├── .gitignore ├── LICENSE ├── README.rst ├── docker-cpu.df ├── docker-gpu.df ├── docs ├── BankLobby_example.gif ├── EnhanceCSI_example.png ├── Faces_example.png ├── OldStation_example.gif └── StreetView_example.gif ├── enhance.py ├── requirements.txt └── train ├── ne1x-photo-repair.sh └── ne2x-photo-default.sh /.gitignore: -------------------------------------------------------------------------------- 1 | # NeuralEnhance 2 | train/ 3 | valid/ 4 | *.pkl.bz2 5 | 6 | # Byte-compiled / optimized / DLL files 7 | __pycache__/ 8 | *.py[cod] 9 | *$py.class 10 | 11 | # C extensions 12 | *.so 13 | 14 | # Distribution / packaging 15 | .Python 16 | env/ 17 | build/ 18 | develop-eggs/ 19 | dist/ 20 | downloads/ 21 | eggs/ 22 | .eggs/ 23 | lib/ 24 | lib64/ 25 | parts/ 26 | sdist/ 27 | var/ 28 | *.egg-info/ 29 | .installed.cfg 30 | *.egg 31 | 32 | # PyInstaller 33 | # Usually these files are written by a python script from a template 34 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 35 | *.manifest 36 | *.spec 37 | 38 | # Installer logs 39 | pip-log.txt 40 | pip-delete-this-directory.txt 41 | 42 | # Unit test / coverage reports 43 | htmlcov/ 44 | .tox/ 45 | .coverage 46 | .coverage.* 47 | .cache 48 | nosetests.xml 49 | coverage.xml 50 | *,cover 51 | .hypothesis/ 52 | 53 | # Translations 54 | *.mo 55 | *.pot 56 | 57 | # Django stuff: 58 | *.log 59 | local_settings.py 60 | 61 | # Flask stuff: 62 | instance/ 63 | .webassets-cache 64 | 65 | # Scrapy stuff: 66 | .scrapy 67 | 68 | # Sphinx documentation 69 | docs/_build/ 70 | 71 | # PyBuilder 72 | target/ 73 | 74 | # IPython Notebook 75 | .ipynb_checkpoints 76 | 77 | # pyenv 78 | .python-version 79 | 80 | # celery beat schedule file 81 | celerybeat-schedule 82 | 83 | # dotenv 84 | .env 85 | 86 | # virtualenv 87 | venv/ 88 | ENV/ 89 | 90 | # Spyder project settings 91 | .spyderproject 92 | 93 | # Rope project settings 94 | .ropeproject 95 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 611 | 612 | If the disclaimer of warranty and limitation of liability provided 613 | above cannot be given local legal effect according to their terms, 614 | reviewing courts shall apply local law that most closely approximates 615 | an absolute waiver of all civil liability in connection with the 616 | Program, unless a warranty or assumption of liability accompanies a 617 | copy of the Program in return for a fee. 618 | 619 | END OF TERMS AND CONDITIONS 620 | 621 | How to Apply These Terms to Your New Programs 622 | 623 | If you develop a new program, and you want it to be of the greatest 624 | possible use to the public, the best way to achieve this is to make it 625 | free software which everyone can redistribute and change under these terms. 626 | 627 | To do so, attach the following notices to the program. It is safest 628 | to attach them to the start of each source file to most effectively 629 | state the exclusion of warranty; and each file should have at least 630 | the "copyright" line and a pointer to where the full notice is found. 631 | 632 | 633 | Copyright (C) 634 | 635 | This program is free software: you can redistribute it and/or modify 636 | it under the terms of the GNU Affero General Public License as published 637 | by the Free Software Foundation, either version 3 of the License, or 638 | (at your option) any later version. 639 | 640 | This program is distributed in the hope that it will be useful, 641 | but WITHOUT ANY WARRANTY; without even the implied warranty of 642 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 643 | GNU Affero General Public License for more details. 644 | 645 | You should have received a copy of the GNU Affero General Public License 646 | along with this program. If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | Neural Enhance 2 | ============== 3 | 4 | .. image:: docs/OldStation_example.gif 5 | 6 | **Example #1** — Old Station: `view comparison `_ in 24-bit HD, `original photo `_ CC-BY-SA @siv-athens. 7 | 8 | ---- 9 | 10 | `As seen on TV! `_ What if you could increase the resolution of your photos using technology from CSI laboratories? Thanks to deep learning and ``#NeuralEnhance``, it's now possible to train a neural network to zoom in to your images at 2x or even 4x. You'll get even better results by increasing the number of neurons or training with a dataset similar to your low resolution image. 11 | 12 | The catch? The neural network is hallucinating details based on its training from example images. It's not reconstructing your photo exactly as it would have been if it was HD. That's only possible in Hollywood — but using deep learning as "Creative AI" works and it is just as cool! Here's how you can get started... 13 | 14 | 1. `Examples & Usage <#1-examples--usage>`_ 15 | 2. `Installation <#2-installation--setup>`_ 16 | 3. `Background & Research <#3-background--research>`_ 17 | 4. `Troubleshooting <#4-troubleshooting-problems>`_ 18 | 5. `Frequent Questions <#5-frequent-questions>`_ 19 | 20 | |Python Version| |License Type| |Project Stars| 21 | 22 | .. image:: docs/EnhanceCSI_example.png 23 | :target: http://enhance.nucl.ai/w/8581db92-9d61-11e6-990b-c86000be451f/view 24 | 25 | 1. Examples & Usage 26 | =================== 27 | 28 | The main script is called ``enhance.py``, which you can run with Python 3.4+ once it's `setup <#2-installation--setup>`_ as below. The ``--device`` argument that lets you specify which GPU or CPU to use. For the samples above, here are the performance results: 29 | 30 | * **GPU Rendering HQ** — Assuming you have CUDA setup and enough on-board RAM to fit the image and neural network, generating 1080p output should complete in 5 seconds, or 2s per image if multiple at the same time. 31 | * **CPU Rendering HQ** — This will take roughly 20 to 60 seconds for 1080p output, however on most machines you can run 4-8 processes simultaneously given enough system RAM. Runtime depends on the neural network size. 32 | 33 | The default is to use ``--device=cpu``, if you have NVIDIA card setup with CUDA already try ``--device=gpu0``. On the CPU, you can also set environment variable to ``OMP_NUM_THREADS=4``, which is most useful when running the script multiple times in parallel. 34 | 35 | 1.a) Enhancing Images 36 | --------------------- 37 | 38 | A list of example command lines you can use with the pre-trained models provided in the GitHub releases: 39 | 40 | .. code:: bash 41 | 42 | # Run the super-resolution script to repair JPEG artefacts, zoom factor 1:1. 43 | python3 enhance.py --type=photo --model=repair --zoom=1 broken.jpg 44 | 45 | # Process multiple good quality images with a single run, zoom factor 2:1. 