I am using the following code to do tracking and detection. However, the FPS I am getting is around 7-8. I have experimented with a few parameters but they don't seem to make a difference on the FPS. Can someone please help in improving this?

from pathlib import Path

import sys

import cv2

import depthai as dai

import numpy as np

import time

# Get argument first

nnPath = str((Path(__file__).parent / Path('yolov7-tiny_openvino_2022.1_6shave.blob')).resolve().absolute())
labelMap = [ "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
syncNN = True

# Create pipeline

pipeline = dai.Pipeline()
# Define sources and outputs

camRgb = pipeline.create(dai.node.ColorCamera)

detectionNetwork = pipeline.create(dai.node.YoloDetectionNetwork)

xoutRgb = pipeline.create(dai.node.XLinkOut)

nnOut = pipeline.create(dai.node.XLinkOut)
xoutRgb.setStreamName("rgb")nnOut.setStreamName("nn")

#Properties

camRgb.setPreviewSize(640,640)

camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)

camRgb.setInterleaved(False)

camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)

camRgb.setFps(20)

# Network specific settingsdetectionNetwork.setConfidenceThreshold(0.5)

detectionNetwork.setNumClasses(80)

detectionNetwork.setCoordinateSize(4)

detectionNetwork.setAnchors([12.0, 16.0, 19.0, 36.0, 40.0, 28.0, 36.0, 75.0, 76.0, 55.0, 72.0, 146.0, 142.0, 110.0, 192.0, 243.0, 459.0, 401.0])

detectionNetwork.setAnchorMasks({"side80": [0, 1, 2], "side40": [3, 4, 5], "side20": [6, 7, 8]})

detectionNetwork.setIouThreshold(0.5)

detectionNetwork.setBlobPath(nnPath)

detectionNetwork.setNumInferenceThreads(2)

detectionNetwork.input.setBlocking(False)

# Linking

camRgb.preview.link(detectionNetwork.input)

if syncNN:

detectionNetwork.passthrough.link(xoutRgb.input)

else:

camRgb.preview.link(xoutRgb.input)
detectionNetwork.out.link(nnOut.input)
# Connect to device and start pipeline

device_info = dai.DeviceInfo("169.254.1.222")

with dai.Device(pipeline, device_info) as device:
# usbSpeed = dai.UsbSpeed.SUPER

# 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)
# nn data, being the 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, 255)

cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)

cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2) # Show the frame cv2.imshow(name, frame)
while True:

if syncNN:

inRgb = qRgb.get()

inDet = qDet.get()

else:

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 frame is not None:

displayFrame("rgb", frame)

if cv2.waitKey(1) == ord('q'):

break