更新 detacttracking
This commit is contained in:
lee
1
detecttracking/ultralytics/trackers/utils/__init__.py
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1
detecttracking/ultralytics/trackers/utils/__init__.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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279
detecttracking/ultralytics/trackers/utils/gmc.py
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279
detecttracking/ultralytics/trackers/utils/gmc.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import copy
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import cv2
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import numpy as np
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from ultralytics.utils import LOGGER
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class GMC:
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def __init__(self, method='sparseOptFlow', downscale=2):
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"""Initialize a video tracker with specified parameters."""
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super().__init__()
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self.method = method
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self.downscale = max(1, int(downscale))
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if self.method == 'orb':
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self.detector = cv2.FastFeatureDetector_create(20)
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self.extractor = cv2.ORB_create()
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self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING)
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elif self.method == 'sift':
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self.detector = cv2.SIFT_create(nOctaveLayers=3, contrastThreshold=0.02, edgeThreshold=20)
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self.extractor = cv2.SIFT_create(nOctaveLayers=3, contrastThreshold=0.02, edgeThreshold=20)
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self.matcher = cv2.BFMatcher(cv2.NORM_L2)
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elif self.method == 'ecc':
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number_of_iterations = 5000
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termination_eps = 1e-6
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self.warp_mode = cv2.MOTION_EUCLIDEAN
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self.criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, number_of_iterations, termination_eps)
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elif self.method == 'sparseOptFlow':
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self.feature_params = dict(maxCorners=1000,
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qualityLevel=0.01,
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minDistance=1,
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blockSize=3,
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useHarrisDetector=False,
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k=0.04)
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elif self.method in ['none', 'None', None]:
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self.method = None
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else:
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raise ValueError(f'Error: Unknown GMC method:{method}')
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self.prevFrame = None
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self.prevKeyPoints = None
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self.prevDescriptors = None
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self.initializedFirstFrame = False
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def apply(self, raw_frame, detections=None):
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"""Apply object detection on a raw frame using specified method."""
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if self.method in ['orb', 'sift']:
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return self.applyFeatures(raw_frame, detections)
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elif self.method == 'ecc':
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return self.applyEcc(raw_frame, detections)
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elif self.method == 'sparseOptFlow':
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return self.applySparseOptFlow(raw_frame, detections)
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else:
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return np.eye(2, 3)
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def applyEcc(self, raw_frame, detections=None):
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"""Initialize."""
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height, width, _ = raw_frame.shape
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frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
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H = np.eye(2, 3, dtype=np.float32)
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# Downscale image (TODO: consider using pyramids)
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if self.downscale > 1.0:
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frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
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frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
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width = width // self.downscale
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height = height // self.downscale
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# Handle first frame
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if not self.initializedFirstFrame:
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# Initialize data
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self.prevFrame = frame.copy()
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# Initialization done
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self.initializedFirstFrame = True
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return H
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# Run the ECC algorithm. The results are stored in warp_matrix.
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# (cc, H) = cv2.findTransformECC(self.prevFrame, frame, H, self.warp_mode, self.criteria)
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try:
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(cc, H) = cv2.findTransformECC(self.prevFrame, frame, H, self.warp_mode, self.criteria, None, 1)
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except Exception as e:
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LOGGER.warning(f'WARNING: find transform failed. Set warp as identity {e}')
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return H
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def applyFeatures(self, raw_frame, detections=None):
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"""Initialize."""
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height, width, _ = raw_frame.shape
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frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
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H = np.eye(2, 3)
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# Downscale image (TODO: consider using pyramids)
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if self.downscale > 1.0:
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# frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
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frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
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width = width // self.downscale
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height = height // self.downscale
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# Find the keypoints
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mask = np.zeros_like(frame)
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# mask[int(0.05 * height): int(0.95 * height), int(0.05 * width): int(0.95 * width)] = 255
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mask[int(0.02 * height):int(0.98 * height), int(0.02 * width):int(0.98 * width)] = 255
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if detections is not None:
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for det in detections:
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tlbr = (det[:4] / self.downscale).astype(np.int_)
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mask[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2]] = 0
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keypoints = self.detector.detect(frame, mask)
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# Compute the descriptors
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keypoints, descriptors = self.extractor.compute(frame, keypoints)
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# Handle first frame
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if not self.initializedFirstFrame:
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# Initialize data
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self.prevFrame = frame.copy()
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self.prevKeyPoints = copy.copy(keypoints)
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self.prevDescriptors = copy.copy(descriptors)
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# Initialization done
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self.initializedFirstFrame = True
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return H
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# Match descriptors.
