Mercurial Hosting > traffic-intelligence
view python/moving.py @ 744:ed6ff2ec0aeb dev
bug correction from Laurent Gauthier
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Thu, 10 Sep 2015 15:48:01 -0400 |
parents | c6d4ea05a2d0 |
children | 15ddc8715236 |
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#! /usr/bin/env python '''Libraries for moving objects, trajectories...''' import utils, cvutils from base import VideoFilenameAddable from math import sqrt, atan2, cos, sin from numpy import median, array, zeros, hypot, NaN, std, floor, float32 from matplotlib.pyplot import plot from scipy.stats import scoreatpercentile from scipy.spatial.distance import cdist try: from shapely.geometry import Polygon, Point as shapelyPoint from shapely.prepared import prep shapelyAvailable = True except ImportError: print('Shapely library could not be loaded') shapelyAvailable = False class Interval(object): '''Generic interval: a subset of real numbers (not iterable)''' def __init__(self, first=0, last=-1, revert = False): if revert and last<first: self.first=last self.last=first else: self.first=first self.last=last def __str__(self): return '[{0}, {1}]'.format(self.first, self.last) def __repr__(self): return self.__str__() def empty(self): return self.first > self.last def center(self): return (self.first+self.last)/2. def length(self): '''Returns the length of the interval''' return float(max(0,self.last-self.first)) def equal(self, i2): return self.first==i2.first and self.last == i2.last def getList(self): return [self.first, self.last] def contains(self, instant): return (self.first<=instant and self.last>=instant) def inside(self, interval2): '''Indicates if the temporal interval of self is comprised in interval2''' return (self.first >= interval2.first) and (self.last <= interval2.last) @classmethod def union(cls, interval1, interval2): '''Smallest interval comprising self and interval2''' return cls(min(interval1.first, interval2.first), max(interval2.last, interval2.last)) @classmethod def intersection(cls, interval1, interval2): '''Largest interval comprised in both self and interval2''' return cls(max(interval1.first, interval2.first), min(interval1.last, interval2.last)) def distance(self, interval2): if not Interval.intersection(self, interval2).empty(): return 0 elif self.first > interval2.last: return self.first - interval2.last elif self.last < interval2.first: return interval2.first - self.last else: return None @classmethod def unionIntervals(cls, intervals): 'returns the smallest interval containing all intervals' inter = cls(intervals[0].first, intervals[0].last) for i in intervals[1:]: inter = cls.union(inter, i) return inter class TimeInterval(Interval): '''Temporal interval: set of instants at fixed time step, between first and last, included For example: based on frame numbers (hence the modified length method) It may be modified directly by setting first and last''' def __init__(self, first=0, last=-1): super(TimeInterval, self).__init__(first, last, False) @staticmethod def fromInterval(inter): return TimeInterval(inter.first, inter.last) def __getitem__(self, i): if not self.empty(): if isinstance(i, int): return self.first+i else: raise TypeError, "Invalid argument type." #elif isinstance( key, slice ): def __iter__(self): self.iterInstantNum = -1 return self def next(self): if self.iterInstantNum >= self.length()-1: raise StopIteration else: self.iterInstantNum += 1 return self[self.iterInstantNum] def length(self): '''Returns the length of the interval''' return float(max(0,self.last-self.first+1)) def __len__(self): return self.length() # class BoundingPolygon: # '''Class for a polygon bounding a set of points # with methods to create intersection, unions... # ''' # We will use the polygon class of Shapely class STObject(object): '''Class for spatio-temporal object, i.e. with temporal and spatial existence (time interval and bounding polygon for positions (e.g. rectangle)). It may not mean that the object is defined for all time instants within the time interval''' def __init__(self, num = None, timeInterval = None, boundingPolygon = None): self.num = num self.timeInterval = timeInterval self.boundingPolygon = boundingPolygon def empty(self): return self.timeInterval.empty()# or not self.boudingPolygon def getNum(self): return self.num def __len__(self): return self.timeInterval.length() def length(self): return self.timeInterval.length() def getFirstInstant(self): return self.timeInterval.first def getLastInstant(self): return self.timeInterval.last def getTimeInterval(self): return self.timeInterval def existsAtInstant(self, t): return self.timeInterval.contains(t) def commonTimeInterval(self, obj2): return TimeInterval.intersection(self.getTimeInterval(), obj2.getTimeInterval()) class Point(object): def __init__(self, x, y): self.x = x self.y = y def __str__(self): return '({:f},{:f})'.format(self.x,self.y) def __repr__(self): return self.__str__() def __add__(self, other): return Point(self.x+other.x, self.y+other.y) def __sub__(self, other): return Point(self.x-other.x, self.y-other.y) def __neg__(self): return Point(-self.x, -self.y) def __getitem__(self, i): if i == 0: return self.x elif i == 1: return self.y else: raise IndexError() def orthogonal(self, clockwise = True): 'Returns the orthogonal vector' if clockwise: return Point(self.y, -self.x) else: return Point(-self.y, self.x) def multiply(self, alpha): 'Warning, returns a new Point' return Point(self.x*alpha, self.y*alpha) def divide(self, alpha): 'Warning, returns a new Point' return Point(self.x/alpha, self.y/alpha) def plot(self, options = 'o', **kwargs): plot([self.x], [self.y], options, **kwargs) def norm2Squared(self): '''2-norm distance (Euclidean distance)''' return self.x**2+self.y**2 def norm2(self): '''2-norm distance (Euclidean distance)''' return sqrt(self.norm2Squared()) def norm1(self): return abs(self.x)+abs(self.y) def normMax(self): return max(abs(self.x),abs(self.y)) def aslist(self): return [self.x, self.y] def astuple(self): return (self.x, self.y) def asint(self): return Point(int(self.x), int(self.y)) if shapelyAvailable: def asShapely(self): return shapelyPoint(self.x, self.y) def project(self, homography): projected = cvutils.projectArray(homography, array([[self.x], [self.y]])) return Point(projected[0], projected[1]) def inPolygon(self, polygon): '''Indicates if the point x, y is inside the polygon (array of Nx2 coordinates of the polygon vertices) taken from http://www.ariel.com.au/a/python-point-int-poly.html Use Polygon.contains if Shapely is installed''' n = polygon.shape[0]; counter = 0; p1 = polygon[0,:]; for i in range(n+1): p2 = polygon[i % n,:]; if self.y > min(p1[1],p2[1]): if self.y <= max(p1[1],p2[1]): if self.x <= max(p1[0],p2[0]): if p1[1] != p2[1]: xinters = (self.y-p1[1])*(p2[0]-p1[0])/(p2[1]-p1[1])+p1[0]; if p1[0] == p2[0] or self.x <= xinters: counter+=1; p1=p2 return (counter%2 == 1); @staticmethod def fromList(p): return Point(p[0], p[1]) @staticmethod def dot(p1, p2): 'Scalar product' return p1.x*p2.x+p1.y*p2.y @staticmethod def cross(p1, p2): 'Cross product' return p1.x*p2.y-p1.y*p2.x @staticmethod def cosine(p1, p2): return