drawWindow from area.field import drawWindow
from area.tractor import Tractor, do_actions from area.tractor import Tractor, do_actions
from area.field import tiles, fieldX, fieldY from area.field import tiles, fieldX, fieldY
from area.field import get_tile_coordinates from area.field import get_tile_coordinates, get_tile_index
from ground import Dirt from ground import Dirt
from plant import Plant from plant import Plant
from bfs import graphsearch, Istate, succ from bfs import graphsearch, Istate, succ
from astar import a_star from astar import a_star
WIN = pygame.display.set_mode((WIDTH, HEIGHT)) from NN.neural_network import load_model, load_image, guess_image, display_image, display_result
from PIL import Image
pygame.init()
WIN_WIDTH = WIDTH + 300
WIN = pygame.display.set_mode((WIN_WIDTH, HEIGHT))
pygame.display.set_caption('Intelligent tractor') pygame.display.set_caption('Intelligent tractor')
@ -23,7 +27,7 @@ def main():
pygame.display.update() pygame.display.update()
#getting coordinates of our "goal tile": #getting coordinates of our "goal tile":
tile_index=127 tile_index = get_tile_index()
tile_x, tile_y = get_tile_coordinates(tile_index) tile_x, tile_y = get_tile_coordinates(tile_index)
if tile_x is not None and tile_y is not None: if tile_x is not None and tile_y is not None:
print(f"Coordinates of tile {tile_index} are: ({tile_x}, {tile_y})") print(f"Coordinates of tile {tile_index} are: ({tile_x}, {tile_y})")
@ -62,14 +66,39 @@ def main():
for event in pygame.event.get(): for event in pygame.event.get():
if event.type == pygame.QUIT: if event.type == pygame.QUIT:
run = False run = False
#small test of work_on_field method:
time.sleep(1) time.sleep(1)
tile1 = tiles[0]
# movement based on route-planning (test):
tractor.draw_tractor(WIN)
time.sleep(1)
if moves != False:
do_actions(tractor, WIN, moves)
#guessing the image under the tile:
goalTile = tiles[tile_index]
image_path = goalTile.photo
display_image(WIN, goalTile.photo, (WIDTH-20 , 300)) #displays photo next to the field
pygame.display.update()
image_tensor = load_image(image_path)
prediction = guess_image(load_model(), image_tensor)
display_result(WIN, (WIDTH - 50 , 600), prediction) #display text under the photo
pygame.display.update()
print(f"The predicted image is: {prediction}")
p1 = Plant('wheat', 'cereal', random.randint(1,100), random.randint(1,100), random.randint(1,100)) p1 = Plant('wheat', 'cereal', random.randint(1,100), random.randint(1,100), random.randint(1,100))
goalTile.plant = p1
d1 = Dirt(random.randint(1, 100), random.randint(1,100)) d1 = Dirt(random.randint(1, 100), random.randint(1,100))
d1.pests_and_weeds() d1.pests_and_weeds()
tile1.ground=d1 goalTile.ground=d1
#getting the name and type of the recognized plant:
p1.update_name(prediction)
#decission tree test:
if d1.pest: if d1.pest:
pe = 1 pe = 1
else: else:
@ -116,19 +145,13 @@ def main():
model = joblib.load('model.pkl') model = joblib.load('model.pkl')
nowe_dane = pd.read_csv('model_data.csv') nowe_dane = pd.read_csv('model_data.csv')
predykcje = model.predict(nowe_dane) predykcje = model.predict(nowe_dane)
# movement based on route-planning (test):
tractor.draw_tractor(WIN)
time.sleep(1)
if moves != False:
do_actions(tractor, WIN, moves)
print(predykcje) print(predykcje)
#work on field:
if predykcje == 'work': if predykcje == 'work':
tractor.work_on_field(tile1, d1, p1) tractor.work_on_field(goalTile, d1, p1)
time.sleep(30) time.sleep(50)
print("\n") print("\n")

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@ -19,7 +19,19 @@ class Plant:
else: else:
print("Unable to grow due to bad condition of the ground") print("Unable to grow due to bad condition of the ground")
# more properties def update_name(self, predicted_class):
if predicted_class == "Apple":
self.name = "apple"
self.plant_type = 'fruit'
elif predicted_class == "Radish":
self.name = "radish"
self.plant_type = 'vegetable'
# add init, getters,setters elif predicted_class == "Cauliflower":
self.name = "cauliflower"
self.plant_type = 'vegetable'
elif predicted_class == "Wheat":
self.name = "wheat"
self.plant_type = 'cereal'

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