46 | python3 enhance.py --type=photo --zoom=2 file1.jpg file2.jpg 47 | 48 | # Display output images that were given `_ne?x.png` suffix. 49 | open *_ne?x.png 50 | 51 | Here's a list of currently supported models, image types, and zoom levels in one table. 52 | 53 | ================== ===================== ==================== ===================== ==================== 54 | FEATURES ``--model=default`` ``--model=repair`` ``--model=denoise`` ``--model=deblur`` 55 | ================== ===================== ==================== ===================== ==================== 56 | ``--type=photo`` 2x 1x … … 57 | ================== ===================== ==================== ===================== ==================== 58 | 59 | 60 | 1.b) Training Super-Resolution 61 | ------------------------------ 62 | 63 | Pre-trained models are provided in the GitHub releases. Training your own is a delicate process that may require you to pick parameters based on your image dataset. 64 | 65 | .. code:: bash 66 | 67 | # Remove the model file as don't want to reload the data to fine-tune it. 68 | rm -f ne?x*.pkl.bz2 69 | 70 | # Pre-train the model using perceptual loss from paper [1] below. 71 | python3.4 enhance.py --train "data/*.jpg" --model custom --scales=2 --epochs=50 \ 72 | --perceptual-layer=conv2_2 --smoothness-weight=1e7 --adversary-weight=0.0 \ 73 | --generator-blocks=4 --generator-filters=64 74 | 75 | # Train the model using an adversarial setup based on [4] below. 76 | python3.4 enhance.py --train "data/*.jpg" --model custom --scales=2 --epochs=250 \ 77 | --perceptual-layer=conv5_2 --smoothness-weight=2e4 --adversary-weight=1e3 \ 78 | --generator-start=5 --discriminator-start=0 --adversarial-start=5 \ 79 | --discriminator-size=64 80 | 81 | # The newly trained model is output into this file... 82 | ls ne?x-custom-*.pkl.bz2 83 | 84 | 85 | .. image:: docs/BankLobby_example.gif 86 | 87 | **Example #2** — Bank Lobby: `view comparison `_ in 24-bit HD, `original photo `_ CC-BY-SA @benarent. 88 | 89 | 2. Installation & Setup 90 | ======================= 91 | 92 | 2.a) Using Docker Image [recommended] 93 | ------------------------------------- 94 | 95 | The easiest way to get up-and-running is to `install Docker `_. Then, you should be able to download and run the pre-built image using the ``docker`` command line tool. Find out more about the ``alexjc/neural-enhance`` image on its `Docker Hub `_ page. 96 | 97 | Here's the simplest way you can call the script using ``docker``, assuming you're familiar with using ``-v`` argument to mount folders you can use this directly to specify files to enhance: 98 | 99 | .. code:: bash 100 | 101 | # Download the Docker image and show the help text to make sure it works. 102 | docker run --rm -v `pwd`:/ne/input -it alexjc/neural-enhance --help 103 | 104 | **Single Image** — In practice, we suggest you setup an alias called ``enhance`` to automatically expose the folder containing your specified image, so the script can read it and store results where you can access them. This is how you can do it in your terminal console on OSX or Linux: 105 | 106 | .. code:: bash 107 | 108 | # Setup the alias. Put this in your .bashrc or .zshrc file so it's available at startup. 109 | alias enhance='function ne() { docker run --rm -v "$(pwd)/`dirname ${@:$#}`":/ne/input -it alexjc/neural-enhance ${@:1:$#-1} "input/`basename ${@:$#}`"; }; ne' 110 | 111 | # Now run any of the examples above using this alias, without the `.py` extension. 112 | enhance --zoom=1 --model=repair images/broken.jpg 113 | 114 | **Multiple Images** — To enhance multiple images in a row (faster) from a folder or wildcard specification, make sure to quote the argument to the alias command: 115 | 116 | .. code:: bash 117 | 118 | # Process multiple images, make sure to quote the argument! 119 | enhance --zoom=2 "images/*.jpg" 120 | 121 | If you want to run on your NVIDIA GPU, you can instead change the alias to use the image ``alexjc/neural-enhance:gpu`` which comes with CUDA and CUDNN pre-installed. Then run it within `nvidia-docker `_ and it should use your physical hardware! 122 | 123 | 124 | 2.b) Manual Installation [developers] 125 | ------------------------------------- 126 | 127 | This project requires Python 3.4+ and you'll also need ``numpy`` and ``scipy`` (numerical computing libraries) as well as ``python3-dev`` installed system-wide. If you want more detailed instructions, follow these: 128 | 129 | 1. `Linux Installation of Lasagne `_ **(intermediate)** 130 | 2. `Mac OSX Installation of Lasagne `_ **(advanced)** 131 | 3. `Windows Installation of Lasagne `_ **(expert)** 132 | 133 | Afterward fetching the repository, you can run the following commands from your terminal to setup a local environment: 134 | 135 | .. code:: bash 136 | 137 | # Create a local environment for Python 3.x to install dependencies here. 138 | python3 -m venv pyvenv --system-site-packages 139 | 140 | # If you're using bash, make this the active version of Python. 141 | source pyvenv/bin/activate 142 | 143 | # Setup the required dependencies simply using the PIP module. 144 | python3 -m pip install --ignore-installed -r requirements.txt 145 | 146 | After this, you should have ``pillow``, ``theano`` and ``lasagne`` installed in your virtual environment. You'll also need to download this `pre-trained neural network `_ (VGG19, 80Mb) and put it in the same folder as the script to run. To de-install everything, you can just delete the ``#/pyvenv/`` folder. 147 | 148 | .. image:: docs/Faces_example.png 149 | 150 | **Example #3** — Specialized super-resolution for faces, trained on HD examples of celebrity faces only. The quality is significantly higher when narrowing the domain from "photos" in general. 151 | 152 | 3. Background & Research 153 | ======================== 154 | 155 | This code uses a combination of techniques from the following papers, as well as some minor improvements yet to be documented (watch this repository for updates): 156 | 157 | 1. `Perceptual Losses for Real-Time Style Transfer and Super-Resolution `_ 158 | 2. `Real-Time Super-Resolution Using Efficient Sub-Pixel Convolution `_ 159 | 3. `Deeply-Recursive Convolutional Network for Image Super-Resolution `_ 160 | 4. `Photo-Realistic Super-Resolution Using a Generative Adversarial Network `_ 161 | 162 | Special thanks for their help and support in various ways: 163 | 164 | * Eder Santana — Discussions, encouragement, and his ideas on `sub-pixel deconvolution `_. 165 | * Andrew Brock — This sub-pixel layer code is based on `his project repository `_ using Lasagne. 