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 [email protected]: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

mrjoulin / blurred Goto Github PK

blurred's Introduction

Blurred

1. Installation

You can install the project in 3 ways:

1.1.1 Clone Docker image (if you have access)

1.1.2 Clone and run Docker (recommended)

1.1.3 Python Installation

WARNING

1.2 And run the executable file

2. Options for use for run.py

2.1 Option --render_directory <path-directory>

2.2 Option --render_video <path-video>

2.3 Option --render_image <path-image>

2.4 Option --render_online

Wait kays:

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

~ "q" or Esc - to exit

3. API to get a render image

3.1 Init page /

3.2 WebRTC connection /offer

Connection json:

Data Channel:

Input json:

Output json:

Examples:

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2. Options for use for `run.py`

2.1 Option `--render_directory <path-directory>`

2.2 Option `--render_video <path-video>`

2.3 Option `--render_image <path-image>`

2.4 Option `--render_online`

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

`~ "q"` or Esc - to exit

3.1 Init page `/`

3.2 WebRTC connection `/offer`