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dijkstra.py
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dijkstra.py
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import heapq
import cv2, random
import matplotlib.pyplot as plt
import numpy as np
import phyre
# Helper functions and classes
class Vertex:
def __init__(self, x_coord, y_coord):
self.x = x_coord
self.y = y_coord
self.d = float('inf') # distance from source
self.parent_x = None
self.parent_y = None
self.processed = False
self.index_in_queue = None
# Return neighbor directly above, below, right, and left
def get_neighbors(mat, r, c):
shape = mat.shape
neighbors = []
# ensure neighbors are within image boundaries
if r > 0 and not mat[r - 1][c].processed:
neighbors.append(mat[r - 1][c])
if r < shape[0] - 1 and not mat[r + 1][c].processed:
neighbors.append(mat[r + 1][c])
if c > 0 and not mat[r][c - 1].processed:
neighbors.append(mat[r][c - 1])
if c < shape[1] - 1 and not mat[r][c + 1].processed:
neighbors.append(mat[r][c + 1])
return neighbors
def bubble_up(queue, index):
if index <= 0:
return queue
p_index = (index - 1) // 2
if queue[index].d < queue[p_index].d:
queue[index], queue[p_index] = queue[p_index], queue[index]
queue[index].index_in_queue = index
queue[p_index].index_in_queue = p_index
quque = bubble_up(queue, p_index)
return queue
def bubble_down(queue, index):
length = len(queue)
lc_index = 2 * index + 1
rc_index = lc_index + 1
if lc_index >= length:
return queue
if lc_index < length and rc_index >= length: # just left child
if queue[index].d > queue[lc_index].d:
queue[index], queue[lc_index] = queue[lc_index], queue[index]
queue[index].index_in_queue = index
queue[lc_index].index_in_queue = lc_index
queue = bubble_down(queue, lc_index)
else:
small = lc_index
if queue[lc_index].d > queue[rc_index].d:
small = rc_index
if queue[small].d < queue[index].d:
queue[index], queue[small] = queue[small], queue[index]
queue[index].index_in_queue = index
queue[small].index_in_queue = small
queue = bubble_down(queue, small)
return queue
def get_distance(img, u, v):
return 0.1 + (float(img[v][0]) - float(img[u][0])) ** 2 + (float(img[v][1]) - float(img[u][1])) ** 2 + (float(img[v][2]) - float(img[u][2])) ** 2
def get_distance_obj(img, u, v, obj):
if obj[u] and obj[v]:
#return 0.0 + (float(img[v][0]) - float(img[u][0])) ** 2 + (float(img[v][1]) - float(img[u][1])) ** 2 + (float(img[v][2]) - float(img[u][2])) ** 2
return 0.0
else:
#return 0.1 + (float(img[v][0]) - float(img[u][0])) ** 2 + (float(img[v][1]) - float(img[u][1])) ** 2 + (float(img[v][2]) - float(img[u][2])) ** 2
return 1 if (img[v]==img[u]).all() or obj[u] else 1000
def drawPath(img, path, thickness=2):
'''path is a list of (x,y) tuples'''
x0, y0 = path[0]
for vertex in path[1:]:
x1, y1 = vertex
cv2.line(img, (x0, y0), (x1, y1), (255, 0, 0), thickness)
x0, y0 = vertex
def find_shortest_path(img, src, dst):
pq = [] # min-heap priority queue
source_x = src[0]
source_y = src[1]
dest_x = dst[0]
dest_y = dst[1]
imagerows, imagecols = img.shape[0], img.shape[1]
matrix = np.full((imagerows, imagecols), None) # access by matrix[row][col]
for r in range(imagerows):
for c in range(imagecols):
matrix[r][c] = Vertex(c, r)
matrix[r][c].index_in_queue = len(pq)
pq.append(matrix[r][c])
matrix[source_y][source_x].d = 0
pq = bubble_up(pq, matrix[source_y][source_x].index_in_queue)
while len(pq) > 0:
u = pq[0]
u.processed = True
pq[0] = pq[-1]
pq[0].index_in_queue = 0
pq.pop()
pq = bubble_down(pq, 0)
neighbors = get_neighbors(matrix, u.y, u.x)
for v in neighbors:
dist = get_distance(img, (u.y, u.x), (v.y, v.x))
if u.d + dist < v.d:
v.d = u.d + dist
v.parent_x = u.x
v.parent_y = u.y
idx = v.index_in_queue
pq = bubble_down(pq, idx)
pq = bubble_up(pq, idx)
path = []
iter_v = matrix[dest_y][dest_x]
path.append((dest_x, dest_y))
while (iter_v.y != source_y or iter_v.x != source_x):
path.append((iter_v.x, iter_v.y))
iter_v = matrix[iter_v.parent_y][iter_v.parent_x]
path.append((source_x, source_y))
return path
def find_distance_map(img, src):
pq = [] # min-heap priority queue
source_x = src[0]
source_y = src[1]
imagerows, imagecols = img.shape[0], img.shape[1]
matrix = np.full((imagerows, imagecols), None) # access by matrix[row][col]
for r in range(imagerows):
for c in range(imagecols):
matrix[r][c] = Vertex(c, r)
matrix[r][c].index_in_queue = len(pq)
pq.append(matrix[r][c])
matrix[source_y][source_x].d = 0
pq = bubble_up(pq, matrix[source_y][source_x].index_in_queue)
while len(pq) > 0:
u = pq[0]
u.processed = True
pq[0] = pq[-1]
pq[0].index_in_queue = 0
pq.pop()
pq = bubble_down(pq, 0)
neighbors = get_neighbors(matrix, u.y, u.x)
for v in neighbors:
dist = get_distance(img, (u.y, u.x), (v.y, v.x))
if u.d + dist < v.d:
v.d = u.d + dist
v.parent_x = u.x
v.parent_y = u.y
idx = v.index_in_queue
pq = bubble_down(pq, idx)
pq = bubble_up(pq, idx)
distance_map = np.ones((imagerows, imagecols)) * 256.0 # access by matrix[row][col]
for r in range(imagerows):
for c in range(imagecols):
distance_map[r][c] = float(matrix[r][c].d)
trg_norm = True
if trg_norm:
dd = distance_map.copy()
dd[dd > 255.] = 0. # numpy.unique(np.array(dd, dtype=int))
dmax = np.max(dd)
#print('dmax =', dmax)
distance_map = distance_map / dmax * 255.