166 | * Casper Kaae Sønderby — For suggesting a more stable alternative to sigmoid + log as GAN loss functions. 167 | 168 | 169 | 4. Troubleshooting Problems 170 | =========================== 171 | 172 | Can't install or Unable to find pgen, not compiling formal grammar. 173 | ------------------------------------------------------------------- 174 | 175 | There's a Python extension compiler called Cython, and it's missing or improperly installed. Try getting it directly from the system package manager rather than PIP. 176 | 177 | **FIX:** ``sudo apt-get install cython3`` 178 | 179 | 180 | NotImplementedError: AbstractConv2d theano optimization failed. 181 | --------------------------------------------------------------- 182 | 183 | This happens when you're running without a GPU, and the CPU libraries were not found (e.g. ``libblas``). The neural network expressions cannot be evaluated by Theano and it's raising an exception. 184 | 185 | **FIX:** ``sudo apt-get install libblas-dev libopenblas-dev`` 186 | 187 | 188 | TypeError: max_pool_2d() got an unexpected keyword argument 'mode' 189 | ------------------------------------------------------------------ 190 | 191 | You need to install Lasagne and Theano directly from the versions specified in ``requirements.txt``, rather than from the PIP versions. These alternatives are older and don't have the required features. 192 | 193 | **FIX:** ``python3 -m pip install -r requirements.txt`` 194 | 195 | 196 | ValueError: unknown locale: UTF-8 197 | --------------------------------- 198 | 199 | It seems your terminal is misconfigured and not compatible with the way Python treats locales. You may need to change this in your ``.bashrc`` or other startup script. Alternatively, this command will fix it once for this shell instance. 200 | 201 | **FIX:** ``export LC_ALL=en_US.UTF-8`` 202 | 203 | .. image:: docs/StreetView_example.gif 204 | 205 | **Example #4** — Street View: `view comparison `_ in 24-bit HD, `original photo `_ CC-BY-SA @cyalex. 206 | 207 | ---- 208 | 209 | |Python Version| |License Type| |Project Stars| 210 | 211 | .. |Python Version| image:: http://aigamedev.github.io/scikit-neuralnetwork/badge_python.svg 212 | :target: https://www.python.org/ 213 | 214 | .. |License Type| image:: https://img.shields.io/badge/license-AGPL-blue.svg 215 | :target: https://github.com/alexjc/neural-enhance/blob/master/LICENSE 216 | 217 | .. |Project Stars| image:: https://img.shields.io/github/stars/alexjc/neural-enhance.svg?style=flat 218 | :target: https://github.com/alexjc/neural-enhance/stargazers 219 | -------------------------------------------------------------------------------- /docker-cpu.df: -------------------------------------------------------------------------------- 1 | FROM ubuntu:14.04 2 | 3 | # Install dependencies 4 | RUN apt-get -qq update && \ 5 | apt-get -qq install --assume-yes \ 6 | "build-essential" \ 7 | "git" \ 8 | "wget" \ 9 | "libopenblas-dev" \ 10 | "liblapack-dev" \ 11 | "pkg-config" && \ 12 | rm -rf /var/lib/apt/lists/* 13 | 14 | # Miniconda. 15 | RUN wget --quiet https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \ 16 | /bin/bash ~/miniconda.sh -b -p /opt/conda && \ 17 | rm ~/miniconda.sh 18 | 19 | # Install requirements before copying project files 20 | WORKDIR /ne 21 | COPY requirements.txt . 22 | RUN /opt/conda/bin/conda install -q -y conda numpy scipy pip pillow 23 | RUN /opt/conda/bin/python3.5 -m pip install -q -r "requirements.txt" 24 | 25 | # Copy only required project files 26 | COPY enhance.py . 27 | 28 | # Get a pre-trained neural networks, non-commercial & attribution. 29 | RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne1x-photo-deblur-0.3.pkl.bz2" 30 | RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne1x-photo-repair-0.3.pkl.bz2" 31 | RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne2x-photo-default-0.3.pkl.bz2" 32 | RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne4x-photo-default-0.3.pkl.bz2" 33 | # Set an entrypoint to the main enhance.py script 34 | ENTRYPOINT ["/opt/conda/bin/python3.5", "enhance.py", "--device=cpu"] 35 | -------------------------------------------------------------------------------- /docker-gpu.df: -------------------------------------------------------------------------------- 1 | FROM nvidia/cuda:8.0-cudnn5-devel 2 | 3 | # Install dependencies 4 | RUN apt-get -qq update && \ 5 | apt-get -qq install --assume-yes \ 6 | "build-essential" \ 7 | "git" \ 8 | "wget" \ 9 | "pkg-config" && \ 10 | rm -rf /var/lib/apt/lists/* 11 | 12 | # Miniconda. 13 | RUN wget --quiet https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \ 14 | /bin/bash ~/miniconda.sh -b -p /opt/conda && \ 15 | rm ~/miniconda.sh 16 | 17 | # Install requirements before copying project files 18 | WORKDIR /ne 19 | COPY requirements.txt . 20 | RUN /opt/conda/bin/conda install -q -y conda numpy scipy pip pillow 21 | RUN /opt/conda/bin/python3.5 -m pip install -q -r "requirements.txt" 22 | 23 | # Copy only required project files 24 | COPY enhance.py . 25 | 26 | # Get a pre-trained neural networks, non-commercial & attribution. 27 | RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne1x-photo-deblur-0.3.pkl.bz2" 28 | RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne1x-photo-repair-0.3.pkl.bz2" 29 | RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne2x-photo-default-0.3.pkl.bz2" 30 | RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne4x-photo-default-0.3.pkl.bz2" 31 | 32 | # Set an entrypoint to the main enhance.py script 33 | ENTRYPOINT ["/opt/conda/bin/python3.5", "enhance.py", "--device=gpu"] 34 | -------------------------------------------------------------------------------- /docs/BankLobby_example.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexjc/neural-enhance/2fd67de7eddae3e8dc48f8abdc791ddbcc867112/docs/BankLobby_example.gif -------------------------------------------------------------------------------- /docs/EnhanceCSI_example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexjc/neural-enhance/2fd67de7eddae3e8dc48f8abdc791ddbcc867112/docs/EnhanceCSI_example.png -------------------------------------------------------------------------------- /docs/Faces_example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexjc/neural-enhance/2fd67de7eddae3e8dc48f8abdc791ddbcc867112/docs/Faces_example.png -------------------------------------------------------------------------------- /docs/OldStation_example.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexjc/neural-enhance/2fd67de7eddae3e8dc48f8abdc791ddbcc867112/docs/OldStation_example.gif -------------------------------------------------------------------------------- /docs/StreetView_example.