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knnMatches = self.matcher.knnMatch(self.prevDescriptors, descriptors, 2)
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# Filtered matches based on smallest spatial distance
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matches = []
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spatialDistances = []
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maxSpatialDistance = 0.25 * np.array([width, height])
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# Handle empty matches case
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if len(knnMatches) == 0:
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# Store to next iteration
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self.prevFrame = frame.copy()
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self.prevKeyPoints = copy.copy(keypoints)
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self.prevDescriptors = copy.copy(descriptors)
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return H
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for m, n in knnMatches:
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if m.distance < 0.9 * n.distance:
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prevKeyPointLocation = self.prevKeyPoints[m.queryIdx].pt
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currKeyPointLocation = keypoints[m.trainIdx].pt
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spatialDistance = (prevKeyPointLocation[0] - currKeyPointLocation[0],
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prevKeyPointLocation[1] - currKeyPointLocation[1])
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if (np.abs(spatialDistance[0]) < maxSpatialDistance[0]) and \
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(np.abs(spatialDistance[1]) < maxSpatialDistance[1]):
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spatialDistances.append(spatialDistance)
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matches.append(m)
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meanSpatialDistances = np.mean(spatialDistances, 0)
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stdSpatialDistances = np.std(spatialDistances, 0)
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inliers = (spatialDistances - meanSpatialDistances) < 2.5 * stdSpatialDistances
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goodMatches = []
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prevPoints = []
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currPoints = []
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for i in range(len(matches)):
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if inliers[i, 0] and inliers[i, 1]:
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goodMatches.append(matches[i])
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prevPoints.append(self.prevKeyPoints[matches[i].queryIdx].pt)
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currPoints.append(keypoints[matches[i].trainIdx].pt)
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prevPoints = np.array(prevPoints)
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currPoints = np.array(currPoints)
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# Draw the keypoint matches on the output image
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# if False:
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# import matplotlib.pyplot as plt
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# matches_img = np.hstack((self.prevFrame, frame))
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# matches_img = cv2.cvtColor(matches_img, cv2.COLOR_GRAY2BGR)
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# W = np.size(self.prevFrame, 1)
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# for m in goodMatches:
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# prev_pt = np.array(self.prevKeyPoints[m.queryIdx].pt, dtype=np.int_)
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# curr_pt = np.array(keypoints[m.trainIdx].pt, dtype=np.int_)
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# curr_pt[0] += W
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# color = np.random.randint(0, 255, 3)
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# color = (int(color[0]), int(color[1]), int(color[2]))
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#
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# matches_img = cv2.line(matches_img, prev_pt, curr_pt, tuple(color), 1, cv2.LINE_AA)
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# matches_img = cv2.circle(matches_img, prev_pt, 2, tuple(color), -1)
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# matches_img = cv2.circle(matches_img, curr_pt, 2, tuple(color), -1)
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#
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# plt.figure()
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# plt.imshow(matches_img)
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# plt.show()
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# Find rigid matrix
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if (np.size(prevPoints, 0) > 4) and (np.size(prevPoints, 0) == np.size(prevPoints, 0)):
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H, inliers = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)
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# Handle downscale
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if self.downscale > 1.0:
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H[0, 2] *= self.downscale
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H[1, 2] *= self.downscale
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else:
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LOGGER.warning('WARNING: not enough matching points')
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# Store to next iteration
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self.prevFrame = frame.copy()
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self.prevKeyPoints = copy.copy(keypoints)
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self.prevDescriptors = copy.copy(descriptors)
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return H
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def applySparseOptFlow(self, raw_frame, detections=None):
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"""Initialize."""
