CNN Model Body
This commit is contained in:
Steffen Illium
@ -1,125 +1,132 @@
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from pathlib import Path
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import copy
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from math import sqrt
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import numpy as np
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from PIL import Image, ImageDraw
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import networkx as nx
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from matplotlib import pyplot as plt
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from lib.objects.trajectory import Trajectory
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class Map(object):
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white = [1, 255]
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black = [0]
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def __copy__(self):
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return copy.deepcopy(self)
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@property
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def shape(self):
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return self.map_array.shape
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@property
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def width(self):
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return self.shape[0]
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@property
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def height(self):
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return self.shape[1]
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@property
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def as_graph(self):
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return self._G
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@property
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def as_array(self):
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return self.map_array
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def __init__(self, name='', array_like_map_representation=None):
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self.map_array: np.ndarray = array_like_map_representation
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self.name = name
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pass
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def __setattr__(self, key, value):
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super(Map, self).__setattr__(key, value)
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if key == 'map_array' and self.map_array is not None:
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self._G = self._build_graph()
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def _build_graph(self, full_neighbors=True):
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graph = nx.Graph()
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# Do checks in order: up - left - upperLeft - lowerLeft
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neighbors = [(0, -1, 1), (-1, 0, 1), (-1, -1, sqrt(2)), (-1, 1, sqrt(2))]
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# Check pixels for their color (determine if walkable)
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for idx, value in np.ndenumerate(self.map_array):
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if value in self.white:
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y, x = idx
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# IF walkable, add node
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graph.add_node((y, x), count=0)
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# Fully connect to all surrounding neighbors
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for n, (xdif, ydif, weight) in enumerate(neighbors):
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# Differentiate between 8 and 4 neighbors
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if not full_neighbors and n >= 2:
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break
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query_node = (y + ydif, x + xdif)
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if graph.has_node(query_node):
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graph.add_edge(idx, query_node, weight=weight)
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return graph
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@classmethod
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def from_image(cls, imagepath: Path):
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with Image.open(imagepath) as image:
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return cls(name=imagepath.name, array_like_map_representation=np.array(image))
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def simple_trajectory_between(self, start, dest):
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vertices = list(nx.shortest_path(self._G, start, dest))
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trajectory = Trajectory(vertices)
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return trajectory
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def get_valid_position(self):
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not_found, valid_position = True, (-9999, -9999)
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while not_found:
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valid_position = int(np.random.choice(self.height, 1)), int(np.random.choice(self.width, 1))
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if self._G.has_node(valid_position):
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not_found = False
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pass
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return valid_position
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def get_trajectory_from_vertices(self, *args):
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coords = list()
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for start, dest in zip(args[:-1], args[1:]):
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coords.extend(nx.shortest_path(self._G, start, dest))
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return Trajectory(coords)
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def get_random_trajectory(self):
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start = self.get_valid_position()
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dest = self.get_valid_position()
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return self.simple_trajectory_between(start, dest)
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def are_homotopic(self, trajectory, other_trajectory):
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if not all(isinstance(x, Trajectory) for x in [trajectory, other_trajectory]):
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raise TypeError
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polyline = trajectory.vertices.copy()
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polyline.extend(reversed(other_trajectory.vertices))
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img = Image.new('L', (self.height, self.width), 0)
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ImageDraw.Draw(img).polygon(polyline, outline=1, fill=1)
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a = (np.array(img) * self.map_array).sum()
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if a >= 1:
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return False
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else:
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return True
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def draw(self):
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fig, ax = plt.gcf(), plt.gca()
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# The standard colormaps also all have reversed versions.
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# They have the same names with _r tacked on to the end.