Point.dot(p1,p2)/(p1.norm2()*p2.norm2()) @staticmethod def distanceNorm2(p1, p2): return (p1-p2).norm2() @staticmethod def plotAll(points, **kwargs): from matplotlib.pyplot import scatter scatter([p.x for p in points],[p.y for p in points], **kwargs) def similarOrientation(self, refDirection, cosineThreshold): 'Indicates whether the cosine of the vector and refDirection is smaller than cosineThreshold' return Point.cosine(self, refDirection) >= cosineThreshold @staticmethod def timeToCollision(p1, p2, v1, v2, collisionThreshold): '''Computes exact time to collision with a distance threshold The unknown of the equation is the time to reach the intersection between the relative trajectory of one road user and the circle of radius collisionThreshold around the other road user''' dv = v1-v2 dp = p1-p2 a = dv.norm2Squared()#(v1.x-v2.x)**2 + (v1.y-v2.y)**2 b = 2*Point.dot(dv, dp)#2 * ((p1.x-p2.x) * (v1.x-v2.x) + (p1.y-p2.y) * (v1.y-v2.y)) c = dp.norm2Squared() - collisionThreshold**2#(p1.x-p2.x)**2 + (p1.y-p2.y)**2 - collisionThreshold**2 delta = b**2 - 4*a*c if delta >= 0: deltaRoot = sqrt(delta) ttc1 = (-b + deltaRoot)/(2*a) ttc2 = (-b - deltaRoot)/(2*a) if ttc1 >= 0 and ttc2 >= 0: ttc = min(ttc1,ttc2) elif ttc1 >= 0: ttc = ttc1 elif ttc2 >= 0: ttc = ttc2 else: # ttc1 < 0 and ttc2 < 0: ttc = None else: ttc = None return ttc @staticmethod def midPoint(p1, p2): 'Returns the middle of the segment [p1, p2]' return Point(0.5*p1.x+0.5*p2.x, 0.5*p1.y+0.5*p2.y) if shapelyAvailable: def pointsInPolygon(points, polygon): '''Optimized tests of a series of points within (Shapely) polygon ''' prepared_polygon = prep(polygon) return filter(prepared_polygon.contains, points) # Functions for coordinate transformation # From Paul St-Aubin's PVA tools def subsec_spline_dist(splines): ''' Prepare list of spline subsegments from a spline list. Output: ======= ss_spline_d[spline #][mode][station] where: mode=0: incremental distance mode=1: cumulative distance mode=2: cumulative distance with trailing distance ''' ss_spline_d = [] #Prepare subsegment distances for spline in range(len(splines)): ss_spline_d.append([[],[],[]]) ss_spline_d[spline][0] = zeros(len(splines[spline])-1) #Incremental distance ss_spline_d[spline][1] = zeros(len(splines[spline])-1) #Cumulative distance ss_spline_d[spline][2] = zeros(len(splines[spline])) #Cumulative distance with trailing distance for spline_p in range(len(splines[spline])): if spline_p > (len(splines[spline]) - 2): break ss_spline_d[spline][0][spline_p] = utils.pointDistanceL2(splines[spline][spline_p][0],splines[spline][spline_p][1],splines[spline][(spline_p+1)][0],splines[spline][(spline_p+1)][1]) ss_spline_d[spline][1][spline_p] = sum(ss_spline_d[spline][0][0:spline_p]) ss_spline_d[spline][2][spline_p] = ss_spline_d[spline][1][spline_p]#sum(ss_spline_d[spline][0][0:spline_p]) ss_spline_d[spline][2][-1] = ss_spline_d[spline][2][-2] + ss_spline_d[spline][0][-1] return ss_spline_d def ppldb2p(qx,qy, p0x,p0y, p1x,p1y): ''' Point-projection (Q) on line defined by 2 points (P0,P1). http://cs.nyu.edu/~yap/classes/visual/03s/hw/h2/math.pdf ''' if(p0x == p1x and p0y == p1y): return None try: #Approximate slope singularity by giving some slope roundoff; account for roundoff error if(round(p0x, 10) == round(p1x, 10)): p1x += 0.0000000001 if(round(p0y, 10) == round(p1y, 10)): p1y += 0.0000000001 #make the calculation Y = (-(qx)*(p0y-p1y)-(qy*(p0y-p1y)**2)/(p0x-p1x)+p0x**2*(p0y-p1y)/(p0x-p1x)-p0x*p1x*(p0y-p1y)/(p0x-p1x)-p0y*(p0x-p1x))/(p1x-p0x-(p0y-p1y)**2/(p0x-p1x)) X = (-Y*(p1y-p0y)+qx*(p1x-p0x)+qy*(p1y-p0y))/(p1x-p0x) except ZeroDivisionError: print('Error: Division by zero in ppldb2p. Please report this error with the full traceback:') print('qx={0}, qy={1}, p0x={2}, p0y={3}, p1x={4}, p1y={5}...'.format(qx, qy, p0x, p0y, p1x, p1y)) import pdb; pdb.set_trace() return Point(X,Y) def getSYfromXY(p, splines, goodEnoughSplineDistance = 0.5): ''' Snap a point p to it's nearest subsegment of it's nearest spline (from the list splines). A spline is a list of points (class Point), most likely a trajectory. Output: ======= [spline index, subsegment leading point index, snapped point, subsegment distance, spline distance, orthogonal point offset] or None ''' minOffsetY = float('inf') #For each spline for spline in range(len(splines)): #For each spline point index for spline_p in range(len(splines[spline])-1): #Get closest point on spline closestPoint = ppldb2p(p.x,p.y,splines[spline][spline_p][0],splines[spline][spline_p][1],splines[spline][spline_p+1][0],splines[spline][spline_p+1][1]) if closestPoint is None: print('Error: Spline {0}, segment {1} has identical bounds and therefore is not a vector. Projection cannot continue.'.format(spline, spline_p)) return None # check if the if utils.inBetween(splines[spline][spline_p][0], splines[spline][spline_p+1][0], closestPoint.x) and utils.inBetween(splines[spline][spline_p][1], splines[spline][spline_p+1][1], closestPoint.y): offsetY = Point.distanceNorm2(closestPoint, p) if offsetY < minOffsetY: minOffsetY = offsetY snappedSpline = spline snappedSplineLeadingPoint = spline_p snappedPoint = Point(closestPoint.x, closestPoint.y) #Jump loop if significantly close if offsetY < goodEnoughSplineDistance: break #Get sub-segment distance if minOffsetY != float('inf'): subsegmentDistance = Point.distanceNorm2(snappedPoint, splines[snappedSpline][snappedSplineLeadingPoint]) #Get cumulative alignment distance (total segment distance) splineDistanceS = splines[snappedSpline].getCumulativeDistance(snappedSplineLeadingPoint) + subsegmentDistance orthogonalSplineVector = (splines[snappedSpline][snappedSplineLeadingPoint+1]-splines[snappedSpline][snappedSplineLeadingPoint]).orthogonal() offsetVector = p-snappedPoint if Point.dot(orthogonalSplineVector, offsetVector) < 0: minOffsetY = -minOffsetY return [snappedSpline, snappedSplineLeadingPoint, snappedPoint, subsegmentDistance, splineDistanceS, minOffsetY] else: return None def getXYfromSY(s, y, splineNum, splines, mode = 0): ''' Find X,Y coordinate from S,Y data. if mode = 0 : return Snapped X,Y if mode !=0 : return Real X,Y ''' #(buckle in, it gets ugly from here on out) ss_spline_d = subsec_spline_dist(splines) #Find subsegment snapped_x = None snapped_y = None for spline_ss_index in range(len(ss_spline_d[splineNum][1])): if(s < ss_spline_d[splineNum][1][spline_ss_index]): ss_value = s - ss_spline_d[splineNum][1][spline_ss_index-1] #Get normal vector and then snap vector_l_x = (splines[splineNum][spline_ss_index][0] - splines[splineNum][spline_ss_index-1][0]) vector_l_y = (splines[splineNum][spline_ss_index][1] - splines[splineNum][spline_ss_index-1][1]) magnitude = sqrt(vector_l_x**2 + vector_l_y**2) n_vector_x = vector_l_x/magnitude n_vector_y = vector_l_y/magnitude snapped_x = splines[splineNum][spline_ss_index-1][0] + ss_value*n_vector_x snapped_y = splines[splineNum][spline_ss_index-1][1] + ss_value*n_vector_y #Real values (including orthogonal projection of y)) real_x = snapped_x - y*n_vector_y real_y = snapped_y + y*n_vector_x break if mode == 0 or (not snapped_x): if(not snapped_x): snapped_x = splines[splineNum][-1][0] snapped_y = splines[splineNum][-1][1] return [snapped_x,snapped_y] else: return [real_x,real_y] class NormAngle(object): '''Alternate encoding of a point, by its norm and orientation''' def __init__(self, norm, angle): self.norm = norm self.angle = angle @staticmethod def fromPoint(p): norm = p.norm2() if norm > 0: angle = atan2(p.y, p.x) else: angle = 0. return NormAngle(norm, angle) def __add__(self, other): 'a norm cannot become negative' return NormAngle(max(self.norm+other.norm, 0), self.angle+other.angle) def getPoint(self): return Point(self.norm*cos(self.angle), self.norm*sin(self.angle)) def predictPositionNoLimit(nTimeSteps, initialPosition, initialVelocity, initialAcceleration = Point(0,0)): '''Predicts the position in nTimeSteps at constant speed/acceleration''' return initialVelocity + initialAcceleration.multiply(nTimeSteps),initialPosition+initialVelocity.multiply(nTimeSteps) + initialAcceleration.multiply(nTimeSteps**2*0.5) def predictPosition(position, speedOrientation, control, maxSpeed = None): '''Predicts the position (moving.Point) at the next time step with given control input (deltaSpeed, deltaTheta) speedOrientation is the other encoding of velocity, (speed, orientation) speedOrientation and control are NormAngle''' predictedSpeedTheta = speedOrientation+control if maxSpeed: predictedSpeedTheta.norm = min(predictedSpeedTheta.norm, maxSpeed) predictedPosition = position+predictedSpeedTheta.getPoint() return predictedPosition, predictedSpeedTheta class FlowVector(object): '''Class to represent 4-D flow vectors, ie a position and a velocity''' def __init__(self, position, velocity): 'position and velocity should be Point instances' self.position = position self.velocity = velocity def __add__(self, other): return FlowVector(self.position+other.position, self.velocity+other.velocity) def multiply(self, alpha): return FlowVector(self.position.multiply(alpha), self.velocity.multiply(alpha)) def plot(self, options = '', **kwargs): plot([self.position.x, self.position.x+self.velocity.x], [self.position.y, self.position.y+self.velocity.y], options, **kwargs) self.position.plot(options+'x', **kwargs) @staticmethod def similar(f1, f2, maxDistance2, maxDeltavelocity2): return (f1.position-f2.position).norm2Squared()<maxDistance2 and (f1.velocity-f2.velocity).norm2Squared()<maxDeltavelocity2 def intersection(p1, p2, p3, p4): ''' Intersection point (x,y) of lines formed by the vectors p1-p2 and p3-p4 http://paulbourke.net/geometry/pointlineplane/''' dp12 = p2-p1 dp34 = p4-p3 #det = (p4.y-p3.y)*(p2.x-p1.x)-(p4.x-p3.x)*(p2.y-p1.y) det = float(dp34.y*dp12.x-dp34.x*dp12.y) if det == 0.: return None else: ua = (dp34.x*(p1.y-p3.y)-dp34.y*(p1.x-p3.x))/det return p1+dp12.multiply(ua) # def intersection(p1, p2, dp1, dp2): # '''Returns the intersection point between the two lines # defined by the respective vectors (dp) and origin points (p)''' # from numpy import matrix # from numpy.linalg import linalg # A = matrix([[dp1.y, -dp1.x], # [dp2.y, -dp2.x]]) # B = matrix([[dp1.y*p1.x-dp1.x*p1.y], # [dp2.y*p2.x-dp2.x*p2.y]]) # if linalg.det(A) == 0: # return None # else: # intersection = linalg.solve(A,B) # return Point(intersection[0,0], intersection[1,0]) def segmentIntersection(p1, p2, p3, p4): '''Returns the intersecting point of the segments [p1, p2] and [p3, p4], None otherwise''' if (Interval.intersection(Interval(p1.x,p2.x,True), Interval(p3.x,p4.x,True)).empty()) or (Interval.intersection(Interval(p1.y,p2.y,True), Interval(p3.y,p4.y,True)).empty()): return None else: inter = intersection(p1, p2, p3, p4) if (inter is not None and utils.inBetween(p1.x, p2.x, inter.x) and utils.inBetween(p3.x, p4.x, inter.x) and utils.inBetween(p1.y, p2.y, inter.y) and utils.inBetween(p3.y, p4.y, inter.y)): return inter else: return None def segmentLineIntersection(p1, p2, p3, p4): '''Indicates if the line going through p1 and p2 intersects inside p3, p4''' inter = intersection(p1, p2, p3, p4) if inter is not None and utils.inBetween(p3.x, p4.x, inter.x) and utils.inBetween(p3.y, p4.y, inter.y): return inter else: return None class Trajectory(object): '''Class for trajectories: temporal sequence of positions The class is iterable''' def __init__(self, positions=None): if positions is not None: self.positions = positions else: self.positions = [[],[]] @staticmethod def generate(p, v, nPoints): t = Trajectory() p0 = Point(p.x, p.y) t.addPosition(p0) for i in xrange(nPoints-1): p0 += v t.addPosition(p0) return t, Trajectory([[v.x]*nPoints, [v.y]*nPoints]) @staticmethod def load(line1, line2): return Trajectory([[float(n) for n in line1.split(' ')], [float(n) for n in line2.split(' ')]]) @staticmethod def fromPointList(points): t = Trajectory() if isinstance(points[0], list) or isinstance(points[0], tuple): for p in points: t.addPositionXY(p[0],p[1]) else: for p in points: t.addPosition(p) return t def __len__(self): return len(self.positions[0]) def length(self): return self.__len__() def empty(self): return self.__len__() == 0 def __getitem__(self, i): if isinstance(i, int): return Point(self.positions[0][i], self.positions[1][i]) else: raise TypeError, "Invalid argument type." #elif isinstance( key, slice ): def __str__(self): return ' '.join([self.__getitem__(i).__str__() for i in xrange(self.length())]) def __repr__(self): return self.__str__() def __iter__(self): self.iterInstantNum = 0 return self def next(self): if self.iterInstantNum >= self.length(): raise StopIteration else: self.iterInstantNum += 1 return self[self.iterInstantNum-1] def setPositionXY(self, i, x, y): if i < self.__len__(): self.positions[0][i] = x self.positions[1][i] = y def setPosition(self, i, p): self.setPositionXY(i, p.x, p.y) def addPositionXY(self, x, y): self.positions[0].append(x) self.positions[1].append(y) def addPosition(self, p): self.addPositionXY(p.x, p.y) def duplicateLastPosition(self): self.positions[0].append(self.positions[0][-1]) self.positions[1].append(self.positions[1][-1]) @staticmethod def _plot(positions, options = '', withOrigin = False, lastCoordinate = None, timeStep = 1, **kwargs): if lastCoordinate is None: plot(positions[0][::timeStep], positions[1][::timeStep], options, **kwargs) elif 0 <= lastCoordinate <= len(positions[0]): plot(positions[0][:lastCoordinate:timeStep], positions[1][:lastCoordinate:timeStep], options, **kwargs) if withOrigin: plot([positions[0][0]], [positions[1][0]], 'ro', **kwargs) def project(self, homography): return Trajectory(cvutils.projectTrajectory(homography, self.positions).tolist()) def plot(self, options = '', withOrigin = False, timeStep = 1, **kwargs): Trajectory._plot(self.positions, options, withOrigin, None, timeStep, **kwargs) def plotAt(self, lastCoordinate, options = '', withOrigin = False, timeStep = 1, **kwargs): Trajectory._plot(self.positions, options, withOrigin, lastCoordinate, timeStep, **kwargs) def plotOnWorldImage(self, nPixelsPerUnitDistance, options = '', withOrigin = False, timeStep = 1, **kwargs): imgPositions = [[x*nPixelsPerUnitDistance for x in self.positions[0]], [x*nPixelsPerUnitDistance for x in self.positions[1]]] Trajectory._plot(imgPositions, options, withOrigin, None, timeStep, **kwargs) def getXCoordinates(self): return self.positions[0] def