下载免费网站模板下载,临沂市建设局网站,网络架构必须满足哪些特性,萨wordpress安装配置MMSegmentation环境
为了验证 MMSegmentation 和所需的环境是否安装正确#xff0c;我们可以运行示例 python 代码来初始化分段器并推断演示图像#xff1a;
from mmseg.apis import inference_segmentor, init_segmentor
import mmcvconfig_file configs/pspnet/…安装配置MMSegmentation环境
为了验证 MMSegmentation 和所需的环境是否安装正确我们可以运行示例 python 代码来初始化分段器并推断演示图像
from mmseg.apis import inference_segmentor, init_segmentor
import mmcvconfig_file configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
checkpoint_file checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth# build the model from a config file and a checkpoint file
model init_segmentor(config_file, checkpoint_file, devicecuda:0)# test a single image and show the results
img test.jpg # or img mmcv.imread(img), which will only load it once
result inference_segmentor(model, img)
# visualize the results in a new window
model.show_result(img, result, showTrue)
# or save the visualization results to image files
# you can change the opacity of the painted segmentation map in (0, 1].
model.show_result(img, result, out_fileresult.jpg, opacity0.5) 安装MMCV官方
mmsegmention环境安装 Ubuntu20运行SegNeXt代码提取道路水体(一)——从mmsegmentation安装到测试代码环境配置全过程摸索
怎么把图片保存出来 在SegNeXt/mmseg/models/segmentors/base.py里的show_result函数里加了out_file demo/result_demo.png 但是会报错 cv2.error: OpenCV(4.5.5) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function cvtColor 出现的问题
后面下载了ade数据集刚开始运行
python tools/train.py configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py 还能跑起来但是不知道为什么就报错AttributeError: ConfigDict object has no attribute log_level 解决AttributeError: ‘ConfigDict‘ object has no attribute ‘log_level‘ 训练与测试
训练
python tools/train.py configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py
测试
python tools/test.py work_dirs/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k.py work_dirs/pspnet_r50-d8_512x512_80k_ade20k/iter_80000.pth --eval mIoU
想要把预测的图片生成出来
python tools/test.py work_dirs/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k.py work_dirs/pspnet_r50-d8_512x512_80k_ade20k/iter_80000.pth --show-dir work_results/ --eval mIoU 使用SegNeXt进行训练及预测
下载ImageNet预训练模型 用相同的方法进行训练 看看ade数据效果如何 把这个预训练放在这个路径下pretrained/mscan_b.pth 修改数据的位置放到这个路径下/root/ADEChallengeData2016/images/training
出现错误PermissionError: ADE20KDataset: [Errno 13] Permission denied: ‘/root/ADEChallengeData2016/images/training’
修改之后
训练
python tools/train.py local_configs/segnext/base/segnext.base.512x512.ade.160k.py
预测
python tools/test.py work_dirs/segnext.base.512x512.ade.160k/segnext.base.512x512.ade.160k.py work_dirs/segnext.base.512x512.ade.160k/latest.pth --show-dir work_results_segnext/ --eval mIoU结果 代码详解
代码详解
参考 Ubuntu20运行SegNeXt代码提取道路水体(一)——从mmsegmentation安装到测试代码环境配置全过程摸索 Ubuntu20运行SegNeXt代码提取道路水体(二)——SegNeXt源代码安装到测试环境配置全过程摸索 MMSegmentation V0.27.0训练与推理自己的数据集二