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# https: // matplotlib.org / api / pyplot_summary.html?highlight = colormaps
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img = ax.imshow(self.as_array, cmap='Greys_r')
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return dict(img=img, fig=fig, ax=ax)
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from pathlib import Path
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import copy
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from math import sqrt
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from random import choice
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import numpy as np
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from PIL import Image, ImageDraw
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import networkx as nx
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from matplotlib import pyplot as plt
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from lib.objects.trajectory import Trajectory
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class Map(object):
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# This setting is for Img mode "L" aka GreyScale Image; values: 0-255
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white = 255
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black = 0
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def __copy__(self):
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return copy.deepcopy(self)
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@property
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def shape(self):
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return self.map_array.shape
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@property
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def width(self):
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return self.shape[0]
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@property
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def height(self):
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return self.shape[1]
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@property
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def as_graph(self):
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return self._G
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@property
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def as_array(self):
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return self.map_array
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def __init__(self, name='', array_like_map_representation=None):
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self.map_array: np.ndarray = array_like_map_representation
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self.name = name
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pass
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def __setattr__(self, key, value):
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super(Map, self).__setattr__(key, value)
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if key == 'map_array' and self.map_array is not None:
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self._G = self._build_graph()
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def _build_graph(self, full_neighbors=True):
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graph = nx.Graph()
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# Do checks in order: up - left - upperLeft - lowerLeft
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neighbors = [(0, -1, 1), (-1, 0, 1), (-1, -1, sqrt(2)), (-1, 1, sqrt(2))]
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# Check pixels for their color (determine if walkable)
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for idx, value in np.ndenumerate(self.map_array):
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if value == self.white:
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try:
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y, x = idx
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except ValueError:
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y, x, channels = idx
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idx = (y, x)
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# IF walkable, add node
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graph.add_node((y, x), count=0)
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# Fully connect to all surrounding neighbors
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for n, (xdif, ydif, weight) in enumerate(neighbors):
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# Differentiate between 8 and 4 neighbors
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if not full_neighbors and n >= 2:
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break
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query_node = (y + ydif, x + xdif)
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if graph.has_node(query_node):
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graph.add_edge(idx, query_node, weight=weight)
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return graph
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@classmethod
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def from_image(cls, imagepath: Path):
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with Image.open(imagepath) as image:
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# Turn the image to single Channel Greyscale
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if image.mode != 'L':
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image = image.convert('L')
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map_array = np.array(image)
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return cls(name=imagepath.name, array_like_map_representation=map_array)
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def simple_trajectory_between(self, start, dest):
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vertices = list(nx.shortest_path(self._G, start, dest))
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trajectory = Trajectory(vertices)
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return trajectory
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def get_valid_position(self):
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valid_position = choice(list(self._G.nodes))
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return valid_position
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def get_trajectory_from_vertices(self, *args):
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coords = list()
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for start, dest in zip(args[:-1], args[1:]):
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coords.extend(nx.shortest_path(self._G, start, dest))
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return Trajectory(coords)
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def get_random_trajectory(self):
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start = self.get_valid_position()
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dest = self.get_valid_position()
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return self.simple_trajectory_between(start, dest)
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def are_homotopic(self, trajectory, other_trajectory):
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if not all(isinstance(x, Trajectory) for x in [trajectory, other_trajectory]):
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raise TypeError
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polyline = trajectory.vertices.copy()
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polyline.extend(reversed(other_trajectory.vertices))
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img = Image.new('L', (self.height, self.width), 0)
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draw = ImageDraw.Draw(img)
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draw.polygon(polyline, outline=255, fill=255)
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a = (np.array(img) * np.where(self.map_array == self.white, 0, 1)).sum()
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if a >= 1:
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return False
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else:
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return True
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def draw(self):
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fig, ax = plt.gcf(), plt.gca()
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# The standard colormaps also all have reversed versions.
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# They have the same names with _r tacked on to the end.
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# https: // matplotlib.org / api / pyplot_summary.html?highlight = colormaps
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img = ax.imshow(self.as_array, cmap='Greys_r')
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return dict(img=img, fig=fig, ax=ax)
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