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antTrail.py
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antTrail.py
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#!/usr/bin/env python3
from pathlib import Path
import cv2
import depthai as dai
import numpy as np
import time
import blobconverter
'''
Spatial detection network demo.
Performs inference on RGB camera and retrieves spatial location coordinates: x,y,z relative to the center of depth map.
'''
# Get argument first
#nnBlobPath = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute())
'''
# NN for license plate detection, PROBLEM: need front facing cars
nnBlobPath=blobconverter.from_zoo(name="vehicle-license-plate-detection-barrier-0106", shaves=6)
frame_dimension = [300,300]
'''
# NN for vehicle detection
nnBlobPath=blobconverter.from_zoo(name="vehicle-detection-0202", shaves=6)
frame_dimension = [512,512]
print("nnBlobPath: ", nnBlobPath)
if not Path(nnBlobPath).exists():
import sys
raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
# MobilenetSSD label texts
labelMap = ["vehicle"]
syncNN = True
# Create pipeline
pipeline = dai.Pipeline()
# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
spatialDetectionNetwork = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork)
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)
xoutRgb = pipeline.create(dai.node.XLinkOut)
xoutNN = pipeline.create(dai.node.XLinkOut)
xoutBoundingBoxDepthMapping = pipeline.create(dai.node.XLinkOut)
xoutDepth = pipeline.create(dai.node.XLinkOut)
xoutRgb.setStreamName("rgb")
xoutNN.setStreamName("detections")
xoutBoundingBoxDepthMapping.setStreamName("boundingBoxDepthMapping")
xoutDepth.setStreamName("depth")
# Properties
camRgb.setPreviewSize(frame_dimension) #depends on the chosen NN
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
# Setting node configs
stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
spatialDetectionNetwork.setBlobPath(nnBlobPath)
spatialDetectionNetwork.setConfidenceThreshold(0.5)
spatialDetectionNetwork.input.setBlocking(False)
spatialDetectionNetwork.setBoundingBoxScaleFactor(0.5)
spatialDetectionNetwork.setDepthLowerThreshold(100)
spatialDetectionNetwork.setDepthUpperThreshold(5000)
#spatialDetectionNetwork.setDepthAlign()
# Linking
monoLeft.out.link(stereo.left)
monoRight.out.link(stereo.right)
camRgb.preview.link(spatialDetectionNetwork.input)
if syncNN:
spatialDetectionNetwork.passthrough.link(xoutRgb.input)
else:
camRgb.preview.link(xoutRgb.input)
spatialDetectionNetwork.out.link(xoutNN.input)
spatialDetectionNetwork.boundingBoxMapping.link(xoutBoundingBoxDepthMapping.input)
stereo.depth.link(spatialDetectionNetwork.inputDepth)
spatialDetectionNetwork.passthroughDepth.link(xoutDepth.input)
# Connect to device and start pipeline
with dai.Device(pipeline) as device:
# Output queues will be used to get the rgb frames and nn data from the outputs defined above
previewQueue = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
detectionNNQueue = device.getOutputQueue(name="detections", maxSize=4, blocking=False)
xoutBoundingBoxDepthMapping = device.getOutputQueue(name="boundingBoxDepthMapping", maxSize=4, blocking=False)
depthQueue = device.getOutputQueue(name="depth", maxSize=4, blocking=False)
startTime = time.monotonic()
counter = 0
fps = 0
# Previous distance detected
previous_distance = np.zeros(3)
# Previous timestamp
previous_time = time.monotonic()
while True:
inPreview = previewQueue.get()
inDet = detectionNNQueue.get()
depth = depthQueue.get()
counter+=1
current_time = time.monotonic()
if (current_time - startTime) > 1 :
fps = counter / (current_time - startTime)
counter = 0
startTime = current_time
frame = inPreview.getCvFrame()
depthFrame = depth.getFrame() # depthFrame values are in millimeters
depthFrameColor = cv2.normalize(depthFrame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
depthFrameColor = cv2.equalizeHist(depthFrameColor)
depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
detections = inDet.detections
if len(detections) != 0:
boundingBoxMapping = xoutBoundingBoxDepthMapping.get()
roiDatas = boundingBoxMapping.getConfigData()
for roiData in roiDatas:
roi = roiData.roi
roi = roi.denormalize(depthFrameColor.shape[1], depthFrameColor.shape[0])
topLeft = roi.topLeft()
bottomRight = roi.bottomRight()
xmin = int(topLeft.x)
ymin = int(topLeft.y)
xmax = int(bottomRight.x)
ymax = int(bottomRight.y)
cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), 255, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX)
# If the frame is available, draw bounding boxes on it and show the frame
height = frame.shape[0]
width = frame.shape[1]
for detection in detections:
# Denormalize bounding box
x1 = int(detection.xmin * width)
x2 = int(detection.xmax * width)
y1 = int(detection.ymin * height)
y2 = int(detection.ymax * height)
try:
label = labelMap[detection.label]
except:
label = detection.label
cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, "{:.2f}".format(detection.confidence*100), (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"X: {int(detection.spatialCoordinates.x)} mm", (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"Y: {int(detection.spatialCoordinates.y)} mm", (x1 + 10, y1 + 65), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"Z: {int(detection.spatialCoordinates.z)} mm", (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), cv2.FONT_HERSHEY_SIMPLEX)
# Just for first detection...
if(len(detections) != 0):
detection = detections[0]
x = int(detection.xmin * width)
y = int(detection.ymin * height)
# Current distance
current_distance = np.zeros(3)
current_distance[0] = detection.spatialCoordinates.x
current_distance[1] = detection.spatialCoordinates.y
current_distance[2] = detection.spatialCoordinates.z
# Compute relative speed [m/s]
try:
speed = (current_distance-previous_distance)/(current_time-previous_time)/1000
except:
speed = (current_distance-previous_distance)/1000
# Display speed
cv2.putText(frame, f"V_X: {float(round(speed[0],3))} m/s", (x + 10, y + 110), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"V_Y: {float(round(speed[1],3))} m/s", (x + 10, y + 125), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"V_Z: {float(round(speed[2],3))} m/s", (x + 10, y + 140), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
# Save previous location and timestamp
previous_distance = current_distance
previous_time = current_time
cv2.putText(frame, "NN fps: {:.2f}".format(fps), (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, (255,255,255))
cv2.imshow("depth", depthFrameColor)
cv2.imshow("preview", frame)
if cv2.waitKey(1) == ord('q'):
break