distance_map[distance_map > 255.] = 255.
distance_map = 255. - distance_map
return distance_map
def find_distance_map_obj(img, obj, trg_norm=False, len_norm=True):
pq = [] # min-heap priority queue
src = np.transpose(np.where(obj))
source_y = src[0][0]
source_x = src[0][1]
imagerows, imagecols = img.shape[0], img.shape[1]
matrix = np.full((imagerows, imagecols), None) # access by matrix[row][col]
for r in range(imagerows):
for c in range(imagecols):
matrix[r][c] = Vertex(c, r)
matrix[r][c].index_in_queue = len(pq)
pq.append(matrix[r][c])
matrix[source_y][source_x].d = 0
pq = bubble_up(pq, matrix[source_y][source_x].index_in_queue)
while len(pq) > 0:
u = pq[0]
u.processed = True
pq[0] = pq[-1]
pq[0].index_in_queue = 0
pq.pop()
pq = bubble_down(pq, 0)
neighbors = get_neighbors(matrix, u.y, u.x)
for v in neighbors:
# dist = get_distance(img, (u.y, u.x), (v.y, v.x))
dist = get_distance_obj(img, (u.y, u.x), (v.y, v.x), obj)
if u.d + dist < v.d:
v.d = u.d + dist
v.parent_x = u.x
v.parent_y = u.y
idx = v.index_in_queue
pq = bubble_down(pq, idx)
pq = bubble_up(pq, idx)
distance_map = np.ones((imagerows, imagecols)) * 255.0 # access by matrix[row][col]
for r in range(imagerows):
for c in range(imagecols):
distance_map[r][c] = float(matrix[r][c].d)
if trg_norm:
dd = distance_map.copy()
dd[dd > 255.] = 255. # numpy.unique(np.array(dd, dtype=int))
dmax = np.max(dd)
print('dmax =', dmax)
distance_map = distance_map / dmax * 255.
if len_norm:
distance_map = 255*distance_map / (img.shape[0]*2)
distance_map[distance_map > 255.] = 255.
distance_map = 255. - distance_map
return distance_map
# improve/debug time-step selection for injection
# implement 5 random positions at the goal object
# take into account grey obstacles
# run the benchmark with stats on compute on GPU cluster
if __name__ == "__main__":
x = 42
y = 42
sim = phyre.initialize_simulator(['00002:017'], "ball")
# img = cv2.imread('maze.png') # read image
init_scene = sim.initial_scenes[0]
img = phyre.observations_to_float_rgb(init_scene) # read image
img = cv2.resize(img, (64,64))
print(img)
cv2.imwrite('00002_017_scene.png', img*255)
target = np.flip((init_scene == 4), axis=0).astype(float)
target = cv2.resize(target, (64,64))
# cv2.imwrite('maze-initial.png', img)
distance_map = find_distance_map_obj(img, target)
#distance_map[y-1, x] = 0.
#distance_map[y, x] = 0.
#distance_map[y+1, x] = 0.
#distance_map[y, x-1] = 0.
#distance_map[y, x+1] = 0.
cv2.imwrite('00002_017_solution.png', distance_map)
print('DONE')
# %%
# img = cv2.imread('maze.png') # read image
# cv2.imwrite('maze-initial.png', img)
# p = find_shortest_path(img, (25, 5), (5, 220))
# drawPath(img, p)
# cv2.imwrite('maze-solution.png', img)
# plt.figure(figsize=(7, 7))
# plt.imshow(img) # show the image on the screen
# plt.show()
# %%