ConvertDataturkData-To-COCO

Code to convert dataturk data to PascalVOOC and then to COCO dataset format

How to Use

Long Procedure Incoming !!

Sorry for that !!

STEP 1

# STEP 1

• First create the folder structure

Create a folder downloads , pascal and pascal/downloads

File Struture:
root
├──code
    ├──getimageslist.py
├──downloads
├──pascal
   ├──downloads
├──convertData.py
├──convertJsontoCOCOformat.py
├──createids.py
├──dataturk.json
├──removeUselessFilesRejectedbymodel.py
├──xml_to_csv.py

STEP 2

# STEP 2

• Open terminal go to this folder.

        python convertData.py dataturk.json downloads pascal
        
        This will fill files in the downloads folder and pascal
        Cut paste downloaded images from downloads folder to pascal/downloads

• Convert it to CSV.

         python xml_to_csv.py

STEP 3

# STEP 3

• Before converting to coco we need to create labels.txt and ids.txt

            python createids.py

        For labels.txt add your own labels in the file.

• Note : It is advised to add the labels into the list in LINE NO. 22 of ```convertJsontoCOCOformat.py```
• The reason is some times text file is not read with next line character so you may face this issue, better add the labels manually

STEP 4

# STEP 4

• Now paste labels.txt and ids.txt into pascal and new filestructure is:

File Struture:
root
├──code
    ├──getimageslist.py
├──downloads
    ├──Multiple Images
├──pascal
   ├──downloads
      ├──Multiple images copied from downloads folder
   ├──Multiple XMLS
   ├──ids.txt
   ├──labels.txt
├──convertData.py
├──convertJsontoCOCOformat.py
├──createids.py
├──dataturk.json
├──removeUselessFilesRejectedbymodel.py
├──train.csv   ------->Will come from xml_to_csv file
├──xml_to_csv.py

STEP 5

# STEP 5

• Now run the main coco convert file.

         python convertJsontoCOCOformat.py  --ann_dir pascal --ann_ids pascal/ids.txt --labels pascal/labels.txt --output  mainTrain.json

• Now you will get your COCO format json end.

Note: if using with Fastai , run the get_annotations functions and print the images

    Some images get rejected, so we need to remove that completely.
    Copy the output of images from get_annotation function and paste it as list l in the file removeUselessFilesRejectedbymodel.py

EXTRA STEP 6

# STEP 6

• So you will run command below if you you dont have fastai locally and will run get_annotations in some different environment like google colab or kaggle kernel
• Run

            python removeUselessFilesRejectedbymodel.py

• If you have Fastai locally run

            cd code
            python getimageslist.py

• This gives you a folder untagged, remove that folder from pascal directory and save it somewhere else if you want it else delete the whole folder.

Now we need to repeat Step 3 till step 5 again

---

• Once useless images are removed you need to run STEP 3 Again since Ids are basically filenames
• After that again paste that ids.txt in the pascal folder that is Step 4
• Run Step 5 again

Finally your Dataset will contain

root  
├──code
   ├──getimageslist.py
├──pascal
   ├──downloads
       ├──Multiple Images
   ├──Multiple XMLS
   ├──ids.txt
   ├──labels.txt
├──dataturk.json
├──mainout.json

Finally it can be used in Fastai