Implemented

.gitignore

@@ -200,4 +200,8 @@ fabric.properties
 .idea/httpRequests
 
 # Android studio 3.1+ serialized cache file
-.idea/caches/build_file_checksums.ser
+.idea/caches/build_file_checksums.ser
+
+# logs
+logs/**/*
+!logs/.keep

.vscode/settings.json

@@ -1,7 +1,6 @@
 {
-    "python.pythonPath": "venv/bin/python3",
+    "python.pythonPath": "venv/bin/python",
 
-    "python.linting.pylintEnabled": true,
     "python.languageServer": "Pylance",
 
     "python.testing.pytestArgs": [

README.md

@@ -1,9 +1,36 @@
-# Python Data Science Template
-## How to install
+# Fast Reflection Removal
+Removes reflections quickly and easily.
+
+![demo](docs/demo.png)
 
+## How to install
 **Prerequisities**
 - Python: 3.8
 
+### As a package
+In your own project, just perform the following command:
+```bash
+# python refers to the virtual environment to install package to
+python -m pip install git+https://github.com/JanPalasek/fast-reflection-removal
+```
+
+Now, you can use the reflection removal in your project in the following manner:
+```python
+from frr.core import FastReflectionRemoval
+
+...
+# numpy array with values between 0 and 1 of shape (H, W, C)
+img = ...
+# instantiate the algoroithm class
+alg = FastReflectionRemoval(h = 0.11)
+# run the algorithm and get result of shape (H, W, C)
+dereflected_img = alg.remove_reflection(img)
+
+
+...
+```
+
+### As a repository
 1. Clone the project and go to its root directory.
 2. Create and activate the virtual environment:
     ```bash
@@ -19,31 +46,31 @@
     ```
 4. Install the packages from requirements and the project:
     ```bash
-    # install from 'requirements.txt'
-    python -m piptools sync
-    # install the current modules
-    python -m pip install -e .
+    make sync # on windows just perform the following commands: python -m piptools sync requirements.txt; python -m pip install -e .
     ```
 
-## How to work with pip-tools
-Pip-tools has 2 operations: *compile* and *sync*.
+## How to run the project
+To run the project, we need to run script bin/run.py. The important parameters of this script are:
+- ***h***: larger h leads to more reflections removed, but the result image will be blurrier,
+- ***input_path***: path to the input image,
+- ***output_path***: path to the output image.
 
-1. **compile**: Generates *requirements.in* into *requirements.txt*. Do not modify requirements.txt manually, it must be generated.
+Example:
 ```bash
-python -m piptools compile
+# activate the environment
+source venv/bin/activate # on Windows ./venv/Scripts/activate.ps1 in Powershell
+python bin/run.py --h=0.11 --input_path="docs/toy_example.jpg" --output_path="docs/toy_example_out.jpg"
 ```
 
-2. **sync**: Based on *requirements.txt*, updates the installed packages of the virtual environment. If you keep your local packages of the project (installed by pip install -e .) separately, after this step you need to install them again with the same command. If you add them to *requirements.in*, then it is generated as an absolute path into *requirements.txt* and the project is not well distributable.
-```bash
-python -m piptools sync
-# (optionally) python -m pip install -e .
-```
+The program, in this example, loads input image from the path docs/toy_example.jpg, processes it with parameter h=0.11 and outputs it into docs/toy_example_out.jpg.
+
 
 ## Project structure
 Folders:
-- *bin*: executable python files. They should be included into the root namespace by setup.py scripts.
-- *config*: configuration files, usually in yaml or ini.
+- *bin*: executable python files.
 - *docs*: documentation.
-- *notebooks*: jupyter notebooks for analysis etc.
 - *src*: contains list of folders for sources, e.g. python.
 - *tests*: follows the structure of src/python.
+
+## Credits
+This repository implements paper [Fast Single Image Reflection Suppression via Convex Optimization](https://arxiv.org/pdf/1903.03889.pdf) authored by Yang Yang, Wenye Ma, Yin Zheng, Jian-Feng Cai and Weiyu Xu.

bin/run.py

@@ -0,0 +1,28 @@
+from frr.core import FastReflectionRemoval
+from frr.utils import FileWriter
+import matplotlib.pyplot as plt
+import argparse
+
+def main(args):
+    frr = FastReflectionRemoval(h=args.h, debug_writer=FileWriter(path="logs") if args.debug else None)
+
+    # read image and normalize it into [0, 1]
+    img = plt.imread(args.input_path) / 255
+
+    # remove reflection
+    result_img = frr.remove_reflection(img)
+
+    # store image
+    plt.imsave(args.output_path, result_img)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_path", default=None, type=str)
+    parser.add_argument("--output_path", default=None, type=str)
+    parser.add_argument("--debug", action="store_true", default=None)
+
+    parser.add_argument("--h", default=0.03, type=float)
+    args = parser.parse_args()
+
+    main(args)

makefile

@@ -0,0 +1,14 @@
+PYTHON="venv/bin/python"
+
+install:
+	@ ${PYTHON} -m pip install -r requirements.txt
+
+compile:
+	@ ${PYTHON} -m piptools compile requirements.in
+
+sync:
+	@ ${PYTHON} -m piptools sync requirements.txt
+	@ ${PYTHON} -m pip install -e .
+
+clean_logs:
+	@ rm -r logs && mkdir logs && touch logs/.keep

