Upload

CONTRIBUTING.md

@@ -0,0 +1,33 @@
+# Contribution Guide
+
+We welcome any contributions whether it's,
+
+- Submitting feedback
+- Fixing bugs
+- Or implementing a new feature.
+
+Please read this guide before making any contributions.
+
+#### Submit Feedback
+The feedback should be submitted by creating an issue at [GitHub issues](https://github.com/idealo/image-super-resolution/issues).
+Select the related template (bug report, feature request, or custom) and add the corresponding labels.
+
+#### Fix Bugs:
+You may look through the [GitHub issues](https://github.com/idealo/image-super-resolution/issues) for bugs.
+
+#### Implement Features
+You may look through the [GitHub issues](https://github.com/idealo/image-super-resolution/issues) for feature requests.
+
+## Pull Requests (PR)
+1. Fork the repository and a create a new branch from the master branch.
+2. For bug fixes, add new tests and for new features please add changes to the documentation.
+3. Do a PR from your new branch to our `dev` branch of the original Image Super-Resolution repo.
+
+## Documentation
+- Make sure any new function or class you introduce has proper docstrings.
+
+## Testing
+- We use [pytest](https://docs.pytest.org/en/latest/) for our testing. Make sure to write tests for any new feature and/or bug fixes.
+
+## Main Contributor List
+We maintain a list of main contributors to appreciate all the contributions.

Dockerfile.cpu

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+FROM tensorflow/tensorflow:1.13.1-py3
+
+# Install system packages
+RUN apt-get update && apt-get install -y --no-install-recommends \
+      bzip2 \
+      g++ \
+      git \
+      graphviz \
+      libgl1-mesa-glx \
+      libhdf5-dev \
+      openmpi-bin \
+      screen \
+      wget && \
+    rm -rf /var/lib/apt/lists/* \
+    apt-get upgrade
+
+ENV TENSOR_HOME /home/isr
+WORKDIR $TENSOR_HOME
+
+COPY ISR ./ISR
+COPY scripts ./scripts
+COPY weights ./weights
+COPY config.yml ./
+COPY setup.py ./
+
+RUN pip install --upgrade pip
+RUN pip install -e .
+
+ENV PYTHONPATH ./ISR/:$PYTHONPATH
+ENTRYPOINT ["sh", "./scripts/entrypoint.sh"]

Dockerfile.gpu

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+FROM tensorflow/tensorflow:1.13.1-gpu-py3
+
+# Install system packages
+RUN apt-get update && apt-get install -y --no-install-recommends \
+      bzip2 \
+      g++ \
+      git \
+      graphviz \
+      libgl1-mesa-glx \
+      libhdf5-dev \
+      openmpi-bin \
+      screen \
+      wget && \
+    rm -rf /var/lib/apt/lists/* \
+    apt-get upgrade
+
+ENV TENSOR_HOME /home/isr
+WORKDIR $TENSOR_HOME
+
+COPY ISR ./ISR
+COPY scripts ./scripts
+COPY weights ./weights
+COPY config.yml ./
+COPY setup.py ./
+
+RUN pip install --upgrade pip
+RUN pip install -e ".[gpu]" --ignore-installed
+
+ENV PYTHONPATH ./ISR/:$PYTHONPATH
+ENTRYPOINT ["sh", "./scripts/entrypoint.sh"]