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexjc/neural-enhance/2fd67de7eddae3e8dc48f8abdc791ddbcc867112/docs/StreetView_example.gif -------------------------------------------------------------------------------- /enhance.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | """ _ _ 3 | _ __ ___ _ _ _ __ __ _| | ___ _ __ | |__ __ _ _ __ ___ ___ 4 | | '_ \ / _ \ | | | '__/ _` | | / _ \ '_ \| '_ \ / _` | '_ \ / __/ _ \ 5 | | | | | __/ |_| | | | (_| | | | __/ | | | | | | (_| | | | | (_| __/ 6 | |_| |_|\___|\__,_|_| \__,_|_| \___|_| |_|_| |_|\__,_|_| |_|\___\___| 7 | 8 | """ 9 | # 10 | # Copyright (c) 2016, Alex J. Champandard. 11 | # 12 | # Neural Enhance is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General 13 | # Public License version 3. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; 14 | # without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 15 | # 16 | 17 | __version__ = '0.3' 18 | 19 | import io 20 | import os 21 | import sys 22 | import bz2 23 | import glob 24 | import math 25 | import time 26 | import pickle 27 | import random 28 | import argparse 29 | import itertools 30 | import threading 31 | import collections 32 | 33 | 34 | # Configure all options first so we can later custom-load other libraries (Theano) based on device specified by user. 35 | parser = argparse.ArgumentParser(description='Generate a new image by applying style onto a content image.', 36 | formatter_class=argparse.ArgumentDefaultsHelpFormatter) 37 | add_arg = parser.add_argument 38 | add_arg('files', nargs='*', default=[]) 39 | add_arg('--zoom', default=2, type=int, help='Resolution increase factor for inference.') 40 | add_arg('--rendering-tile', default=80, type=int, help='Size of tiles used for rendering images.') 41 | add_arg('--rendering-overlap', default=24, type=int, help='Number of pixels padding around each tile.') 42 | add_arg('--rendering-histogram',default=False, action='store_true', help='Match color histogram of output to input.') 43 | add_arg('--type', default='photo', type=str, help='Name of the neural network to load/save.') 44 | add_arg('--model', default='default', type=str, help='Specific trained version of the model.') 45 | add_arg('--train', default=False, type=str, help='File pattern to load for training.') 46 | add_arg('--train-scales', default=0, type=int, help='Randomly resize images this many times.') 47 | add_arg('--train-blur', default=None, type=int, help='Sigma value for gaussian blur preprocess.') 48 | add_arg('--train-noise', default=None, type=float, help='Radius for preprocessing gaussian blur.') 49 | add_arg('--train-jpeg', default=[], nargs='+', type=int, help='JPEG compression level & range in preproc.') 50 | add_arg('--epochs', default=10, type=int, help='Total number of iterations in training.') 51 | add_arg('--epoch-size', default=72, type=int, help='Number of batches trained in an epoch.') 52 | add_arg('--save-every', default=10, type=int, help='Save generator after every training epoch.') 53 | add_arg('--batch-shape', default=192, type=int, help='Resolution of images in training batch.') 54 | add_arg('--batch-size', default=15, type=int, help='Number of images per training batch.') 55 | add_arg('--buffer-size', default=1500, type=int, help='Total image fragments kept in cache.') 56 | add_arg('--buffer-fraction', default=5, type=int, help='Fragments cached for each image loaded.') 57 | add_arg('--learning-rate', default=1E-4, type=float, help='Parameter for the ADAM optimizer.') 58 | add_arg('--learning-period', default=75, type=int, help='How often to decay the learning rate.') 59 | add_arg('--learning-decay', default=0.5, type=float, help='How much to decay the learning rate.') 60 | add_arg('--generator-upscale', default=2, type=int, help='Steps of 2x up-sampling as post-process.') 61 | add_arg('--generator-downscale',default=0, type=int, help='Steps of 2x down-sampling as preprocess.') 62 | add_arg('--generator-filters', default=[64], nargs='+', type=int, help='Number of convolution units in network.') 63 | add_arg('--generator-blocks', default=4, type=int, help='Number of residual blocks per iteration.') 64 | add_arg('--generator-residual', default=2, type=int, help='Number of layers in a residual block.') 65 | add_arg('--perceptual-layer', default='conv2_2', type=str, help='Which VGG layer to use as loss component.') 66 | add_arg('--perceptual-weight', default=1e0, type=float, help='Weight for VGG-layer perceptual loss.') 67 | add_arg('--discriminator-size', default=32, type=int, help='Multiplier for number of filters in D.') 68 | add_arg('--smoothness-weight', default=2e5, type=float, help='Weight of the total-variation loss.') 69 | add_arg('--adversary-weight', default=5e2, type=float, help='Weight of adversarial loss compoment.') 70 | add_arg('--generator-start', default=0, type=int, help='Epoch count to start training generator.') 71 | add_arg('--discriminator-start',default=1, type=int, help='Epoch count to update the discriminator.') 72 | add_arg('--adversarial-start', default=2, type=int, help='Epoch for generator to use discriminator.') 73 | add_arg('--device', default='cpu', type=str, help='Name of the CPU/GPU to use, for Theano.') 74 | args = parser.parse_args() 75 | 76 | 77 | #---------------------------------------------------------------------------------------------------------------------- 78 | 79 | # Color coded output helps visualize the information a little better, plus it looks cool! 80 | class ansi: 81 | WHITE = '\033[0;97m' 82 | WHITE_B = '\033[1;97m' 83 | YELLOW = '\033[0;33m' 84 | YELLOW_B = '\033[1;33m' 85 | RED = '\033[0;31m' 86 | RED_B = '\033[1;31m' 87 | BLUE = '\033[0;94m' 88 | BLUE_B = '\033[1;94m' 89 | CYAN = '\033[0;36m' 90 | CYAN_B = '\033[1;36m' 91 | ENDC = '\033[0m' 92 | 93 | def error(message, *lines): 94 | string = "\n{}ERROR: " + message + "{}\n" + "\n".join(lines) + ("{}\n" if lines else "{}") 95 | print(string.format(ansi.RED_B, ansi.RED, ansi.ENDC)) 96 | sys.exit(-1) 97 | 98 | def warn(message, *lines): 99 | string = "\n{}WARNING: " + message + "{}\n" + "\n".join(lines) + "{}\n" 100 | print(string.format(ansi.YELLOW_B, ansi.YELLOW, ansi.ENDC)) 101 | 102 | def extend(lst): return itertools.chain(lst, itertools.repeat(lst[-1])) 103 | 104 | print("""{} {}Super Resolution for images and videos powered by Deep Learning!{} 105 | - Code licensed as AGPLv3, models under CC BY-NC-SA.{}""".format(ansi.CYAN_B, __doc__, ansi.CYAN, ansi.ENDC)) 106 | 107 | # Load the underlying deep learning libraries based on the device specified. If you specify THEANO_FLAGS manually, 108 | # the code assumes you know what you are doing and they are not overriden! 