語義分割學習筆記3——運作語義分割程式

• 一、linux安裝依賴環境
• • 1、建立open-mmlab環境
  • 2、安裝pytorch
  • 3、 安裝 MMCV
  • 4、安裝 MMSegmentation
• 三、測試是否安裝成功
• 二、windows安裝依賴環境

準備運作經典的open-mmlab mmsegmentation架構

源碼位址：https://github.com/open-mmlab/mmsegmentation

安裝說明：https://github.com/open-mmlab/mmsegmentation/blob/master/README_zh-CN.md

cat /usr/local/cuda/version.txt
           

or

deepstream-app --version-all
           

deepstream-app version 5.0.0

CUDA Runtime Version: 10.2

這裡我省略了。

語義分割學習筆記2——Jetson Xavier安裝pytorch

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.6.0/index.html
           

pip install mmsegmentation 
           

三、測試是否安裝成功

下載下傳需要的模型

https://github.com/open-mmlab/mmsegmentation/tree/master/configs

下載下傳下文源碼中對應的cfg和checkpoint檔案。

from mmseg.apis import inference_segmentor, init_segmentor
import mmcv

config_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, device='cuda: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, show=True)
# 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_file='result.jpg', opacity=0.5)

# test a video and show the results
video = mmcv.VideoReader('video.mp4')
for frame in video:
   result = inference_segmentor(model, frame)
   model.show_result(frame, result, wait_time=1)

           

python demo/image_demo.py demo/demo.jpg configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py \
    checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth --device cuda:0 --palette cityscapes