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height, width, _ = raw_frame.shape
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frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
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H = np.eye(2, 3)
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# Downscale image
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if self.downscale > 1.0:
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# frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
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frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
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# Find the keypoints
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keypoints = cv2.goodFeaturesToTrack(frame, mask=None, **self.feature_params)
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# Handle first frame
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if not self.initializedFirstFrame:
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# Initialize data
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self.prevFrame = frame.copy()
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self.prevKeyPoints = copy.copy(keypoints)
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# Initialization done
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self.initializedFirstFrame = True
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return H
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# Find correspondences
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matchedKeypoints, status, err = cv2.calcOpticalFlowPyrLK(self.prevFrame, frame, self.prevKeyPoints, None)
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# Leave good correspondences only
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prevPoints = []
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currPoints = []
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for i in range(len(status)):
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if status[i]:
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prevPoints.append(self.prevKeyPoints[i])
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currPoints.append(matchedKeypoints[i])
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prevPoints = np.array(prevPoints)
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currPoints = np.array(currPoints)
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# Find rigid matrix
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if (np.size(prevPoints, 0) > 4) and (np.size(prevPoints, 0) == np.size(prevPoints, 0)):
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H, inliers = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)
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# Handle downscale
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if self.downscale > 1.0:
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H[0, 2] *= self.downscale
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H[1, 2] *= self.downscale
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else:
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LOGGER.warning('WARNING: not enough matching points')
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# Store to next iteration
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self.prevFrame = frame.copy()
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self.prevKeyPoints = copy.copy(keypoints)
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return H
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368
detecttracking/ultralytics/trackers/utils/kalman_filter.py
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368
detecttracking/ultralytics/trackers/utils/kalman_filter.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import numpy as np
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import scipy.linalg
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class KalmanFilterXYAH:
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"""
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For bytetrack. A simple Kalman filter for tracking bounding boxes in image space.
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The 8-dimensional state space (x, y, a, h, vx, vy, va, vh) contains the bounding box center position (x, y),
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aspect ratio a, height h, and their respective velocities.
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Object motion follows a constant velocity model. The bounding box location (x, y, a, h) is taken as direct
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observation of the state space (linear observation model).
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"""
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def __init__(self):
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"""Initialize Kalman filter model matrices with motion and observation uncertainty weights."""
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ndim, dt = 4, 1.
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# Create Kalman filter model matrices.
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self._motion_mat = np.eye(2 * ndim, 2 * ndim)
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for i in range(ndim):
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self._motion_mat[i, ndim + i] = dt
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self._update_mat = np.eye(ndim, 2 * ndim)
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# Motion and observation uncertainty are chosen relative to the current state estimate. These weights control
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# the amount of uncertainty in the model. This is a bit hacky.
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self._std_weight_position = 1. / 20
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self._std_weight_velocity = 1. / 160
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def initiate(self, measurement):
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"""
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Create track from unassociated measurement.
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Parameters
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----------
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measurement : ndarray
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Bounding box coordinates (x, y, a, h) with center position (x, y),
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aspect ratio a, and height h.
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Returns
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-------
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(ndarray, ndarray)
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Returns the mean vector (8 dimensional) and covariance matrix (8x8
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dimensional) of the new track. Unobserved velocities are initialized
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to 0 mean.
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"""
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mean_pos = measurement
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mean_vel = np.zeros_like(mean_pos)
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mean = np.r_[mean_pos, mean_vel]
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std = [
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2 * self._std_weight_position * measurement[3], 2 * self._std_weight_position * measurement[3], 1e-2,
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2 * self._std_weight_position * measurement[3], 10 * self._std_weight_velocity * measurement[3],
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10 * self._std_weight_velocity * measurement[3], 1e-5, 10 * self._std_weight_velocity * measurement[3]]
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covariance = np.diag(np.square(std))
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return mean, covariance
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def predict(self, mean, covariance):
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"""
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Run Kalman filter prediction step.
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Parameters
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----------
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mean : ndarray
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The 8 dimensional mean vector of the object state at the previous time step.
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covariance : ndarray
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The 8x8 dimensional covariance matrix of the object state at the previous time step.
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Returns
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-------
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(ndarray, ndarray)
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Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
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initialized to 0 mean.
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"""
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std_pos = [
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self._std_weight_position * mean[3], self._std_weight_position * mean[3], 1e-2,
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self._std_weight_position * mean[3]]
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std_vel = [
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self._std_weight_velocity * mean[3], self._std_weight_velocity * mean[3], 1e-5,
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self._std_weight_velocity * mean[3]]
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motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
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# mean = np.dot(self._motion_mat, mean)
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mean = np.dot(mean, self._motion_mat.T)
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covariance = np.linalg.multi_dot((self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
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return mean, covariance
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def project(self, mean, covariance):
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"""
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Project state distribution to measurement space.
