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from ultralytics import YOLO
import cv2
# Load the YOLO model
model = YOLO("make.pt")
# Define the mapping of class indices to car types
class_map = {
0: 'beige',
1: 'black',
2: 'blue',
3: 'brown',
4: 'gold',
5: 'green',
6: 'grey',
7: 'orange',
8: 'pink',
9: 'purple',
10: 'red',
11: 'sivler',
12: 'tan',
13: 'white',
14: 'yellow'
}
# Open the video file
video_path = 'DATA\greencar.png'
result = model(video_path)
# cap = cv2.VideoCapture(video_path)
# while True:
# ret, frame = cap.read()
# if not ret:
# break
# # Perform object detection
# results = model(frame)
# # Assuming the top prediction is what you're interested in
# top_prediction_index = results[0].probs.top5[0] # Index of the highest probability class
# top_prediction_prob = results[0].probs.top5conf[0].item() # Highest probability
# # Get the car type from the class_map
# # car_type = class_map[top_prediction_index]
# print('/n')
# print(f"{class_map[top_prediction_index]}")
# cap.release()
# cv2.destroyAllWindows() |