LICENSE

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README.md

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+# Image Super-Resolution (ISR)
+
+<img src="figures/butterfly.png">
+
+[![Build Status](https://travis-ci.org/idealo/image-super-resolution.svg?branch=master)](https://travis-ci.org/idealo/image-super-resolution)
+[![Docs](https://img.shields.io/badge/docs-online-brightgreen)](https://idealo.github.io/image-super-resolution/)
+[![License](https://img.shields.io/badge/License-Apache%202.0-orange.svg)](https://github.com/idealo/image-super-resolution/blob/master/LICENSE)
+
+The goal of this project is to upscale and improve the quality of low resolution images.
+
+Since the code is no longer actively maintained, it will be archived on 2025-01-03.
+
+This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components.  
+
+The implemented networks include:
+
+- The super-scaling Residual Dense Network described in [Residual Dense Network for Image Super-Resolution](https://arxiv.org/abs/1802.08797) (Zhang et al. 2018)
+- The super-scaling Residual in Residual Dense Network described in [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://arxiv.org/abs/1809.00219) (Wang et al. 2018)
+- A multi-output version of the Keras VGG19 network for deep features extraction used in the perceptual loss
+- A custom discriminator network based on the one described in [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) (SRGANS, Ledig et al. 2017)
+
+Read the full documentation at: [https://idealo.github.io/image-super-resolution/](https://idealo.github.io/image-super-resolution/).
+
+[Docker scripts](https://idealo.github.io/image-super-resolution/tutorials/docker/) and [Google Colab notebooks](https://github.com/idealo/image-super-resolution/tree/master/notebooks) are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) with only a few commands.
+
+ISR is compatible with Python 3.6 and is distributed under the Apache 2.0 license. We welcome any kind of contribution. If you wish to contribute, please see the [Contribute](#contribute) section.
+
+## Contents
+- [Pre-trained networks](#pre-trained-networks)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Additional Information](#additional-information)
+- [Contribute](#contribute)
+- [Citation](#citation)
+- [Maintainers](#maintainers)
+- [License](#copyright)
+
+## Troubleshooting
+### Training not delivering good/patchy results
+When training your own model, start with only PSNR loss (50+ epochs, depending on the dataset) and only then introduce GANS and feature loss. This can be controlled by the loss weights argument.
+
+This is just sample, you will need to tune these parameters.
+
+PSNR only:
+```
+loss_weights = {
+  'generator': 1.0,
+  'feature_extractor': 0.0,
+  'discriminator': 0.00
+}
+```
+
+Later:
+```
+loss_weights = {
+  'generator': 0.0,
+  'feature_extractor': 0.0833,
+  'discriminator': 0.01
+}
+```
+### Weights loading
+If you are having trouble loading your own weights or the pre-trained weights (`AttributeError: 'str' object has no attribute 'decode'`), try:
+```bash
+pip install 'h5py==2.10.0' --force-reinstall
+```
+[Issue](https://github.com/idealo/image-super-resolution/issues/197#issue-877826405)
+
+## Pre-trained networks
+
+The weights used to produced these images are available directly when creating the model object. 
+
+Currently 4 models are available:
+  - RDN: psnr-large, psnr-small, noise-cancel
+  - RRDN: gans
+ 
+Example usage:
+
+```
+model = RRDN(weights='gans')
+```
+  
+The network parameters will be automatically chosen.
+(see [Additional Information](#additional-information)).
+
+#### Basic model
+RDN model, PSNR driven, choose the option ```weights='psnr-large'``` or ```weights='psnr-small'``` when creating a RDN model.
+
+|![butterfly-sample](figures/butterfly_comparison_SR_baseline.png)|
+|:--:|
+| Low resolution image (left), ISR output (center), bicubic scaling (right). Click to zoom. |
+#### GANS model
+RRDN model, trained with Adversarial and VGG features losses, choose the option ```weights='gans'``` when creating a RRDN model.
+
+|![baboon-comparison](figures/baboon-compare.png)|
+|:--:|
+| RRDN GANS model (left), bicubic upscaling (right). |
+-> [more detailed comparison](http://www.framecompare.com/screenshotcomparison/PGZPNNNX)
+
+#### Artefact Cancelling GANS model
+RDN model, trained with Adversarial and VGG features losses, choose the option ```weights='noise-cancel'``` when creating a RDN model.
+
+|![temple-comparison](figures/temple_comparison.png)|
+|:--:|
+| Standard vs GANS model. Click to zoom. |
+
+
+|![sandal-comparison](figures/sandal-compare.png)|
+|:--:|
+| RDN GANS artefact cancelling model (left), RDN standard PSNR driven model (right). |
+-> [more detailed comparison](http://www.framecompare.com/screenshotcomparison/2ECCNNNU)
+
+
+## Installation
+There are two ways to install the Image Super-Resolution package:
+
+- Install ISR from PyPI (recommended):
+```
+pip install ISR
+```
+- Install ISR from the GitHub source:
+```
+git clone https://github.com/idealo/image-super-resolution
+cd image-super-resolution
+python setup.py install
+```
+
+## Usage
+
+### Prediction
+
+Load image and prepare it
+```python
+import numpy as np
+from PIL import Image
+
+img = Image.open('data/input/test_images/sample_image.jpg')
+lr_img = np.array(img)
+```
+
+Load a pre-trained model and run prediction (check the prediction tutorial under notebooks for more details)
+```python
+from ISR.models import RDN
