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96 lines
4.3 KiB
Markdown
96 lines
4.3 KiB
Markdown
# Auto Annotation Runner
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A small command line program to test and run AutoAnnotation Scripts.
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## Instructions
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There are two modes to run this script in. If you already have a model uploaded into the server, and you're having
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issues with running it in production, you can pass in the model name and a task id that you want to test against.
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```shell
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# Note that this module can be found in cvat/utils/auto_annotation/run_model.py
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$ python /path/to/run_model.py --model-name mymodel --task-id 4
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```
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If you're running in docker, this can be useful way to debug your model.
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``` shell
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$ docker exec -it cvat bash -ic 'python3 ~/cvat/apps/auto_annotation/run_model.py --model-name my-model --task-id 4
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```
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If you are developing an auto annotation model or you can't get something uploaded into the server,
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then you'll need to specify the individual inputs.
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```shell
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# Note that this module can be found in cvat/utils/auto_annotation/run_model.py
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$ python path/to/run_model.py --py /path/to/python/interp.py \
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--xml /path/to/xml/file.xml \
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--bin /path/to/bin/file.bin \
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--json /path/to/json/mapping/mapping.json
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```
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Some programs need to run unrestricted or as an administer. Use the `--unrestriced` flag to simulate.
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You can pass image files in to fully simulate your findings. Images are passed in as a list
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```shell
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$ python /path/to/run_model.py --py /path/to/python/interp.py \
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--xml /path/to/xml/file.xml \
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--bin /path/to/bin/file.bin \
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--json /path/to/json/mapping/mapping.json \
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--image-files /path/to/img.jpg /path2/to/img2.png /path/to/img3.jpg
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```
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Additionally, it's sometimes useful to visualize your images.
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Use the `--show-images` flag to have each image with the annotations pop up.
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```shell
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$ python /path/to/run_model.py --py /path/to/python/interp.py \
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--xml /path/to/xml/file.xml \
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--bin /path/to/bin/file.bin \
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--json /path/to/json/mapping/mapping.json \
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--image-files /path/to/img.jpg /path2/to/img2.png /path/to/img3.jpg \
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--show-images
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```
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If you'd like to see the labels printed on the image, use the `--show-labels` flag
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```shell
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$ python /path/to/run_model.py --py /path/to/python/interp.py \
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--xml /path/to/xml/file.xml \
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--bin /path/to/bin/file.bin \
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--json /path/to/json/mapping/mapping.json \
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--image-files /path/to/img.jpg /path2/to/img2.png /path/to/img3.jpg \
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--show-images \
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--show-labels
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```
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There's a command that let's you scan quickly by setting the length of time (in milliseconds) to display each image.
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Use the `--show-image-delay` flag and set the appropriate time.
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In this example, 2000 milliseconds is 2 seconds for each image.
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```shell
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# Display each image in a window for 2 seconds
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$ python /path/to/run_model.py --py /path/to/python/interp.py \
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--xml /path/to/xml/file.xml \
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--bin /path/to/bin/file.bin \
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--json /path/to/json/mapping/mapping.json \
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--image-files /path/to/img.jpg /path2/to/img2.png /path/to/img3.jpg \
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--show-images \
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--show-image-delay 2000
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```
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Visualization isn't always enough.
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The CVAT has a serialization step that can throw errors on model upload even after successful visualization.
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You must install the necessary packages installed, but then you can add the `--serialize` command to ensure that your
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results will serialize correctly.
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```shell
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$ python /path/to/run_model.py --py /path/to/python/interp.py \
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--xml /path/to/xml/file.xml \
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--bin /path/to/bin/file.bin \
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--json /path/to/json/mapping/mapping.json \
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--image-files /path/to/img.jpg /path2/to/img2.png /path/to/img3.jpg \
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--serialize
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```
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