add GitHub issues/docs

* add testing py3.7
* add gitter badge
* update setup

docs/ISSUE_TEMPLATE.md → .github/ISSUE_TEMPLATE/Bug_report.md

@@ -1,3 +1,9 @@
+---
+name: Bug report
+about: Create a report to help us improve
+
+---
+
 ## Description
 [Please provide a general introduction to the issue/proposal.]
 

.github/ISSUE_TEMPLATE/Feature_request.md

@@ -0,0 +1,17 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.travis.yml

@@ -21,7 +21,7 @@ python:
   # - 3.4  # will be deprecated for pandas
   - 3.5
   - 3.6
-  # - "3.7-dev"
+  - 3.7
 
 # See http://docs.travis-ci.com/user/caching/#pip-cache
 cache: pip

MANIFEST.in

@@ -47,6 +47,7 @@ include data_images/others/lena.png
     data_images/langerhans_islets/image/gtExoIsl_21.jpg
 
 prune .git
+prune .github
 prune libs
 prune venv
 prune build

README.md

@@ -3,11 +3,16 @@
 [![Build Status](https://travis-ci.org/Borda/pyImSegm.svg?branch=master)](https://travis-ci.org/Borda/pyImSegm)
 [![codecov](https://codecov.io/gh/Borda/pyImSegm/branch/master/graph/badge.svg?token=BCvf6F5sFP)](https://codecov.io/gh/Borda/pyImSegm)
 [![Codacy Badge](https://api.codacy.com/project/badge/Grade/48b7976bbe9d42bc8452f6f9e573ee70)](https://www.codacy.com/app/Borda/pyImSegm?utm_source=github.com&utm_medium=referral&utm_content=Borda/pyImSegm&utm_campaign=Badge_Grade)
+[![CircleCI](https://circleci.com/gh/Borda/pyImSegm.svg?style=svg&circle-token=a30180a28ae7e490c0c0829d1549fcec9a5c59d0)](https://circleci.com/gh/Borda/pyImSegm)
 [![Run Status](https://api.shippable.com/projects/5962ea48a125960700c197f8/badge?branch=master)](https://app.shippable.com/github/Borda/pyImSegm)
 [![Coverage Badge](https://api.shippable.com/projects/5962ea48a125960700c197f8/coverageBadge?branch=master)](https://app.shippable.com/github/Borda/pyImSegm)
-[![CircleCI](https://circleci.com/gh/Borda/pyImSegm.svg?style=svg&circle-token=a30180a28ae7e490c0c0829d1549fcec9a5c59d0)](https://circleci.com/gh/Borda/pyImSegm)
 [![CodeFactor](https://www.codefactor.io/repository/github/borda/pyimsegm/badge)](https://www.codefactor.io/repository/github/borda/pyimsegm)
+[![Gitter](https://badges.gitter.im/pyImSegm/community.svg)](https://gitter.im/pyImSegm/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+<!--
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Borda/pyImSegm/master?filepath=notebooks)
+-->
 
+---
 
 ## Superpixel segmentation with GraphCut regularisation
 
@@ -25,7 +30,7 @@ Image segmentation is widely used as an initial phase of many image processing t
 ![input image](figures/insitu7545.jpg)
 ![segmentation](figures/insitu7545_gc.png)
 
-Borovec J., Svihlik J., Kybic J., Habart D. (2017). **Supervised and unsupervised segmentation using superpixels, model estimation, and Graph Cut.** In: Journal of Electronic Imaging.
+Reference: _Borovec J., Svihlik J., Kybic J., Habart D. (2017). **Supervised and unsupervised segmentation using superpixels, model estimation, and Graph Cut.** In: Journal of Electronic Imaging._
 
 
 ## Object centre detection and Ellipse approximation
@@ -41,7 +46,7 @@ An image processing pipeline to detect and localize Drosophila egg chambers that
 ![estimated centres](figures/insitu7545_center_clusters.jpg)
 ![ellipse fitting](figures/insitu7545_ellipse_fit.png)
 
-Borovec J., Kybic J., Nava R. (2017) **Detection and Localization of Drosophila Egg Chambers in Microscopy Images.** In: Machine Learning in Medical Imaging.
+Reference: _Borovec J., Kybic J., Nava R. (2017) **Detection and Localization of Drosophila Egg Chambers in Microscopy Images.** In: Machine Learning in Medical Imaging._
 
 ## Superpixel Region Growing with Shape prior
 
@@ -57,7 +62,7 @@ Region growing is a classical image segmentation method based on hierarchical re
 ![growing RG](figures/insitu7545_RG2SP_animation.gif)
 ![ellipse fitting](figures/insitu7545_RG2SP-gc-mm.png)
 
-Borovec J., Kybic J., Sugimoto, A. (2017). **Region growing using superpixels with learned shape prior.** In: Journal of Electronic Imaging.
+Reference: _Borovec J., Kybic J., Sugimoto, A. (2017). **Region growing using superpixels with learned shape prior.** In: Journal of Electronic Imaging._
 