getYCoordinates(self): return self.positions[1] def asArray(self): return array(self.positions) def xBounds(self): # look for function that does min and max in one pass return Interval(min(self.getXCoordinates()), max(self.getXCoordinates())) def yBounds(self): # look for function that does min and max in one pass return Interval(min(self.getYCoordinates()), max(self.getYCoordinates())) def add(self, traj2): '''Returns a new trajectory of the same length''' if self.length() != traj2.length(): print 'Trajectories of different lengths' return None else: return Trajectory([[a+b for a,b in zip(self.getXCoordinates(),traj2.getXCoordinates())], [a+b for a,b in zip(self.getYCoordinates(),traj2.getYCoordinates())]]) def subtract(self, traj2): '''Returns a new trajectory of the same length''' if self.length() != traj2.length(): print 'Trajectories of different lengths' return None else: return Trajectory([[a-b for a,b in zip(self.getXCoordinates(),traj2.getXCoordinates())], [a-b for a,b in zip(self.getYCoordinates(),traj2.getYCoordinates())]]) def multiply(self, alpha): '''Returns a new trajectory of the same length''' return Trajectory([[alpha*x for x in self.getXCoordinates()], [alpha*y for y in self.getYCoordinates()]]) def differentiate(self, doubleLastPosition = False): diff = Trajectory() for i in xrange(1, self.length()): diff.addPosition(self[i]-self[i-1]) if doubleLastPosition: diff.addPosition(diff[-1]) return diff def norm(self): '''Returns the list of the norms at each instant''' # def add(x, y): return x+y # sq = map(add, [x*x for x in self.positions[0]], [y*y for y in self.positions[1]]) # return sqrt(sq) return hypot(self.positions[0], self.positions[1]) # def cumulatedDisplacement(self): # 'Returns the sum of the distances between each successive point' # displacement = 0 # for i in xrange(self.length()-1): # displacement += Point.distanceNorm2(self.__getitem__(i),self.__getitem__(i+1)) # return displacement def computeCumulativeDistances(self): '''Computes the distance from each point to the next and the cumulative distance up to the point Can be accessed through getDistance(idx) and getCumulativeDistance(idx)''' self.distances = [] self.cumulativeDistances = [0.] p1 = self[0] cumulativeDistance = 0. for i in xrange(self.length()-1): p2 = self[i+1] self.distances.append(Point.distanceNorm2(p1,p2)) cumulativeDistance += self.distances[-1] self.cumulativeDistances.append(cumulativeDistance) p1 = p2 def getDistance(self,i): '''Return the distance between points i and i+1''' if i < self.length()-1: return self.distances[i] else: print('Index {} beyond trajectory length {}-1'.format(i, self.length())) def getCumulativeDistance(self, i): '''Return the cumulative distance between the beginning and point i''' if i < self.length(): return self.cumulativeDistances[i] else: print('Index {} beyond trajectory length {}'.format(i, self.length())) def similarOrientation(self, refDirection, cosineThreshold, minProportion = 0.5): '''Indicates whether the minProportion (<=1.) (eg half) of the trajectory elements (vectors for velocity) have a cosine with refDirection is smaller than cosineThreshold''' count = 0 lengthThreshold = float(self.length())*minProportion for p in self: if p.similarOrientation(refDirection, cosineThreshold): count += 1 return count >= lengthThreshold def wiggliness(self): return self.getCumulativeDistance(self.length()-1)/float(Point.distanceNorm2(self.__getitem__(0),self.__getitem__(self.length()-1))) def getIntersections(self, p1, p2): '''Returns a list of the indices at which the trajectory intersects with the segment of extremities p1 and p2 the list is empty if there is no crossing''' indices = [] intersections = [] for i in xrange(self.length()-1): q1=self.__getitem__(i) q2=self.__getitem__(i+1) p = utils.segmentIntersection(q1, q2, p1, p2) if p is not None: if q1.x != q2.x: ratio = (p.x-q1.x)/(q2.x-q1.x) elif q1.y != q2.y: ratio = (p.y-q1.y)/(q2.y-q1.y) else: ratio = 0 indices.append(i+ratio) intersections.append(p) return indices def getLineIntersections(self, p1, p2): '''Returns a list of the indices at which the trajectory intersects with the segment of extremities p1 and p2 the list is empty if there is no crossing''' indices = [] intersections = [] for i in xrange(self.length()-1): q1=self.__getitem__(i) q2=self.__getitem__(i+1) p = utils.segmentLineIntersection(p1, p2, q1, q2) if p is not None: if q1.x != q2.x: ratio = (p.x-q1.x)/(q2.x-q1.x) elif q1.y != q2.y: ratio = (p.y-q1.y)/(q2.y-q1.y) else: ratio = 0 indices.append(i+ratio) intersections.append(p) return indices, intersections def getTrajectoryInInterval(self, inter): 'Returns all position between index inter.first and index.last (included)' if inter.first >=0 and inter.last<= self.length(): return Trajectory([self.positions[0][inter.first:inter.last+1], self.positions[1][inter.first:inter.last+1]]) else: return None if shapelyAvailable: def getTrajectoryInPolygon(self, polygon): '''Returns the trajectory built with the set of points inside the (shapely) polygon''' traj = Trajectory() points = [p.asShapely() for p in self] for p in pointsInPolygon(points, polygon): traj.addPositionXY(p.x, p.y) return traj def proportionInPolygon(self, polygon, minProportion = 0.5): pointsIn = pointsInPolygon([p.asShapely() for p in self], polygon) lengthThreshold = float(self.length())*minProportion return len(pointsIn) >= lengthThreshold else: def getTrajectoryInPolygon(self, polygon): '''Returns the trajectory built with the set of points inside the polygon (array of Nx2 coordinates of the polygon vertices)''' traj = Trajectory() for p in self: if p.inPolygon(polygon): traj.addPosition(p) return traj def proportionInPolygon(self, polygon, minProportion = 0.5): pointsInPolygon = [p.inPolygon(polygon) for p in self] lengthThreshold = float(self.length())*minProportion return len(pointsInPolygon) >= lengthThreshold @staticmethod def lcss(t1, t2, lcss): return lcss.compute(t1, t2) class CurvilinearTrajectory(Trajectory): '''Sub class of trajectory for trajectories with curvilinear coordinates and lane assignements longitudinal coordinate is stored as first coordinate (exterior name S) lateral coordiante is stored as second coordinate''' def __init__(self, S = None, Y = None, lanes = None): if S is None or Y is None or len(S) != len(Y): self.positions = [[],[]] if S is not None and Y is not None and len(S) != len(Y): print("S and Y coordinates of different lengths\nInitializing to empty lists") else: self.positions = [S,Y] if lanes is None or len(lanes) != self.length(): self.lanes = [] else: self.lanes = lanes def __getitem__(self,i): if isinstance(i, int): return [self.positions[0][i], self.positions[1][i], self.lanes[i]] else: raise TypeError, "Invalid argument type." #elif isinstance( key, slice ): def getSCoordinates(self): return self.getXCoordinates() def getLanes(self): return self.lanes def addPositionSYL(self, s, y, lane): self.addPositionXY(s,y) self.lanes.append(lane) def addPosition(self, p): 'Adds position in the point format for curvilinear of list with 3 values' self.addPositionSYL(p[0], p[1], p[2]) def setPosition(self, i, s, y, lane): self.setPositionXY(i, s, y) if i < self.