Blurred

Have you ever wanted to remove disturbing objects from photos / videos or even online?

Our application will provide you with this opportunity.

1. Installation

You can install the project in 3 ways:

1.1.1 Clone Docker image (if you have access)

docker pull joulin/blurred:latest

1.1.2 Clone and run Docker (recommended)

# Clone directory
git clone -b develop git@github.com:Mrjoulin/AR_remove_object.git
cd AR_remove_object
sudo sh setup.sh
# And after Docker start and build

1.1.3 Python Installation

And then install all dependencies using python pip.

# Installing a newer python (if necessary)
sudo apt-get install python3
# Install all dependensies
apt-get install -y libsm6 libxext6 libxrender-dev python3-tk libavdevice-dev libavfilter-dev libopus-dev libvpx-dev pkg-config
pip install -r Docker/requirements.txt

WARNING

All objects detected by "Tensorflow Object Detection" algoritm. Therefore, before using the application, make sure that it is installed (installation guide).

1.2 And run the executable file

# if you use Docker, then after Docker start and build, your terminal will open
# To run server
python wsgi.py [any options]
# To run app localy (use webcam)
python run.py [any options]

Run python <name_file>.py --help to watch all options

2. Options for use for run.py

Our application offers several options for processing directories with video, online, etc.

2.1 Option --render_directory <path-directory>

Processing all videos in a folder using one of the algorithms (see clause 2.1)

Example:

# Use a --inpaint algoritm to render directory with videos
python run.py --render_directory videos/input_videos/to_render/  

The final videos will be in videos/out_videos directory

2.2 Option --render_video <path-video>

Vidos processing using the selected algorithm (see clause 2.1)

Example:

# Use a --inpaint algoritm to render a video
python run.py --render_video videos/input_videos/to_render/<you_video>.MOV 

The final video will be in videos/out_videos directory

2.3 Option --render_image <path-image>

Images processing using the selected algorithm (see clause 2.1)

Example:

# Use a --inpaint algoritm to render an image
python run.py --render_image server/imgs/render_img.jpeg

The final image will be in AR_remover directory

2.4 Option --render_online

Use a web camera to online processing objects

Example:

# Use a web camera for online rendering
python run.py --render_online

Wait kays:

~ " "(Spaсe) - to impainting (press again to turn off)

~ "q" or Esc - to exit

3. API to get a render image

These are requests to our server xxx.xxx.xxx.xxx:5000 to receive the processed frame.

3.1 Init page /

Start page with buttons for testing server response

3.2 WebRTC connection /offer

POST request to WebRTC connection with server. Rendering a stream video

Connection json:

{
    sdp, type: <string>, <string> - for WebRTC connection
    video_transform: (check Input in Data Channel) - it's need to start
}

Data Channel:

Input json:

    {
        "message_id": <message id>
        "name": <name_algorithm>, - name algorithm. Options: "", "edges", "boxes", "inpaint"
        "src": [<additional variables>] - for "inpaint" -- [<class id objects>] (For example: [1, 15]; ["all"])
                                          for others -- []
    }

Output json:

    {
        "message_id": <message id>,
        "data": [
            {
                "class_id": <class id object>, - for example: 23 (int)
                "position: {
                    "x_min": <position top left point of the rectangle>, - from the left edge (example: 0,1325..)
                    "y_min": <position top left point of the rectangle>, - from the top edge (example: 0,3271..)
                    "x_max": <position bottom right point of the rectangle>, - from the left edge (example: 0,562..)
                    "y_max": <position bottom right point of the rectangle> - from the top edge (example: 0,8932..)
                }
            },
            ...

        ] - detected objects: for "inpaint" - removed objects
                              for "boxes" - all detected objects
                              for others - []
    }

Examples:

To see examples follow this link