├── logo ├── logo.png └── tiles.png ├── plakakia ├── __init__.py ├── config_example.yaml ├── images.py ├── make_tiles.py ├── settings.py ├── annotations.py └── tiling.py ├── .pre-commit-config.yaml ├── requirements.txt ├── CONTRIBUTING.md ├── setup.py ├── .gitignore ├── demo └── explore_tiling_output.py ├── requirements_conda.yaml ├── README.md ├── tests ├── test_utils_tiling.py └── Test_outputs.ipynb └── LICENSE /logo/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kalfasyan/plakakia/HEAD/logo/logo.png -------------------------------------------------------------------------------- /logo/tiles.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kalfasyan/plakakia/HEAD/logo/tiles.png -------------------------------------------------------------------------------- /plakakia/__init__.py: -------------------------------------------------------------------------------- 1 | # __init__.py 2 | from .annotations import * 3 | from .tiling import * 4 | from .settings import * 5 | from .make_tiles import * -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | # See https://pre-commit.com for more information 2 | # See https://pre-commit.com/hooks.html for more hooks 3 | repos: 4 | - repo: https://github.com/pre-commit/pre-commit-hooks 5 | rev: v3.2.0 6 | hooks: 7 | - id: trailing-whitespace 8 | - id: end-of-file-fixer 9 | - id: check-yaml 10 | - id: check-added-large-files 11 | - id: pytest 12 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | certifi==2022.12.7 2 | charset-normalizer==3.1.0 3 | contourpy==1.0.7 4 | cycler==0.11.0 5 | fonttools==4.39.0 6 | idna==3.4 7 | kiwisolver==1.4.4 8 | matplotlib==3.7.1 9 | numpy==1.24.2 10 | opencv-python==4.7.0.72 11 | packaging==23.0 12 | pandas==1.5.3 13 | Pillow==9.4.0 14 | pip==23.0.1 15 | psutil==5.9.4 16 | pyparsing==3.0.9 17 | python-dateutil==2.8.2 18 | pytz==2022.7.1 19 | PyYAML==6.0 20 | requests==2.28.2 21 | scipy==1.10.1 22 | seaborn==0.12.2 23 | sentry-sdk==1.16.0 24 | setuptools==65.6.3 25 | six==1.16.0 26 | tqdm==4.65.0 27 | urllib3==1.26.14 28 | wheel==0.38.4 29 | pre-commit 30 | imagesize 31 | pytest 32 | lxml==4.9.2 -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | ## How to contribute 2 | 3 | At the moment, the project is in its early stages. If you want to contribute, you can do so by: 4 | - [Opening an issue](https://github.com/kalfasyan/plakakia/issues) to report a bug or request a feature. 5 | - [Opening a pull request](https://github.com/kalfasyan/plakakia/pulls) to fix a bug or add a feature. 6 | - [Contacting me](mailto:kalfasyan@gmail.com) to discuss the project. 7 | 8 | The response time might be a bit slow depending on my [postdoc duties](https://www.kuleuven.be/wieiswie/en/person/00107087), but I will try to get back to you as soon as possible. 9 | 10 | My goal is to make this library as useful as possible for the community, while trying to learn new things myself. Note that this is my first open-source "library", so I am still learning how to do things properly. Any feedback is welcome! -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | # setup.py 2 | 3 | from setuptools import setup 4 | 5 | setup( 6 | name='plakakia', 7 | version='0.2.1', 8 | author='Yannis Kalfas', 9 | author_email='kalfasyan@gmail.com', 10 | description='Python image tiling library for image processing, object detection, etc.', 11 | packages=['plakakia'], 12 | install_requires=[ 13 | 'numpy>=1.24.2', 14 | 'pandas>=1.5.3', 15 | 'matplotlib>=3.7.1', 16 | 'opencv-python>=4.7.0.72', 