* OpenVINO installation
* Separate tf_annotation -> tf_annotation and cuda support.
* TF Annotation app now supports openVino backend
* Doc for CUDA component
* OpenVINO Readme file was added
* OpenVINO env and pip requirements for model optimizer
* Update logging
* TF annotation Readme file was added
* Update CHANGELOG
* Keep aspect ratio for image, not reverse input channels
* Move analytics into components
main
Boris Sekachev7 years agocommitted byNikita Manovich
The step is necessary only to run tf_annotation app. If you don't have a Nvidia GPU you can skip the step. See detailed installation instructions on repository page.
To build all necessary docker images run `docker-compose build` command. By default, in production mode the tool uses PostgreSQL as database, Redis for caching.
### Run containers without tf_annotation app
### Run docker containers
To start all containers run `docker-compose up -d` command. Go to [localhost:8080](http://localhost:8080/). You should see a login page.
To start default container run `docker-compose up -d` command. Go to [localhost:8080](http://localhost:8080/). You should see a login page.
### Run containers with tf_annotation app
### You can include any additional components. Just add corresponding docker-compose file to build or run command:
If you would like to enable tf_annotation app first of all be sure that nvidia-driver, nvidia-docker and docker-compose>=1.19.0 are installed properly (see instructions above) and `docker info | grep 'Runtimes'` output contains `nvidia`.
Run following command:
```bash
docker-compose -f docker-compose.yml -f docker-compose.nvidia.yml up -d --build
You can [register a user](http://localhost:8080/auth/register) but by default it will not have rights even to view list of tasks. Thus you should create a superuser. The superuser can use admin panel to assign correct groups to the user. Please use the command below:
```bash
docker exec -it cvat sh -c '/usr/bin/python3 ~/manage.py createsuperuser'
Type your login/password for the superuser [on the login page](http://localhost:8080/auth/login) and press **Login** button. Now you should be able to create a new annotation task. Please read documentation for more details.
This application allows you automatically to annotate many various objects on images. [Tensorflow object detector](https://github.com/tensorflow/models/tree/master/research/object_detection) work in backend. It needs NVIDIA GPU for convenience using, but you may run it on CPU (just remove tensorflow-gpu python package and install the CPU tensorflow package version).
#### Enable instructions
1. Download the root dir with this app to cvat/apps if need.
3. Enable this application in ```settings/base.py```
```
INSTALLED_APPS += ['cvat.apps.tf_annotation']
```
1. If you want to run CVAT in container:
* Set TF_ANNOTATION argument to "yes" in ```docker-compose.yml```
* Add ```runtime: nvidia``` (if you have nvidia-gpu) to cvat block ([nvidia-docker2](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) must be installed)
5. Else you must download [model](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017.tar.gz), unpack it and set TF_ANNOTATION_MODEL_PATH environment variable to unpacked file ```frozen_inference_graph.pb```.
This variable must be available from cvat runtime environment.
Boris Sekachev
7 years ago
committed by
Nikita Manovich