new Readme

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/.name

@@ -0,0 +1 @@
+astar.py

.idea/SI_projekt_smieciarka.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.9" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="16">
+            <item index="0" class="java.lang.String" itemvalue="scipy" />
+            <item index="1" class="java.lang.String" itemvalue="pygame" />
+            <item index="2" class="java.lang.String" itemvalue="opencv-python" />
+            <item index="3" class="java.lang.String" itemvalue="scikit-learn" />
+            <item index="4" class="java.lang.String" itemvalue="h5py" />
+            <item index="5" class="java.lang.String" itemvalue="kiwisolver" />
+            <item index="6" class="java.lang.String" itemvalue="torch" />
+            <item index="7" class="java.lang.String" itemvalue="numpy" />
+            <item index="8" class="java.lang.String" itemvalue="torchvision" />
+            <item index="9" class="java.lang.String" itemvalue="mahotas" />
+            <item index="10" class="java.lang.String" itemvalue="tensorflow" />
+            <item index="11" class="java.lang.String" itemvalue="PyQt5" />
+            <item index="12" class="java.lang.String" itemvalue="matplotlib" />
+            <item index="13" class="java.lang.String" itemvalue="grpcio" />
+            <item index="14" class="java.lang.String" itemvalue="sip" />
+            <item index="15" class="java.lang.String" itemvalue="Pillow" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/SI_projekt_smieciarka.iml" filepath="$PROJECT_DIR$/.idea/SI_projekt_smieciarka.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

README.md

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+<h1>Projekt SI - Smieciarka</h1>

TSP.py

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+
+from ortools.constraint_solver import routing_enums_pb2
+from ortools.constraint_solver import pywrapcp
+from bfs import distance, bfs
+from house import create_houses
+
+
+def create_data_model(multi_trash, truck):
+    data = {}
+    graphrow = []
+    graph = []
+    lista = multi_trash
+    for i in range(8):
+        list1=multi_trash
+        if (i == 0):
+            pos = truck.pos
+        else:
+            pos = lista[i-1].pos
+        for j in range(8):
+            if(j==0):
+                endpos = truck.pos
+            else:
+                endpos = list1[j-1].pos
+            if(i==j):
+                graphrow.append(0)
+            else:
+                dist=distance(pos, endpos)
+                graphrow.append(dist)
+
+        graph.append(graphrow.copy())
+        graphrow.clear()
+    data['distance_matrix'] = graph.copy()
+    data['num_vehicles'] = 1
+    data['depot'] = 0
+    print(data['distance_matrix'])
+    return data
+
+
+def print_solution(manager, routing, solution):
+    index = routing.Start(0)
+    plan_output = []
+    route_distance = 0
+    while not routing.IsEnd(index):
+        plan_output.append(manager.IndexToNode(index))
+        previous_index = index
+        index = solution.Value(routing.NextVar(index))
+        route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)
+    plan_output.append(manager.IndexToNode(index))
+    print(plan_output)
+    return plan_output
+
+
+def tsp(x, y):
+    data = create_data_model(x, y)
+
+    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),
+                                           data['num_vehicles'], data['depot'])
+
+    routing = pywrapcp.RoutingModel(manager)
+
+
+    def distance_callback(from_index, to_index):
+        from_node = manager.IndexToNode(from_index)
+        to_node = manager.IndexToNode(to_index)
+        return data['distance_matrix'][from_node][to_node]
+
+    transit_callback_index = routing.RegisterTransitCallback(distance_callback)
+
+    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
+
+    #search_parameters = pywrapcp.DefaultRoutingSearchParameters()
+   # search_parameters.first_solution_strategy = (
+       # routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
+    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
+    search_parameters.local_search_metaheuristic = (
+        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
+    search_parameters.time_limit.seconds = 30
+    search_parameters.log_search = True
+
+    solution = routing.SolveWithParameters(search_parameters)
+
+    if solution:
+        sol = print_solution(manager, routing, solution)
+
+    return sol
+
+def tspmove(order, truck, Ltrash):
+    houses = create_houses(40)
+    path = []
+    endpos=truck.pos
+    for i in range(1, 8):
+        startpos = endpos
+        endpos = Ltrash[order[i]-1].pos
+        #print(i,startpos,endpos)
+        x = bfs(startpos, truck.dir_control, endpos, houses)
+        #print("$$$$",x)
+        for i in x:
+            if(i==97):
+                truck.rotate(-1)
+            elif(i==100):
+                truck.rotate(1)
+        path.append(x)
+    x = bfs(endpos, truck.dir_control, truck.pos, houses)
+    for i in x:
+        if (i == 97):
+            truck.rotate(-1)
+        elif (i == 100):
+            truck.rotate(1)
+    path.append(x)
+    #print("###############################\n", path)
+    truck.dir_control=0
+    truck.direction = [1, 0]
+    return path
+
+

