Hello community
I want to build a vehicle detection with the camera OAK-1 and a RaspberryPi on which I use the image OAK_CM4_POE.
I have tried this example:
.../dephtai-python/examples/MobileNet/rgb_mobilenet_2.py
I copied the file rgb_mobilenet.py and named it rgb_mobilenet_2.py because I still have the software for PiFace2 installed on the Raspberry and wanted to integrate the PiFace2 into this example.
Now it is about recognizing 3 types of vehicles.
Namely bus, train and car.
The relay of the PiFace2 should switch as soon as one of the 3 objects is detected.
As soon as an object is detected, a frame with the name of the detected object appears in the camera image.
How do I have to write the if-statement, so that I can say for example:
If the frame says "car" or bus or train, then switch the relay.
Here is my code with which I tried this, but nothing worked.
I tried to find the detected object in a variable, but without success....
Therefore also the attempt with: ("train" in detections)...etc
from pathlib import Path
import cv2
import depthai as dai
import numpy as np
import time
import argparse
import pifacedigitalio
nnPathDefault = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute())
parser = argparse.ArgumentParser()
parser.add_argument('nnPath', nargs='?', help="Path to mobilenet detection network blob", default=nnPathDefault)
parser.add_argument('-s', '--sync', action="store_true", help="Sync RGB output with NN output", default=False)
args = parser.parse_args()
if not Path(nnPathDefault).exists():
import sys
raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
# MobilenetSSD label texts
labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
"diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
# Create pipeline
pipeline = dai.Pipeline()
# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
nn = pipeline.create(dai.node.MobileNetDetectionNetwork)
xoutRgb = pipeline.create(dai.node.XLinkOut)
nnOut = pipeline.create(dai.node.XLinkOut)
xoutRgb.setStreamName("rgb")
nnOut.setStreamName("nn")
try:
pfd = pifacedigitalio.PiFaceDigital()
pfd.leds[0].turn_off()
except:
print("No Piface connected!")
pfd = None
# Properties
camRgb.setPreviewSize(300, 300)
camRgb.setInterleaved(False)
camRgb.setFps(30)
# Define a neural network that will make predictions based on the source frames
nn.setConfidenceThreshold(0.5)
nn.setBlobPath(args.nnPath)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(False)
# Linking
if args.sync:
nn.passthrough.link(xoutRgb.input)
else:
camRgb.preview.link(xoutRgb.input)
camRgb.preview.link(nn.input)
nn.out.link(nnOut.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
qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)
frame = None
detections = []
startTime = time.monotonic()
counter = 0
color2 = (255, 255, 255)
#Fahrzeug = labelMap[detection.label]
# nn data (bounding box locations) are in <0..1> range - they need to be normalized with frame width/height
def frameNorm(frame, bbox):
normVals = np.full(len(bbox), frame.shape[0])
normVals[::2] = frame.shape[1]
return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)
def displayFrame(name, frame):
color = (255, 0, 0)
for detection in detections:
bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
# Show the frame
cv2.imshow(name, frame)
while True:
if args.sync:
# Use blocking get() call to catch frame and inference result synced
inRgb = qRgb.get()
inDet = qDet.get()
else:
# Instead of get (blocking), we use tryGet (nonblocking) which will return the available data or None otherwise
inRgb = qRgb.tryGet()
inDet = qDet.tryGet()
if inRgb is not None:
frame = inRgb.getCvFrame()
cv2.putText(frame, "NN fps: {:.2f}".format(counter / (time.monotonic() - startTime)),
(2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color2)
if inDet is not None:
detections = inDet.detections
counter += 1
# If the frame is available, draw bounding boxes on it and show the frame
if frame is not None:
displayFrame("rgb", frame)
#pull relay to 1 if an Object was detected
Objects= labelMap[detection.label]
if pfd is not None:
pfd.leds[0].turn_on() if (("car" in detection) or ("train" in detection) or ("bus" in detection)) else pfd.leds[0].turn_off()
# pfd.leds[0].turn_on() if ((Objects == "car") or (Objects == "train") or (Objects == "bus")) else pfd.leds[0].turn_off()
if cv2.waitKey(1) == ord('q'):
pfd.leds[0].turn_off()
break
`
