def resample(points, n):
eta = PathLength(points) / (n - 1)
delta = 0.0
newpoints = [points[0]]
length = len(points)
i = 1
while i < length:
if points[i].id == points[i - 1].id:
d = Distance(points[i - 1], points[i])
if (delta + d) >= eta:
qx = points[i - 1].x + (
(eta - delta) / d) * (points[i].x - points[i - 1].x)
qy = points[i - 1].y + (
(eta - delta) / d) * (points[i].y - points[i - 1].y)
q = Point.Point(qx, qy, points[i].id)
newpoints.append(q)
points.insert(i, q)
length = length + 1
delta = 0.0
else:
delta = delta + d
i = i + 1
if len(newpoints) == n - 1:
p = Point.Point(points[length - 1].x, points[length - 1].y,
points[length - 1].id)
newpoints.append(p)
return newpoints
def recognize(self, points):
p = [
Point.Point(30, 7, 1),
Point.Point(103, 7, 1),
Point.Point(66, 7, 2),
Point.Point(66, 87, 2)
]
t0 = datetime.datetime.now()
points = resample(points, NumPoints)
points = scale(points)
points = translateto(points, Origin)
b = float('inf')
u = -1
for i in range(0, len(self.pointclouds)):
d = self.greedycloudmatch(points, self.pointclouds[i])
if d < b:
b = d
u = i
t1 = datetime.datetime.now()
if u == -1:
r1 = Result.Result("No match.", 0.0, t1 - t0)
r = r1
else:
r2 = Result.Result(self.pointclouds[u].name,
max((b - 2.0) / -2.0, 0.0), t1 - t0)
r = r2
return r
def __init__(self, x_1, y_1, x_2, y_2, point1=None, point2=None):
if point1 is None and point2 is None:
point1 = Point(x_1, y_1)
point2 = Point(x_2, y_2)
super().__init__(point1, point2)
self.sprite = self.generate_sprite((125, 0, 125))
def __init__(self, x_1, y_1, x_2, y_2, point1=None, point2=None):
if point1 is None and point2 is None:
point1 = Point(x_1, y_1)
point2 = Point(x_2, y_2)
self.distance_lines = []
super().__init__(point1, point2)
self.sprite = self.generate_sprite((0, 125, 125))
self._generate_distance_lines()
def __init__(self):
self._screen = pygame.display.set_mode(self.SIZE)
pygame.display.set_caption(self.TITLE)
self._done = False
self.line = Line()
self.lineByPerceptron = Line()
self._points = [
Point(random.uniform(-1, 1), random.uniform(-1, 1))
for _ in range(50)
]
self.training_point = Point(random.uniform(-1, 1),
random.uniform(-1, 1))
self.trainingIndex = 0
self.brain = Perceptron(3)
def translateto(points, pt):
c = centroid(points)
newpoints = []
for i in range(0, len(points)):
qx = points[i].x = pt.x - c.x
qy = points[i].y + pt.y - c.y
p = Point.Point(qx, qy, points[i].id)
newpoints.append(p)
return newpoints
def centroid(points):
x = 0.0
y = 0.0
for i in range(0, len(points)):
x = x + points[i].x
y = y + points[i].y
x = x / len(points)
y = y / len(points)
p = Point.Point(x, y, 0)
return p
def scale(points):
minX = +float('inf')
maxX = -float('inf')
minY = +float('inf')
maxY = -float('inf')
for i in range(0, len(points)):
minX = min(minX, points[i].x)
minY = min(minY, points[i].y)
maxX = max(maxX, points[i].x)
maxY = max(maxY, points[i].y)
size = max(maxX - minX, maxY - minY)
newpoints = []
for i in range(0, len(points)):
qx = (points[i].x - minX) / size
qy = (points[i].y - minY) / size
newpoints.append(Point.Point(qx, qy, points[i].id))
return newpoints
def _update(self):
"""Updates all the entities and train the neural network"""
for point in self._points:
inputs = [point.x, point.y, point.bias]
guess = self.brain.guess(inputs)
if guess == point.label:
point.color = GREEN
else:
point.color = RED
# The point that serves as a training
input_training = [
self.training_point.x, self.training_point.y,
self.training_point.bias
]
self.brain.train(input_training, self.training_point.label)
self.training_point = Point(random.uniform(-1, 1),
random.uniform(-1, 1))
def update(self):
self.line_of_sight = Line(Point(vector=self.robots[0].center),
Point(vector=self.robots[1].center))
self.line_of_sight.update()
import entity.Point as Point
from Processors.Pointprocessor import *
NumPoints = 32
Origin = Point.Point(0, 0, 0)
class PointCloud:
def __init__(self, name, points):
self.name = name
self.points = resample(points, NumPoints)
self.points = scale(points)
self.points = translateto(points, Origin)
class Recognizer:
pointclouds = []
pc = PointCloud.PointCloud("T", [
Point.Point(30, 7.0, 1),
Point.Point(103, 7, 1),
Point.Point(66, 7, 2),
Point.Point(66, 87, 2)
])
pointclouds.append(pc)
def recognize(self, points):
p = [
Point.Point(30, 7, 1),
Point.Point(103, 7, 1),
Point.Point(66, 7, 2),
Point.Point(66, 87, 2)
]
t0 = datetime.datetime.now()
points = resample(points, NumPoints)
points = scale(points)
points = translateto(points, Origin)
b = float('inf')
u = -1
for i in range(0, len(self.pointclouds)):
d = self.greedycloudmatch(points, self.pointclouds[i])
if d < b:
b = d
u = i
t1 = datetime.datetime.now()
if u == -1:
r1 = Result.Result("No match.", 0.0, t1 - t0)
r = r1
else:
r2 = Result.Result(self.pointclouds[u].name,
max((b - 2.0) / -2.0, 0.0), t1 - t0)
r = r2
return r
def greedycloudmatch(self, points, P):
e = 0.50
step = int(math.floor(math.pow(len(points), 1.0 - e)))
minimum = float('inf')
for i in range(0, len(points), step):
d1 = self.clouddistance(points, P.points, i)
d2 = self.clouddistance(P.points, points, i)
minimum = min(minimum, min(d1, d2))
return minimum
def clouddistance(self, pts1, pts2, start):
matched = []
for k in range(0, len(pts1)):
matched.append(0)
sum = 0
i = start
while i != start:
index = -1
min = 2147483647
for j in range(0, len(matched)):
if matched[j] == 0:
d = PointCloud.PointCloud.Distance(pts1[i], pts2[j])
if d < min:
min = d
index = j
matched[index] = 1
weight = 1 - ((i - start + len(pts1)) % len(pts1)) / len(pts1)
sum = sum + weight * min
i = (i + 1) % len(pts1)
return sum
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the names of the University of Washington nor Microsoft,
nor the names of its contributors may be used to endorse or promote
products derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL Jacob O. Wobbrock OR Andrew D. Wilson
OR Yang Li BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''
from RecognizeService import Recognizer
from entity import Point
# input points
p=[Point.Point(30,7,1),Point.Point(103,7,1),Point.Point(66,7,2),Point.Point(66,87,2)]
r=Recognizer.Recognizer()
print(r.recognize(p).name)
def _generate_distance_line(self, origin, offset):
start = origin
end = [origin[0] + offset[0], origin[1] + offset[1]]
line = Line(Point(vector=start), Point(vector=end))
return line
def __init__(self, size_x, size_y):
self.walls = []
# Create slightly oversized map container
self.container = Square(Point(-1, -1), Point(size_x + 1, size_y + 1))
self.wall_sprites = pygame.sprite.Group()