requirements.in

@@ -1,3 +1,7 @@
-pylint==2.7.4
+#pylint==2.7.4
 autopep8==1.5.6
-pytest==6.2.3
+pytest==6.2.3
+numpy==1.21.2
+scipy==1.7.1
+matplotlib==3.4.3
+argparse==1.4.0

requirements.txt

@@ -1,41 +1,53 @@
 #
-# This file is autogenerated by pip-compile
+# This file is autogenerated by pip-compile with python 3.8
 # To update, run:
 #
-#    pip-compile
+#    pip-compile requirements.in
 #
-astroid==2.5.6
-    # via pylint
+argparse==1.4.0
+    # via -r requirements.in
 attrs==21.2.0
     # via pytest
 autopep8==1.5.6
     # via -r requirements.in
+cycler==0.10.0
+    # via matplotlib
 iniconfig==1.1.1
     # via pytest
-isort==5.8.0
-    # via pylint
-lazy-object-proxy==1.6.0
-    # via astroid
-mccabe==0.6.1
-    # via pylint
+kiwisolver==1.3.2
+    # via matplotlib
+matplotlib==3.4.3
+    # via -r requirements.in
+numpy==1.21.2
+    # via
+    #   -r requirements.in
+    #   matplotlib
+    #   scipy
 packaging==20.9
     # via pytest
+pillow==8.4.0
+    # via matplotlib
 pluggy==0.13.1
     # via pytest
 py==1.10.0
     # via pytest
-pycodestyle==2.7.0
+pycodestyle==2.8.0
     # via autopep8
-pylint==2.7.4
-    # via -r requirements.in
 pyparsing==2.4.7
-    # via packaging
+    # via
+    #   matplotlib
+    #   packaging
 pytest==6.2.3
     # via -r requirements.in
+python-dateutil==2.8.2
+    # via matplotlib
+scipy==1.7.1
+    # via -r requirements.in
+six==1.16.0
+    # via
+    #   cycler
+    #   python-dateutil
 toml==0.10.2
     # via
     #   autopep8
-    #   pylint
     #   pytest
-wrapt==1.12.1
-    # via astroid

setup.py

@@ -5,15 +5,21 @@
     long_description = fh.read()
 
 setuptools.setup(
-    name='template_project',
+    name='fast-reflection-removal',
     version='0.1a',
     description="",
     long_description=long_description,
     long_description_content_type="text/markdown",
     package_dir={
         "": "src/python"
     },
+    install_requires=[
+        "argparse~=1.4.0",
+        "numpy~=1.21.2",
+        "scipy~=1.7.1",
+        "matplotlib~=3.4",
+    ],
     packages=setuptools.find_packages("src/python"),
-    scripts=[],
+    scripts=["bin/run.py"],
     python_requires="~=3.8"
 )

src/python/frr/base.py

@@ -0,0 +1,71 @@
+import numpy as np
+from typing import Tuple
+from scipy.fftpack import dct, idct
+
+def gradient(A: np.ndarray, append_zeros=False) -> Tuple[np.ndarray]:
+    """
+    Computes gradients of input numpy array. Returns tuple, where the first
+    part is gradient in direction of axis 0 (rows), then axis 1 (columns),...
+
+    Args:
+        f (np.ndarray): Input numpy array.
+
+    Returns:
+        Returns tuple of numpy arrays denoting gradients in different directions.
+    """
+
+    rows, cols = A.shape
+
+    grad_x = np.zeros_like(A)
+    grad_x[:, 0: cols - 1] = np.diff(A, axis=1)
+
+    grad_y = np.zeros_like(A)
+    grad_y[0:rows - 1, :] = np.diff(A, axis=0)
+
+    B = np.concatenate((grad_x[..., np.newaxis], grad_y[..., np.newaxis]), axis=-1)
+
+    return B
+
+def divergence(A):
+    m, n, _ = A.shape
+    B = np.zeros(shape=(m, n))
+
+    T = A[:, :, 0]
+    T1 = np.zeros(shape=(m, n))
+    T1[:, 1:n] = T[:, 0:n-1]
+
+    B = B + T - T1
+
+    T = A[:, :, 1]
+    T1 = np.zeros(shape=(m, n))
+    T1[1:m, :] = T[0:m-1, :]
+
+    B = B + T - T1
+    return B
+
+
+def laplacian(f, h: float = None):
+    rows, cols = f.shape
+    dims = 2
+
+    grads = gradient(f)
+
+    if h is not None:
+        # remove edges (gradients) smaller than 0
+        # norm = np.sqrt(np.sum(grads * grads, axis=-1))
+        norm = np.linalg.norm(grads, axis=-1)
+
+        mask = (norm < h)[..., np.newaxis].repeat(dims, axis=-1)
+        grads[mask] = 0
+
+    # and compute its divergence by summing the second-order gradients
+    laplacian = divergence(grads)
+
+    return laplacian
+
+
+def dct2(block):
+    return dct(dct(block.T, norm='ortho').T, norm='ortho')
+
+def idct2(block):
+    return idct(idct(block.T, norm='ortho').T, norm='ortho')