main.py

__author__ = "Yska"

# Importing the required libraries
import sys
import argparse
import numpy as np
import math
from utils import move
import random
import matplotlib.pyplot as plt
from QuadMDP.QuadTree import Point, Rect
from QuadMDP.QuadMDP import QuadMDP
from utils.Obstacle import Obstacle, Agent
from utils.Global import Global
from utils.agent_graph import *
from matplotlib.animation import FuncAnimation
import time
import csv


def path(args):

    # Initializing the world from the picture
    filename = args.input
    image = plt.imread('map/' + filename)
    mapWidth, mapHeight = image.shape
    print("Map Width:", mapWidth)
    print("Map height:", mapHeight)
    mapDepth = (int)(math.log(mapWidth,2))
    searchDepth = 2 # Search depth within QuadMDP

    # Global class for the whole environment
    g = Global((mapWidth,mapHeight))

    # QuadMDP class for generating the quad decomposed states
    quad = QuadMDP(Rect(mapWidth/2-0.5,mapHeight/2-0.5,mapWidth,mapHeight),mapDepth)

    # Initialize time collection
    times = [0] #first entry is time steps, second+third are realtime global path search

    # Load all obstacles
    for x in range(mapWidth):
        for y in range(mapHeight):
            if image[y,x] == 0:
                g.createObstacle(move.NONE,(x,y))
                quad.insert(Point(x,y,True))

    # Generate quad decomposed states
    S = quad.findEmptySpace(searchDepth)
    graph = quad.generateGraph(S, searchDepth)

    # Generate the start position and the goal
    startPos = (8,8)
    goalPos = (119,8)
    startQuadMDP = quad.findContainedQuadMDP(startPos)
    goalQuadMDP = quad.findContainedQuadMDP(goalPos)

    DFS = False  # set to True to use DFS instead of BFS
    Simple = True

    # Get the optimal path in the Quadtree representation

    if DFS:  # DFS
        path_dfs = quad.getPathDFSV2(S,searchDepth,startQuadMDP,goalQuadMDP, path = [], visited = set())
        path = [element.getTuple() for element in path_dfs]
    else:  # BFS
        times.append(time.perf_counter())
        path = quad.getOptimalPath(S,searchDepth,startQuadMDP.getTuple(),goalQuadMDP.getTuple())
        times.append(time.perf_counter())
    path.append(goalPos)



    # Projecting the path to the Global representation
    for i, p in enumerate(path):
        decision = np.random.randint(0, 3)
        if decision == 0:
            path[i] = (math.floor(p[0]), math.floor(p[1]))
        if decision == 1:
            path[i] = (math.ceil(p[0]), math.floor(p[1]))
        if decision == 2:
            path[i] = (math.floor(p[0]), math.ceil(p[1]))
        if decision == 3:
            path[i] = (math.ceil(p[0]), math.ceil(p[1]))
    
    print("Path we want to take: ", path)

    # Create the moving obstacles
    moving_obstacles = []  # store the moving obstacles
    moving_obstacles_positions = dict()  # store the positions of the moving obstacles over time
    num_obstacles = 200
    a = np.random.randint(0, [[mapWidth],[mapHeight]], size = (2,num_obstacles)).T

    for element in a:  # creating random moving obstacles
        obs1 = g.createObstacle(move.RANDOM,tuple(element))  # moving obstacles with random dynamics
        moving_obstacles.append(obs1)
        moving_obstacles_positions[obs1] = [tuple(element)]


    # Create the agent
    agent = g.createAgent(startPos)
    agent.range = 2

    # Print the initial state of the world
    print("Initial position")
    g.plot()


    # Retrieve the dimensions of the picture and the first observation
    Xlim, Ylim = mapWidth, mapHeight
    obs = g.observe()

    # Build observation environment
    Graph = agent_graph(agent, obs, Xlim, Ylim)
    
    # Initialization
    idx_goal = 0  # index of the intermediary goal we are considering
    complete_path = []
    current_state = tuple(agent.location)
    not_moving_count = 0
    while idx_goal < len(path):

        # Grab the current goal and project it to the observation environment
        goal = tuple(path[idx_goal])
        projected_goal = Graph.project_to_surroundings(goal)

        # Get the optimal path on the low level
        if DFS:  # DFS
            inter_path =  Graph.get_path_DFS(current_state, projected_goal, [], set())
        else:  # BFS
            inter_path = Graph.get_optimal_path(current_state, projected_goal)
        

        # Get to the next state on the path
        next_state = inter_path[1]
        action = np.array(next_state) - np.array(current_state)

        # Check that we are not stuck in a corner
        if action == move.NONE:
            not_moving_count += 1
        else:
            not_moving_count = 0

        # If stuck for some reason, go back to the previous goal
        if not_moving_count > 10 and action == move.NONE and idx_goal >= 1:
            idx_goal -= 1
            not_moving_count = 0