__len__(): self.lanes[i] = lane def differentiate(self, doubleLastPosition = False): diff = CurvilinearTrajectory() p1 = self[0] for i in xrange(1, self.length()): p2 = self[i] diff.addPositionSYL(p2[0]-p1[0], p2[1]-p1[1], p1[2]) p1=p2 if doubleLastPosition and self.length() > 1: diff.addPosition(diff[-1]) return diff def getIntersections(self, S1, lane = None): '''Returns a list of the indices at which the trajectory goes past the curvilinear coordinate S1 (in provided lane if lane is not None) the list is empty if there is no crossing''' indices = [] for i in xrange(self.length()-1): q1=self.__getitem__(i) q2=self.__getitem__(i+1) if q1[0] <= S1 < q2[0] and (lane is None or (self.lanes[i] == lane and self.lanes[i+1] == lane)): indices.append(i+(S1-q1[0])/(q2[0]-q1[0])) return indices ################## # Moving Objects ################## userTypeNames = ['unknown', 'car', 'pedestrian', 'motorcycle', 'bicycle', 'bus', 'truck'] userType2Num = utils.inverseEnumeration(userTypeNames) class MovingObject(STObject, VideoFilenameAddable): '''Class for moving objects: a spatio-temporal object with a trajectory and a geometry (constant volume over time) and a usertype (e.g. road user) coded as a number (see userTypeNames) ''' def __init__(self, num = None, timeInterval = None, positions = None, velocities = None, geometry = None, userType = userType2Num['unknown']): super(MovingObject, self).__init__(num, timeInterval) self.positions = positions self.velocities = velocities self.geometry = geometry self.userType = userType self.features = None # compute bounding polygon from trajectory @staticmethod def generate(p, v, timeInterval): positions, velocities = Trajectory.generate(p, v, int(timeInterval.length())) return MovingObject(timeInterval = timeInterval, positions = positions, velocities = velocities) @staticmethod def concatenate(obj1, obj2, num = None): '''Concatenates two objects supposed to overlap temporally ''' commonTimeInterval = obj1.commonTimeInterval(obj2) if commonTimeInterval.empty(): print('The two objects\' time intervals do not overlap: obj1 {} and obj2 {}'.format(obj1.getTimeInterval(), obj2.getTimeInterval())) return None else: if num is None: newNum = obj1.getNum() else: newNum = num newTimeInterval = TimeInterval.union(obj1.getTimeInterval(), obj2.getTimeInterval()) # positions positions = Trajectory() for t in newTimeInterval: nTotal = 0. p = Point(0.,0.) for obj in [obj1, obj2]: if obj.existsAtInstant(t): if obj.hasFeatures(): n = len([f for f in obj.getFeatures() if f.existsAtInstant(t)]) else: n = 1. p += obj.getPositionAtInstant(t).multiply(n) nTotal += n assert nTotal>0, 'there should be at least one point for each instant' positions.addPosition(p.divide(nTotal)) # velocities: if any if hasattr(obj1, 'velocities') and hasattr(obj2, 'velocities'): velocities = Trajectory() for t in newTimeInterval: nTotal = 0. p = Point(0.,0.) for obj in [obj1, obj2]: if obj.existsAtInstant(t): if obj.hasFeatures(): n = len([f for f in obj.getFeatures() if f.existsAtInstant(t)]) else: n = 1. p += obj.getVelocityAtInstant(t).multiply(n) nTotal += n assert n>0, 'there should be at least one point for each instant' velocities.addPosition(p.divide(nTotal)) else: velocities = None # TODO object envelop (polygon) # user type if obj1.getUserType() != obj2.getUserType(): print('The two moving objects have different user types: obj1 {} obj2 {}'.format(userTypeNames[obj1.getUserType()], userTypeNames[obj2.getUserType()])) newObject = MovingObject(newNum, newTimeInterval, positions, velocities, userType = obj1.getUserType()) if obj1.hasFeatures() and obj2.hasFeatures(): newObject.features = obj1.getFeatures()+obj2.getFeatures() return newObject def getObjectInTimeInterval(self, inter): '''Returns a new object extracted from self, restricted to time interval inter''' intersection = TimeInterval.intersection(inter, self.getTimeInterval()) if not intersection.empty(): trajectoryInterval = TimeInterval(intersection.first-self.getFirstInstant(), intersection.last-self.getFirstInstant()) obj = MovingObject(self.num, intersection, self.positions.getTrajectoryInInterval(trajectoryInterval), self.geometry, self.userType) if self.velocities: obj.velocities = self.velocities.getTrajectoryInInterval(trajectoryInterval) return obj else: print 'The object does not exist at '+str(inter) return None def getObjectsInMask(self, mask, homography = None, minLength = 1): '''Returns new objects made of the positions in the mask mask is in the destination of the homography space''' if homography is not None: self.projectedPositions = self.positions.project(homography) else: self.projectedPositions = self.positions def inMask(positions, i, mask): p = positions[i] return mask[p.y, p.x] != 0. #subTimeIntervals self.getFirstInstant()+i filteredIndices = [inMask(self.projectedPositions, i, mask) for i in range(int(self.length()))] # 'connected components' in subTimeIntervals l = 0 intervalLabels = [] prev = True for i in filteredIndices: if i: if not prev: # new interval l += 1 intervalLabels.append(l) else: intervalLabels.append(-1) prev = i intervalLabels = array(intervalLabels) subObjects = [] for l in set(intervalLabels): if l >= 0: if sum(intervalLabels == l) >= minLength: times = [self.getFirstInstant()+i for i in range(len(intervalLabels)) if intervalLabels[i] == l] subTimeInterval = TimeInterval(min(times), max(times)) subObjects.append(self.getObjectInTimeInterval(subTimeInterval)) return subObjects def getPositions(self): return self.positions def getVelocities(self): return self.velocities def getUserType(self): return self.userType def getCurvilinearPositions(self): if hasattr(self, 'curvilinearPositions'): return self.curvilinearPositions else: return None def plotCurvilinearPositions(self, lane = None, options = '', withOrigin = False, **kwargs): if hasattr(self, 'curvilinearPositions'): if lane is None: plot(list(self.getTimeInterval()), self.curvilinearPositions.positions[0], options, **kwargs) if withOrigin: plot([self.getFirstInstant()], [self.curvilinearPositions.positions[0][0]], 'ro', **kwargs) else: instants = [] coords = [] for t, p in zip(self.getTimeInterval(), self.curvilinearPositions): if p[2] == lane: instants.append(t) coords.append(p[0]) else: instants.append(NaN) coords.append(NaN) plot(instants, coords, options, **kwargs) if withOrigin and len(instants)>0: plot([instants[0]], [coords[0]], 'ro', **kwargs) else: print('Object {} has no curvilinear positions'.format(self.getNum())) def setUserType(self, userType): self.userType = userType def setFeatures(self, features): self.features = [features[i] for i in self.featureNumbers] def getFeatures(self): return self.features def hasFeatures(self): return (self.features is not None) def getFeature(self, i): if self.hasFeatures() and i<len(self.features): return self.features[i] else: return None def getFeatureNumbers(self): '''Returns the number of features at each instant dict instant -> number of features''' if self.hasFeatures(): featureNumbers = {} for t in self.getTimeInterval(): n = 0 for f in self.getFeatures(): if f.existsAtInstant(t): n += 1 featureNumbers[t]=n return featureNumbers else: print('Object {} has no features loaded.'