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Transdec environment https://github.com/pwrdc/TransdecEnvironment controlling with Python

Project description

PyTransdec

PyTransdec is a Python library prepared for controlling TransdecEnvironment by PWr Diving Crew (KN Robocik) at Wrocław University of Science and Technology.

It wraps Unity ML-Agents library.

The project is maintained by Pwr Diving Crew software team members (Unity3D section).

KN Robocik website

Should any issues be noticed, please submit a New issue on GitHub.

Installation

Python

To use Python API Python 3.6 is required. On Windows it is recommended to use Anaconda (64-bit or 32-bit, but you can use your own preferred Python installation.

For Anaconda use default installation settings. After installation open Anaconda Navigator to finish the setup.

image

In case environment variables were not created, you will see error conda is not recognized as internal or external command when you type conda into command line. To solve this problem set the environment variables: open Edit environment variables for your account, click Environment Variables button, then double click Path under under System variable. Add the following paths using New button:

%UserProfile%\Anaconda3\Scripts
%UserProfile%\Anaconda3\Scripts\conda.exe
%UserProfile%\Anaconda3
%UserProfile%\Anaconda3\python.exe

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Before proceeding check your installation by executing python --version, which should output something like this:

Python 3.6.x :: ...

You also need pip (which is installed by default in Anaconda). Check if it is correctly installed by executing pip --version. The output should look like this:

pip x.x...

If any error occurred, please check your installation.

PyTransdec package

To use the package install it using pip (it is recommended to use a virtual environment such as Pipenv (preferred), conda env or virtualenv.

Installation command:

pip install git+https://github.com/PiotrJZielinski/PyTransdec

PyTransdec package automatically installs all required dependencies.

Usage

PyTransdec package contains TransdecCommunication([file_name, worker_id]) class, which can be used to communicate with TransdecEnvironment. To import the package use following Python script:

from pytransdec import TransdecCommunication

You can then apply it using with statement:

with TransdecCommunication() as tc:
  ...

Parameters:

  • file_name: str, optional - Unity Environment file to operate on; defaults to None (connect to Unity Editor)
  • worker_id: int, optional - for more than 1 parallel workers - port incremental to be used for connection; defaults to 0

Methods

method/property description
tc.reset([message, training]) Reset the environment with reset message; update observations
tc.step([action]) Make a step in the environment (specified with action); update observations
tc.reward Current reward value
tc.vector Current vector observations dictionary
tc.visual Current visual observations list
tc.collect_data(positive, add_noise, add_background, n_images[, save_dir, start_num, annotation_margin, used_observations, object_number, show_img, draw_annotations, print_annotations, progress_bar]) Automatically collect data from Transdec Environment
tc.reset(self, message={}, training=True) [source]

Reset the environment with reset message and update observations.

Parameters:

  • message: Dict[str, int], optional - a dictionary specifying TransdecEnvironment reset settings; defaults to empty Dict; available keys:
    • 'CollectData': if 0 - navigation mode; if 1 - data collection mode
    • 'EnableNoise': has effect only when 'CollectData' == 1; if 0 - no noise added; if 1 - noise objects added on the image
    • 'Positive': has effect only when 'CollectData' == 1; if 0 - collect negative examples (target object hidden); if 1 - collect positive examples (target object visible)
    • FocusedObject - has effect only when 'CollectData' == 1; specify which object is focused on collecting data (input: object number from Data collection settings)
    • EnableBackgroundImage - has effect only when 'CollectData' == 1; if 0 - transdec is background; if 1 - random images is background
    • ForceToSaveAsNegative - focus to save image as negative example, even if Postive is set to True
    • 'AgentMaxSteps': after how many steps is the agent reset; if 0 - never
  • training: bool, optional - use TransdecEnvironment in training mode (if True) or in inference mode (if False); defaults to true
tc.step(action=[0.0, 0.0, 0.0, 0.0]) [source]

Make a step in the environment (specified with action) and update observations.

Parameters:

  • action: Tuple[float, float, float, float], optional - movement settings for the robot in range [-1, 1]; defaults to [0.0, 0.0, 0.0, 0.0]; action sequence:
    • longitudinal movement (1.0: max forward, -1.0: max backward)
    • lateral movement (1.0: max right, -1.0: max left)
    • vertical movement (1.0: max upward, -1.0: max downward)
    • yaw rotation (1.0: max right turn, -1.0: max left turn)
    • camera focus (0: front camera, 1: bottom camera)
tc.reward [source]

Get current reward value.

