Multi-Purpose-MPC/map.py

127 lines
4.4 KiB
Python

import numpy as np
import matplotlib.pyplot as plt
from skimage.morphology import remove_small_holes
from PIL import Image
from skimage.draw import line_aa
class Map:
def __init__(self, file_path, threshold_occupied=100,
origin=(-30.0, -24.0), resolution=0.06):
"""
Constructor for map object. Map contains occupancy grid map data of
environment as well as meta information.
:param file_path: path to image of map
:param threshold_occupied: threshold value for binarization of map
image
:param origin: x and y coordinates of map origin in world coordinates
[m]
:param resolution: resolution in m/px
"""
# Set binarization threshold
self.threshold_occupied = threshold_occupied
# Numpy array containing map data
self.data = np.array(Image.open(file_path))[:, :, 0]
# Process raw map image
self.process_map()
# Store meta information
self.height = self.data.shape[0] # height of the map in px
self.width = self.data.shape[1] # width of the map in px
self.resolution = resolution # resolution of the map in m/px
self.origin = origin # x and y coordinates of map origin
# (bottom-left corner) in m
# Containers for user-specified additional obstacles and boundaries
self.obstacles = list()
self.boundaries = list()
def w2m(self, x, y):
"""
World2Map. Transform coordinates from global coordinate system to
map coordinates.
:param x: x coordinate in global coordinate system
:param y: y coordinate in global coordinate system
:return: discrete x and y coordinates in px
"""
dx = int(np.floor((x - self.origin[0]) / self.resolution))
dy = int(np.floor((y - self.origin[1]) / self.resolution))
return dx, dy
def m2w(self, dx, dy):
"""
Map2World. Transform coordinates from map coordinate system to
global coordinates.
:param dx: x coordinate in map coordinate system
:param dy: y coordinate in map coordinate system
:return: x and y coordinates of cell center in global coordinate system
"""
x = (dx + 0.5) * self.resolution + self.origin[0]
y = (dy + 0.5) * self.resolution + self.origin[1]
return x, y
def add_obstacles(self, obstacles):
"""
Add obstacles to the map.
:param obstacles: list of obstacle objects
"""
# Extend list of obstacles
self.obstacles.extend(obstacles)
# Iterate over list of new obstacles
for obstacle in obstacles:
# Compute radius of circular object in pixels
radius_px = int(np.ceil(obstacle.radius / self.resolution))
# Get center coordinates of obstacle in map coordinates
cx_px, cy_px = self.w2m(obstacle.cx, obstacle.cy)
# Add circular object to map
y, x = np.ogrid[-radius_px: radius_px, -radius_px: radius_px]
index = x ** 2 + y ** 2 <= radius_px ** 2
self.data[cy_px-radius_px:cy_px+radius_px, cx_px-radius_px:
cx_px+radius_px][index] = 0
def add_boundary(self, boundaries):
"""
Add boundaries to the map.
:param boundaries: list of tuples containing coordinates of boundaries'
start and end points
"""
# Extend list of boundaries
self.boundaries.extend(boundaries)
# Iterate over list of boundaries
for boundary in boundaries:
sx = self.w2m(boundary[0][0], boundary[0][1])
gx = self.w2m(boundary[1][0], boundary[1][1])
path_x, path_y, _ = line_aa(sx[0], sx[1], gx[0], gx[1])
for x, y in zip(path_x, path_y):
self.data[y, x] = 0
def process_map(self):
"""
Process raw map image. Binarization and removal of small holes in map.
"""
# Binarization using specified threshold
# 1 corresponds to free, 0 to occupied
self.data = np.where(self.data >= self.threshold_occupied, 1, 0)
# Remove small holes in map corresponding to spurious measurements
self.data = remove_small_holes(self.data, area_threshold=5,
connectivity=8).astype(np.int8)
if __name__ == '__main__':
map = Map('map_floor2.png')
plt.imshow(map.data, cmap='gray')
plt.show()