17 | 'scipy>=1.10.1', 18 | 'seaborn>=0.12.2', 19 | 'lxml>=4.9.2', 20 | 'imagesize>=1.4.1', 21 | 'psutil>=5.9.5', 22 | 'tqdm>=4.65.0', 23 | 'PyYAML>=6.0', 24 | 'pyarrow>=12.0.0', 25 | 'fastparquet>=2023.4.0', 26 | ], 27 | entry_points={ 28 | 'console_scripts': [ 29 | 'make_tiles = plakakia.make_tiles:main', 30 | ], 31 | }, 32 | ) -------------------------------------------------------------------------------- /plakakia/config_example.yaml: -------------------------------------------------------------------------------- 1 | { 2 | "input_extension_images": "jpg", 3 | "output_extension_images": "jpg", 4 | "tile_size": 70, # tile size in pixels 5 | "step_size": 70, # step size in pixels for sliding window 6 | "input_dir_images": "/home/kalfasyan/data/OBJECT_DETECTION/solar-panels/images", 7 | "input_dir_annotations": "/home/kalfasyan/data/OBJECT_DETECTION/solar-panels/annotations", 8 | "input_format_annotations": "yolo", # yolo, pascal_voc, coco or segmentation 9 | "output_dir_images": "output/images", 10 | "output_dir_annotations": "output/annotations", 11 | "output_format_annotations": "yolo", # yolo or pascal_voc 12 | "annotation_mapping": { # mapping of annotation classes to integers 13 | "kernel": 0, 14 | "person": 1, 15 | "bottle": 2, 16 | "empty": 3, 17 | "trafficlight": 4, 18 | "speedlimit": 5, 19 | "crosswalk": 6, 20 | "stop": 7 21 | }, 22 | "validate_settings": true, # validate settings before running 23 | "log": false, # log to file 24 | "log_folder": "logs", # folder to save logs 25 | "num_workers": -1, # number of workers for multiprocessing 26 | "clear_duplicates": false, # clear duplicate images in the output folder 27 | "draw_boxes": true, # draw bounding boxes on the output images 28 | } 29 | -------------------------------------------------------------------------------- /plakakia/images.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | from pathlib import Path 3 | 4 | def read_input_image(im_fname=None, settings=None): 5 | extension = settings.input_extension_images 6 | 7 | if extension in ['jpg', 'png']: 8 | image_filename = str(Path(settings.input_dir_images).joinpath(f"{im_fname}.{extension}")) 9 | 10 | image = cv2.imread(image_filename) 11 | # Pad the image if needed 12 | if settings.pad_image: 13 | image = add_border(image, settings=settings, color=[0, 0, 0]) 14 | else: 15 | raise NotImplementedError(f"Extension {extension} not implemented.") 16 | 17 | return image 18 | 19 | def read_input_mask(im_fname=None, settings=None): 20 | extension = settings.format_to_extension['segmentation'] 21 | 22 | if extension in ['png']: 23 | mask_filename = str(Path(settings.input_dir_annotations).joinpath(f"{im_fname}.{extension}")) 24 | 25 | mask = cv2.imread(mask_filename) 26 | # Pad the mask if needed 27 | if settings.pad_image: 28 | mask = add_border(mask, settings=settings, color=[0, 0, 0]) 29 | else: 30 | raise NotImplementedError(f"Extension {extension} not implemented for reading masks.") 31 | 32 | return mask 33 | 34 | def add_border(image, settings, color=[0, 0, 0]): 35 | """ Add border to an image. """ 36 | 37 | top=settings.pad_size 38 | bottom=settings.pad_size 39 | left=settings.pad_size 40 | right=settings.pad_size 41 | 42 | if isinstance(image, str): 43 | image = cv2.imread(image) 44 | 45 | # Create border 46 | border = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) 47 | 48 | return border 49 | -------------------------------------------------------------------------------- /plakakia/make_tiles.