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JunoCamProcessing.md

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Processing JunoCam Images

This guide assumes you have a working installation of this software. Additionally, you will need to have the latest version of ISIS3 and the latest Juno spice kernels. You'll need to modify the 'makedb' script in the 'spk' directory to include the predicted trajectory kernels.

Preparing Raw JunoCam Data

Raw JunoCam images are posted to the mission website a couple days after Perijove. These are provided as PNG images with metadata in a JSON file.

The raw data needs to be in a format that can be ingested by ISIS3 while preserving all the metadata. I do that by converting it into a format more-or-less matching PDS archive VICAR files. To do this, you can use junocam_png_to_img.py:

junocam_png_to_img.py -p input_junocam_file.png -m input_metadata_file.json

This will create an .img file with the image, and a .lbl file as a seperate metadata label.

Processing JunoCam Images

Process a PDS formatted JunoCam image using process.py:

process.py -d input_junocam_file.lbl

This will perform the following steps:

  1. Convert JunoCam images to ISIS Cube files (junocam2isis)

  2. Optionally trims top and bottom of framelets by a few pixels. (trim)

    This is primarily useful when processing from raw data which tends to result in lines on the top an bottom of the framelets. Specifying the option, for example, -o vt=4 will trim each framelets by 4 pixels off the top and bottom.

  3. Initialize spice kernels on each framelet (spiceinit)

  4. Map projects framelets to equirectangular (cam2map)

    Specify another projection by using, for example, -o projection=mercator. Projection must have a corresponding map file in the ISIS system.

  5. Assembles framelets into mosaics (automos)

  6. Optionally runs a histogram equalization on the mosaics (histeq)

    To run this, specify -o histeq=true.

  7. Exports the map projected mosaics to tiff files (unsigned 16 bit). (isis2std)

  8. Reprojects the mosaics to match the camera perspective mid-observation (map2cam)

  9. Optionally runs a histogram equalization on the camera-projected mosaics (histeq)

    To run this, specify -o histeq=true.

  10. Exports the camera-projected mosaics to tiff files (unsigned 16 bit), both as individual grayscale images and as a combined RGB image. (isis2std)

  11. Cleans up. Using -n option will skip this step and leave all the intermediate cube files in the work directory.

At this point you are left with three grayscale map-projected images, three grayscale camera-projected imagees, and one color camera-projected image. It is now up to you to decide which of the output images to use and how to finalize them. I've been using the grayscale camera-projected images, bringing them into Photoshop for fine-tuned alignment, then Lightroom for color and contrast adjustments.