Skip to content

Latest commit

 

History

History
67 lines (58 loc) · 3.79 KB

README.md

File metadata and controls

67 lines (58 loc) · 3.79 KB
Raw

vision_dev

Scripts

  • split_data.sh

    This script splits a directory full of images into multiple numbered directories with 100 images each

    • Usage

      ./split_data.sh <source directory>
      • source directory: [string] The directory storing the original set of image

Sloth

Sloth is an image tagging tool.

Configuration

Provided, is the sloth configuration file, robosub_config.py, to specialize sloth for Robosub use. Included, are the required annotation conversions for any frameworks used by robosub.

  • Annotation types

    • Darknet

      Darknet has its own specific format which requires multiple files, one per image. For this, we have created a special *.darknet format to bridge the gap between sloth's one file system to darknet's multiple file system. The *.darknet format is as follows

      ---
<class name> : <class number>
class_name_1 : 0
class_name_2 : 1
      :
      :
---
>> "filename_1.jpg"
<class number> <x_position_ratio> <y_position_ratio> <width_ratio> <height_ratio> //Darknet format line
<<
>> "filename_2.png"
<<
    :
    :
      • class name: [string] The name of the class within sloth for the tag
      • class number: [integer] The number assotiated with the class name used within darknet
      • x_position_ratio: [0.0...1.0] The ratio between the x position of the center of the rectangle and the image width
      • y_position_ratio: [0.0...1.0] The ratio between the y position of the center of the rectangle and the image height
      • width_ratio: [0.0...1.0] The ratio between the width of the rectangle and the image width
      • height_ratio: [0.0...1.0] The ratio between the height of the rectangle and the image height

      Utilize the sloth_to_darknet.py script to generate the darknet files from the above annotation format.

Scripts

  • sloth_to_darknet.py

    Converts from the *.darknet format specified above to multiple files within an output directory and a training list used with Darknet

    • Usage

      ./sloth_to_darknet.py -f <filename> -o <output directory> [-t <training list filename>]
      • filename: [string] The filepath to the *.darknet file from which to extract information.
      • output directory: [string] The directory into which the darknet compatible annotation files are output.
      • (optional) training list filename: [string] The filename of the output training list

  • sloth_to_tfrecord.py

    Converts json format files into tenserflow format.

    • Usage

      ./sloth_to_tfrecord.py <config_file> <output directory> --crossvalidation <crossvalidation> --test <testratio> --evaluation <evaluationratio>
      • input [STRING]: resourcename from REST resource or json-file ending in .json
        • .json for offline-usage
      • outputdir [STRING]: folder in which the tfrecord files will be written + labelMap
      • crossvalidation [INT] (default 1): determines amounts of training- & test-datasets that will be generated using the crossvalidation algorithm
        • eg: 10 -> evaluationratio is substracted from all files - the remaining are used to create X sets. Has higher priority than the testratio parameter. crossvalidation == 1 -> data is split into [trainingset / testset / validationset] according to testratio & evaluationratio
      • testratio [FLOAT] (default 0.1): determines the ratio of files will be used as a test record. Will be ignored when crossvalidation parameter is greater than 1
        • eg: 0.1 -> 10% of the labelled files will be written to test.tfrecord
      • evaluationratio [FLOAT] (default 0.2): determines the ratio of files will be used as an evaluation record
        • eg: 0.1 -> 10% of the labelled files will be written to eval.tfrecord