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding: utf-8 3 | 4 | # Author: Ioannis Kalfas (@kalfasyan) (kalfasyan at gmail dot com) 5 | 6 | import argparse 7 | import multiprocessing as mp 8 | import os 9 | import random 10 | import shutil 11 | from pathlib import Path 12 | from time import perf_counter 13 | 14 | import yaml 15 | from tqdm import tqdm 16 | 17 | from plakakia.settings import Settings 18 | from plakakia.tiling import clear_duplicates, process_tiles 19 | 20 | random.seed(3) 21 | 22 | def process_tiles_wrapper(args): 23 | """Wrapper function for the process_tile function.""" 24 | t, input_image, input_annotation, settings = args 25 | return process_tiles(t, input_image, input_annotation, settings) 26 | 27 | 28 | def main(): 29 | # Parse command-line arguments 30 | parser = argparse.ArgumentParser() 31 | parser.add_argument('--config', help='Path to config.yaml file') 32 | args = parser.parse_args() 33 | print(100*'-') 34 | config_path=args.config 35 | 36 | start_time = perf_counter() 37 | 38 | if config_path is None: 39 | print(f"No config file provided. Using default file.") 40 | # If no config_path is provided, use the default config.yaml file in the package 41 | package_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'plakakia') 42 | config_file = os.path.join(package_dir, 'config_example.yaml') 43 | else: 44 | config_file = config_path 45 | 46 | with open(config_file, 'r', encoding='utf-8') as f: 47 | config = yaml.load(f, Loader=yaml.FullLoader) 48 | 49 | # Create a settings object 50 | settings = Settings(**config) 51 | 52 | # Create a process pool with the desired number of workers 53 | with mp.Pool(processes=settings.num_workers) as pool: 54 | # Prepare the arguments for each task 55 | args = [(t, input_image, input_annotation, settings) \ 56 | for t, (input_image, input_annotation) in \ 57 | enumerate(zip(settings.input_images, settings.input_annotations))] 58 | 59 | # Submit the tasks to the pool 60 | results = pool.map(process_tiles_wrapper, args) 61 | 62 | end_time = perf_counter() - start_time 63 | 64 | print(f"Finished making tiles! Elapsed time: {end_time:.2f} seconds") 65 | 66 | if settings.clear_duplicates: 67 | clear_duplicates(settings) 68 | 69 | if __name__ == '__main__': 70 | # Call the main function with the provided config path 71 | main() -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | .ipynb_checkpoints/ 132 | runs/ 133 | datasets/ 134 | tiles/ 135 | output/ 136 | images/ 137 | annotations/ 138 | src/ 139 | logs/ 140 | __pycache__/ 141 | *.jpg 142 | *.Identifier 143 | *.xlsx 144 | *.csv 145 | #*.txt 146 | *.xml 147 | *.json 148 | *.log 149 | -------------------------------------------------------------------------------- /demo/explore_tiling_output.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | import cv2 3 | import numpy as np 4 | from natsort import natsorted 5 | import os 6 | 7 | st.set_page_config(page_title="Plakakia Tiling Explorer", layout="centered") 8 | 9 | st.title("Plakakia Tiling Explorer") 10 | 11 | # Let the user select the image they want to use 12 | image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"]) 13 | if image is None: st.info("Please upload an image file."); st.stop() 14 | 15 | image = cv2.imdecode(np.frombuffer(image.read(), np.uint8), 1) 16 | # Delete any existing image.png file and altered.png file 17 | try: os.remove("image.png") 18 | except: pass 19 | try: os.remove("altered.png") 20 | except: pass 21 | 22 | 23 | with