解决Python中supervision模块导入错误的完整指南

Traceback (most recent call last):
  File "/Users/yahyahajali/Python/ComputerVision-master/yolov8.py", line 8, in <module>
    from supervision.tools.detections import Detections, BoxAnnotator
ModuleNotFoundError: No module named 'supervision.tools'

from supervision.tools.detections import Detections, BoxAnnotator

from supervision import Detections, BoxAnnotator

import torch
import numpy as np
import cv2
from time import time
from ultralytics import YOLO

# 从supervision库直接导入Detections和BoxAnnotator
from supervision import Detections, BoxAnnotator 
from supervision.draw.color import ColorPalette # ColorPalette通常在supervision.draw模块中

# 假设 IMAGE 是一个已经加载的图像，例如：
# IMAGE = cv2.imread("path/to/your/image.jpg")

def process_frame(image_path):
    """
    加载YOLO模型并对图像进行目标检测，然后使用supervision进行标注。
    """
    # 1. 加载YOLOv8模型
    model = YOLO('yolov8s.pt')

    # 2. 读取图像
    image = cv2.imread(image_path)
    if image is None:
        print(f"Error: Could not load image from {image_path}")
        return

    # 3. 执行目标检测
    yolov8_results = model(image)[0]

    # 4. 将YOLO结果转换为supervision的Detections对象
    detections = Detections.from_yolov8(yolov8_results)

    # 5. 初始化BoxAnnotator用于绘制检测框
    box_annotator = BoxAnnotator(color=ColorPalette(), thickness=2, text_thickness=1, text_scale=0.5)

    # 6. 在图像上绘制检测结果
    annotated_image = box_annotator.annotate(scene=image.copy(), detections=detections)

    # 7. 显示或保存结果 (这里仅为演示，实际应用中可根据需求处理)
    # cv2.imshow("YOLOv8 Detections", annotated_image)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()
    print("Detection and annotation successful.")
    return annotated_image

# 运行示例 (请替换为您的图片路径)
# processed_img = process_frame("path/to/your/image.jpg")

pip install supervision

python
>>> import supervision
>>> from supervision import Detections, BoxAnnotator