109 | os.environ.setdefault('THEANO_FLAGS', 'floatX=float32,device={},force_device=True,allow_gc=True,'\ 110 | 'print_active_device=False'.format(args.device)) 111 | 112 | # Scientific & Imaging Libraries 113 | import numpy as np 114 | import scipy.ndimage, scipy.misc, PIL.Image 115 | 116 | # Numeric Computing (GPU) 117 | import theano, theano.tensor as T 118 | T.nnet.softminus = lambda x: x - T.nnet.softplus(x) 119 | 120 | # Support ansi colors in Windows too. 121 | if sys.platform == 'win32': 122 | import colorama 123 | 124 | # Deep Learning Framework 125 | import lasagne 126 | from lasagne.layers import Conv2DLayer as ConvLayer, Deconv2DLayer as DeconvLayer, Pool2DLayer as PoolLayer 127 | from lasagne.layers import InputLayer, ConcatLayer, ElemwiseSumLayer, batch_norm 128 | 129 | print('{} - Using the device `{}` for neural computation.{}\n'.format(ansi.CYAN, theano.config.device, ansi.ENDC)) 130 | 131 | 132 | #====================================================================================================================== 133 | # Image Processing 134 | #====================================================================================================================== 135 | class DataLoader(threading.Thread): 136 | 137 | def __init__(self): 138 | super(DataLoader, self).__init__(daemon=True) 139 | self.data_ready = threading.Event() 140 | self.data_copied = threading.Event() 141 | 142 | self.orig_shape, self.seed_shape = args.batch_shape, args.batch_shape // args.zoom 143 | 144 | self.orig_buffer = np.zeros((args.buffer_size, 3, self.orig_shape, self.orig_shape), dtype=np.float32) 145 | self.seed_buffer = np.zeros((args.buffer_size, 3, self.seed_shape, self.seed_shape), dtype=np.float32) 146 | self.files = glob.glob(args.train) 147 | if len(self.files) == 0: 148 | error("There were no files found to train from searching for `{}`".format(args.train), 149 | " - Try putting all your images in one folder and using `--train=data/*.jpg`") 150 | 151 | self.available = set(range(args.buffer_size)) 152 | self.ready = set() 153 | 154 | self.cwd = os.getcwd() 155 | self.start() 156 | 157 | def run(self): 158 | while True: 159 | random.shuffle(self.files) 160 | for f in self.files: 161 | self.add_to_buffer(f) 162 | 163 | def add_to_buffer(self, f): 164 | filename = os.path.join(self.cwd, f) 165 | try: 166 | orig = PIL.Image.open(filename).convert('RGB') 167 | scale = 2 ** random.randint(0, args.train_scales) 168 | if scale > 1 and all(s//scale >= args.batch_shape for s in orig.size): 169 | orig = orig.resize((orig.size[0]//scale, orig.size[1]//scale), resample=PIL.Image.LANCZOS) 170 | if any(s < args.batch_shape for s in orig.size): 171 | raise ValueError('Image is too small for training with size {}'.format(orig.size)) 172 | except Exception as e: 173 | warn('Could not load `{}` as image.'.format(filename), 174 | ' - Try fixing or removing the file before next run.') 175 | self.files.remove(f) 176 | return 177 | 178 | seed = orig 179 | if args.train_blur is not None: 180 | seed = seed.filter(PIL.ImageFilter.GaussianBlur(radius=random.randint(0, args.train_blur*2))) 181 | if args.zoom > 1: 182 | seed = seed.resize((orig.size[0]//args.zoom, orig.size[1]//args.zoom), resample=PIL.Image.LANCZOS) 183 | if len(args.train_jpeg) > 0: 184 | buffer, rng = io.BytesIO(), args.train_jpeg[-1] if len(args.train_jpeg) > 1 else 15 185 | seed.save(buffer, format='jpeg', quality=args.train_jpeg[0]+random.randrange(-rng, +rng)) 186 | seed = PIL.Image.open(buffer) 187 | 188 | orig = scipy.misc.fromimage(orig).astype(np.float32) 189 | seed = scipy.misc.fromimage(seed).astype(np.float32) 190 | 191 | if args.train_noise is not None: 192 | seed += scipy.random.normal(scale=args.train_noise, size=(seed.shape[0], seed.shape[1], 1)) 193 | 194 | for _ in range(seed.shape[0] * seed.shape[1] // (args.buffer_fraction * self.seed_shape ** 2)): 195 | h = random.randint(0, seed.shape[0] - self.seed_shape) 196 | w = random.randint(0, seed.shape[1] - self.seed_shape) 197 | seed_chunk = seed[h:h+self.seed_shape, w:w+self.seed_shape] 198 | h, w = h * args.zoom, w * args.zoom 199 | orig_chunk = orig[h:h+self.orig_shape, w:w+self.orig_shape] 200 | 201 | while len(self.available) == 0: 202 | self.data_copied.wait() 203 | self.data_copied.clear() 204 | 205 | i = self.available.pop() 206 | self.orig_buffer[i] = np.transpose(orig_chunk.astype(np.float32) / 255.0 - 0.5, (2, 0, 1)) 207 | self.seed_buffer[i] = np.transpose(seed_chunk.astype(np.float32) / 255.0 - 0.5, (2, 0, 1)) 208 | self.ready.add(i) 209 | 210 | if len(self.ready) >= args.batch_size: 211 | self.data_ready.set() 212 | 213 | def copy(self, origs_out, seeds_out): 214 | self.data_ready.wait() 215 | self.data_ready.clear() 216 | 217 | for i, j in enumerate(random.sample(self.ready, args.batch_size)): 218 | origs_out[i] = self.orig_buffer[j] 219 | seeds_out[i] = self.seed_buffer[j] 220 | self.available.add(j) 221 | self.data_copied.set() 222 | 223 | 224 | #====================================================================================================================== 225 | # Convolution Networks 226 | #====================================================================================================================== 227 | 228 | class SubpixelReshuffleLayer(lasagne.layers.Layer): 229 | """Based on the code by ajbrock: https://github.com/ajbrock/Neural-Photo-Editor/ 230 | """ 231 | 232 | def __init__(self, incoming, channels, upscale, **kwargs): 233 | super(SubpixelReshuffleLayer, self).__init__(incoming, **kwargs) 234 | self.upscale = upscale 235 | self.channels = channels 236 | 237 | def get_output_shape_for(self, input_shape): 238 | def up(d): return self.upscale * d if d else d 239 | return (input_shape[0], self.channels, up(input_shape[2]), up(input_shape[3])) 240 | 241 | def get_output_for(self, input, deterministic=False, **kwargs): 242 | out, r = T.zeros(self.get_output_shape_for(input.shape)), self.upscale 243 | for y, x in itertools.product(range(r), repeat=2): 244 | out=T.inc_subtensor(out[:,:,y::r,x::r], input[:,r*y+x::r*r,:,:]) 245 | return out 246 | 247 | 248 | class Model(object): 249 | 250 | def __init__(self): 251 | self.network = collections.OrderedDict() 252 | self.network['img'] = InputLayer((None, 3, None, None)) 253 | self.network['seed'] = InputLayer((None, 3, None, None)) 254 | 255 | config, params = self.load_model() 256 | self.setup_generator(self.last_layer(), config) 257 | 258 | if args.train: 259 | concatenated = lasagne.layers.ConcatLayer([self.network['img'], self.network['out']], axis=0) 260 | self.setup_perceptual(concatenated) 261 | self.load_perceptual() 