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indoor_Navigation_master.py

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+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+
+from __future__ import division, absolute_import
+from PIL import Image
+import numpy as np
+from pylab import imshow, savefig  # , show, get_cmap
+from tools import Worker, Isovist, TrackCollection, IndoorToolset  # , Track, IsovistCollection
+
+# import tensorflow as tf
+
+
+if __name__ == '__main__':
+    walkableTiles = 255
+    w = Worker()
+    # file = 'maps\Tate.bmp'
+    # file = 'maps\Map.bmp'
+    # file = 'maps\Maze.bmp'
+    # file = 'maps\oet.bmp'
+    # file = 'maps\doom.bmp'
+    # file = 'maps\priz.bmp'
+    # file = 'maps\\tum.bmp'
+    file = 'maps\crossing.bmp'
+    with Image.open(file) as f:
+        baseToolset = IndoorToolset(np.array(f), walkableTiles, worker=w, isoVistSize=30)
+
+    baseToolset.refresh_random_clock()
+
+    """HEATMAP, Random"""
+    # Heatmap with random starts/targets
+    if False:
+        pCol = TrackCollection(baseToolset, worker=w)
+        pCol.add_n_bunch_random(100, penalty=2, safe=True)
+        pCol.show(graphUpdate=True, saveIMG=True)
+
+    """Homotroph Analysis"""
+    # Homotrphic Colored Pixels with same Starts/Targets, vertices iterating over all walkable pixels.
+    if False:
+        st = baseToolset.getRandomPos()
+        baseToolset.refresh_random_clock()
+        ta = baseToolset.getRandomPos()
+        pCol = TrackCollection(baseToolset)
+
+        pCol.add_n_bunch_tracks('all', st, ta)
+
+        pCol.homotopic_classification()
+        pCol.show(hClass=True, saveIMG=True)
+
+    """Principal Convex Hull Analysis (PCHA) / Archetypal Analysis."""
+    # refer to https://github.com/ulfaslak/py_pcha for further Information and original Editor
+    if False:
+        if True:
+            st = baseToolset.getRandomPos()
+            baseToolset.refresh_random_clock()
+            ta = baseToolset.getRandomPos()
+        if False:                       # NOTE: use this with Maze.bmp!!!!!!
+            st = (98, 6)
+            ta = (98, 195)
+        if False:                        # NOTE: use this with Map.bmp!!!!!!
+            st = (15, 15)
+            ta = (190, 280)
+        pCol = TrackCollection(baseToolset)
+        # ToDo: Print the k value as chart
+        # noinspection PyUnboundLocalVariable
+        pCol.add_n_bunch_tracks(20, st, ta, penalty=2)
+        pCol.homotopic_classification()
+        if False:
+            ArchetypeList = pCol.return_archetypes(2)
+            pCol.show(saveIMG=True, trackList=ArchetypeList, allTracks=True)
+            ArchetypeList = pCol.return_archetypes(4)
+            pCol.show(saveIMG=True, trackList=ArchetypeList, allTracks=True)
+            ArchetypeList = pCol.return_archetypes(5)
+            pCol.show(saveIMG=True, trackList=ArchetypeList, allTracks=True)
+        ArchetypeList = pCol.return_archetypes(3)
+        pCol.show(saveIMG=True, trackList=ArchetypeList, allTracks=True)
+
+    """Isovists Testing"""
+    # Some Explanation text here
+    if False:
+        point = baseToolset.getRandomPos()
+        print(point)
+        i = Isovist(*point, array=baseToolset.imgArray, walkable=walkableTiles, rangeLimit=30)
+        i.saveImg()
+        baseToolset.imgArray[point] = 160
+        imshow(baseToolset.imgArray)
+        savefig('startpos.tif', interpolation='none', cmap='gray')
+
+    """Synthesyse n-bunch Tracks/ minimum length / storage for TensorFlow"""
+    # Some Explenation text here
+    if True:
+        pCol = TrackCollection(baseToolset)
+        pCol.add_single_track((67, 103), (67, 64), qhull=False)
+        pCol.add_single_track((47, 82), (88, 82), qhull=False)
+        #pCol.add_n_bunch_random(
+        #    1000, penalty=None, safe=False,
+        #    minLen=50
+        #    # minLen=int(sqrt(baseToolset.width * baseToolset.height) / 4)
+        #)
+
+        pCol.save_to_disc('crossing')
+
+        # pCol3 = TrackCollection(baseToolset)
+        # pCol3.recover_from_disc('synthetic_tracks_sizeDIV4')
+
+    print('Success!')
+    pass
+    pass