# PIL/Pillow Basics - The essential image library
from PIL import Image

# Open and display image
img = Image.open("input.jpg")
img.show()

# Convert formats
img.save("output.png")
img.convert("L").save("grayscale.jpg")  # RGB to grayscale

# Basic transformations
img.rotate(90).save("rotated.jpg")
img.resize((300, 300)).save("resized.jpg")
img.transpose(Image.FLIP_LEFT_RIGHT).save("mirrored.jpg")

pip install ultralytics opencv-python numpy

import cv2
import numpy as np
from ultralytics import YOLO

# Hashtags: #Setup #Python #OpenCV #YOLOv8

# Load a pre-trained YOLOv8 model (yolov8n.pt is small and fast)
model = YOLO('yolov8n.pt')

# Path to your video file
VIDEO_PATH = 'traffic_video.mp4'

# Define the polygons for two lanes.
# IMPORTANT: You MUST adjust these coordinates for your video's perspective.
# Each polygon is a numpy array of [x, y] coordinates.
LANE_1_POLYGON = np.array([[20, 400], [450, 400], [450, 250], [20, 250]], np.int32)
LANE_2_POLYGON = np.array([[500, 400], [980, 400], [980, 250], [500, 250]], np.int32)

# Define the congestion threshold. If vehicle count > this, the lane is congested.
CONGESTION_THRESHOLD = 10

# Hashtags: #Configuration #AIModel #SmartCity

cap = cv2.VideoCapture(VIDEO_PATH)

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        break

    # Run YOLOv8 inference on the frame
    results = model(frame)

    # Initialize vehicle counts for each lane for the current frame
    lane_1_count = 0
    lane_2_count = 0

    # Process detection results
    for r in results:
        for box in r.boxes:
            # Check if the detected object is a vehicle
            class_id = int(box.cls[0])
            class_name = model.names[class_id]
            
            if class_name in ['car', 'truck', 'bus', 'motorbike']:
                # Get bounding box coordinates
                x1, y1, x2, y2 = map(int, box.xyxy[0])
                
                # Calculate the center point of the bounding box
                center_x = (x1 + x2) // 2
                center_y = (y1 + y2) // 2

                # Check if the center point is inside Lane 1
                if cv2.pointPolygonTest(LANE_1_POLYGON, (center_x, center_y), False) >= 0:
                    lane_1_count += 1
                
                # Check if the center point is inside Lane 2
                elif cv2.pointPolygonTest(LANE_2_POLYGON, (center_x, center_y), False) >= 0:
                    lane_2_count += 1

# Hashtags: #RealTime #ObjectDetection #VideoProcessing