Getting Started ================================= Installation ------------ The latest version of PyCDA is available for download via pip. As things are improving rapidly, you'll want the latest version. PyCDA currently supports Python 3.6 only; we recommend using a virtual environment (such as `conda `_ or `virtualenv `_) to keep your dependencies straight. As PyCDA has not been tested for dependency version ranges, pip will want to upgrade packages in the environment with which you install PyCDA; this can cause dependency issues in other places. From your Python 3.6 environment, run: ``pip install pycda`` pip will install the dependencies for you. You've installed PyCDA! Make Detections --------------- To use PyCDA, you'll need to chop up your image data into reasonably-sized pieces; 2,000 x 2,000 pixels per segment is very reasonable, and better machines can handle much bigger. It really depends on the size of your RAM. Put your image segments into a directory and denote its path. For this example, we'll save this 1592 x 1128 pixel image (taken from the Mars Express HRSC instrument): .. image:: image1.bmp to the path: ``/data/image1.bmp`` Now we're ready to begin. Open your python environment:: -> % python3 Python 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19) [GCC 7.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>>from pycda import CDA, load_image >>>cda = CDA() >>>image = load_image('/data/image1.bmp') >>>prediction = cda.get_prediction(image) If you'd like to see the progress of your detection, pass the verbose=True keyword argument to the .get_prediction call, like:: >>>prediction = cda.get_prediction(image, verbose=True) The CDA object will return a prediction object which here is assigned to the alias "prediction." You can now save your results with:: >>>prediction.to_csv('/data/results1.csv') To see your detections plotted over the input image, call:: >>>prediction.show() This should open a popup window on your system with the plot. For our Mars image, it will look like this: .. image:: plot1.png You'll see the detector performs but isn't perfect; the large crater in the lower left corner is conspicuous, but the model is designed to detect craters with 80 pixels of diameter or less; to capture larger craters, reduce the image resolution. Set the image resolution and save the image for later reference:: >>>prediction.set_scale(12.5) >>>prediction.show(save_plot='/data/myplot1.png') And you've begun. Happy crater hunting! Read about the submodules to learn how to modify your CDA pipeline and quantify detection errors; for a more in-depth example, look at the `demo notebook `_