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Parameters
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----------
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mean : ndarray
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The state's mean vector (8 dimensional array).
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covariance : ndarray
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The state's covariance matrix (8x8 dimensional).
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Returns
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-------
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(ndarray, ndarray)
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Returns the projected mean and covariance matrix of the given state estimate.
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"""
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std = [
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self._std_weight_position * mean[3], self._std_weight_position * mean[3], 1e-1,
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self._std_weight_position * mean[3]]
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innovation_cov = np.diag(np.square(std))
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mean = np.dot(self._update_mat, mean)
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covariance = np.linalg.multi_dot((self._update_mat, covariance, self._update_mat.T))
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return mean, covariance + innovation_cov
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def multi_predict(self, mean, covariance):
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"""
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Run Kalman filter prediction step (Vectorized version).
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Parameters
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----------
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mean : ndarray
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The Nx8 dimensional mean matrix of the object states at the previous time step.
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covariance : ndarray
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The Nx8x8 dimensional covariance matrix of the object states at the previous time step.
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Returns
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-------
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(ndarray, ndarray)
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Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
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initialized to 0 mean.
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"""
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std_pos = [
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self._std_weight_position * mean[:, 3], self._std_weight_position * mean[:, 3],
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1e-2 * np.ones_like(mean[:, 3]), self._std_weight_position * mean[:, 3]]
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std_vel = [
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self._std_weight_velocity * mean[:, 3], self._std_weight_velocity * mean[:, 3],
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1e-5 * np.ones_like(mean[:, 3]), self._std_weight_velocity * mean[:, 3]]
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sqr = np.square(np.r_[std_pos, std_vel]).T
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motion_cov = [np.diag(sqr[i]) for i in range(len(mean))]
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motion_cov = np.asarray(motion_cov)
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mean = np.dot(mean, self._motion_mat.T)
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left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))
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covariance = np.dot(left, self._motion_mat.T) + motion_cov
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return mean, covariance
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def update(self, mean, covariance, measurement):
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"""
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Run Kalman filter correction step.
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Parameters
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----------
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mean : ndarray
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The predicted state's mean vector (8 dimensional).
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covariance : ndarray
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The state's covariance matrix (8x8 dimensional).
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measurement : ndarray
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The 4 dimensional measurement vector (x, y, a, h), where (x, y) is the center position, a the aspect
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ratio, and h the height of the bounding box.
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Returns
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-------
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(ndarray, ndarray)
|
||||
Returns the measurement-corrected state distribution.
|
||||
"""
|
||||
projected_mean, projected_cov = self.project(mean, covariance)
|
||||
|
||||
chol_factor, lower = scipy.linalg.cho_factor(projected_cov, lower=True, check_finite=False)
|
||||
kalman_gain = scipy.linalg.cho_solve((chol_factor, lower),
|
||||
np.dot(covariance, self._update_mat.T).T,
|
||||
check_finite=False).T
|
||||
innovation = measurement - projected_mean
|
||||
|
||||
new_mean = mean + np.dot(innovation, kalman_gain.T)
|
||||
new_covariance = covariance - np.linalg.multi_dot((kalman_gain, projected_cov, kalman_gain.T))
|
||||
return new_mean, new_covariance
|
||||
|
||||
def gating_distance(self, mean, covariance, measurements, only_position=False, metric='maha'):
|
||||
"""
|
||||
Compute gating distance between state distribution and measurements. A suitable distance threshold can be
|
||||
obtained from `chi2inv95`. If `only_position` is False, the chi-square distribution has 4 degrees of
|
||||
freedom, otherwise 2.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mean : ndarray
|
||||
Mean vector over the state distribution (8 dimensional).
|
||||
covariance : ndarray
|
||||
Covariance of the state distribution (8x8 dimensional).
|
||||
measurements : ndarray
|
||||
An Nx4 dimensional matrix of N measurements, each in format (x, y, a, h) where (x, y) is the bounding box
|
||||
center position, a the aspect ratio, and h the height.
|
||||
only_position : Optional[bool]
|
||||
If True, distance computation is done with respect to the bounding box center position only.