astar.py

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+from truck import Truck
+
+class Node():
+    def __init__(self, parent=None, position=None):
+        self.parent = parent
+        self.position = position
+
+        self.g = 0
+        self.h = 0
+        self.f = 0
+
+    def __eq__(self, other):
+        return self.position == other.position
+
+
+def astar(maze, start, end):
+    start_node = Node(position=start)
+    end_node = Node(position=end)
+    x=0
+    open_list = []
+    closed_list = []
+
+    open_list.append(start_node)
+
+    while len(open_list) > 0:
+
+        current_node = open_list[0]
+        current_index = 0
+        for index, item in enumerate(open_list):
+            if item.f < current_node.f:
+                current_node = item
+                current_index = index
+
+        open_list.pop(current_index)
+        closed_list.append(current_node)
+
+        if current_node == end_node:
+            path = []
+
+            current = current_node
+            while current is not None:
+                path.append(current.position)
+                if (maze[current.position[0]][current.position[1]] == 1):
+                    x = x + 1
+                if (maze[current.position[0]][current.position[1]] == 2):
+                    x = x + 3
+                if (maze[current.position[0]][current.position[1]] == 6):
+                    x = x + 20
+                if (maze[current.position[0]][current.position[1]] == 7):
+                    x = x + 5
+                if (maze[current.position[0]][current.position[1]] == 8):
+                    x = x + 1
+
+                current = current.parent
+            return path[::-1]
+            #return x
+
+        children = []
+        for new_position in [(0, -1), (0, 1), (-1, 0), (1, 0)]:
+
+            node_position = (
+                current_node.position[0] + new_position[0], current_node.position[1] + new_position[1])
+
+            if node_position[0] > (len(maze) - 1) or node_position[0] < 0 or node_position[1] > (len(maze[len(maze)-1]) - 1) or node_position[1] < 0:
+                continue
+
+            if Node(current_node, node_position) in closed_list:
+                continue
+
+            if maze[node_position[0]][node_position[1]] == 9:
+                continue
+
+            new_node = Node(current_node, node_position)
+
+            children.append(new_node)
+
+        for child in children:
+
+            for closed_child in closed_list:
+                if child == closed_child:
+                    continue
+
+            if(maze[child.position[0]][child.position[1]]==1):
+                child.g = 1
+            if(maze[child.position[0]][child.position[1]]==2):
+                child.g = 3
+            if(maze[child.position[0]][child.position[1]]==6):
+                child.g = 20
+            if(maze[child.position[0]][child.position[1]]==7):
+                child.g = 5
+            if(maze[child.position[0]][child.position[1]]==8):
+                child.g = 1
+            child.h = ((child.position[0] - end_node.position[0]) **
+                       2) + ((child.position[1] - end_node.position[1]) ** 2)
+            child.f = child.g + child.h
+
+            for open_node in open_list:
+                if child == open_node and child.g > open_node.g:
+                    continue
+
+            open_list.append(child)
+
+
+def main():
+
+    maze = [[9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],
+            [9, 9, 9, 9, 9, 1, 1, 9, 9, 9, 1, 1, 1, 9, 9, 9, 1,
+                1, 9, 9, 1, 1, 1, 9, 9, 1, 1, 9, 1, 1, 9, 9],
+            [9, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 9, 1, 1,
+                8, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 9],
+            [9, 1, 1, 1, 2, 1, 1, 6, 1, 1, 1, 1, 1, 1, 1, 1, 9,
+             1, 0, 1, 7, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 9],
+            [9, 1, 1, 1, 1, 9, 9, 9, 9, 9, 9, 1, 1, 1, 1, 9, 9,
+             9, 9, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 9, 9, 9],
+            [9, 9, 9, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1, 2, 1,
+             1, 1, 1, 9, 6, 6, 9, 1, 1, 2, 1, 1, 9, 1, 9],
+            [9, 1, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+             1, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 9],
+            [9, 1, 1, 1, 9, 9, 9, 9, 9, 1, 1, 1, 1, 1, 9, 9, 9,
+             9, 9, 9, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 9, 9],
+            [9, 1, 1, 1, 1, 1, 1, 9, 9, 9, 9, 9, 9, 1, 1, 1, 1,
+             1, 9, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 9],
+            [9, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 9, 1, 1, 1, 9,
+             9, 9, 7, 6, 9, 9, 9, 9, 6, 2, 1, 9, 9, 9, 9],
+            [9, 1, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+             1, 9, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 9],
+            [9, 1, 1, 1, 9, 9, 9, 9, 9, 1, 1, 9, 9, 9, 1, 1, 9,
+             9, 9, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 9],
+            [9, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+             1, 1, 2, 1, 1, 9, 9, 1, 9, 1, 1, 7, 1, 1, 9],
+            [9, 1, 1, 1, 1, 1, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+             1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 9],
+            [9, 1, 1, 9, 1, 1, 9, 9, 9, 1, 9, 1, 1, 1, 9, 9, 9,
+             1, 1, 1, 1, 1, 9, 1, 1, 1, 9, 9, 1, 1, 1, 9],
+            [9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 9, 1, 1, 2, 1,
+             1, 1, 1, 9, 9, 9, 9, 1, 1, 1, 2, 1, 1, 9, 9],
+            [9, 1, 1, 1, 9, 9, 9, 9, 1, 1, 1, 1, 1, 1, 9, 1, 1,
+             1, 9, 1, 1, 9, 9, 1, 1, 1, 1, 1, 1, 1, 1, 9],
+            [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]]
+
+    start = (13, 7)
+    end = (3, 18)
+
+    path = astar(maze, start, end)
+    print(path)
+    return path
+
+
+if __name__ == '__main__':
+    main()

