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73 lines
1.9 KiB
Markdown
73 lines
1.9 KiB
Markdown
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### Install
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0. clone the project
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```
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git clone https://github.com/naurril/SUSTechPOINTS
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```
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1. Install packages
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```
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pip install -r requirement.txt
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```
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2. Download model
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download pretrained model file [deep_annotation_inference.h5](https://github.com/naurril/SUSTechPOINTS/releases/download/0.1/deep_annotation_inference.h5), put it into `./algos/models`
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```
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wget https://github.com/naurril/SUSTechPOINTS/releases/download/0.1/deep_annotation_inference.h5 -P algos/models
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```
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### Start
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Run the following command in shell, then go to http://127.0.0.1:8081
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```
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python main.py
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```
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## Object type configuration
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Default object configuration is in [obj_cfg.js](src/public/js/../../../public/js/obj_cfg.js)
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Adjust the contents to customize.
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## Data preparation
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````
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+- data
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+- scene1
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+- lidar
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+- 0000.pcd
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+- 0001.pcd
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+- camera
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+- front
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+- 0000.jpg
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+- 0001.jpg
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+- left
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+- ...
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+- aux_lidar
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+- front
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+- 0000.pcd
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+- 0001.pcd
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+- radar
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+- front_points
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+- 0000.pcd
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+- 0001.pcd
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+- front_tracks
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+- ...
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+- calib
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+- camera
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+- front.json
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+- left.json
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+- radar
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+- front_points.json
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+- front_tracks.json
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+- label
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+- 0000.json
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+- 0001.json
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+- scene2
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````
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label is the directory to save the annotation result.
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calib is the calibration matrix from point cloud to image. it's optional, but if provided, the box is projected on the image so as to assist the annotation.
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check examples in `./data/example` |