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Fixed ellipse and ellipsoid methoids
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30hours
parent
21c2d549f3
commit
9f31e3190c
5 changed files with 112 additions and 100 deletions
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@ -189,4 +189,7 @@ class Geometry:
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return math.sqrt(
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(point2[0]-point1[0])**2 +
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(point2[1]-point1[1])**2 +
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(point2[2]-point1[2])**2)
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(point2[2]-point1[2])**2)
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def average_points(points):
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return [sum(coord) / len(coord) for coord in zip(*points)]
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@ -20,15 +20,16 @@ class EllipseParametric:
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@see blah2 at https://github.com/30hours/blah2.
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"""
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def __init__(self):
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def __init__(self, method="mean", nSamples=150, threshold=500):
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"""
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@brief Constructor for the EllipseParametric class.
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"""
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self.ellipsoids = []
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self.nSamples = 150
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self.threshold = 800
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self.nSamples = nSamples
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self.threshold = threshold
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self.method = method
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def process(self, assoc_detections, radar_data):
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@ -87,54 +88,56 @@ class EllipseParametric:
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radar_keys = list(target_samples[target].keys())
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samples_intersect = []
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# loop points in master ellipsoid
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# for point1 in target_samples[target][radar_keys[0]]:
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# valid_point = True
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# # loop over each other list
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# for i in range(1, len(radar_keys)):
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# # loop points in other list
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# if not any(Geometry.distance_ecef(point1, point2) < self.threshold
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# for point2 in target_samples[target][radar_keys[i]]):
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# valid_point = False
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# break
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# if valid_point:
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# samples_intersect.append(point1)
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if self.method == "mean":
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# average_point = self.average_points(samples_intersect)
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# samples_intersect = [average_point]
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# loop points in main ellipsoid
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for point1 in target_samples[target][radar_keys[0]]:
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valid_point = True
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# loop over each other list
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for i in range(1, len(radar_keys)):
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# loop points in other list
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if not any(Geometry.distance_ecef(point1, point2) < self.threshold
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for point2 in target_samples[target][radar_keys[i]]):
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valid_point = False
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break
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if valid_point:
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samples_intersect.append(point1)
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average_point = Geometry.average_points(samples_intersect)
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samples_intersect = [average_point]
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elif self.method == "minimum":
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min_distance = self.threshold
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min_point1 = None
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# loop points in main ellipsoid
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for point1 in target_samples[target][radar_keys[0]]:
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valid_point = True
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distance_from_point1 = [self.threshold]*(len(radar_keys)-1)
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# loop over each other list
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for i in range(1, len(radar_keys)):
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if i > 1 and distance_from_point1[i-1] > self.threshold:
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valid_point = False
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break
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# loop points in other list
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for point2 in target_samples[target][radar_keys[i]]:
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distance = Geometry.distance_ecef(point1, point2)
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if distance < distance_from_point1[i-1]:
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distance_from_point1[i-1] = distance
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norm = math.sqrt(sum(x ** 2 for x in distance_from_point1))
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if valid_point and norm < min_distance:
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min_distance = norm
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min_point1 = point1
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if min_point1 is not None:
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samples_intersect.append(min_point1)
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else:
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return output
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min_distance = self.threshold
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min_point1 = None
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for point1 in target_samples[target][radar_keys[0]]:
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valid_point = True
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distance_from_point1 = [self.threshold]*(len(radar_keys)-1)
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# loop over each other list
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for i in range(1, len(radar_keys)):
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if i > 1 and distance_from_point1[i-1] > self.threshold:
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valid_point = False
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break
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# loop points in other list
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for point2 in target_samples[target][radar_keys[i]]:
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distance = Geometry.distance_ecef(point1, point2)
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if distance < distance_from_point1[i-1]:
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distance_from_point1[i-1] = distance
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norm = math.sqrt(sum(x ** 2 for x in distance_from_point1))
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if valid_point and norm < min_distance:
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min_distance = norm
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min_point1 = point1
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if min_point1 is not None:
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samples_intersect.append(min_point1)
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else:
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print('Invalid method.')
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return output
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# find closest points bruteforce
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# points = list(target_samples[target].values())
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# result_points, result_distance = self.closest_points_bruteforce(points)
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# average_point = self.average_points(result_points)
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# if result_distance < self.threshold:
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# samples_intersect.append(average_point)
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# remove duplicates and convert to LLA
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output[target] = {}
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output[target]["points"] = []
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@ -190,37 +193,4 @@ class EllipseParametric:
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output.append([x, y, z])
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return output
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def euclidean_distance(self, point1, point2):
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return np.linalg.norm(np.array(point1) - np.array(point2))
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def closest_points_bruteforce(self, point_sets):
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closest_distance = float('inf')
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closest_points = None
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for combination in itertools.product(*point_sets):
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distance = sum(self.euclidean_distance(combination[i], combination[i+1]) for i in range(len(point_sets)-1))
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if distance < closest_distance:
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closest_distance = distance
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closest_points = combination
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return closest_points, closest_distance
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# def closest_points_bruteforce(point_sets):
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# closest_distance = float('inf')
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# closest_points = None
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# def calculate_distance(combination):
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# nonlocal closest_distance, closest_points
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# distance = sum(euclidean_distance(combination[i], combination[i+1]) for i in range(len(point_sets)-1))
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# if distance < closest_distance:
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# closest_distance = distance
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# closest_points = combination
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# with ThreadPoolExecutor() as executor:
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# executor.map(calculate_distance, itertools.product(*point_sets))
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# return closest_points, closest_distance
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def average_points(self, points):
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return [sum(coord) / len(coord) for coord in zip(*points)]
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@ -17,15 +17,16 @@ class EllipsoidParametric:
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@see blah2 at https://github.com/30hours/blah2.