262 | self.setup_discriminator() 263 | self.load_generator(params) 264 | self.compile() 265 | 266 | #------------------------------------------------------------------------------------------------------------------ 267 | # Network Configuration 268 | #------------------------------------------------------------------------------------------------------------------ 269 | 270 | def last_layer(self): 271 | return list(self.network.values())[-1] 272 | 273 | def make_layer(self, name, input, units, filter_size=(3,3), stride=(1,1), pad=(1,1), alpha=0.25): 274 | conv = ConvLayer(input, units, filter_size, stride=stride, pad=pad, nonlinearity=None) 275 | prelu = lasagne.layers.ParametricRectifierLayer(conv, alpha=lasagne.init.Constant(alpha)) 276 | self.network[name+'x'] = conv 277 | self.network[name+'>'] = prelu 278 | return prelu 279 | 280 | def make_block(self, name, input, units): 281 | self.make_layer(name+'-A', input, units, alpha=0.1) 282 | # self.make_layer(name+'-B', self.last_layer(), units, alpha=1.0) 283 | return ElemwiseSumLayer([input, self.last_layer()]) if args.generator_residual else self.last_layer() 284 | 285 | def setup_generator(self, input, config): 286 | for k, v in config.items(): setattr(args, k, v) 287 | args.zoom = 2**(args.generator_upscale - args.generator_downscale) 288 | 289 | units_iter = extend(args.generator_filters) 290 | units = next(units_iter) 291 | self.make_layer('iter.0', input, units, filter_size=(7,7), pad=(3,3)) 292 | 293 | for i in range(0, args.generator_downscale): 294 | self.make_layer('downscale%i'%i, self.last_layer(), next(units_iter), filter_size=(4,4), stride=(2,2)) 295 | 296 | units = next(units_iter) 297 | for i in range(0, args.generator_blocks): 298 | self.make_block('iter.%i'%(i+1), self.last_layer(), units) 299 | 300 | for i in range(0, args.generator_upscale): 301 | u = next(units_iter) 302 | self.make_layer('upscale%i.2'%i, self.last_layer(), u*4) 303 | self.network['upscale%i.1'%i] = SubpixelReshuffleLayer(self.last_layer(), u, 2) 304 | 305 | self.network['out'] = ConvLayer(self.last_layer(), 3, filter_size=(7,7), pad=(3,3), nonlinearity=None) 306 | 307 | def setup_perceptual(self, input): 308 | """Use lasagne to create a network of convolution layers using pre-trained VGG19 weights. 309 | """ 310 | offset = np.array([103.939, 116.779, 123.680], dtype=np.float32).reshape((1,3,1,1)) 311 | self.network['percept'] = lasagne.layers.NonlinearityLayer(input, lambda x: ((x+0.5)*255.0) - offset) 312 | 313 | self.network['mse'] = self.network['percept'] 314 | self.network['conv1_1'] = ConvLayer(self.network['percept'], 64, 3, pad=1) 315 | self.network['conv1_2'] = ConvLayer(self.network['conv1_1'], 64, 3, pad=1) 316 | self.network['pool1'] = PoolLayer(self.network['conv1_2'], 2, mode='max') 317 | self.network['conv2_1'] = ConvLayer(self.network['pool1'], 128, 3, pad=1) 318 | self.network['conv2_2'] = ConvLayer(self.network['conv2_1'], 128, 3, pad=1) 319 | self.network['pool2'] = PoolLayer(self.network['conv2_2'], 2, mode='max') 320 | self.network['conv3_1'] = ConvLayer(self.network['pool2'], 256, 3, pad=1) 321 | self.network['conv3_2'] = ConvLayer(self.network['conv3_1'], 256, 3, pad=1) 322 | self.network['conv3_3'] = ConvLayer(self.network['conv3_2'], 256, 3, pad=1) 323 | self.network['conv3_4'] = ConvLayer(self.network['conv3_3'], 256, 3, pad=1) 324 | self.network['pool3'] = PoolLayer(self.network['conv3_4'], 2, mode='max') 325 | self.network['conv4_1'] = ConvLayer(self.network['pool3'], 512, 3, pad=1) 326 | self.network['conv4_2'] = ConvLayer(self.network['conv4_1'], 512, 3, pad=1) 327 | self.network['conv4_3'] = ConvLayer(self.network['conv4_2'], 512, 3, pad=1) 328 | self.network['conv4_4'] = ConvLayer(self.network['conv4_3'], 512, 3, pad=1) 329 | self.network['pool4'] = PoolLayer(self.network['conv4_4'], 2, mode='max') 330 | self.network['conv5_1'] = ConvLayer(self.network['pool4'], 512, 3, pad=1) 331 | self.network['conv5_2'] = ConvLayer(self.network['conv5_1'], 512, 3, pad=1) 332 | self.network['conv5_3'] = ConvLayer(self.network['conv5_2'], 512, 3, pad=1) 333 | self.network['conv5_4'] = ConvLayer(self.network['conv5_3'], 512, 3, pad=1) 334 | 335 | def setup_discriminator(self): 336 | c = args.discriminator_size 337 | self.make_layer('disc1.1', batch_norm(self.network['conv1_2']), 1*c, filter_size=(5,5), stride=(2,2), pad=(2,2)) 338 | self.make_layer('disc1.2', self.last_layer(), 1*c, filter_size=(5,5), stride=(2,2), pad=(2,2)) 339 | self.make_layer('disc2', batch_norm(self.network['conv2_2']), 2*c, filter_size=(5,5), stride=(2,2), pad=(2,2)) 340 | self.make_layer('disc3', batch_norm(self.network['conv3_2']), 3*c, filter_size=(3,3), stride=(1,1), pad=(1,1)) 341 | hypercolumn = ConcatLayer([self.network['disc1.2>'], self.network['disc2>'], self.network['disc3>']]) 342 | self.make_layer('disc4', hypercolumn, 4*c, filter_size=(1,1), stride=(1,1), pad=(0,0)) 343 | self.make_layer('disc5', self.last_layer(), 3*c, filter_size=(3,3), stride=(2,2)) 344 | self.make_layer('disc6', self.last_layer(), 2*c, filter_size=(1,1), stride=(1,1), pad=(0,0)) 345 | self.network['disc'] = batch_norm(ConvLayer(self.last_layer(), 1, filter_size=(1,1), 346 | nonlinearity=lasagne.nonlinearities.linear)) 347 | 348 | 349 | #------------------------------------------------------------------------------------------------------------------ 350 | # Input / Output 351 | #------------------------------------------------------------------------------------------------------------------ 352 | 353 | def load_perceptual(self): 354 | """Open the serialized parameters from a pre-trained network, and load them into the model created. 355 | """ 356 | vgg19_file = os.path.join(os.path.dirname(__file__), 'vgg19_conv.pkl.bz2') 357 | if not os.path.exists(vgg19_file): 358 | error("Model file with pre-trained convolution layers not found. Download here...", 359 | "https://github.com/alexjc/neural-doodle/releases/download/v0.0/vgg19_conv.pkl.bz2") 360 | 361 | data = pickle.load(bz2.open(vgg19_file, 'rb')) 362 | layers = lasagne.layers.get_all_layers(self.last_layer(), treat_as_input=[self.network['percept']]) 363 | for p, d in zip(itertools.chain(*[l.get_params() for l in layers]), data): p.set_value(d) 364 | 365 | def list_generator_layers(self): 366 | for l in lasagne.layers.get_all_layers(self.network['out'], treat_as_input=[self.network['img']]): 367 | if not l.get_params(): continue 368 | name = list(self.network.keys())[list(self.network.values()).index(l)] 369 | yield (name, l) 370 | 371 | def get_filename(self, absolute=False): 372 | filename = 'ne%ix-%s-%s-%s.pkl.bz2' % (args.zoom, args.type, args.model, __version__) 373 | return os.path.join(os.path.dirname(__file__), filename) if absolute else filename 374 | 375 | def save_generator(self): 376 | def cast(p): return p.get_value().astype(np.float16) 377 | params = {k: [cast(p) for p in l.get_params()] for (k, l) in self.list_generator_layers()} 378 | config = {k: getattr(args, k) for k in ['generator_blocks', 'generator_residual', 'generator_filters'] + \ 379 | ['generator_upscale', 'generator_downscale']} 380 | 381 | pickle.dump((config, params), bz2.open(self.get_filename(absolute=True), 'wb')) 382 | print(' - Saved model as `{}` after training.'