|
||||
|
||||
Returns
|
||||
-------
|
||||
ndarray
|
||||
Returns an array of length N, where the i-th element contains the squared Mahalanobis distance between
|
||||
(mean, covariance) and `measurements[i]`.
|
||||
"""
|
||||
mean, covariance = self.project(mean, covariance)
|
||||
if only_position:
|
||||
mean, covariance = mean[:2], covariance[:2, :2]
|
||||
measurements = measurements[:, :2]
|
||||
|
||||
d = measurements - mean
|
||||
if metric == 'gaussian':
|
||||
return np.sum(d * d, axis=1)
|
||||
elif metric == 'maha':
|
||||
cholesky_factor = np.linalg.cholesky(covariance)
|
||||
z = scipy.linalg.solve_triangular(cholesky_factor, d.T, lower=True, check_finite=False, overwrite_b=True)
|
||||
return np.sum(z * z, axis=0) # square maha
|
||||
else:
|
||||
raise ValueError('invalid distance metric')
|
||||
|
||||
|
||||
class KalmanFilterXYWH(KalmanFilterXYAH):
|
||||
"""
|
||||
For BoT-SORT. A simple Kalman filter for tracking bounding boxes in image space.
|
||||
|
||||
The 8-dimensional state space (x, y, w, h, vx, vy, vw, vh) contains the bounding box center position (x, y),
|
||||
width w, height h, and their respective velocities.
|
||||
|
||||
Object motion follows a constant velocity model. The bounding box location (x, y, w, h) is taken as direct
|
||||
observation of the state space (linear observation model).
|
||||
"""
|
||||
|
||||
def initiate(self, measurement):
|
||||
"""
|
||||
Create track from unassociated measurement.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
measurement : ndarray
|
||||
Bounding box coordinates (x, y, w, h) with center position (x, y), width w, and height h.
|
||||
|
||||
Returns
|
||||
-------
|
||||
(ndarray, ndarray)
|
||||
Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of the new track.
|
||||
Unobserved velocities are initialized to 0 mean.
|
||||
"""
|
||||
mean_pos = measurement
|
||||
mean_vel = np.zeros_like(mean_pos)
|
||||
mean = np.r_[mean_pos, mean_vel]
|
||||
|
||||
std = [
|
||||
2 * self._std_weight_position * measurement[2], 2 * self._std_weight_position * measurement[3],
|
||||
2 * self._std_weight_position * measurement[2], 2 * self._std_weight_position * measurement[3],
|
||||
10 * self._std_weight_velocity * measurement[2], 10 * self._std_weight_velocity * measurement[3],
|
||||
10 * self._std_weight_velocity * measurement[2], 10 * self._std_weight_velocity * measurement[3]]
|
||||
covariance = np.diag(np.square(std))
|
||||
return mean, covariance
|
||||
|
||||
def predict(self, mean, covariance):
|
||||
"""
|
||||
Run Kalman filter prediction step.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mean : ndarray
|
||||
The 8 dimensional mean vector of the object state at the previous time step.
|
||||
covariance : ndarray
|
||||
The 8x8 dimensional covariance matrix of the object state at the previous time step.
|
||||
|
||||
Returns
|
||||
-------
|
||||
(ndarray, ndarray)
|
||||
Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
|
||||
initialized to 0 mean.
|
||||
"""
|
||||
std_pos = [
|
||||
self._std_weight_position * mean[2], self._std_weight_position * mean[3],
|
||||
self._std_weight_position * mean[2], self._std_weight_position * mean[3]]
|
||||
std_vel = [
|
||||
self._std_weight_velocity * mean[2], self._std_weight_velocity * mean[3],
|
||||
self._std_weight_velocity * mean[2], self._std_weight_velocity * mean[3]]
|
||||
motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
|
||||
|
||||
mean = np.dot(mean, self._motion_mat.T)
|
||||
covariance = np.linalg.multi_dot((self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
|
||||
|
||||
return mean, covariance
|
||||
|
||||
def project(self, mean, covariance):
|
||||
"""
|
||||
Project state distribution to measurement space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mean : ndarray
|
||||
The state's mean vector (8 dimensional array).
|
||||
covariance : ndarray
|
||||
The state's covariance matrix (8x8 dimensional).