st.form(key='my_form', clear_on_submit=False): 24 | st.subheader("Crop & Resize Image") 25 | col1, col2 = st.columns(2) 26 | top_crop = col1.number_input("Top Crop %", 0, 100, 0) 27 | bottom_crop = col2.number_input("Bottom Crop %", 0, 100, 0) 28 | col1, col2 = st.columns(2) 29 | left_crop = col1.number_input("Left Crop", 0, 100, 0) 30 | right_crop = col2.number_input("Right Crop", 0, 100, 0) 31 | 32 | # Resize the image to a user selected size 33 | resize = st.slider('Resize', 0, 100, 100) 34 | 35 | # Select the tile size and step size 36 | col1, col2 = st.columns(2) 37 | with col1: tile_size = st.slider('Tile Size', 0, 1000, 50, step=5) 38 | with col2: step_size = st.slider('Step Size', 0, 1000, 50, step=5) 39 | 40 | # Add a submit button 41 | submit_button = st.form_submit_button(label='▶️ Run') 42 | 43 | if submit_button: 44 | # Crop the image 45 | image = image[:, int(image.shape[1] * (left_crop / 100.0)):int(image.shape[1] * (1 - (right_crop / 100.0))), :] 46 | # Crop the image 47 | image = image[int(image.shape[0] * (top_crop / 100.0)):int(image.shape[0] * (1 - (bottom_crop / 100.0))), :, :] 48 | image = cv2.resize(image, (0, 0), fx=(resize / 100.0), fy=(resize / 100.0)) 49 | cv2.imwrite("image.png", image) 50 | 51 | try: 52 | from plakakia.tiling import tile_image 53 | tiles, coordinates = tile_image(image, tile_size, step_size) 54 | except Exception as e: 55 | st.error("Error: " + str(e)) 56 | st.stop() 57 | 58 | # Draw a rectangle around each tile and make it smaller by 1 pixel to see the grid 59 | tile_size = int(tile_size) 60 | step_size = int(step_size) 61 | for i in range(len(coordinates)): 62 | x1, y1, x2, y2 = coordinates[i] 63 | # Make the color the inverse of the average color of the tile 64 | inv_color = (255 - int(np.average(tiles[i, :, :, 0])), 255 - int(np.average(tiles[i, :, :, 1])), 255 - int(np.average(tiles[i, :, :, 2]))) 65 | # Make the rectangle 1 pixel smaller to see the grid 66 | cv2.rectangle(image, (x1, y1), (x2-1, y2-1), inv_color, 1) 67 | st.write("Image size: ", image.shape) 68 | 69 | if image is not None: 70 | cv2.imwrite("altered.png", image) 71 | else: 72 | st.error("Please initialize") 73 | 74 | if image is not None: 75 | st.image("altered.png", use_column_width=True) 76 | 77 | else: 78 | st.stop() 79 | -------------------------------------------------------------------------------- /requirements_conda.yaml: -------------------------------------------------------------------------------- 1 | name: plakakia 2 | channels: 3 | - anaconda 4 | - conda-forge 5 | - defaults 6 | dependencies: 7 | - appnope=0.1.3=pyhd8ed1ab_0 8 | - asttokens=2.4.1=pyhd8ed1ab_0 9 | - backports=1.0=pyhd8ed1ab_3 10 | - backports.functools_lru_cache=1.6.5=pyhd8ed1ab_0 11 | - blas=1.0=openblas 12 | - bottleneck=1.3.5=py311ha0d4635_0 13 | - brotli=1.1.0=hb547adb_1 14 | - brotli-bin=1.1.0=hb547adb_1 15 | - bzip2=1.0.8=h620ffc9_4 16 | - c-ares=1.21.0=h93a5062_0 17 | - ca-certificates=2023.08.22=hca03da5_0 18 | - cairo=1.16.0=h29d4eff_2 19 | - colorama=0.4.6=pyhd8ed1ab_0 20 | - comm=0.1.4=pyhd8ed1ab_0 21 | - contourpy=1.0.5=py311h48ca7d4_0 22 | - cycler=0.12.1=pyhd8ed1ab_0 23 | - debugpy=1.6.7=py311h313beb8_0 24 | - decorator=5.1.1=pyhd8ed1ab_0 25 | - eigen=3.4.0=h1995070_0 26 | - exceptiongroup=1.1.3=pyhd8ed1ab_0 27 | - executing=2.0.1=pyhd8ed1ab_0 28 | - ffmpeg=4.2.2=h04105a8_0 29 | - fontconfig=2.13.94=heb65262_0 30 | - fonttools=4.25.0=pyhd3eb1b0_0 31 | - freetype=2.10.4=h17b34a0_1 32 | - gettext=0.21.1=h0186832_0 