Convert Data created with labelme to coco format data set. This repo is aimed at creating a full dataset which include the following subsets:

    1. train set ~60% of the input images by default  
    2. test set ~20% of the input images bt default 
    3. validation set ~20% of the input images. 

The output has been tasted (so far all I can tell is that the training began) on facebookresearch/maskrcnn-benchmark for instance segmentation.

create the class with the file: CreateDastaSet.py

I have based this script on the labelme labelme2coco script therefore follow the labelme installation instraction at wkentaro/labelme

1. Use the wkentaro/labelme to annotate your data

2. Edit CreateDataSet inputDict as follows -input = path\to\your\Inputfolder at the following format : (Bug therefore the fornat is required )

   • Inputfoder -subfolder1 - containing data - Imgs and annotations
     -subfolder2 - containing data - Imgs and annotations -subfolder3 - containing data - Imgs and annotations
   • outputfoler= path\to\where\output\data\will\be\created
   • labels = 'path\to\label.txt'
   3. Verify the output with the following notebook from [pycococreator] (https://github.com/waspinator/pycococreator)

3. run the CreateDataSet.py script

• 1. add annotations field BoundingBox
• 2. convert to work with iscrowded =0 , aka polygons dots
• 3. add support to appending exsiting dataset (MUST Backup Previous version to dir\old\setname+timeframe )
• 4. re-enble parser
• 5. solve directory in directory bug for list of json files gathering
• 6. add a change directory function to \usedforcreatingdataset of input images in order to avoid using them for appending the dataset.
• 7. use imantics - Annotation to calculate BBox and area in stade of labelme function
• 8. figure how to add rotated boundingboxes

• solved - appended empty annotations to the sets
• Input dircteroy has to be parent directory bug
• Image Resize doesn't mach with mask

labelme

pycococreator

cocoapi