import cv2
import torch
from yolov5_model import YOLOv5Model
 
model = YOLOv5Model()
 
def process_frame(frame):
    with torch.no_grad():
        detections = model(frame)
    
    results = process_detections(detections)
    return results
 
cap = cv2.VideoCapture(0)  # 使用攝像頭
 
while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    results = process_frame(frame)
    display_results(frame, results)
 
    cv2.imshow('YOLOv5 License Plate Recognition', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
 
cap.release()
cv2.destroyAllWindows()

import time
 
def count_plates(results):
    return len(results)
 
frame_count = 0
plate_count = 0
start_time = time.time()
 
while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    frame_count += 1
    results = process_frame(frame)
    plate_count += count_plates(results)
    
    if frame_count % 100 == 0:
        elapsed_time = time.time() - start_time
        plates_per_second = plate_count / elapsed_time
        print(f'Plates detected per second: {plates_per_second:.2f}')
        start_time = time.time()
        plate_count = 0
 
    display_results(frame, results)
    cv2.imshow('YOLOv5 License Plate Recognition', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

from brand_recognition_model import BrandRecognitionModel
 
brand_model = BrandRecognitionModel()
 
def recognize_brands(vehicles):
    brands = []
    for vehicle in vehicles:
        brand = brand_model.recognize(vehicle)
        brands.append(brand)
    return brands
 
def display_results_with_brands(frame, results, brands):
    for i, result in enumerate(results):
        draw_bounding_box(frame, result)
        draw_brand_label(frame, result, brands[i])
 
while True:
    ret, frame = cap.read()
    if not ret:
        break
 
    results = process_frame(frame)
    vehicles = extract_vehicles_from_plates(frame, results)
    brands = recognize_brands(vehicles)
    
    display_results_with_brands(frame, results, brands)
    cv2.imshow('YOLOv5 License Plate Recognition', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

from vehicle_behavior_analysis import VehicleBehaviorAnalysis
 
behavior_analysis = VehicleBehaviorAnalysis()
 
def analyze_vehicle_behavior(previous_results, current_results):
    behaviors = behavior_analysis.compare(previous_results, current_results)
    return behaviors
 
def display_results_with_behavior(frame, results, behaviors):
    for i, result in enumerate(results):
        draw_bounding_box(frame, result)
        draw_behavior_label(frame, result, behaviors[i])
 
previous_results = None
 
while True:
    ret, frame = cap.read()
    if not ret:
        break
 
    current_results = process_frame(frame)
    
    if previous_results is not None:
        behaviors = analyze_vehicle_behavior(previous_results, current_results)
        display_results_with_behavior(frame, current_results, behaviors)
    else:
        display_results(frame, current_results)
 
    previous_results = current_results
    cv2.imshow('YOLOv5 License Plate Recognition', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break