+
+rdn = RDN(weights='psnr-small')
+sr_img = rdn.predict(lr_img)
+Image.fromarray(sr_img)
+```
+
+#### Large image inference
+To predict on large images and avoid memory allocation errors, use the `by_patch_of_size` option for the predict method, for instance
+```
+sr_img = model.predict(image, by_patch_of_size=50)
+```
+Check the documentation of the `ImageModel` class for further details.
+
+### Training
+
+Create the models
+```python
+from ISR.models import RRDN
+from ISR.models import Discriminator
+from ISR.models import Cut_VGG19
+
+lr_train_patch_size = 40
+layers_to_extract = [5, 9]
+scale = 2
+hr_train_patch_size = lr_train_patch_size * scale
+
+rrdn  = RRDN(arch_params={'C':4, 'D':3, 'G':64, 'G0':64, 'T':10, 'x':scale}, patch_size=lr_train_patch_size)
+f_ext = Cut_VGG19(patch_size=hr_train_patch_size, layers_to_extract=layers_to_extract)
+discr = Discriminator(patch_size=hr_train_patch_size, kernel_size=3)
+```
+
+Create a Trainer object using the desired settings and give it the models (`f_ext` and `discr` are optional)
+```python
+from ISR.train import Trainer
+loss_weights = {
+  'generator': 0.0,
+  'feature_extractor': 0.0833,
+  'discriminator': 0.01
+}
+losses = {
+  'generator': 'mae',
+  'feature_extractor': 'mse',
+  'discriminator': 'binary_crossentropy'
+}
+
+log_dirs = {'logs': './logs', 'weights': './weights'}
+
+learning_rate = {'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30}
+
+flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
+
+trainer = Trainer(
+    generator=rrdn,
+    discriminator=discr,
+    feature_extractor=f_ext,
+    lr_train_dir='low_res/training/images',
+    hr_train_dir='high_res/training/images',
+    lr_valid_dir='low_res/validation/images',
+    hr_valid_dir='high_res/validation/images',
+    loss_weights=loss_weights,
+    learning_rate=learning_rate,
+    flatness=flatness,
+    dataname='image_dataset',
+    log_dirs=log_dirs,
+    weights_generator=None,
+    weights_discriminator=None,
+    n_validation=40,
+)
+```
+
+Start training
+```python
+trainer.train(
+    epochs=80,
+    steps_per_epoch=500,
+    batch_size=16,
+    monitored_metrics={'val_PSNR_Y': 'max'}
+)
+```
+
+## Additional Information
+You can read about how we trained these network weights in our Medium posts:
+- part 1: [A deep learning based magnifying glass](https://medium.com/idealo-tech-blog/a-deep-learning-based-magnifying-glass-dae1f565c359)
+- part 2: [Zoom in... enhance](https://medium.com/idealo-tech-blog/zoom-in-enhance-a-deep-learning-based-magnifying-glass-part-2-c021f98ebede
+)
+
+### RDN Pre-trained weights
+The weights of the RDN network trained on the [DIV2K dataset](https://data.vision.ee.ethz.ch/cvl/DIV2K) are available in ```weights/sample_weights/rdn-C6-D20-G64-G064-x2/PSNR-driven/rdn-C6-D20-G64-G064-x2_PSNR_epoch086.hdf5```. <br>
+The model was trained using ```C=6, D=20, G=64, G0=64``` as parameters (see architecture for details) for 86 epochs of 1000 batches of 8 32x32 augmented patches taken from LR images.
+
+The artefact can cancelling weights obtained with a combination of different training sessions using different datasets and perceptual loss with VGG19 and GAN can be found at `weights/sample_weights/rdn-C6-D20-G64-G064-x2/ArtefactCancelling/rdn-C6-D20-G64-G064-x2_ArtefactCancelling_epoch219.hdf5`
+We recommend using these weights only when cancelling compression artefacts is a desirable effect.
+
+### RDN Network architecture
+The main parameters of the architecture structure are:
+- D - number of Residual Dense Blocks (RDB)
+- C - number of convolutional layers stacked inside a RDB
+- G - number of feature maps of each convolutional layers inside the RDBs
+- G0 - number of feature maps for convolutions outside of RDBs and of each RBD output
+
+<img src="figures/RDN.png" width="600">
+<br>
+
+<img src="figures/RDB.png" width="600">
+
+source: [Residual Dense Network for Image Super-Resolution](https://arxiv.org/abs/1802.08797)
+
+### RRDN Network architecture
+The main parameters of the architecture structure are:
+- T - number of Residual in Residual Dense Blocks (RRDB)
+- D - number of Residual Dense Blocks (RDB) insider each RRDB
+- C - number of convolutional layers stacked inside a RDB
+- G - number of feature maps of each convolutional layers inside the RDBs
+- G0 - number of feature maps for convolutions outside of RDBs and of each RBD output
+
+<img src="figures/RRDN.jpg" width="600">
+<br>
+
+<img src="figures/RRDB.png" width="600">
+
+source: [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://arxiv.org/abs/1809.00219)
+
+## Contribute
+We welcome all kinds of contributions, models trained on different datasets, new model architectures and/or hyperparameters combinations that improve the performance of the currently published model.
+
+Will publish the performances of new models in this repository.
+
+See the [Contribution](CONTRIBUTING.md) guide for more details.
+
+#### Bump version
+To bump up the version, use
+```
+bumpversion {part} setup.py
+```
+
+## Citation
+Please cite our work in your publications if it helps your research.
+
+```BibTeX
+@misc{cardinale2018isr,
+  title={ISR},
+  author={Francesco Cardinale et al.},
+  year={2018},
+  howpublished={\url{https://github.com/idealo/image-super-resolution}},
+}
+```
+
+## Maintainers
+* Francesco Cardinale, github: [cfrancesco](https://github.com/cfrancesco)
+* Dat Tran, github: [datitran](https://github.com/datitran)
+
+## Copyright
+
+See [LICENSE](LICENSE) for details.

