Dynamic Drafting Paper

Dependencies

Python libraries to install:

• cv2
• numpy
• scipy
• scikit-image
• networkx
• cairosvg
  • cairo: brew install cairo

For printing:

• lpr

For camera server:

• gphoto2
• websocket-client

(TODO: Make sure this list is complete.)

Usage

Run a pipeline with, e.g.

python ddp extract_paper

Check out ddp/__main__.py for command line options.

Code Organization

camera_server/. Code that runs on the computer with the camera. Creates a web server that one can query for the current picture. (TODO)

ddp/. The codebase for doing dynamic drafting paper (ddp).

ddp/core/. All the core logic. A bunch of functions for processing images, graphs, constraint networks, etc.

ddp/infrastructure/. Code for interfacing with the outside world.

ddp/pipeline/. Pipelines for performing particular tasks. These chain together the functions from ddp/core/.

ddp/__main__.py. This is the "runner" that runs pipelines in various ways.

input/. Sample input images and data for running through pipelines.

log/. Images and data from a pipeline run will be written here when a pipeline is run with file logging.

Pipelines

Each pipeline module must contain at least these two functions:

1. run(input_data). The main pipeline function. Should make copious calls to infrastructure.log.

2. sample(). Returns a sample input that can be run through the pipeline. That is, run(sample()) should be a typical run for the pipeline.

Logging

In order to visualize pipeline execution, pipeline run functions should make calls to infrastructure.log.image(...).

If the runner is in file mode, calls to log will output files to the log/ directory. In watch mode, calls to log.image(...) will show logged images in a window (TODO).