yolov5里test.py的运行代码
时间: 2023-08-06 08:04:23 浏览: 91
您好!YOLOv5是一个目标检测算法,test.py是其中的一个测试脚本。以下是YOLOv5中test.py的运行代码示例:
```python
import torch
import argparse
import sys
sys.path.append('path/to/yolov5') # 替换为YOLOv5的路径
from models.experimental import attempt_load
from utils.general import check_img_size, non_max_suppression, scale_coords
from utils.torch_utils import select_device, time_synchronized
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
opt = parser.parse_args()
# 设置设备
opt.device = select_device(opt.device)
# 加载模型
model = attempt_load(opt.weights, map_location=opt.device)
imgsz = check_img_size(opt.img_size, s=model.stride.max())
# 设置模型为评估模式
model.eval()
# 进行推理
with torch.no_grad():
for path, img, im0s, _ in dataset:
img = torch.from_numpy(img).to(opt.device)
img = img.float()
# 图片预处理
img /= 255.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# 推理
pred = model(img)[0]
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes,
agnostic=opt.agnostic_nms)
# 处理预测结果
for i, det in enumerate(pred):
if len(det):
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0s.shape).round()
# 绘制边界框、标签等
for *xyxy, conf, cls in reversed(det):
label = f'{names[int(cls)]} {conf:.2f}'
plot_one_box(xyxy, im0s, label=label, color=colors[int(cls)], line_thickness=3)
# 显示结果
cv2.imshow('', im0s)
cv2.waitKey(0) # 按任意键退出
if __name__ == '__main__':
main()
```
请注意,以上代码只是示例代码,具体使用时需要根据您的环境和需求进行相应的修改。