mmdetection测试结果保存
时间: 2023-09-02 22:11:24 浏览: 253
在mmdetection框架中,可以通过以下方法将测试结果保存下来:
1. 首先,创建一个test.py文件,并添加以下代码:
```
from mmdet.apis import init_detector, inference_detector, show_result_pyplot
import os
imagepath = r'D:\software\opensources\mmdet214\data\new_cell_cocoformat\valset' # 需要加载的测试图片的文件路径
savepath = r'D:\software\opensources\mmdet214\configs\retinanet\cell\test_show' # 保存测试图片的路径
config_file = r'D:\software\opensources\mmdet214\configs\cell\retinanet_r101_fpn_1x_cell.py' # 网络模型
checkpoint_file = r'D:\software\opensources\mmdet214\work_dirs\retinanet_r101_fpn_1x_cell_1300_472\epoch_100.pth' # 训练好的模型参数
device = 'cuda:0'
# 初始化检测器模型
model = init_detector(config_file, checkpoint_file, device=device)
# 对每张测试图片进行推理和结果保存
for filename in os.listdir(imagepath):
img = os.path.join(imagepath, filename)
result = inference_detector(model, img)
out_file = os.path.join(savepath, filename)
show_result_pyplot(model, img, result, out_file, score_thr=0.6)
```
2. 修改mmdet\apis\inference.py文件中的show_result_pyplot函数,添加一个out_file参数,并将其传递给model.show_result函数。具体修改内容如下:
```
def show_result_pyplot(model, img, result, out_file, score_thr=0.3, title='result', wait_time=0):
"""
Visualize the detection results on the image.
Args:
model (nn.Module): The loaded detector.
img (str or np.ndarray): Image filename or loaded image.
result (tuple[list or list): The detection result, can be either (bbox, segm) or just bbox.
score_thr (float): The threshold to visualize the bboxes and masks.
title (str): Title of the pyplot figure.
wait_time (float): Value of waitKey param. Default: 0.
out_file (str): Path to save the visualization result.
"""
if hasattr(model, 'module'):
model = model.module
model.show_result(img, result, out_file, score_thr=score_thr, show=True, wait_time=wait_time, win_name=title, bbox_color=(72, 101, 241), text_color=(72, 101, 241))
```
通过以上两个步骤,你可以在mmdetection框架中测试图片并保存结果。每个测试图片的结果将保存在指定的路径下。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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