After counting the vehicles, we need to visualize the results on the video frame. We will draw the lane polygons, display the vehicle count for each, and change the lane's color to red if it is considered congested based on our threshold.

# --- Visualization --- (This code continues inside the while loop)

    # Draw the lane polygons on the frame
    cv2.polylines(frame, [LANE_1_POLYGON], isClosed=True, color=(255, 255, 0), thickness=2)
    cv2.polylines(frame, [LANE_2_POLYGON], isClosed=True, color=(255, 255, 0), thickness=2)

    # Check for congestion and display status for Lane 1
    if lane_1_count > CONGESTION_THRESHOLD:
        status_1 = "CONGESTED"
        color_1 = (0, 0, 255) # Red
    else:
        status_1 = "NORMAL"
        color_1 = (0, 255, 0) # Green
    
    cv2.putText(frame, f"Lane 1: {lane_1_count} ({status_1})", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color_1, 2)

    # Check for congestion and display status for Lane 2
    if lane_2_count > CONGESTION_THRESHOLD:
        status_2 = "CONGESTED"
        color_2 = (0, 0, 255) # Red
    else:
        status_2 = "NORMAL"
        color_2 = (0, 255, 0) # Green

    cv2.putText(frame, f"Lane 2: {lane_2_count} ({status_2})", (530, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color_2, 2)
    
    # Display the frame with detections and status
    cv2.imshow("Traffic Congestion Monitor", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

# Hashtags: #DataVisualization #OpenCV #TrafficFlow

---

#Step 5: Results and Discussion

When you run the script, a video window will appear. You will see:
• Yellow polygons outlining the defined lanes.
• Text at the top indicating the number of vehicles in each lane and its status ("NORMAL" or "CONGESTED").
• The status text and its color will change in real-time based on the vehicle count exceeding the CONGESTION_THRESHOLD.

Discussion of Results:
Threshold is Key: The CONGESTION_THRESHOLD is the most important variable to tune. A value of 10 might be too high for a short lane or too low for a long one. It must be calibrated based on the specific camera view and what is considered "congested" for that road.
Polygon Accuracy: The system's accuracy is highly dependent on how well you define the LANE_POLYGON coordinates. They must accurately map to the lanes in the video, accounting for perspective.
Limitations: This method only measures vehicle density (number of cars in an area). It does not measure traffic flow (vehicle speed). A lane could have many cars moving quickly (high density, but not congested) or a few stopped cars (low density, but very congested).
Potential Improvements:
Object Tracking: Implement an object tracker (like DeepSORT or BoT-SORT) to assign a unique ID to each car. This would allow you to calculate the average speed of vehicles within each lane, providing a much more reliable measure of congestion.
Time-Based Analysis: Analyze data over time. A lane that is consistently above the threshold for more than a minute is a stronger indicator of a traffic jam than a brief spike in vehicle count.

#ProjectComplete #AIforCities #Transportation

━━━━━━━━━━━━━━━
By: @CodeProgrammer ✨