.format(self.getNum())) return None def getSpeeds(self, nInstantsIgnoredAtEnds = 0): speeds = self.getVelocities().norm() if nInstantsIgnoredAtEnds > 0: n = min(nInstantsIgnoredAtEnds, int(floor(self.length()/2.))) return speeds[n:-n] else: return speeds def getSpeedIndicator(self): from indicators import SeverityIndicator return SeverityIndicator('Speed', {t:self.getVelocityAtInstant(t).norm2() for t in self.getTimeInterval()}) def getPositionAt(self, i): return self.positions[i] def getVelocityAt(self, i): return self.velocities[i] def getPositionAtInstant(self, i): return self.positions[i-self.getFirstInstant()] def getVelocityAtInstant(self, i): return self.velocities[i-self.getFirstInstant()] def getXCoordinates(self): return self.positions.getXCoordinates() def getYCoordinates(self): return self.positions.getYCoordinates() def plot(self, options = '', withOrigin = False, timeStep = 1, withFeatures = False, **kwargs): if withFeatures and self.hasFeatures(): for f in self.getFeatures(): f.positions.plot('r', True, timeStep, **kwargs) self.positions.plot('bx-', True, timeStep, **kwargs) else: self.positions.plot(options, withOrigin, timeStep, **kwargs) def plotOnWorldImage(self, nPixelsPerUnitDistance, options = '', withOrigin = False, timeStep = 1, **kwargs): self.positions.plotOnWorldImage(nPixelsPerUnitDistance, options, withOrigin, timeStep, **kwargs) def play(self, videoFilename, homography = None, undistort = False, intrinsicCameraMatrix = None, distortionCoefficients = None, undistortedImageMultiplication = 1.): cvutils.displayTrajectories(videoFilename, [self], homography = homography, firstFrameNum = self.getFirstInstant(), lastFrameNumArg = self.getLastInstant(), undistort = undistort, intrinsicCameraMatrix = intrinsicCameraMatrix, distortionCoefficients = distortionCoefficients, undistortedImageMultiplication = undistortedImageMultiplication) def speedDiagnostics(self, framerate = 1., display = False): speeds = framerate*self.getSpeeds() coef = utils.linearRegression(range(len(speeds)), speeds) print('min/5th perc speed: {} / {}\nspeed diff: {}\nspeed stdev: {}\nregression: {}'.format(min(speeds), scoreatpercentile(speeds, 5), speeds[-2]-speeds[1], std(speeds), coef[0])) if display: from matplotlib.pyplot import figure, axis figure(1) self.plot() axis('equal') figure(2) plot(list(self.getTimeInterval()), speeds) @staticmethod def minMaxDistance(obj1, obj2): '''Computes the min max distance used for feature grouping''' commonTimeInterval = obj1.commonTimeInterval(obj2) if not commonTimeInterval.empty(): minDistance = (obj1.getPositionAtInstant(commonTimeInterval.first)-obj2.getPositionAtInstant(commonTimeInterval.first)).norm2() maxDistance = minDistance for t in list(commonTimeInterval)[1:]: d = (obj1.getPositionAtInstant(t)-obj2.getPositionAtInstant(t)).norm2() if d<minDistance: minDistance = d elif d>maxDistance: maxDistance = d return int(commonTimeInterval.length()), minDistance, maxDistance else: return int(commonTimeInterval.length()), None, None @staticmethod def distances(obj1, obj2, instant1, _instant2 = None): '''Returns the distances between all features of the 2 objects at the same instant instant1 or at instant1 and instant2''' if _instant2 is None: instant2 = instant1 else: instant2 = _instant2 positions1 = [f.getPositionAtInstant(instant1).astuple() for f in obj1.features if f.existsAtInstant(instant1)] positions2 = [f.getPositionAtInstant(instant2).astuple() for f in obj2.features if f.existsAtInstant(instant2)] return cdist(positions1, positions2, metric = 'euclidean') @staticmethod def minDistance(obj1, obj2, instant1, instant2 = None): return MovingObject.distances(obj1, obj2, instant1, instant2).min() @staticmethod def maxDistance(obj1, obj2, instant, instant2 = None): return MovingObject.distances(obj1, obj2, instant1, instant2).max() def maxSize(self): '''Returns the max distance between features at instant there are the most features''' if hasattr(self, 'features'): nFeatures = -1 tMaxFeatures = 0 for t in self.getTimeInterval(): n = len([f for f in self.features if f.existsAtInstant(t)]) if n > nFeatures: nFeatures = n tMaxFeatures = t return MovingObject.maxDistance(self, self, tMaxFeatures) else: print('Load features to compute a maximum size') return None def setRoutes(self, startRouteID, endRouteID): self.startRouteID = startRouteID self.endRouteID = endRouteID def getInstantsCrossingLane(self, p1, p2): '''Returns the instant(s) at which the object passes from one side of the segment to the other empty list if there is no crossing''' indices, intersections = self.positions.getIntersections(p1, p2) return [t+self.getFirstInstant() for t in indices] @staticmethod def computePET(obj1, obj2, collisionDistanceThreshold): '''Post-encroachment time based on distance threshold Returns the smallest time difference when the object positions are within collisionDistanceThreshold''' #for i in xrange(int(obj1.length())-1): # for j in xrange(int(obj2.length())-1): # inter = segmentIntersection(obj1.getPositionAt(i), obj1.getPositionAt(i+1), obj2.getPositionAt(i), obj2.getPositionAt(i+1)) positions1 = [p.astuple() for p in obj1.getPositions()] positions2 = [p.astuple() for p in obj2.getPositions()] pets = zeros((int(obj1.length()), int(obj2.length()))) for i,t1 in enumerate(obj1.getTimeInterval()): for j,t2 in enumerate(obj2.getTimeInterval()): pets[i,j] = abs(t1-t2) distances = cdist(positions1, positions2, metric = 'euclidean') if distances.min() <= collisionDistanceThreshold: return pets[distances <= collisionDistanceThreshold].min() else: return None def predictPosition(self, instant, nTimeSteps, externalAcceleration = Point(0,0)): '''Predicts the position of object at instant+deltaT, at constant speed''' return predictPositionNoLimit(nTimeSteps, self.getPositionAtInstant(instant), self.getVelocityAtInstant(instant), externalAcceleration) def projectCurvilinear(self, alignments, ln_mv_av_win=3): ''' Add, for every object position, the class 'moving.CurvilinearTrajectory()' (curvilinearPositions instance) which holds information about the curvilinear coordinates using alignment metadata. From Paul St-Aubin's PVA tools ====== Input: ====== alignments = a list of alignments, where each alignment is a list of points (class Point). ln_mv_av_win = moving average window (in points) in which to smooth lane changes. As per tools_math.cat_mvgavg(), this term is a search *radius* around the center of the window. ''' self.curvilinearPositions = CurvilinearTrajectory() #For each point for i in xrange(int(self.length())): result = getSYfromXY(self.getPositionAt(i), alignments) # Error handling if(result is None): print('Warning: trajectory {} at point {} {} has alignment errors (spline snapping)\nCurvilinear trajectory could not be computed'.format(self.getNum(), i, self.getPositionAt(i))) else: [align, alignPoint, snappedPoint, subsegmentDistance, S, Y] = result self.curvilinearPositions.addPositionSYL(S, Y, align) ## Go back through points and correct lane #Run through objects looking for outlier point smoothed_lanes = utils.cat_mvgavg(self.curvilinearPositions.getLanes(),ln_mv_av_win) ## Recalculate projected point to new lane lanes = self.curvilinearPositions.getLanes() if(lanes != smoothed_lanes): for i in xrange(len(lanes)): if(lanes[i] != smoothed_lanes[i]): result = getSYfromXY(self.getPositionAt(i),[alignments[smoothed_lanes[i]]]) # Error handling if(result is None): ## This can be triggered by tracking errors when the trajectory jumps around passed another alignment. print(' Warning: trajectory {} at point {} {} has alignment errors during trajectory smoothing and will not be corrected.'