Returns:

  • float representing current reward value calculated inside TransdecEnvironment
tc.vector [source]

Get current vector observations dictionary.

Returns:

  • The dictionary of observations with keys:
    • 'acceleration_x' - linear acceleration value in all axes,
    • 'acceleration_y',
    • 'acceleration_z',
    • 'angular_acceleration_x' - angular acceleration value in all axes,
    • 'angular_acceleration_y',
    • 'angular_acceleration_z',
    • 'rotation_x' - rotation position value in all axes,
    • 'rotation_y',
    • 'rotation_z',
    • 'depth' - robot's depth measured from water surface,
    • 'bounding_box_x' - bounding box parameters (central point coordinates, width and height),
    • 'bounding_box_y',
    • 'bounding_box_w',
    • 'bounding_box_h',
    • 'bounding_box_p' - probability of containing target (1 or 0),
    • 'relative_x' - robot's position relative to target in all axes,
    • 'relative_y',
    • 'relative_z',
    • 'relative_yaw' - robot's orientation relative to target in vertical axis
tc.visual [source]

Get current visual observations list.

Returns:

  • List of all available visual observations as Numpy arrays in range [0, 255]
tc.collect_data(positive, add_noise, n_images, save_dir='collected_data', start_num=1, annotation_margin=5, used_observations=('x', 'y', 'w', 'h', 'p'), show_img=False, draw_annotations=False, print_annotations=False, progress_bar=True) [source]

Automatically collect data from Transdec Environment, saving images to save_dir, together with annotations.csv of preset content.

Parameters:

  • positive: bool - if True collect positive examples (target object visible), else collect negative examples (target object hidden)
  • add_noise: bool - if `True add noise objects on the image, else do not
  • n_images: int - number of images to be saved
  • save_dir: str, optional - folder to put images and annotations file; defaults to 'collected_data'
  • start_num: int, optional - starting number to for image filename; defaults to 1
  • annotation_margin: int, optional - value added to all bounding box' dimensions; defaults to 5
  • used_observations: Union[str, Tuple[str, ...]], optional - which of the observations should be saved to annotations.csv file; if all save all vector observations; defaults to ('x', 'y', 'w', 'h', 'p'); available keys:
    • 'a_x' - linear acceleration value in all axes,
    • 'a_y',
    • 'a_z',
    • 'eps_x' - angular acceleration value in all axes,
    • 'eps_y',
    • 'eps_z',
    • 'phi_x' - rotation position value in all axes,
    • 'phi_y',
    • 'phi_z',
    • 'd' - robot's depth measured from water surface,
    • 'x' - bounding box parameters (central point coordinates, width and height),
    • 'y',
    • 'w',
    • 'h',
    • 'p' - probability of containing target (1 or 0),
    • 'relative_x' - robot's position relative to target in all axes,
    • 'relative_y',
    • 'relative_z',
    • 'relative_yaw' - robot's orientation relative to target in vertical axis
  • show_img: bool, optional - show each collected image; defaults to False
  • draw_annotations: bool, optional - draw annotations on each showed image; has effect only when show_img==True; defaults to False
  • print_annotations: bool, optional - print each annotation in console; defaults to False
  • progress_bar: bool, optional - show neat progressbar for data collection; defaults to True

Example code for data collection

with TransdecCommunication() as tc:
        # collect 1000 positive examples with noise of object 0
        tc.collect_data(positive=True, add_noise=True, add_background=False, n_images=1000, save_dir='collected_data/{}/train'.format(0),
                        used_observations='all', object_number=0, show_img=True, draw_annotations=True)
        # collect 1000 positive examples with custom backgrount of object 0
        tc.collect_data(positive=True, add_noise=False, add_background=True, n_images=1000, save_dir='collected_data/{}/train'.format(0),
                        used_observations='all', object_number=0, show_img=True, draw_annotations=True) 
        # collect 1000 negative examples with noise of object 0
        tc.collect_data(positive=False, add_noise=True, add_background=False, n_images=1000, save_dir='collected_data/{}/train'.format(0),
                        used_observations='all', object_number=0, show_img=True, draw_annotations=True)

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