yolov8+SAHI
时间: 2025-01-13 18:53:55 浏览: 64
### 集成YOLOv8与SAHI进行对象检测和图像处理
#### 使用场景概述
对于大规模推理项目,结合YOLOv8模型和SAHI框架能够显著提升目标检测的效果。通过利用SAHI的关键特性[^1],可以实现更精确的对象分割以及复杂环境下的高效推断。
#### 安装依赖库
为了使两者协同工作,需先安装必要的Python包:
```bash
pip install ultralytics sahi
```
#### 加载预训练的YOLOv8模型并配置SAHI参数
创建一个脚本来加载YOLOv8权重文件,并设置用于切片操作的具体选项:
```python
from ultralytics import YOLO
from sahi.model import YoloModelAdapter
from sahi.predict import get_sliced_prediction
model_path = "path/to/yolov8_model.pt"
yolo_model = YOLO(model_path)
sahi_adapter = YoloModelAdapter(yolo_model=yolo_model)
image_path = 'input_image.jpg'
slice_height = 512
slice_width = 512
overlap_ratio = 0.2
confidence_threshold = 0.3
nms_threshold = 0.4
```
#### 执行分片预测过程
调用`get_sliced_prediction()`函数来执行基于切片的目标检测任务,该方法会自动将输入图片分成多个子区域分别进行分析后再汇总结果:
```python
result = get_sliced_prediction(
image=image_path,
slice_height=slice_height,
slice_width=slice_width,
overlap_ratio=overlap_ratio,
confidence_threshold=confidence_threshold,
nms_threshold=nms_threshold,
detection_model=sahi_adapter
)
```
#### 处理输出数据
最后一步是对返回的结果做进一步解析,提取感兴趣的信息如边界框坐标、类别标签等,并将其可视化展示出来:
```python
for obj in result.object_prediction_list:
bbox = obj.bbox.to_voc_bbox()
category_name = obj.category.name
score = obj.score.value
print(f"Detected {category_name} at location {bbox} with confidence {score:.2f}")
```
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