.format(self.getNum(), i, self.getPositionAt(i))) else: [align, alignPoint, snappedPoint, subsegmentDistance, S, Y] = result self.curvilinearPositions.setPosition(i, S, Y, align) def computeSmoothTrajectory(self, minCommonIntervalLength): '''Computes the trajectory as the mean of all features if a feature exists, its position is Warning work in progress TODO? not use the first/last 1-.. positions''' nFeatures = len(self.features) if nFeatures == 0: print('Empty object features\nCannot compute smooth trajectory') else: # compute the relative position vectors relativePositions = {} # relativePositions[(i,j)] is the position of j relative to i for i in xrange(nFeatures): for j in xrange(i): fi = self.features[i] fj = self.features[j] inter = fi.commonTimeInterval(fj) if inter.length() >= minCommonIntervalLength: xi = array(fi.getXCoordinates()[inter.first-fi.getFirstInstant():int(fi.length())-(fi.getLastInstant()-inter.last)]) yi = array(fi.getYCoordinates()[inter.first-fi.getFirstInstant():int(fi.length())-(fi.getLastInstant()-inter.last)]) xj = array(fj.getXCoordinates()[inter.first-fj.getFirstInstant():int(fj.length())-(fj.getLastInstant()-inter.last)]) yj = array(fj.getYCoordinates()[inter.first-fj.getFirstInstant():int(fj.length())-(fj.getLastInstant()-inter.last)]) relativePositions[(i,j)] = Point(median(xj-xi), median(yj-yi)) relativePositions[(j,i)] = -relativePositions[(i,j)] ### # User Type Classification ### def classifyUserTypeSpeedMotorized(self, threshold, aggregationFunc = median, nInstantsIgnoredAtEnds = 0): '''Classifies slow and fast road users slow: non-motorized -> pedestrians fast: motorized -> cars aggregationFunc can be any function that can be applied to a vector of speeds, including percentile: aggregationFunc = lambda x: percentile(x, percentileFactor) # where percentileFactor is 85 for 85th percentile''' speeds = self.getSpeeds(nInstantsIgnoredAtEnds) if aggregationFunc(speeds) >= threshold: self.setUserType(userType2Num['car']) else: self.setUserType(userType2Num['pedestrian']) def classifyUserTypeSpeed(self, speedProbabilities, aggregationFunc = median, nInstantsIgnoredAtEnds = 0): '''Classifies road user per road user type speedProbabilities are functions return P(speed|class) in a dictionary indexed by user type names Returns probabilities for each class for simple threshold classification, simply pass non-overlapping indicator functions (membership) e.g. def indic(x): if abs(x-mu) < sigma: return 1 else: return x''' if not hasattr(self, 'aggregatedSpeed'): self.aggregatedSpeed = aggregationFunc(self.getSpeeds(nInstantsIgnoredAtEnds)) userTypeProbabilities = {} for userTypename in speedProbabilities: userTypeProbabilities[userType2Num[userTypename]] = speedProbabilities[userTypename](self.aggregatedSpeed) self.setUserType(utils.argmaxDict(userTypeProbabilities)) return userTypeProbabilities def initClassifyUserTypeHoGSVM(self, aggregationFunc, pedBikeCarSVM, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), nInstantsIgnoredAtEnds = 0): '''Initializes the data structures for classification TODO? compute speed for longest feature?''' self.aggregatedSpeed = aggregationFunc(self.getSpeeds(nInstantsIgnoredAtEnds)) if self.aggregatedSpeed < pedBikeSpeedTreshold or bikeCarSVM is None: self.appearanceClassifier = pedBikeCarSVM elif self.aggregatedSpeed < bikeCarSpeedThreshold: self.appearanceClassifier = bikeCarSVM else: class CarClassifier: def predict(self, hog): return userType2Num['car'] self.appearanceClassifier = CarClassifier() self.userTypes = {} def classifyUserTypeHoGSVMAtInstant(self, img, instant, homography, width, height, px = 0.2, py = 0.2, minNPixels = 800): '''Extract the image box around the object and applies the SVM model on it''' croppedImg, yCropMin, yCropMax, xCropMin, xCropMax = cvutils.imageBox(img, self, instant, homography, width, height, px, py, minNPixels) if croppedImg is not None and len(croppedImg) > 0: hog = cvutils.HOG(croppedImg)#HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False) self.userTypes[instant] = int(self.appearanceClassifier.predict(hog)) else: self.userTypes[instant] = userType2Num['unknown'] def classifyUserTypeHoGSVM(self, pedBikeCarSVM = None, width = 0, height = 0, homography = None, images = None, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), minSpeedEquiprobable = -1, speedProbabilities = None, aggregationFunc = median, nInstantsIgnoredAtEnds = 0, px = 0.2, py = 0.2, minNPixels = 800): '''Agregates SVM detections in each image and returns probability (proportion of instants with classification in each category) images is a dictionary of images indexed by instant With default parameters, the general (ped-bike-car) classifier will be used Considered categories are the keys of speedProbabilities''' if not hasattr(self, 'aggregatedSpeed') or not hasattr(self, 'userTypes'): print('Initilize the data structures for classification by HoG-SVM') self.initClassifyUserTypeHoGSVM(aggregationFunc, pedBikeCarSVM, bikeCarSVM, pedBikeSpeedTreshold, bikeCarSpeedThreshold, nInstantsIgnoredAtEnds) if len(self.userTypes) != self.length() and images is not None: # if classification has not been done previously for t in self.getTimeInterval(): if t not in self.userTypes: self.classifyUserTypeHoGSVMAtInstant(images[t], t, homography, width, height, px, py, minNPixels) # compute P(Speed|Class) if speedProbabilities is None or self.aggregatedSpeed < minSpeedEquiprobable: # equiprobable information from speed userTypeProbabilities = {userType2Num['car']: 1., userType2Num['pedestrian']: 1., userType2Num['bicycle']: 1.} else: userTypeProbabilities = {userType2Num[userTypename]: speedProbabilities[userTypename](self.aggregatedSpeed) for userTypename in speedProbabilities} # result is P(Class|Appearance) x P(Speed|Class) nInstantsUserType = {userTypeNum: 0 for userTypeNum in userTypeProbabilities}# number of instants the object is classified as userTypename for t in self.userTypes: nInstantsUserType[self.userTypes[t]] = nInstantsUserType.get(self.userTypes[t], 0) + 1 for userTypeNum in userTypeProbabilities: userTypeProbabilities[userTypeNum] *= nInstantsUserType[userTypeNum] # class is the user type that maximizes usertype probabilities self.setUserType(utils.argmaxDict(userTypeProbabilities)) def classifyUserTypeArea(self, areas, homography): '''Classifies the object based on its location (projected to image space) areas is a dictionary of matrix of the size of the image space for different road users possible locations, indexed by road user type names TODO: areas could be a wrapper object with a contains method that would work for polygons and images (with wrapper class) skip frames at beginning/end?''' print('not implemented/tested