def doCompute(self, drawer, landmark, figure, **args):
fPoints = list(
math2d.stepAlongLine(figure,
math2d.length(figure) / 100.0))
gPoints = [math2d.closestPointOnPolygon(landmark, p) for p in fPoints]
distances = math2d.squareDistances(fPoints, gPoints)
#[math2d.squaredist(f1, g1) for f1, g1 in zip(fPoints, gPoints)]
i, dist = math2d.argMin(distances)
fPoint = fPoints[i]
gPoint = math2d.closestPointOnPolygon(landmark, fPoint)
axes = [fPoint, gPoint]
m = {}
drawer.drawSegment("angleFigureToPastAxes", fPoint, gPoint)
bborigin, (width, height) = math2d.boundingBox(figure)
scale = pow(pow(width, 2) + pow(height, 2), 0.5)
if scale == 0:
bborigin, (width, height) = math2d.boundingBox(landmark)
scale = pow(pow(width, 2) + pow(height, 2), 0.5)
if math2d.isDegenerate(axes):
m['angleFigureToPastAxes'] = 0
else:
m['angleFigureToPastAxes'] = math2d.angleBetweenLines(
axes, [figure[0], figure[-1]])
drawer.distanceFeature(m, "pastAxesLength", "Axes Start", "Axes End",
axes[0], axes[1], scale)
return m
def nearestNeighbor(self, x_rand, tree):
nodes = [n for n in tree.nodes()]
x1, x2 = x_rand
distances = [
math2d.dist(p1, x1) + math2d.dist(p2, x2) for p1, p2 in nodes
]
minI, min = math2d.argMin(distances)
return nodes[minI]
def doCompute(self, drawer, landmark, figure, **args):
figureSteps = list(
math2d.stepAlongLine(figure,
math2d.length(figure) / 100.0))
points = [
math2d.closestPointOnPolygon(landmark, p) for p in figureSteps
]
interiorPoints = math2d.interiorPoints(landmark, figureSteps)
landmarkCentroid = math2d.centroid(landmark)
distances = [math2d.squaredist(p, (0, 0)) for p in points]
bborigin, (width,
height) = math2d.boundingBox(na.append(landmark, figure, 0))
scale = pow(pow(width, 2) + pow(height, 2), 0.5)
out = {}
i, dist = math2d.argMin(distances)
drawer.distanceFeature(out, "minimumDistanceToLandmark",
"Closest point on landmark", "Figure End",
figureSteps[i], points[i], scale)
for p in interiorPoints:
drawer.drawPoint("numInteriorPoints", p)
if len(interiorPoints) == 0:
drawer.drawText("numInteriorPoints", landmarkCentroid,
"No interior points.")
#if out["numInteriorPoints"] > 0:
#out["numInteriorPoints"] = 1
out["numInteriorPoints"] = len(interiorPoints)
if math2d.isInteriorPoint(landmark, figureSteps[i]):
out["minimumDistanceToLandmark"] = 0
if math2d.isInteriorPoint(landmark, figure[-1]):
out["distFigureEndToLandmark"] = 0
differences = [d1 - d2 for d1, d2 in zip(distances, distances[1:])]
differences = [d / d if d != 0 else 0 for d in differences]
if len(differences) == 0:
out["averageDistanceDifference"] = 0
else:
out["averageDistanceDifference"] = math2d.mean(differences)
for p1, p2 in zip(points, points[1:]):
drawer.drawLine("averageDistanceDifference", [p1, p2])
return out
def doCompute(self, drawer, ground, figure, **args):
steps = 100
fPoints = list(
math2d.stepAlongLine(figure,
math2d.length(figure) / 100))
gPoints = [math2d.closestPointOnPolygon(ground, p) for p in fPoints]
distances = [
math2d.squaredist(f1, g1) for f1, g1 in zip(fPoints, gPoints)
]
i, minDist = math2d.argMin(distances)
bborigin, (width, height) = math2d.boundingBox(ground + figure)
scale = pow(pow(width, 2) + pow(height, 2), 0.5)
featureMap = {}
drawer.distanceFeature(self, featureMap, "minimumDistanceToGround",
"Figure", "Ground", fPoints[i], gPoints[i],
scale)
for f1, g1 in zip(fPoints, gPoints):
self.drawSegment("averageDistanceToGround", f1, g1)
featureMap["averageDistanceToGround"] = math2d.mean(distances) / scale
return featureMap
def nearestNeighbor(self, x_rand, tree):
nodes = [n for n in tree.nodes()]
distances = [math2d.dist(n, x_rand) for n in nodes]
minI, min = math2d.argMin(distances)
return nodes[minI]