config.yml

@@ -0,0 +1,83 @@
+---
+default:
+  generator: rdn
+  feature_extractor: false
+  discriminator: false
+  training_set: div2k
+  test_set: sample
+log_dirs:
+  logs: ./logs
+  weights: ./weights
+feature_extractor:
+  vgg19:
+    layers_to_extract:
+      - 5
+      - 9
+generators:
+  rrdn:
+    C: 4
+    D: 3
+    G: 32
+    G0: 32
+    T: 4
+    x: 4
+  rdn:
+    C: 6
+    D: 20
+    G: 64
+    G0: 64
+    x: 2
+loss_weights:
+  generator: 1.0
+  feature_extractor: 0.0833
+  discriminator: 0.01
+losses:
+  generator: mae
+  discriminator: binary_crossentropy
+  feature_extractor: mse
+session:
+  prediction:
+    patch_size:
+  training:
+    steps_per_epoch: 1000
+    patch_size: 32
+    batch_size: 16
+    epochs: 300
+    n_validation_samples: 100
+    learning_rate:
+      initial_value: 0.0004
+      decay_frequency: 50
+      decay_factor: 0.5
+    fallback_save_every_n_epochs: 2
+    flatness:
+      min: 0.0
+      increase_frequency: null
+      increase: 0.0
+      max: 0.0
+    metrics:
+      generator: PSNR_Y
+    monitored_metrics:
+      val_loss: min
+      val_PSNR_Y: max
+    adam_optimizer:
+      beta1: 0.9
+      beta2: 0.999
+      epsilon: null
+test_sets:
+  sample: ./data/input/sample
+training_sets:
+  custom data:
+    lr_train_dir: ./data/custom/lr/train
+    hr_train_dir: ./data/custom/hr/train
+    lr_valid_dir: ./data/custom/lr/validation
+    hr_valid_dir: ./data/custom/hr/validation
+    data_name: custom
+  div2k:
+    lr_train_dir: ./data/DIV2K/DIV2K_train_LR_bicubic/X2
+    hr_train_dir: ./data/DIV2K/DIV2K_train_HR
+    lr_valid_dir: ./data/DIV2K/DIV2K_valid_LR_bicubic/X2
+    hr_valid_dir: ./data/DIV2K/DIV2K_valid_HR
+    data_name: div2k
+weights_paths:
+  discriminator:
+  generator:

pypi.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+rm -rf dist/
+python setup.py sdist bdist_wheel
+twine upload dist/*

setup.cfg

@@ -0,0 +1,2 @@
+[metadata]
+description-file = README.md

setup.py

@@ -0,0 +1,38 @@
+from setuptools import setup, find_packages
+
+long_description = '''
+ISR (Image Super-Resolution) is a library to upscale and improve the quality of low resolution images.
+
+Read the documentation at: https://idealo.github.io/image-super-resolution/
+
+ISR is compatible with Python 3.6 and is distributed under the Apache 2.0 license.
+'''
+
+setup(
+    name='ISR',
+    version='2.2.0',
+    author='Francesco Cardinale',
+    author_email='testadicardi@gmail.com',
+    description='Image Super Resolution',
+    long_description=long_description,
+    license='Apache 2.0',
+    install_requires=['imageio', 'numpy', 'tensorflow==2.*', 'tqdm', 'pyaml', 'h5py==2.10.0'],
+    extras_require={
+        'tests': ['pytest==4.3.0', 'pytest-cov==2.6.1'],
+        'docs': ['mkdocs==1.0.4', 'mkdocs-material==4.0.2'],
+        'gpu': ['tensorflow-gpu==2.*'],
+        'dev': ['bumpversion==0.5.3'],
+    },
+    classifiers=[
+        'Development Status :: 4 - Beta',
+        'Intended Audience :: Developers',
+        'Intended Audience :: Education',
+        'Intended Audience :: Science/Research',
+        'License :: OSI Approved :: Apache Software License',
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: 3.6',
+        'Topic :: Software Development :: Libraries',
+        'Topic :: Software Development :: Libraries :: Python Modules',
+    ],
+    packages=find_packages(exclude=('tests',)),
+)