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feature-tracker.py
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import cv2
import depthai as dai
from collections import deque
class FeatureTrackerDrawer:
lineColor = (200, 0, 200)
pointColor = (0, 0, 255)
circleRadius = 2
maxTrackedFeaturesPathLength = 30
# for how many frames the feature is tracked
trackedFeaturesPathLength = 10
trackedIDs = None
trackedFeaturesPath = None
def onTrackBar(self, val):
FeatureTrackerDrawer.trackedFeaturesPathLength = val
pass
def trackFeaturePath(self, features):
newTrackedIDs = set()
for currentFeature in features:
currentID = currentFeature.id
newTrackedIDs.add(currentID)
if currentID not in self.trackedFeaturesPath:
self.trackedFeaturesPath[currentID] = deque()
path = self.trackedFeaturesPath[currentID]
path.append(currentFeature.position)
while(len(path) > max(1, FeatureTrackerDrawer.trackedFeaturesPathLength)):
path.popleft()
self.trackedFeaturesPath[currentID] = path
featuresToRemove = set()
for oldId in self.trackedIDs:
if oldId not in newTrackedIDs:
featuresToRemove.add(oldId)
for id in featuresToRemove:
self.trackedFeaturesPath.pop(id)
self.trackedIDs = newTrackedIDs
def drawFeatures(self, img):
cv2.setTrackbarPos(self.trackbarName, self.windowName, FeatureTrackerDrawer.trackedFeaturesPathLength)
for featurePath in self.trackedFeaturesPath.values():
path = featurePath
for j in range(len(path) - 1):
src = (int(path[j].x), int(path[j].y))
dst = (int(path[j + 1].x), int(path[j + 1].y))
cv2.line(img, src, dst, self.lineColor, 1, cv2.LINE_AA, 0)
j = len(path) - 1
cv2.circle(img, (int(path[j].x), int(path[j].y)), self.circleRadius, self.pointColor, -1, cv2.LINE_AA, 0)
def __init__(self, trackbarName, windowName):
self.trackbarName = trackbarName
self.windowName = windowName
cv2.namedWindow(windowName)
cv2.createTrackbar(trackbarName, windowName, FeatureTrackerDrawer.trackedFeaturesPathLength, FeatureTrackerDrawer.maxTrackedFeaturesPathLength, self.onTrackBar)
self.trackedIDs = set()
self.trackedFeaturesPath = dict()
# Create pipeline
pipeline = dai.Pipeline()
# Define sources and outputs
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
featureTrackerLeft = pipeline.create(dai.node.FeatureTracker)
featureTrackerRight = pipeline.create(dai.node.FeatureTracker)
xoutPassthroughFrameLeft = pipeline.create(dai.node.XLinkOut)
xoutTrackedFeaturesLeft = pipeline.create(dai.node.XLinkOut)
xoutPassthroughFrameRight = pipeline.create(dai.node.XLinkOut)
xoutTrackedFeaturesRight = pipeline.create(dai.node.XLinkOut)
xinTrackedFeaturesConfig = pipeline.create(dai.node.XLinkIn)
xoutPassthroughFrameLeft.setStreamName("passthroughFrameLeft")
xoutTrackedFeaturesLeft.setStreamName("trackedFeaturesLeft")
xoutPassthroughFrameRight.setStreamName("passthroughFrameRight")
xoutTrackedFeaturesRight.setStreamName("trackedFeaturesRight")
xinTrackedFeaturesConfig.setStreamName("trackedFeaturesConfig")
# Properties
monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_720_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_720_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
# Linking
monoLeft.out.link(featureTrackerLeft.inputImage)
featureTrackerLeft.passthroughInputImage.link(xoutPassthroughFrameLeft.input)
featureTrackerLeft.outputFeatures.link(xoutTrackedFeaturesLeft.input)
xinTrackedFeaturesConfig.out.link(featureTrackerLeft.inputConfig)
monoRight.out.link(featureTrackerRight.inputImage)
featureTrackerRight.passthroughInputImage.link(xoutPassthroughFrameRight.input)
featureTrackerRight.outputFeatures.link(xoutTrackedFeaturesRight.input)
xinTrackedFeaturesConfig.out.link(featureTrackerRight.inputConfig)
# By default the least mount of resources are allocated
# increasing it improves performance
numShaves = 2
numMemorySlices = 2
featureTrackerLeft.setHardwareResources(numShaves, numMemorySlices)
featureTrackerRight.setHardwareResources(numShaves, numMemorySlices)
featureTrackerConfig = featureTrackerRight.initialConfig.get()
print("Press 's' to switch between Lucas-Kanade optical flow and hardware accelerated motion estimation!")
# Connect to device and start pipeline
with dai.Device(pipeline) as device:
# Output queues used to receive the results
passthroughImageLeftQueue = device.getOutputQueue("passthroughFrameLeft", 8, False)
outputFeaturesLeftQueue = device.getOutputQueue("trackedFeaturesLeft", 8, False)
passthroughImageRightQueue = device.getOutputQueue("passthroughFrameRight", 8, False)
outputFeaturesRightQueue = device.getOutputQueue("trackedFeaturesRight", 8, False)
inputFeatureTrackerConfigQueue = device.getInputQueue("trackedFeaturesConfig")
leftWindowName = "left"
leftFeatureDrawer = FeatureTrackerDrawer("Feature tracking duration (frames)", leftWindowName)
rightWindowName = "right"
rightFeatureDrawer = FeatureTrackerDrawer("Feature tracking duration (frames)", rightWindowName)
while True:
inPassthroughFrameLeft = passthroughImageLeftQueue.get()
passthroughFrameLeft = inPassthroughFrameLeft.getFrame()
leftFrame = cv2.cvtColor(passthroughFrameLeft, cv2.COLOR_GRAY2BGR)
inPassthroughFrameRight = passthroughImageRightQueue.get()
passthroughFrameRight = inPassthroughFrameRight.getFrame()
rightFrame = cv2.cvtColor(passthroughFrameRight, cv2.COLOR_GRAY2BGR)
trackedFeaturesLeft = outputFeaturesLeftQueue.get().trackedFeatures
leftFeatureDrawer.trackFeaturePath(trackedFeaturesLeft)
leftFeatureDrawer.drawFeatures(leftFrame)
trackedFeaturesRight = outputFeaturesRightQueue.get().trackedFeatures
rightFeatureDrawer.trackFeaturePath(trackedFeaturesRight)
rightFeatureDrawer.drawFeatures(rightFrame)
# Show the frame
cv2.imshow(leftWindowName, leftFrame)
cv2.imshow(rightWindowName, rightFrame)
key = cv2.waitKey(1)
if key == 27:
break
elif key == ord('s'):
if featureTrackerConfig.motionEstimator.type == dai.FeatureTrackerConfig.MotionEstimator.Type.LUCAS_KANADE_OPTICAL_FLOW:
featureTrackerConfig.motionEstimator.type = dai.FeatureTrackerConfig.MotionEstimator.Type.HW_MOTION_ESTIMATION
print("Switching to hardware accelerated motion estimation")
else:
featureTrackerConfig.motionEstimator.type = dai.FeatureTrackerConfig.MotionEstimator.Type.LUCAS_KANADE_OPTICAL_FLOW
print("Switching to Lucas-Kanade optical flow")
cfg = dai.FeatureTrackerConfig()
cfg.set(featureTrackerConfig)
inputFeatureTrackerConfigQueue.send(cfg)