bfs.py

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+from house import *
+import pygame
+
+
+class Node:
+    def __init__(self, pos, direction, parent=None, action=None):
+        self.pos = pos
+        self.direction = direction
+        self.parent = parent
+        self.action = action
+
+    def __eq__(self, other):
+        if isinstance(other, Node):
+            return self.pos == other.pos and self.direction == other.direction
+        return False
+
+
+def successor(pos, direction, houses):
+    neighbours = []
+    axis = 0 if direction in [0, 2] else 1
+    move = 1 if direction in [0, 1] else -1
+
+    neighbours.append([pos, (direction - 1) % 4, pygame.K_a])
+    neighbours.append([pos, (direction + 1) % 4, pygame.K_d])
+
+    if not axis:  # x
+        new_pos = [pos[0] + (move * 40), pos[1]]
+        if not is_house(new_pos, houses):
+            neighbours.append([new_pos, direction, pygame.K_w])
+
+    else:  # y
+        new_pos = [pos[0], pos[1] + (move * 40)]
+        if not is_house(new_pos, houses):
+            neighbours.append([new_pos, direction, pygame.K_w])
+    return neighbours
+
+
+def bfs(pos, direction, end_pos, houses):
+    visited = []
+    queue = []
+    actions = []
+    queue.append(Node(pos, direction))
+
+    while queue:
+        curr_node = queue.pop(0)
+
+        if not is_house(curr_node.pos, houses) and curr_node.pos == end_pos:
+            while curr_node.parent:
+
+               # print(curr_node.pos, end_pos)
+                actions.append(curr_node.action)
+                curr_node = curr_node.parent
+            return actions
+
+        visited.append(curr_node)
+        for n_pos, n_direction, action in successor(curr_node.pos, curr_node.direction, houses):
+            neighbour_node = Node(n_pos, n_direction, curr_node, action)
+            if neighbour_node not in visited and neighbour_node not in queue:
+                queue.append(neighbour_node)
+
+    return actions
+
+def distance(pos, endpos):
+    houses = create_houses(40)
+    actions = bfs(pos, 0, endpos, houses)
+    return len(actions)
+