33 | - giflib=5.2.1=h1a8c8d9_3 34 | - glib=2.69.1=h514c7bf_2 35 | - gmp=6.2.1=h9f76cd9_0 36 | - gnutls=3.6.15=h887c41c_0 37 | - graphite2=1.3.14=hc377ac9_1 38 | - gst-plugins-base=1.14.1=h313beb8_1 39 | - gstreamer=1.14.1=h80987f9_1 40 | - harfbuzz=4.3.0=he9eebac_1 41 | - hdf5=1.12.1=h05c076b_3 42 | - icu=68.2=hbdafb3b_0 43 | - imagesize=1.4.1=py311hca03da5_0 44 | - importlib-metadata=6.8.0=pyha770c72_0 45 | - importlib_metadata=6.8.0=hd8ed1ab_0 46 | - ipykernel=6.26.0=pyh3cd1d5f_0 47 | - ipython=8.17.2=pyh31c8845_0 48 | - jedi=0.19.1=pyhd8ed1ab_0 49 | - jpeg=9e=h1a8c8d9_3 50 | - jupyter_client=8.5.0=pyhd8ed1ab_0 51 | - jupyter_core=5.5.0=py311hca03da5_0 52 | - kiwisolver=1.4.4=py311h313beb8_0 53 | - krb5=1.20.1=h69eda48_0 54 | - lame=3.100=h1a8c8d9_1003 55 | - lcms2=2.15=h481adae_0 56 | - lerc=3.0=hc377ac9_0 57 | - libbrotlicommon=1.1.0=hb547adb_1 58 | - libbrotlidec=1.1.0=hb547adb_1 59 | - libbrotlienc=1.1.0=hb547adb_1 60 | - libclang=12.0.0=default_hc321e17_4 61 | - libcurl=8.4.0=h3e2b118_0 62 | - libcxx=16.0.6=h4653b0c_0 63 | - libdeflate=1.17=h80987f9_1 64 | - libedit=3.1.20191231=hc8eb9b7_2 65 | - libev=4.33=h642e427_1 66 | - libffi=3.4.4=hca03da5_0 67 | - libgfortran=5.0.0=11_3_0_hca03da5_28 68 | - libgfortran5=11.3.0=h009349e_28 69 | - libiconv=1.17=he4db4b2_0 70 | - libidn2=2.3.4=h1a8c8d9_0 71 | - libllvm12=12.0.1=h93073aa_2 72 | - libnghttp2=1.57.0=h62f6fdd_0 73 | - libopenblas=0.3.21=h269037a_0 74 | - libopus=1.3.1=h27ca646_1 75 | - libpng=1.6.39=h80987f9_0 76 | - libpq=12.15=h02f6b3c_1 77 | - libsodium=1.0.18=h27ca646_1 78 | - libssh2=1.10.0=h02f6b3c_2 79 | - libtasn1=4.19.0=h1a8c8d9_0 80 | - libtiff=4.5.1=h313beb8_0 81 | - libunistring=0.9.10=h3422bc3_0 82 | - libvpx=1.10.0=hc377ac9_0 83 | - libwebp=1.3.2=ha3663a8_0 84 | - libwebp-base=1.3.2=hb547adb_0 85 | - libxml2=2.10.4=h372ba2a_0 86 | - libxslt=1.1.37=h1bd8bc4_0 87 | - llvm-openmp=14.0.6=hc6e5704_0 88 | - lz4-c=1.9.4=hb7217d7_0 89 | - matplotlib=3.8.0=py311hca03da5_0 90 | - matplotlib-base=3.8.0=py311h7aedaa7_0 91 | - matplotlib-inline=0.1.6=pyhd8ed1ab_0 92 | - munkres=1.1.4=pyh9f0ad1d_0 93 | - ncurses=6.4=h313beb8_0 94 | - nest-asyncio=1.5.8=pyhd8ed1ab_0 95 | - nettle=3.7.3=h84b5d62_1 96 | - nspr=4.35=hb7217d7_0 97 | - nss=3.89.1=h313beb8_0 98 | - numexpr=2.8.7=py311h6dc990b_0 99 | - numpy=1.26.0=py311he598dae_0 100 | - numpy-base=1.26.0=py311hfbfe69c_0 101 | - opencv=4.6.0=py311hbae66a1_5 102 | - openh264=1.8.0=h98b2900_0 103 | - openssl=3.0.11=h1a28f6b_2 104 | - packaging=23.2=pyhd8ed1ab_0 105 | - pandas=2.1.1=py311h7aedaa7_0 106 | - parso=0.8.3=pyhd8ed1ab_0 107 | - pcre=8.45=hbdafb3b_0 108 | - pexpect=4.8.0=pyh1a96a4e_2 109 | - pickleshare=0.7.5=py_1003 110 | - pillow=9.4.0=py311h313beb8_1 111 | - pip=23.3=py311hca03da5_0 112 | - pixman=0.42.2=h13dd4ca_0 113 | - platformdirs=3.11.0=pyhd8ed1ab_0 114 | - prompt-toolkit=3.0.39=pyha770c72_0 115 | - prompt_toolkit=3.0.39=hd8ed1ab_0 116 | - psutil=5.9.0=py311h80987f9_0 117 | - ptyprocess=0.7.0=pyhd3deb0d_0 118 | - pure_eval=0.2.2=pyhd8ed1ab_0 119 | - pygments=2.16.1=pyhd8ed1ab_0 120 | - pyparsing=3.0.9=pyhd8ed1ab_0 121 | - python=3.11.5=hb885b13_0 122 | - python-dateutil=2.8.2=pyhd8ed1ab_0 123 | - python-tzdata=2023.3=pyhd3eb1b0_0 124 | - pytz=2023.3.post1=py311hca03da5_0 125 | - pyzmq=25.1.0=py311h313beb8_0 126 | - qt-main=5.15.2=ha2d02b5_7 127 | - qt-webengine=5.15.9=h2903aaf_7 128 | - qtwebkit=5.212=h19f419d_5 129 | - readline=8.2=h1a28f6b_0 130 | - setuptools=68.0.0=py311hca03da5_0 131 | - six=1.16.0=pyh6c4a22f_0 