[NEW] Combine images of any dimensions (as long as the images' dimens…

…ions match together)

.circleci/config.yml

@@ -120,10 +120,10 @@ jobs:
   visualize_dependency_graphs:
     executor: py38-docker-image
     environment:
-      NEURAL_STYLE_TRANSFER_DEPS_GRAPHS: dependencies-graphs
+      NST_DEPS_GRAPHS: dependency-graphs
     steps:
       - checkout
-      - run: sudo apt-get update -y
+      - run: sudo apt-get update -y --allow-releaseinfo-change
       - run: python -m pip install -U pip
       - run:
           name: Install the dot binary included in the graphviz package/distribution
@@ -135,7 +135,7 @@ jobs:
           name: Visualize dependency graphs as .svg files
           command: tox -e graphs -vv
       - store_artifacts:
-          path: dependencies-graphs
+          path: dependency-graphs
           destination: dep-graphs
       - run:
           name: Visualize uml diagrams as .svg files
@@ -164,13 +164,13 @@ workflows:
           filters:
             tags:
               only: /.*/
-      # - visualize_dependency_graphs:
-      #     filters:
-      #       branches:
-      #         only:
-      #           - master
-      #           - dev
-      #           - release-staging
+      - visualize_dependency_graphs:
+          filters:
+            branches:
+              only:
+                - master
+                - dev
+                - release-staging
       # - build-documentation:
       #     filters:
       #       branches:

README.rst

@@ -1,6 +1,11 @@
 Neural Style Transfer - CLI
 ===========================
 
+Create artificial artwork by transfering the appearance of one image (eg a famous painting) to another
+user-supplied image (eg your favourite photograph).
+
+Uses a Neural Style Transfer algorithm to transfer the appearance, which you can run though a CLI program.
+
 `Neural Style Tranfer` (NST) is an algorithm that applies the `style` of an image to the `contents` of another and produces a `generated` image.
 The idea is to find out how someone, with the `painting style` shown in one image, would depict the `contents` shown in another image.
 
@@ -17,6 +22,11 @@ This Python package runs a Neural Style Tranfer algorithm on input `content` and
     * - tests
       - | |circleci| |codecov|
 
+    * - package
+      - | |pypi| |wheel| |py_versions| |commits_since|
+
+    * - containerization
+      - | |docker| |image_size|
 
     * - code quality
       - |better_code_hub| |code_climate| |maintainability| |codacy| |scrutinizer|
@@ -40,9 +50,9 @@ Key features of the package:
 
 Installation
 ------------
-| The Neural Style Transfer - CLI heavely depends on Tensorflow (tf) and therefor it is crucial that tf is installed correctly in your Python environment.
+| The Neural Style Transfer - CLI heavely depends on Tensorflow (tf) and therefore it is crucial that tf is installed correctly in your Python environment.
 
-Sample commands to install NST CLI using a terminal:
+Sample commands to install the NST CLI from source, using a terminal:
 
 ::
 
@@ -60,6 +70,18 @@ Sample commands to install NST CLI using a terminal:
     # Install NST CLI (in virtual environment)
     pip install -e .
 
+
+Alternative command to install the NST CLI by downloading the `artificial_artwork` python package from pypi:
+
+::
+
+    pip install artificial_artwork
+
+
+Make the cli available for your host system:
+
+::
+
     # Setup a symbolic link (in your host system) in a location in your PATH
     # Assuming ~/.local/bin is in your PATH
     ln -s $PWD/env/bin/neural-style-transfer ~/.local/bin/neural-style-transfer
@@ -71,7 +93,7 @@ Sample commands to install NST CLI using a terminal:
 Usage
 -----
 
-Download the Vgg-Verydeep-19 pretrained `model` from https://mega.nz/file/i5xDWI4Y.
+Download the Vgg-Verydeep-19 pretrained `model` from https://drive.protonmail.com/urls/7RXGN23ZRR#hsw4STil0Hgc.
 
 Exctract the model (weights and layer architecture).
 
@@ -115,6 +137,21 @@ image generated on a different iteration while running the algorithm. The bigger
 Check out your artificial artwork!
 
 
+Docker image
+------------
+
+We have included a docker file that we use to build an image where both the `artificial_artwork` package (source code)
+and the pretrained model are present. That way you can immediately start creating artwork!
+
+::
+
+    docker pull boromir674/neural-style-transfer
+
+    mkdir nst-output
+
+    docker run -it --rm -v nst-output:/app/nst-output boromir674/neural-style-transfer
+
+
 
 
 .. |circleci|  image:: https://img.shields.io/circleci/build/github/boromir674/neural-style-transfer/master?logo=circleci
@@ -124,9 +161,28 @@ Check out your artificial artwork!
 