yet') if not hasattr(self, projectedPositions): if homography is not None: self.projectedPositions = obj.positions.project(homography) else: self.projectedPositions = obj.positions possibleUserTypes = {userType: 0 for userType in range(len(userTypenames))} for p in self.projectedPositions: for userTypename in areas: if areas[userTypename][p.x, p.y] != 0: possibleUserTypes[userType2Enum[userTypename]] += 1 # what to do: threshold for most common type? self.setUserType() return possibleUserTypes @staticmethod def collisionCourseDotProduct(movingObject1, movingObject2, instant): 'A positive result indicates that the road users are getting closer' deltap = movingObject1.getPositionAtInstant(instant)-movingObject2.getPositionAtInstant(instant) deltav = movingObject2.getVelocityAtInstant(instant)-movingObject1.getVelocityAtInstant(instant) return Point.dot(deltap, deltav) @staticmethod def collisionCourseCosine(movingObject1, movingObject2, instant): 'A positive result indicates that the road users are getting closer' return Point.cosine(movingObject1.getPositionAtInstant(instant)-movingObject2.getPositionAtInstant(instant), #deltap movingObject2.getVelocityAtInstant(instant)-movingObject1.getVelocityAtInstant(instant)) #deltav ################## # Annotations ################## class BBAnnotation(MovingObject): '''Class for a series of ground truth annotations using bounding boxes Its center is the center of the containing shape By default in image space ''' def __init__(self, num = None, timeInterval = None, topLeftPositions = None, bottomRightPositions = None, userType = userType2Num['unknown']): super(BBAnnotation, self).__init__(num, timeInterval, userType = userType) self.topLeftPositions = topLeftPositions.getPositions() self.bottomRightPositions = bottomRightPositions.getPositions() def computeCentroidTrajectory(self, homography = None): self.positions = self.topLeftPositions.add(self.bottomRightPositions).multiply(0.5) if homography is not None: self.positions = self.positions.project(homography) def matches(self, obj, instant, matchingDistance): '''Indicates if the annotation matches obj (MovingObject) with threshold matchingDistance Returns distance if below matchingDistance, matchingDistance+1 otherwise (returns an actual value, otherwise munkres does not terminate)''' d = Point.distanceNorm2(self.getPositionAtInstant(instant), obj.getPositionAtInstant(instant)) if d < matchingDistance: return d else: return matchingDistance + 1 def computeClearMOT(annotations, objects, matchingDistance, firstInstant, lastInstant, returnMatches = False, debug = False): '''Computes the CLEAR MOT metrics Reference: Keni, Bernardin, and Stiefelhagen Rainer. "Evaluating multiple object tracking performance: the CLEAR MOT metrics." EURASIP Journal on Image and Video Processing 2008 (2008) objects and annotations are supposed to in the same space current implementation is BBAnnotations (bounding boxes) mathingDistance is threshold on matching between annotation and object TO: tracker output (objects) GT: ground truth (annotations) Output: returns motp, mota, mt, mme, fpt, gt mt number of missed GT.frames (sum of the number of GT not detected in each frame) mme number of mismatches fpt number of false alarm.frames (tracker objects without match in each frame) gt number of GT.frames if returnMatches is True, return as 2 new arguments the GT and TO matches matches is a dict matches[i] is the list of matches for GT/TO i the list of matches is a dict, indexed by time, for the TO/GT id matched at time t (an instant t not present in matches[i] at which GT/TO exists means a missed detection or false alarm) TODO: Should we use the distance as weights or just 1/0 if distance below matchingDistance? (add argument useDistanceForWeights = False)''' from munkres import Munkres munk = Munkres() dist = 0. # total distance between GT and TO ct = 0 # number of associations between GT and tracker output in each frame gt = 0 # number of GT.frames mt = 0 # number of missed GT.frames (sum of the number of GT not detected in each frame) fpt = 0 # number of false alarm.frames (tracker objects without match in each frame) mme = 0 # number of mismatches matches = {} # match[i] is the tracker track associated with GT i (using object references) if returnMatches: gtMatches = {a.getNum():{} for a in annotations} toMatches = {o.getNum():{} for o in objects} for t in xrange(firstInstant, lastInstant+1): previousMatches = matches.copy() # go through currently matched GT-TO and check if they are still matched withing matchingDistance toDelete = [] for a in matches: if a.existsAtInstant(t) and matches[a].existsAtInstant(t): d = a.matches(matches[a], t, matchingDistance) if d < matchingDistance: dist += d else: toDelete.append(a) else: toDelete.append(a) for a in toDelete: del matches[a] # match all unmatched GT-TO matchedGTs = matches.keys() matchedTOs = matches.values() costs = [] unmatchedGTs = [a for a in annotations if a.existsAtInstant(t) and a not in matchedGTs] unmatchedTOs = [o for o in objects if o.existsAtInstant(t) and o not in matchedTOs] nGTs = len(matchedGTs)+len(unmatchedGTs) nTOs = len(matchedTOs)+len(unmatchedTOs) if len(unmatchedTOs) > 0: for a in unmatchedGTs: costs.append([a.matches(o, t, matchingDistance) for o in unmatchedTOs]) if len(costs) > 0: newMatches = munk.compute(costs) for k,v in newMatches: if costs[k][v] < matchingDistance: matches[unmatchedGTs[k]]=unmatchedTOs[v] dist += costs[k][v] if debug: print('{} '.format(t)+', '.join(['{} {}'.format(k.getNum(), v.getNum()) for k,v in matches.iteritems()])) if returnMatches: for a,o in matches.iteritems(): gtMatches[a.getNum()][t] = o.getNum() toMatches[o.getNum()][t] = a.getNum() # compute metrics elements ct += len(matches) mt += nGTs-len(matches) fpt += nTOs-len(matches) gt += nGTs # compute mismatches # for gt that do not appear in both frames, check if the corresponding to was matched to another gt in previous/next frame mismatches = [] for a in matches: if a in previousMatches: if matches[a] != previousMatches[a]: mismatches.append(a) elif matches[a] in previousMatches.values(): mismatches.append(matches[a]) for a in previousMatches: if a not in matches and previousMatches[a] in matches.values(): mismatches.append(previousMatches[a]) if debug: for mm in set(mismatches): print type(mm), mm.getNum() # some object mismatches may appear twice mme += len(set(mismatches)) if ct > 0: motp = dist/ct else: motp = None if gt > 0: mota = 1.-float(mt+fpt+mme)/gt else: mota = None if returnMatches: return motp, mota, mt, mme, fpt, gt, gtMatches, toMatches else: return motp, mota, mt, mme, fpt, gt def plotRoadUsers(objects, colors): '''Colors is a PlottingPropertyValues instance''' from matplotlib.pyplot import figure, axis figure() for obj in objects: obj.plot(colors.get(obj.userType)) axis('equal') if __name__ == "__main__": import doctest import unittest suite = doctest.DocFileSuite('tests/moving.txt') #suite = doctest.DocTestSuite() unittest.TextTestRunner().run(suite) #doctest.testmod() #doctest.testfile("example.txt") if shapelyAvailable: suite = doctest.DocFileSuite('tests/moving_shapely.txt') unittest.TextTestRunner().run(suite)