|
||||
|
||||
Returns
|
||||
-------
|
||||
(ndarray, ndarray)
|
||||
Returns the projected mean and covariance matrix of the given state estimate.
|
||||
"""
|
||||
std = [
|
||||
self._std_weight_position * mean[2], self._std_weight_position * mean[3],
|
||||
self._std_weight_position * mean[2], self._std_weight_position * mean[3]]
|
||||
innovation_cov = np.diag(np.square(std))
|
||||
|
||||
mean = np.dot(self._update_mat, mean)
|
||||
covariance = np.linalg.multi_dot((self._update_mat, covariance, self._update_mat.T))
|
||||
return mean, covariance + innovation_cov
|
||||
|
||||
def multi_predict(self, mean, covariance):
|
||||
"""
|
||||
Run Kalman filter prediction step (Vectorized version).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mean : ndarray
|
||||
The Nx8 dimensional mean matrix of the object states at the previous time step.
|
||||
covariance : ndarray
|
||||
The Nx8x8 dimensional covariance matrix of the object states at the previous time step.
|
||||
|
||||
Returns
|
||||
-------
|
||||
(ndarray, ndarray)
|
||||
Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
|
||||
initialized to 0 mean.
|
||||
"""
|
||||
std_pos = [
|
||||
self._std_weight_position * mean[:, 2], self._std_weight_position * mean[:, 3],
|
||||
self._std_weight_position * mean[:, 2], self._std_weight_position * mean[:, 3]]
|
||||
std_vel = [
|
||||
self._std_weight_velocity * mean[:, 2], self._std_weight_velocity * mean[:, 3],
|
||||
self._std_weight_velocity * mean[:, 2], self._std_weight_velocity * mean[:, 3]]
|
||||
sqr = np.square(np.r_[std_pos, std_vel]).T
|
||||
|
||||
motion_cov = [np.diag(sqr[i]) for i in range(len(mean))]
|
||||
motion_cov = np.asarray(motion_cov)
|
||||
|
||||
mean = np.dot(mean, self._motion_mat.T)
|
||||
left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))
|
||||
covariance = np.dot(left, self._motion_mat.T) + motion_cov
|
||||
|
||||
return mean, covariance
|
||||
|
||||
def update(self, mean, covariance, measurement):
|
||||
"""
|
||||
Run Kalman filter correction step.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mean : ndarray
|
||||
The predicted state's mean vector (8 dimensional).
|
||||
covariance : ndarray
|
||||
The state's covariance matrix (8x8 dimensional).
|
||||
measurement : ndarray
|
||||
The 4 dimensional measurement vector (x, y, w, h), where (x, y) is the center position, w the width,
|
||||
and h the height of the bounding box.
|
||||
|
||||
Returns
|
||||
-------
|
||||
(ndarray, ndarray)
|
||||
Returns the measurement-corrected state distribution.
|
||||
"""
|
||||
return super().update(mean, covariance, measurement)
|
||||
126
detecttracking/ultralytics/trackers/utils/matching.py
Normal file
126
detecttracking/ultralytics/trackers/utils/matching.py
Normal file
@ -0,0 +1,126 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
|
||||
import numpy as np
|
||||
import scipy
|
||||
from scipy.spatial.distance import cdist
|
||||
|
||||
from ultralytics.utils.metrics import bbox_ioa
|
||||
|
||||
try:
|
||||
import lap # for linear_assignment
|
||||
|
||||
assert lap.__version__ # verify package is not directory
|
||||
except (ImportError, AssertionError, AttributeError):
|
||||
from ultralytics.utils.checks import check_requirements
|
||||
|
||||
check_requirements('lapx>=0.5.2') # update to lap package from https://github.com/rathaROG/lapx
|
||||
import lap
|
||||
|
||||
|
||||
def linear_assignment(cost_matrix, thresh, use_lap=True):
|
||||
"""
|
||||
Perform linear assignment using scipy or lap.lapjv.
|
||||
|
||||
Args:
|
||||
cost_matrix (np.ndarray): The matrix containing cost values for assignments.
|
||||
thresh (float): Threshold for considering an assignment valid.
|
||||
use_lap (bool, optional): Whether to use lap.lapjv. Defaults to True.
|
||||
|
||||
Returns:
|
||||
(tuple): Tuple containing matched indices, unmatched indices from 'a', and unmatched indices from 'b'.