 .. |codecov| image:: https://codecov.io/gh/boromir674/neural-style-transfer/branch/master/graph/badge.svg?token=3POTVNU0L4
     :alt: Codecov
-      :target: https://codecov.io/gh/boromir674/neural-style-transfer
+    :target: https://codecov.io/gh/boromir674/neural-style-transfer
 
 
+
+.. |pypi| image:: https://img.shields.io/pypi/v/artificial-artwork?color=blue&label=pypi&logo=pypi&logoColor=%23849ed9
+    :alt: PyPI
+    :target: https://pypi.org/project/artificial-artwork/
+
+.. |wheel| image:: https://img.shields.io/pypi/wheel/artificial-artwork?logo=python&logoColor=%23849ed9
+    :alt: PyPI - Wheel
+    :target: https://pypi.org/project/artificial-artwork
+
+.. |py_versions| image:: https://img.shields.io/pypi/pyversions/artificial-artwork?color=blue&logo=python&logoColor=%23849ed9
+    :alt: PyPI - Python Version
+    :target: https://pypi.org/project/artificial-artwork
+
+.. |commits_since| image:: https://img.shields.io/github/commits-since/boromir674/neural-style-transfer/v0.5/master?color=blue&logo=Github
+    :alt: GitHub commits since tagged version (branch)
+    :target: https://github.com/boromir674/neural-style-transfer/compare/v0.5..master
+
+
+
 .. |better_code_hub| image:: https://bettercodehub.com/edge/badge/boromir674/neural-style-transfer?branch=master
     :alt: Better Code Hub
     :target: https://bettercodehub.com/
@@ -161,3 +217,11 @@ Check out your artificial artwork!
     :alt: Supported versions
     :target: https://pypi.org/project/topic-modeling-toolkit
 
+
+
+.. |docker| image:: https://img.shields.io/docker/v/boromir674/neural-style-transfer/latest?logo=docker&logoColor=%23849ED9
+    :alt: Docker Image Version (tag latest semver)
+    :target: https://hub.docker.com/r/boromir674/neural-style-transfer
+
+.. |image_size| image:: https://img.shields.io/docker/image-size/boromir674/neural-style-transfer/latest?logo=docker&logoColor=%23849ED9
+    :alt: Docker Image Size (tag)

setup.cfg

@@ -1,10 +1,8 @@
 [metadata]
 ## Setuptools specific information
 name = artificial_artwork
-version = 0.5
-description = Create artificial artwork by transfering the appearance of
-    one image (eg a famous painting) to another user-supplied image (eg your favourite photograph).
-    Uses a Neural Style Transfer algorithm to transfer the appearance which you can run though a CLI program.
+version = 0.6
+description = Create artificial artwork by transfering the appearance of one image (eg a famous painting) to another user-supplied image (eg your favourite photograph).
 long_description = file: README.rst
 long_description_content_type = text/x-rst
 license = AGPLv3

src/artificial_artwork/algorithm.py

@@ -1,43 +1,11 @@
-from abc import ABC
 import attr
 
-
-class AlgorithmInterface(ABC):
-    """An algorithm is a series of execution steps, aiming to solve a problem.
-
-    This interface provides a 'run' method that sequentially runs the
-    algorithm's steps.
-    """
-    def run(self, *args, **kwargs) -> None:
-        """Run the algorithm."""
-        raise NotImplementedError
-
-
-class IterativeAlgorithm(AlgorithmInterface):
-
-    def run(self, *args, **kwargs) -> None:
-        pass
-
-
-class LearningAlgorithm(IterativeAlgorithm):
-
-    def run(self, *args, **kwargs) -> None:
-        pass
-
-    def compute_cost(self, *args, **kwargs) -> float:
-        raise NotImplementedError
+from .style_layer_selector import NSTLayersSelection
 
 
 @attr.s
-class NSTAlgorithm(IterativeAlgorithm):
+class NSTAlgorithm:
     parameters = attr.ib()
-    image_config = attr.ib()
-
-    def run(self, *args, **kwargs) -> None:
-        return super().run(*args, **kwargs)
-
-
-from .style_layer_selector import NSTLayersSelection
 
 
 @attr.s
@@ -47,7 +15,6 @@ class AlogirthmParameters:
     # from the algo input (runtime objects that are the INPUT to the algo)
     content_image = attr.ib()
     style_image = attr.ib()
-    cv_model = attr.ib()
     style_layers = attr.ib(converter=NSTLayersSelection.from_tuples)
     termination_condition = attr.ib()
     output_path = attr.ib()