 ---
 
@@ -109,14 +114,13 @@ python setup.py install
 
 ---
 
-
 ## Experiments
 
 Short description of our three sets of experiments that together compose single image processing pipeline in this order:
 
-1. **Structure segmentation**
-1. **Center detection (and ellipse fitting)**
-1. **Region growing with a shape prior**
+1. **Semantic (un/semi)supervised segmentation**
+1. **Center detection and ellipse fitting**
+1. **Region growing with the learned shape prior**
 
 
 ### Annotation tools
@@ -142,7 +146,7 @@ We introduce some useful tools for work with image annotation and segmentation.
         -segs ./data_images/drosophila_ovary_slice/segm \
         -out ./results/overlap_ovary_segment
     ```
-* **Inpainting** selected labels in segmentation.
+* **In-painting** selected labels in segmentation.
     ```bash
     python handling_annotations/run_segm_annot_inpaint.py \
         -imgs "./data_images/drosophila_ovary_slice/segm/*.png" \
@@ -156,7 +160,7 @@ We introduce some useful tools for work with image annotation and segmentation.
     ```
 
 
-### Structure segmentation
+### Semantic (un/semi)supervised segmentation
 
 We utilize (un)supervised segmentation according to given training examples or some expectations.
 ![vusial debug](figures/visual_img_43_debug.jpg)
@@ -169,7 +173,7 @@ We utilize (un)supervised segmentation according to given training examples or s
         --img_type 2d_split \
         --slic_size 20 --slic_regul 0.25 --slico
     ```
-* Perform **Unsupervised** segmentation in images given in CSV
+* Perform **Un-Supervised** segmentation in images given in CSV
     ```bash
     python experiments_segmentation/run_segm_slic_model_graphcut.py \
        -l ./data_images/langerhans_islets/list_lang-isl_imgs-annot.csv -i "" \
@@ -193,6 +197,7 @@ We utilize (un)supervised segmentation according to given training examples or s
         -o ./results -n Ovary --img_type 2d_split --visual --nb_workers 2
     ```
     ![supervised](figures/imag-disk-20_train.jpg)
+* Perform **Semi-Supervised** is using the the supervised pipeline with not fully annotated images.
 * For both experiment you can evaluate segmentation results.
     ```bash
     python experiments_segmentation/run_compute-stat_annot-segm.py \
@@ -332,7 +337,6 @@ pip install --user git+https://github.com/Borda/morph-snakes.git
 
 ---
 
-
 ## References
 
 For complete references see [BibTex](docs/references.bib).

imsegm/utilities/__init__.py

@@ -5,7 +5,7 @@
 import pandas as pd
 
 # in case you are running on machine without display, e.g. server
-if os.environ.get('DISPLAY', '') == '' and matplotlib.rcParams['backend'] != 'agg':
+if not os.environ.get('DISPLAY', '') and matplotlib.rcParams['backend'] != 'agg':
     print('No display found. Using non-interactive Agg backend.')
     # https://matplotlib.org/faq/usage_faq.html
     matplotlib.use('Agg')

setup.py

@@ -58,26 +58,27 @@ def _parse_requirements(file_path):
 
 setup(
     name='ImSegm',
-    version='0.1.4',
+    version='0.1.5',
     url='https://borda.github.io/pyImSegm',
 
     author='Jiri Borovec',
     author_email='jiri.borovec@fel.cvut.cz',
     license='BSD 3-clause',
-    description='superpixel image segmentation: '
-                '(un)supervised, center detection, region growing',
+    description='superpixel image segmentation:'
+                ' (un)supervised, center detection, region growing',
 
-    packages=find_packages(exclude=['docs', 'notebooks',
-                                    'handling_annotations',
-                                    'experiments_*']),
+    packages=find_packages(
+        exclude=['docs', 'notebooks', 'handling_annotations', 'experiments_*']),
     cmdclass={'build_ext': BuildExt},
-    ext_modules=[Extension('imsegm.features_cython',
-                           language='c++',
-                           sources=['imsegm/features_cython.pyx'],
-                           extra_compile_args=['-O3', '-ffast-math',
-                                               '-march=native'],
-                           # extra_link_args=['-fopenmp'],
-                           )],
+    ext_modules=[
+        Extension('imsegm.features_cython',
+                  language='c++',
+                  sources=['imsegm/features_cython.pyx'],
+                  extra_compile_args=['-O3', '-ffast-math', '-march=native'],
+                  # extra_link_args=['-fopenmp'],
+                  )
+    ],
+
     setup_requires=setup_reqs,
     install_requires=install_reqs,
     # include_dirs = [np.get_include()],
@@ -96,8 +97,11 @@ def _parse_requirements(file_path):
         "Natural Language :: English",
         "Operating System :: OS Independent",
         "Topic :: Scientific/Engineering :: Image Segmentation",
+        'Programming Language :: Python :: 2',
         'Programming Language :: Python :: 2.7',
+        'Programming Language :: Python :: 3',
         'Programming Language :: Python :: 3.5',
         'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7',
     ],
 )