|
||||
"""
|
||||
|
||||
if cost_matrix.size == 0:
|
||||
return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
|
||||
|
||||
if use_lap:
|
||||
# https://github.com/gatagat/lap
|
||||
_, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
|
||||
matches = [[ix, mx] for ix, mx in enumerate(x) if mx >= 0]
|
||||
unmatched_a = np.where(x < 0)[0]
|
||||
unmatched_b = np.where(y < 0)[0]
|
||||
else:
|
||||
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html
|
||||
x, y = scipy.optimize.linear_sum_assignment(cost_matrix) # row x, col y
|
||||
matches = np.asarray([[x[i], y[i]] for i in range(len(x)) if cost_matrix[x[i], y[i]] <= thresh])
|
||||
if len(matches) == 0:
|
||||
unmatched_a = list(np.arange(cost_matrix.shape[0]))
|
||||
unmatched_b = list(np.arange(cost_matrix.shape[1]))
|
||||
else:
|
||||
unmatched_a = list(set(np.arange(cost_matrix.shape[0])) - set(matches[:, 0]))
|
||||
unmatched_b = list(set(np.arange(cost_matrix.shape[1])) - set(matches[:, 1]))
|
||||
|
||||
return matches, unmatched_a, unmatched_b
|
||||
|
||||
|
||||
def iou_distance(atracks, btracks):
|
||||
"""
|
||||
Compute cost based on Intersection over Union (IoU) between tracks.
|
||||
|
||||
Args:
|
||||
atracks (list[STrack] | list[np.ndarray]): List of tracks 'a' or bounding boxes.
|
||||
btracks (list[STrack] | list[np.ndarray]): List of tracks 'b' or bounding boxes.
|
||||
|
||||
Returns:
|
||||
(np.ndarray): Cost matrix computed based on IoU.
|
||||
"""
|
||||
|
||||
if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) \
|
||||
or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
|
||||
atlbrs = atracks
|
||||
btlbrs = btracks
|
||||
else:
|
||||
atlbrs = [track.tlbr for track in atracks]
|
||||
btlbrs = [track.tlbr for track in btracks]
|
||||
|
||||
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32)
|
||||
if len(atlbrs) and len(btlbrs):
|
||||
ious = bbox_ioa(np.ascontiguousarray(atlbrs, dtype=np.float32),
|
||||
np.ascontiguousarray(btlbrs, dtype=np.float32),
|
||||
iou=True)
|
||||
return 1 - ious # cost matrix
|
||||
|
||||
|
||||
def embedding_distance(tracks, detections, metric='cosine'):
|
||||
"""
|
||||
Compute distance between tracks and detections based on embeddings.
|
||||
|
||||
Args:
|
||||
tracks (list[STrack]): List of tracks.
|
||||
detections (list[BaseTrack]): List of detections.
|
||||
metric (str, optional): Metric for distance computation. Defaults to 'cosine'.
|
||||
|
||||
Returns:
|
||||
(np.ndarray): Cost matrix computed based on embeddings.
|
||||
"""
|
||||
|
||||
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32)
|
||||
if cost_matrix.size == 0:
|
||||
return cost_matrix
|
||||
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float32)
|
||||
# for i, track in enumerate(tracks):
|
||||
# cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
|
||||
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float32)
|
||||
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Normalized features
|
||||
return cost_matrix
|
||||
|
||||
|
||||
def fuse_score(cost_matrix, detections):
|
||||
"""
|
||||
Fuses cost matrix with detection scores to produce a single similarity matrix.
|
||||
|
||||
Args:
|
||||
cost_matrix (np.ndarray): The matrix containing cost values for assignments.
|
||||
detections (list[BaseTrack]): List of detections with scores.
|
||||
|
||||
Returns:
|
||||
(np.ndarray): Fused similarity matrix.
|
||||
"""
|
||||
|
||||
if cost_matrix.size == 0:
|
||||
return cost_matrix
|
||||
iou_sim = 1 - cost_matrix
|
||||
det_scores = np.array([det.score for det in detections])
|
||||
det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
|
||||
fuse_sim = iou_sim * det_scores
|
||||
return 1 - fuse_sim # fuse_cost
|
||||
Reference in New Issue
Block a user