.format(self.get_filename())) 383 | 384 | def load_model(self): 385 | if not os.path.exists(self.get_filename(absolute=True)): 386 | if args.train: return {}, {} 387 | error("Model file with pre-trained convolution layers not found. Download it here...", 388 | "https://github.com/alexjc/neural-enhance/releases/download/v%s/%s"%(__version__, self.get_filename())) 389 | print(' - Loaded file `{}` with trained model.'.format(self.get_filename())) 390 | return pickle.load(bz2.open(self.get_filename(absolute=True), 'rb')) 391 | 392 | def load_generator(self, params): 393 | if len(params) == 0: return 394 | for k, l in self.list_generator_layers(): 395 | assert k in params, "Couldn't find layer `%s` in loaded model.'" % k 396 | assert len(l.get_params()) == len(params[k]), "Mismatch in types of layers." 397 | for p, v in zip(l.get_params(), params[k]): 398 | assert v.shape == p.get_value().shape, "Mismatch in number of parameters for layer {}.".format(k) 399 | p.set_value(v.astype(np.float32)) 400 | 401 | #------------------------------------------------------------------------------------------------------------------ 402 | # Training & Loss Functions 403 | #------------------------------------------------------------------------------------------------------------------ 404 | 405 | def loss_perceptual(self, p): 406 | return lasagne.objectives.squared_error(p[:args.batch_size], p[args.batch_size:]).mean() 407 | 408 | def loss_total_variation(self, x): 409 | return T.mean(((x[:,:,:-1,:-1] - x[:,:,1:,:-1])**2 + (x[:,:,:-1,:-1] - x[:,:,:-1,1:])**2)**1.25) 410 | 411 | def loss_adversarial(self, d): 412 | return T.mean(1.0 - T.nnet.softminus(d[args.batch_size:])) 413 | 414 | def loss_discriminator(self, d): 415 | return T.mean(T.nnet.softminus(d[args.batch_size:]) - T.nnet.softplus(d[:args.batch_size])) 416 | 417 | def compile(self): 418 | # Helper function for rendering test images during training, or standalone inference mode. 419 | input_tensor, seed_tensor = T.tensor4(), T.tensor4() 420 | input_layers = {self.network['img']: input_tensor, self.network['seed']: seed_tensor} 421 | output = lasagne.layers.get_output([self.network[k] for k in ['seed','out']], input_layers, deterministic=True) 422 | self.predict = theano.function([seed_tensor], output) 423 | 424 | if not args.train: return 425 | 426 | output_layers = [self.network['out'], self.network[args.perceptual_layer], self.network['disc']] 427 | gen_out, percept_out, disc_out = lasagne.layers.get_output(output_layers, input_layers, deterministic=False) 428 | 429 | # Generator loss function, parameters and updates. 430 | self.gen_lr = theano.shared(np.array(0.0, dtype=theano.config.floatX)) 431 | self.adversary_weight = theano.shared(np.array(0.0, dtype=theano.config.floatX)) 432 | gen_losses = [self.loss_perceptual(percept_out) * args.perceptual_weight, 433 | self.loss_total_variation(gen_out) * args.smoothness_weight, 434 | self.loss_adversarial(disc_out) * self.adversary_weight] 435 | gen_params = lasagne.layers.get_all_params(self.network['out'], trainable=True) 436 | print(' - {} tensors learned for generator.'.format(len(gen_params))) 437 | gen_updates = lasagne.updates.adam(sum(gen_losses, 0.0), gen_params, learning_rate=self.gen_lr) 438 | 439 | # Discriminator loss function, parameters and updates. 440 | self.disc_lr = theano.shared(np.array(0.0, dtype=theano.config.floatX)) 441 | disc_losses = [self.loss_discriminator(disc_out)] 442 | disc_params = list(itertools.chain(*[l.get_params() for k, l in self.network.items() if 'disc' in k])) 443 | print(' - {} tensors learned for discriminator.'.format(len(disc_params))) 444 | grads = [g.clip(-5.0, +5.0) for g in T.grad(sum(disc_losses, 0.0), disc_params)] 445 | disc_updates = lasagne.updates.adam(grads, disc_params, learning_rate=self.disc_lr) 446 | 447 | # Combined Theano function for updating both generator and discriminator at the same time. 448 | updates = collections.OrderedDict(list(gen_updates.items()) + list(disc_updates.items())) 449 | self.fit = theano.function([input_tensor, seed_tensor], gen_losses + [disc_out.mean(axis=(1,2,3))], updates=updates) 450 | 451 | 452 | 453 | class NeuralEnhancer(object): 454 | 455 | def __init__(self, loader): 456 | if args.train: 457 | print('{}Training {} epochs on random image sections with batch size {}.{}'\ 458 | .format(ansi.BLUE_B, args.epochs, args.batch_size, ansi.BLUE)) 459 | else: 460 | if len(args.files) == 0: error("Specify the image(s) to enhance on the command-line.") 461 | print('{}Enhancing {} image(s) specified on the command-line.{}'\ 462 | .format(ansi.BLUE_B, len(args.files), ansi.BLUE)) 463 | 464 | self.thread = DataLoader() if loader else None 465 | self.model = Model() 466 | 467 | print('{}'.format(ansi.ENDC)) 468 | 469 | def imsave(self, fn, img): 470 | scipy.misc.toimage(np.transpose(img + 0.5, (1, 2, 0)).clip(0.0, 1.0) * 255.0, cmin=0, cmax=255).save(fn) 471 | 472 | def show_progress(self, orign, scald, repro): 473 | os.makedirs('valid', exist_ok=True) 474 | for i in range(args.batch_size): 475 | self.imsave('valid/%s_%03i_origin.png' % (args.model, i), orign[i]) 476 | self.imsave('valid/%s_%03i_pixels.png' % (args.model, i), scald[i]) 477 | self.imsave('valid/%s_%03i_reprod.png' % (args.model, i), repro[i]) 478 | 479 | def decay_learning_rate(self): 480 | l_r, t_cur = args.learning_rate, 0 481 | 482 | while True: 483 | yield l_r 484 | t_cur += 1 485 | if t_cur % args.learning_period == 0: l_r *= args.learning_decay 486 | 487 | def train(self): 488 | seed_size = args.batch_shape // args.zoom 489 | images = np.zeros((args.batch_size, 3, args.batch_shape, args.batch_shape), dtype=np.float32) 490 | seeds = np.zeros((args.batch_size, 3, seed_size, seed_size), dtype=np.float32) 491 | learning_rate = self.decay_learning_rate() 492 | try: 493 | average, start = None, time.time() 494 | for epoch in range(args.epochs): 495 | total, stats = None, None 496 | l_r = next(learning_rate) 497 | if epoch >= args.generator_start: self.model.gen_lr.set_value(l_r) 498 | if