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"""
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def __init__(self):
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def __init__(self, method="mean", nSamples=100, threshold=500):
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"""
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@brief Constructor for the EllipsoidParametric class.
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"""
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self.ellipsoids = []
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self.nSamples = 150
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self.threshold = 800
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self.nSamples = nSamples
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self.threshold = threshold
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self.method = method
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def process(self, assoc_detections, radar_data):
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@ -84,18 +85,55 @@ class EllipsoidParametric:
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radar_keys = list(target_samples[target].keys())
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samples_intersect = []
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# loop points in master ellipsoid
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for point1 in target_samples[target][radar_keys[0]]:
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valid_point = True
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# loop over each other list
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for i in range(1, len(radar_keys)):
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# loop points in other list
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if not any(Geometry.distance_ecef(point1, point2) < self.threshold
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for point2 in target_samples[target][radar_keys[i]]):
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valid_point = False
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break
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if valid_point:
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samples_intersect.append(point1)
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if self.method == "mean":
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# loop points in main ellipsoid
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for point1 in target_samples[target][radar_keys[0]]:
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valid_point = True
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# loop over each other list
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for i in range(1, len(radar_keys)):
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# loop points in other list
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if not any(Geometry.distance_ecef(point1, point2) < self.threshold
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for point2 in target_samples[target][radar_keys[i]]):
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valid_point = False
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break
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if valid_point:
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samples_intersect.append(point1)
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average_point = Geometry.average_points(samples_intersect)
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samples_intersect = [average_point]
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elif self.method == "minimum":
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min_distance = self.threshold
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min_point1 = None
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# loop points in main ellipsoid
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for point1 in target_samples[target][radar_keys[0]]:
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valid_point = True
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distance_from_point1 = [self.threshold]*(len(radar_keys)-1)
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# loop over each other list
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for i in range(1, len(radar_keys)):
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if i > 1 and distance_from_point1[i-1] > self.threshold:
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valid_point = False
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break
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# loop points in other list
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for point2 in target_samples[target][radar_keys[i]]:
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distance = Geometry.distance_ecef(point1, point2)
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if distance < distance_from_point1[i-1]:
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distance_from_point1[i-1] = distance
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norm = math.sqrt(sum(x ** 2 for x in distance_from_point1))
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if valid_point and norm < min_distance:
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min_distance = norm
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min_point1 = point1
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if min_point1 is not None:
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samples_intersect.append(min_point1)
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else:
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return output
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else:
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print('Invalid method.')
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return output
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# remove duplicates and convert to LLA
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output[target] = {}
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@ -162,4 +200,4 @@ class EllipsoidParametric:
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[x, y, z] = Geometry.lla2ecef(x, y, z)
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output.append([round(x, 3), round(y, 3), round(z)])
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return output
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return output
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@ -107,11 +107,12 @@ class SphericalIntersection:
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a = S_star @ z_vec
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b = S_star @ r
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R_t = [0, 0]
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discrimninant = 4*((a.T @ b)**2) - 4*((b.T @ b) - 1)*(a.T @ a)
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discriminant = 4*((a.T @ b)**2) - 4*((b.T @ b) - 1)*(a.T @ a)
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if discriminant >= 0:
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R_t[0] = (-2*(a.T @ b) - np.sqrt(discriminant))/(2*((b.T @ b)-1))
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R_t[1] = (-2*(a.T @ b) + np.sqrt(discriminant))/(2*((b.T @ b)-1))
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else:
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print('@@@ discriminant < 0', flush=True)
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R_t[0] = np.real((-2*(a.T @ b) - np.sqrt(discriminant + 0j))/(2*((b.T @ b)-1)))
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R_t[1] = np.real((-2*(a.T @ b) + np.sqrt(discriminant + 0j))/(2*((b.T @ b)-1)))
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x_t = [0, 0]
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@ -29,8 +29,8 @@ api = []
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# init config
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tDelete = 60
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adsbAssociator = AdsbAssociator()
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ellipseParametric = EllipseParametric()
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ellipsoidParametric = EllipsoidParametric()
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ellipseParametric = EllipseParametric("mean", 200, 500)
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ellipsoidParametric = EllipsoidParametric("mean", 100, 500)
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sphericalIntersection = SphericalIntersection()
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adsbTruth = AdsbTruth(5)
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save = True
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