catOrNotTest/catOrNotTest.py

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+
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers.normalization import BatchNormalization
+from PIL import Image
+from random import shuffle, choice
+import numpy as np
+import os
+
+IMAGE_SIZE = 256
+IMAGE_DIRECTORY = './data/training_set'
+
+def label_img(name):
+    if name == 'cats': return np.array([1, 0])
+    elif name == 'notcats' : return np.array([0, 1])
+
+
+def load_data():
+  print("Loading images...")
+  train_data = []
+  directories = next(os.walk(IMAGE_DIRECTORY))[1]
+
+  for dirname in directories:
+    print("Loading {0}".format(dirname))
+    file_names = next(os.walk(os.path.join(IMAGE_DIRECTORY, dirname)))[2]
+    for i in range(200):
+      image_name = choice(file_names)
+      image_path = os.path.join(IMAGE_DIRECTORY, dirname, image_name)
+      label = label_img(dirname)
+      if "DS_Store" not in image_path:
+        img = Image.open(image_path)
+        img = img.convert('L')
+        img = img.resize((IMAGE_SIZE, IMAGE_SIZE), Image.ANTIALIAS)
+        train_data.append([np.array(img), label])
+  
+  return train_data
+
+def create_model():
+  model = Sequential()
+  model.add(Conv2D(32, kernel_size = (3, 3), activation='relu', input_shape=(IMAGE_SIZE, IMAGE_SIZE, 1)))
+  model.add(MaxPooling2D(pool_size=(2,2)))
+  model.add(BatchNormalization())
+  model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
+  model.add(MaxPooling2D(pool_size=(2,2)))
+  model.add(BatchNormalization())
+  model.add(Conv2D(128, kernel_size=(3,3), activation='relu'))
+  model.add(MaxPooling2D(pool_size=(2,2)))
+  model.add(BatchNormalization())
+  model.add(Conv2D(256, kernel_size=(3,3), activation='relu'))
+  model.add(MaxPooling2D(pool_size=(2,2)))
+  model.add(BatchNormalization())
+  model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
+  model.add(MaxPooling2D(pool_size=(2,2)))
+  model.add(BatchNormalization())
+  model.add(Dropout(0.2))
+  model.add(Flatten())
+  model.add(Dense(256, activation='relu'))
+  model.add(Dropout(0.2))
+  model.add(Dense(128, activation='relu'))
+  model.add(Dense(2, activation = 'softmax'))
+
+  return model
+
+
+training_data = load_data()
+training_images = np.array([i[0] for i in training_data]).reshape(-1, IMAGE_SIZE, IMAGE_SIZE, 1)
+training_labels = np.array([i[1] for i in training_data])
+
+print('creating model')
+model = create_model()
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+print('training model')
+model.fit(training_images, training_labels, batch_size=50, epochs=10, verbose=1)
+model.save("model2.h5")

catOrNotTest/catOrNotTest.pyproj

@@ -0,0 +1,65 @@
+<Project DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003" ToolsVersion="4.0">
+  <PropertyGroup>
+    <Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
+    <SchemaVersion>2.0</SchemaVersion>
+    <ProjectGuid>909f8871-bfd4-48ea-bee8-31c07b881648</ProjectGuid>
+    <ProjectHome>.</ProjectHome>
+    <StartupFile>catOrNotTest.py</StartupFile>
+    <SearchPath>
+    </SearchPath>
+    <WorkingDirectory>.</WorkingDirectory>
+    <OutputPath>.</OutputPath>
+    <Name>catOrNotTest</Name>
+    <RootNamespace>catOrNotTest</RootNamespace>
+    <InterpreterId>MSBuild|env2|$(MSBuildProjectFullPath)</InterpreterId>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)' == 'Debug' ">
+    <DebugSymbols>true</DebugSymbols>
+    <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)' == 'Release' ">
+    <DebugSymbols>true</DebugSymbols>
+    <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
+  </PropertyGroup>
+  <ItemGroup>
+    <Compile Include="catOrNotTest.py" />
+    <Compile Include="kopiamain.py">
+      <SubType>Code</SubType>
+    </Compile>
+    <Compile Include="retrain.py">
+      <SubType>Code</SubType>
+    </Compile>
+    <Compile Include="test.py">
+      <SubType>Code</SubType>
+    </Compile>
+  </ItemGroup>
+  <ItemGroup>
+    <Interpreter Include="env2\">
+      <Id>env2</Id>
+      <Version>3.7</Version>
+      <Description>env2 (Python 3.7 (64-bit))</Description>
+      <InterpreterPath>Scripts\python.exe</InterpreterPath>
+      <WindowsInterpreterPath>Scripts\pythonw.exe</WindowsInterpreterPath>
+      <PathEnvironmentVariable>PYTHONPATH</PathEnvironmentVariable>
+      <Architecture>X64</Architecture>
+    </Interpreter>
+    <Interpreter Include="env\">
+      <Id>env</Id>
+      <Version>3.7</Version>
+      <Description>env (Python 3.7 (64-bit))</Description>
+      <InterpreterPath>Scripts\python.exe</InterpreterPath>
+      <WindowsInterpreterPath>Scripts\pythonw.exe</WindowsInterpreterPath>
+      <PathEnvironmentVariable>PYTHONPATH</PathEnvironmentVariable>
+      <Architecture>X64</Architecture>
+    </Interpreter>
+  </ItemGroup>
+  <Import Project="$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion)\Python Tools\Microsoft.PythonTools.targets" />
+  <!-- Uncomment the CoreCompile target to enable the Build command in
+       Visual Studio and specify your pre- and post-build commands in
+       the BeforeBuild and AfterBuild targets below. -->
+  <!--<Target Name="CoreCompile" />-->
+  <Target Name="BeforeBuild">
+  </Target>
+  <Target Name="AfterBuild">
+  </Target>
+</Project>