        # Updating the state of the agents and the moving obstacles
        agent.action = action
        g.next()
        times[0] += 1


        # Position of the agent after moving
        current_state = tuple(agent.location)
        complete_path += [current_state]
        print(current_state)
        print(goal)

        # Check how the environment look now
        #g.plot()  # deactivate for gain of time

        # Get new observation
        obs = g.observe()
        print(obs)

        # Tracking the positions of the obstacles
        for obstacle in moving_obstacles:
            moving_obstacles_positions[obstacle].append(tuple(obstacle.location))

        # Load the new graph corresponding to the new positions of the agent and its new observation
        Graph = agent_graph(agent, obs, Xlim, Ylim)
        if current_state == goal: # If we have reached the goal, we go to the next one
            idx_goal += 1

    # Upload the entire trajectory so as to plot it
    g.complete_path = complete_path

    # Initialize the picture for plotting
    figsize = 4
    fig, ax = plt.subplots(1, 1, figsize=(g.x_dim/g.y_dim*figsize, figsize))
    plt.title("time = " + str(g.time))
    ax.set_xlim([-0.5,g.x_dim-0.5])
    ax.set_ylim([-0.5,g.y_dim-0.5])
    ax.set_aspect('equal')
    ax.invert_yaxis()
    plt.tight_layout()


    # Plot the fixed obstacles outside of plot_agent so that they remain in the figure the entire simulation
    for o in g.obstacles:
        if moving_obstacles_positions.get(o) is None:
            x,y = o.location
            rx = [x-0.5,x+0.5,x+0.5,x-0.5]
            ry = [y-0.5,y-0.5,y+0.5,y+0.5]
            plt.fill(rx,ry,color='red',zorder=-2, alpha=0.8)
    
    # plotting the global path to follow in blue outside of plot_agent so that they remain in the figure the entire simulation
    for state in path:
        x, y = state
        rx = [x-0.5,x+0.5,x+0.5,x-0.5]
        ry = [y-0.5,y-0.5,y+0.5,y+0.5]
        plt.fill(rx,ry,color='blue',zorder=-2, alpha=1)

    # Keeping track of specific artist elements being plotted so as to replace them at each iteration of plot_agent
    moving_obstacles_artists_list = []  # made to contain the plotted positions of the moving obstacles
    observation_artist_list = []  # made to contain the plotted observations 

    def plot_agent(i):  # i = frame number
        # iterative plotting function
        
        plt.title("time = " + str(i))
        
        for element in moving_obstacles_artists_list:  # remove from the figure the previous positions of the moving obstacles
            if len(element) > 0:
                line = element.pop(0)  # remove from the list
                line.remove()  # remove from the figure
        
        
        # Plotting the agents position at time i
        a = g.agents[0]
        x,y = g.complete_path[i]
        rx = [x-0.5,x+0.5,x+0.5,x-0.5]
        ry = [y-0.5,y-0.5,y+0.5,y+0.5]
        plt.fill(rx,ry,color='skyblue',zorder=-2, alpha=0.8)

        # Plotting the observation at time i
        ax_min = max(0, x-a.range)
        ax_max = min(g.x_dim-1, x+a.range)
        ay_min = max(0, y-a.range)
        ay_max = min(g.y_dim-1, y+a.range)
        rx = [ax_min-0.5, ax_max+0.5, ax_max+0.5, ax_min-0.5]
        ry = [ay_min-0.5, ay_min-0.5, ay_max+0.5, ay_max+0.5]
        
        for element in observation_artist_list:
            if len(element)> 0:  # remove the previous observation from the plot
                line = element.pop(0)
                line.remove()

        observation_artist_list.append(plt.fill(rx, ry, color='lightgreen', zorder=-2, alpha=0.4))  # add the new observation to the list of plotted elements (to remove it next iteration)

        # plotting moving obstacles
        for o in moving_obstacles:
            x,y = moving_obstacles_positions[o][i]
            rx = [x-0.5,x+0.5,x+0.5,x-0.5]
            ry = [y-0.5,y-0.5,y+0.5,y+0.5]
            ln = plt.fill(rx,ry,color='red',zorder=-2, alpha=0.8)  # plotting
            moving_obstacles_artists_list.append(ln)  # adding the artist so we can remove it at the next iteration




    # building the animation object
    ani = FuncAnimation(fig, plot_agent, frames=len(complete_path), interval=100, repeat=False)

    # Either show or save as a gif (one cancels the other so you need to execute only one)
    plt.show()
    #ani.save('animation%4f.gif' % (time.time()), writer='imagemagick', fps=10)
    return times

def main(args):
    runs = []
    with open('qtree.csv', 'w') as f:
        writer = csv.writer(f) 
        for i in range(25):
            runs.append(path(args))
            writer.writerow(path(args))
        print(runs)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Quad Decomposition Demo')
    parser.add_argument('-i', '--input', help='Grayscale PNG map file name', default='1.png')
    args = parser.parse_args()
    #main(args)
    path(args)