write code for real time vehicle detection and counting in python
Here is an example code for real-time vehicle detection and counting using Python, OpenCV, and YOLO (You Only Look Once) object detection algorithm:
```
import cv2
import numpy as np

# Load YOLO model
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Set up video capture
cap = cv2.VideoCapture(0)  # Use 0 for default camera, or specify a video file

# Set up vehicle counter
vehicle_count = 0

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Resize frame to improve performance
    frame = cv2.resize(frame, (640, 480))

    # Convert frame to blob
    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)

    # Set input blob for YOLO model
    net.setInput(blob)

    # Run YOLO model
    outs = net.forward(output_layers)

    # Initialize lists to store detected vehicles
 To detect and count vehicles in real-time using Python, you can use a computer vision library like OpenCV. Here's some sample code that demonstrates how to do this:
```
import cv2
import numpy as np

# Load Yolo
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Loading video
cap = cv2.VideoCapture("traffic.mp4")

vehicle_count = 0

while True:
    ret, frame = cap.read()
    height, width, channels = frame.shape

    # Detecting objects
    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)

    class_ids = []
    confidences = []
    boxes = []

    for out in outs:
        for detection in out:
            scores = detection[5