Relax requirements

ashnair1 · Apr 7, 2020 · 6d47135 · 6d47135
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Showing 10 changed files with 17 additions and 16 deletions.
diff --git a/rep_stuff/SpaceNet.png → .github/SpaceNet.png b/rep_stuff/SpaceNet.png → .github/SpaceNet.png
diff --git a/rep_stuff/SpaceNet_mask.png → .github/SpaceNet_mask.png b/rep_stuff/SpaceNet_mask.png → .github/SpaceNet_mask.png
diff --git a/rep_stuff/coco.png → .github/coco.png b/rep_stuff/coco.png → .github/coco.png
diff --git a/rep_stuff/iSAID.png → .github/iSAID.png b/rep_stuff/iSAID.png → .github/iSAID.png
diff --git a/rep_stuff/iSAID_mask.png → .github/iSAID_mask.png b/rep_stuff/iSAID_mask.png → .github/iSAID_mask.png
diff --git a/rep_stuff/visualiser.gif → .github/visualiser.gif b/rep_stuff/visualiser.gif → .github/visualiser.gif
diff --git a/.gitignore b/.gitignore
@@ -2,6 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+*.pyc
 
 # C extensions
 *.so
@@ -131,4 +132,4 @@ annotations/
 images/
 *tar.gz
 .vscode/
-data/
+data/
diff --git a/README.md b/README.md
@@ -2,7 +2,7 @@
 
 ![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&amp;utm_medium=referral&amp;utm_content=ashnair1/COCO-Assistant&amp;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="rep_stuff/coco.png" height="40">
+Helper for dealing with MS-COCO annotations. <img src=".github/coco.png" height="40">
 
 ## 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.
@@ -70,7 +70,7 @@ Done (t=0.06s)
 creating index...
 index created!
 
-In[2]: cas.merge()                                                                                                                                                                                       
+In[2]: cas.merge(merge_images=True)                                                                                                                                                                                       
 Merging image dirs
 100%|█████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 18.33it/s]
 Merging annotations
@@ -132,15 +132,15 @@ Choose directory:
 tiny
 ```
 
-![](./rep_stuff/visualiser.gif)
+![](./.github/visualiser.gif)
 
 ### 5. Generate segmentation masks
-The `cas.get_segmasks()` function allows you to create segmentation masks from your MS COCO object detection datasets. Similar to the Pascal VOC dataset, the mask values are their classes and a colour palette is applied to enable visualisation. The generated masks are stroed in the `./results` folder. Samples are shown below.
+The `cas.get_segmasks()` function allows you to create segmentation masks from your MS COCO object detection datasets. Similar to the Pascal VOC dataset, the mask values are their classes and a colour palette is applied to enable visualisation. The generated masks are stored in the `./results` folder. Samples are shown below.
 
 |                |                            Detection                                             |                                               Segmentation                                       |   
 |----------------|----------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------|
-| **SpaceNet**   | <img src="./rep_stuff/SpaceNet.png" alt="SpaceNet" alt="SpaceNet" width=310 />   |  <img src="./rep_stuff/SpaceNet_mask.png" alt="SpaceNet_mask" title="SpaceNet_mask" width=310 /> |
-| **iSAID**      | <img src="./rep_stuff/iSAID.png" alt="iSAID" title="iSAID" width=310 />          | <img src="./rep_stuff/iSAID_mask.png" alt="iSAID_mask" title="iSAID_mask" width=310 />           |
+| **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.

diff --git a/coco_assistant/VERSION b/coco_assistant/VERSION
@@ -1 +1 @@
-0.2.0
+0.2.1
diff --git a/requirements.txt b/requirements.txt
@@ -1,14 +1,14 @@
 # project requirements
-Cython==0.29.13
-matplotlib==3.0.2
-numpy==1.16.0
-pandas==0.23.4
+Cython
+matplotlib
+numpy
+pandas
 Pillow>=6.2.2
 git+https://github.com/ashnair1/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI
-seaborn==0.9.0
-scikit-image==0.14.2
-tensorflow==1.15.0
-tqdm==4.28.1
+seaborn
+scikit-image
+tensorflow==1.12.0
+tqdm
 
 # packaging
 setuptools==40.6.3