epoch >= args.discriminator_start: self.model.disc_lr.set_value(l_r) 499 | 500 | for _ in range(args.epoch_size): 501 | self.thread.copy(images, seeds) 502 | output = self.model.fit(images, seeds) 503 | losses = np.array(output[:3], dtype=np.float32) 504 | stats = (stats + output[3]) if stats is not None else output[3] 505 | total = total + losses if total is not None else losses 506 | l = np.sum(losses) 507 | assert not np.isnan(losses).any() 508 | average = l if average is None else average * 0.95 + 0.05 * l 509 | print('↑' if l > average else '↓', end='', flush=True) 510 | 511 | scald, repro = self.model.predict(seeds) 512 | self.show_progress(images, scald, repro) 513 | total /= args.epoch_size 514 | stats /= args.epoch_size 515 | totals, labels = [sum(total)] + list(total), ['total', 'prcpt', 'smthn', 'advrs'] 516 | gen_info = ['{}{}{}={:4.2e}'.format(ansi.WHITE_B, k, ansi.ENDC, v) for k, v in zip(labels, totals)] 517 | print('\rEpoch #{} at {:4.1f}s, lr={:4.2e}{}'.format(epoch+1, time.time()-start, l_r, ' '*(args.epoch_size-30))) 518 | print(' - generator {}'.format(' '.join(gen_info))) 519 | 520 | real, fake = stats[:args.batch_size], stats[args.batch_size:] 521 | print(' - discriminator', real.mean(), len(np.where(real > 0.5)[0]), 522 | fake.mean(), len(np.where(fake < -0.5)[0])) 523 | if epoch == args.adversarial_start-1: 524 | print(' - generator now optimizing against discriminator.') 525 | self.model.adversary_weight.set_value(args.adversary_weight) 526 | running = None 527 | if (epoch+1) % args.save_every == 0: 528 | print(' - saving current generator layers to disk...') 529 | self.model.save_generator() 530 | 531 | except KeyboardInterrupt: 532 | pass 533 | 534 | print('\n{}Trained {}x super-resolution for {} epochs.{}'\ 535 | .format(ansi.CYAN_B, args.zoom, epoch+1, ansi.CYAN)) 536 | self.model.save_generator() 537 | print(ansi.ENDC) 538 | 539 | def match_histograms(self, A, B, rng=(0.0, 255.0), bins=64): 540 | (Ha, Xa), (Hb, Xb) = [np.histogram(i, bins=bins, range=rng, density=True) for i in [A, B]] 541 | X = np.linspace(rng[0], rng[1], bins, endpoint=True) 542 | Hpa, Hpb = [np.cumsum(i) * (rng[1] - rng[0]) ** 2 / float(bins) for i in [Ha, Hb]] 543 | inv_Ha = scipy.interpolate.interp1d(X, Hpa, bounds_error=False, fill_value='extrapolate') 544 | map_Hb = scipy.interpolate.interp1d(Hpb, X, bounds_error=False, fill_value='extrapolate') 545 | return map_Hb(inv_Ha(A).clip(0.0, 255.0)) 546 | 547 | def process(self, original): 548 | # Snap the image to a shape that's compatible with the generator (2x, 4x) 549 | s = 2 ** max(args.generator_upscale, args.generator_downscale) 550 | by, bx = original.shape[0] % s, original.shape[1] % s 551 | original = original[by-by//2:original.shape[0]-by//2,bx-bx//2:original.shape[1]-bx//2,:] 552 | 553 | # Prepare paded input image as well as output buffer of zoomed size. 554 | s, p, z = args.rendering_tile, args.rendering_overlap, args.zoom 555 | image = np.pad(original, ((p, p), (p, p), (0, 0)), mode='reflect') 556 | output = np.zeros((original.shape[0] * z, original.shape[1] * z, 3), dtype=np.float32) 557 | 558 | # Iterate through the tile coordinates and pass them through the network. 559 | for y, x in itertools.product(range(0, original.shape[0], s), range(0, original.shape[1], s)): 560 | img = np.transpose(image[y:y+p*2+s,x:x+p*2+s,:] / 255.0 - 0.5, (2, 0, 1))[np.newaxis].astype(np.float32) 561 | *_, repro = self.model.predict(img) 562 | output[y*z:(y+s)*z,x*z:(x+s)*z,:] = np.transpose(repro[0] + 0.5, (1, 2, 0))[p*z:-p*z,p*z:-p*z,:] 563 | print('.', end='', flush=True) 564 | output = output.clip(0.0, 1.0) * 255.0 565 | 566 | # Match color histograms if the user specified this option. 567 | if args.rendering_histogram: 568 | for i in range(3): 569 | output[:,:,i] = self.match_histograms(output[:,:,i], original[:,:,i]) 570 | 571 | return scipy.misc.toimage(output, cmin=0, cmax=255) 572 | 573 | 574 | if __name__ == "__main__": 575 | if args.train: 576 | args.zoom = 2**(args.generator_upscale - args.generator_downscale) 577 | enhancer = NeuralEnhancer(loader=True) 578 | enhancer.train() 579 | else: 580 | enhancer = NeuralEnhancer(loader=False) 581 | for filename in args.files: 582 | print(filename, end=' ') 583 | img = scipy.ndimage.imread(filename, mode='RGB') 584 | out = enhancer.process(img) 585 | out.save(os.path.splitext(filename)[0]+'_ne%ix.png' % args.zoom) 586 | print(flush=True) 587 | print(ansi.ENDC) 588 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | colorama 2 | pillow>=3.2.0 3 | Theano==0.8.2 4 | git+https://github.com/Lasagne/Lasagne.git@61b1ad1#egg=Lasagne==0.2-dev -------------------------------------------------------------------------------- /train/ne1x-photo-repair.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | python3.4 enhance.py \ 4 | --train "$OPEN_IMAGES_PATH/*/*.jpg" --type photo --model repair \ 5 | --epochs=50 --batch-shape=256 --device=gpu1 \ 6 | --generator-downscale=2 --generator-upscale=2 \ 7 | --generator-blocks=8 --generator-filters=128 --generator-residual=0 \ 8 | --perceptual-layer=conv2_2 --smoothness-weight=1e7 --adversary-weight=0.0 \ 9 | --train-noise=2.0 --train-jpeg=30 10 | 11 | python3.4 enhance.py \ 12 | --train "$OPEN_IMAGES_PATH/*/*.jpg" --type photo --model repair \ 13 | --epochs=500 --batch-shape=240 --device=gpu1 \ 14 | --generator-downscale=2 --generator-upscale=2 \ 15 | --perceptual-layer=conv5_2 --smoothness-weight=5e3 --adversary-weight=5e1 \ 16 | --generator-start=10 --discriminator-start=0 --adversarial-start=10 \ 17 | --discriminator-size=48 \ 18 | --train-noise=2.0 --train-jpeg=30 19 | -------------------------------------------------------------------------------- /train/ne2x-photo-default.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | python3.4 enhance.py \ 4 | --train "$OPEN_IMAGES_PATH/*/*.jpg" --type photo --model default \ 5 | --epochs=50 --batch-shape=256 --device=gpu0 \ 6 | --generator-downscale=0 --generator-upscale=1 \ 7 | --generator-blocks=8 --generator-filters=128 --generator-residual=0 \ 8 | --perceptual-layer=conv2_2 --smoothness-weight=1e7 --adversary-weight=0.0 \ 9 | --train-noise=1.0 10 | 11 | python3.4 enhance.py \ 12 | --train "$OPEN_IMAGES_PATH/*/*.jpg" --type photo --model default \ 13 | --epochs=500 --batch-shape=240 --device=gpu0 \ 14 | --generator-downscale=0 --generator-upscale=1 \ 15 | --perceptual-layer=conv5_2 --smoothness-weight=5e3 --adversary-weight=5e1 \ 16 | --generator-start=10 --discriminator-start=0 --adversarial-start=10 \ 17 | --discriminator-size=64 \ 18 | --train-noise=1.0 19 | --------------------------------------------------------------------------------