Remove converter functionality.

Post v0.3.0, converter functionality will not be supported. This was motivated by two reasons:
1. Only TFRecord was supported till now. Tensorflow is a large library and it does not make sense for it to be a project requirement when it's only used for a singular function.
2. There are too many conversion formats. Trying to include even the most popular ones makes the project unwieldy. Better to use one of the readily available scripts online.

README.md

@@ -2,15 +2,15 @@
 
 ![CircleCI](https://circleci.com/gh/ashnair1/COCO-Assistant/tree/master.svg?style=shield&circle-token=553c83e37198fe02a71743d42ee427c292336743) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/5299d18c95da4991b4f3a6ae6e8a0b7a)](https://www.codacy.com/manual/ashnair1/COCO-Assistant?utm_source=github.com&utm_medium=referral&utm_content=ashnair1/COCO-Assistant&utm_campaign=Badge_Grade) [![PyPI version](https://badge.fury.io/py/coco-assistant.svg)](https://badge.fury.io/py/coco-assistant) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
-Helper for dealing with MS-COCO annotations. <img src=".github/coco.png" height="40">
+Helper for dealing with MS-COCO annotations.
 
 ## Overview
 The MS COCO annotation format along with the pycocotools library is quite popular among the computer vision community. Yet I for one found it difficult to play around with the annotations. Deleting a specific category, combining multiple mini datasets to generate a larger dataset, viewing distribution of classes in the annotation file are things I would like to do without writing a separate script for each. The COCO Assistant is designed (or being designed) to assist with this problem. **Please note that currently, the Assistant can only help out with object detection datasets**. Any contributions and/or suggestions are welcome.
 
 ### Requirements
 Your data directory should look as follows:
 
-```shell script
+```markdown
 Example:
 .
 ├── images
@@ -32,7 +32,7 @@ Example:
 `pip install coco-assistant`
 
 ### 2. Installation: From Source
-```shell script
+```markdown
 # Clone the repository
 git clone https://github.com/ashnair1/COCO-Assistant.git
 # Build and install the library
@@ -43,7 +43,7 @@ make
 
 Usage is similar to how you would use `pycocotools`
 
-```shell script
+```markdown
 from coco_assistant import COCO_Assistant
 
 # Specify image and annotation directories
@@ -59,7 +59,7 @@ cas = COCO_Assistant(img_dir, ann_dir)
 
 The `merge` function allows you to merge multiple datasets.
 
-```shell script
+```markdown
 In[1]: cas = COCO_Assistant(img_dir, ann_dir)                                                                                                                                                              
 loading annotations into memory...
 Done (t=0.09s)
@@ -83,7 +83,7 @@ The merged dataset (images and annotation) can be found in `./results/combinatio
 
 Removes a specific category from an annotation file.
 
-```shell script
+```markdown
 In[1]: cas = COCO_Assistant(img_dir, ann_dir)                                                                                                                                                              
 loading annotations into memory...
 Done (t=0.09s)
@@ -125,7 +125,7 @@ The modified annotation can be found in `./results/removal`
 
 Couldn't `pycocotools` visualise annotations (via [showAnns](https://github.com/cocodataset/cocoapi/blob/636becdc73d54283b3aac6d4ec363cffbb6f9b20/PythonAPI/pycocotools/coco.py#L233)) as well? Sure it could, but I required a way to freely view all the annotations of a particular dataset so here we are.
 
-```shell script
+```markdown
 In[1]: cas.visualise()
 Choose directory:
 ['tiny', 'tiny2']
@@ -142,6 +142,3 @@ The `cas.get_segmasks()` function allows you to create segmentation masks from y
 | **SpaceNet**   | <img src="./.github/SpaceNet.png" alt="SpaceNet" title="SpaceNet" width=310 />   |   <img src="./.github/SpaceNet_mask.png" alt="SpaceNet_mask" title="SpaceNet_mask" width=310 />  |
 | **iSAID**      | <img src="./.github/iSAID.png" alt="iSAID" title="iSAID" width=310 />            |   <img src="./.github/iSAID_mask.png" alt="iSAID_mask" title="iSAID_mask" width=310 />           |
 
-### Todo
-1.  Converter for converting COCO annotations to YOLO format.
-2.  Write tests for untested functions :)

coco_assistant/VERSION

@@ -1 +1 @@
-0.2.1
+0.3.0

coco_assistant/coco_assistant.py

@@ -8,11 +8,9 @@
 
 from tqdm import tqdm
 
-from . import coco_converters as converter
 from . import coco_stats as stats
 from . import coco_visualiser as cocovis
-from coco_assistant.utils import anchors
-from coco_assistant.utils import det2seg
+from coco_assistant.utils import anchors, det2seg
 
 logging.basicConfig(level=logging.ERROR)
 logging.getLogger().setLevel(logging.WARNING)
@@ -292,25 +290,6 @@ def get_segmasks(self):
             output_dir = os.path.join(self.res_dir, 'segmasks', name)
             det2seg.det2seg(ann, output_dir)
 
-    def converter(self, to="TFRecord"):
-        """
-        Function for converting annotations to other formats
-
-        :param to: Format to which annotations are to be converted
-        """
-        print("Choose directory:")
-        print(self.imgfolders)
-
-        dir_choice = input()
-
-        if dir_choice.lower() not in [item.lower() for item in self.imgfolders]:
-            raise AssertionError("Choice not in images folder")
-        ind = self.imgfolders.index(dir_choice.lower())
-        ann = self.annfiles[ind]
-        img_dir = os.path.join(self.img_dir, dir_choice)
-
-        converter.convert(ann, img_dir, _format=to)
-
     def visualise(self):
         """
         Function for visualising annotations.

requirements.txt

@@ -7,7 +7,6 @@ Pillow>=6.2.2
 git+https://github.com/ashnair1/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI
 seaborn
 scikit-image
-tensorflow==1.12.0
 tqdm
 
 # packaging