132 | - sqlite=3.41.2=h80987f9_0 133 | - stack_data=0.6.2=pyhd8ed1ab_0 134 | - tk=8.6.12=hb8d0fd4_0 135 | - tornado=6.3.3=py311h80987f9_0 136 | - tqdm=4.66.1=pyhd8ed1ab_0 137 | - traitlets=5.13.0=pyhd8ed1ab_0 138 | - typing-extensions=4.8.0=hd8ed1ab_0 139 | - typing_extensions=4.8.0=pyha770c72_0 140 | - tzdata=2023c=h04d1e81_0 141 | - wcwidth=0.2.9=pyhd8ed1ab_0 142 | - wheel=0.41.2=py311hca03da5_0 143 | - x264=1!152.20180806=h1a28f6b_0 144 | - xz=5.4.2=h80987f9_0 145 | - zeromq=4.3.5=h965bd2d_0 146 | - zipp=3.17.0=pyhd8ed1ab_0 147 | - zlib=1.2.13=h5a0b063_0 148 | - zstd=1.5.5=hd90d995_0 149 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![Static Badge](https://badgen.net/github/release/kalfasyan/plakakia) 2 | ![Static Badge](https://badgen.net/github/license/kalfasyan/plakakia) 3 | ![Static Badge](https://badgen.net/github/stars/kalfasyan/plakakia) 4 | ![Static Badge](https://badgen.net/github/open-issues/kalfasyan/plakakia) 5 | 6 | # plakakia 7 | ### /πλακάκια 8 | *Python image tiling library for image processing, object detection, etc.* 9 | 10 | ![Alt text](logo/logo.png?raw=true "This is a \"plakaki\", meaning tile in Greek.") 11 | 12 | ## What is this? What is it going to be? 13 | `plakakia` is an efficient image tiling tool designed to handle bounding boxes within images. It divides images into rectangular tiles based on specified parameters, seamlessly handling overlapping tiles. The tool assigns bounding boxes to tiles that fully contain them, and it also offers an option to eliminate duplicate bounding boxes. While the current version only supports fully contained bounding boxes, future updates will include support for partial overlap. `plakakia` can handle object detection and segmentation datasets. 14 | 15 | Currently, the library offers online and offline modes for processing data (refer to the [Usage section](https://github.com/kalfasyan/plakakia#usage) section below for more details): 16 | 17 | - In the offline mode, one can use a config file and run a script once to process all data. 18 | - In the online mode, the `tile_image` function allows processing of images of any dimension. 19 | 20 | There are plans to expand `plakakia`'s capabilities in the offline mode to handle images with more than 3 channels. 21 | 22 | ## Performance 23 | To ensure optimal performance, `plakakia` utilizes the `multiprocessing` and `numpy` libraries. This enables efficient processing of thousands of images without the use of nested for-loops commonly used in tiling tasks. For detailed benchmarks on various public datasets, please refer to the information provided below. 24 | 25 | 26 | # Installation 27 | 28 | It is **highly** recommended that you create a new virtual environment for the installation: 29 | 1. Download and install [Mamba](https://mamba.readthedocs.io/en/latest/installation.html) (or [Anaconda](https://www.anaconda.com/products/distribution)). 30 | 2. Create a virtual environment: 31 | `mamba create -n plakakia jupyterlab nb_conda_kernels ipykernel ipywidgets pip -y` 32 | 3. Activate the environment: 33 | `mamba activate plakakia` 34 | 4. Run the following command to install the library: 35 | `pip install plakakia` 36 | 37 | # Usage 38 | 39 | ##### A. Offline tile generation with a config file 40 | 41 | `make_tiles --config path/to/config.yaml` 42 | > Here's an [example config file](plakakia/config_example.yaml). 43 | 44 | ##### B. Online tile generation 45 | 46 | ``` 47 | from plakakia.utils_tiling import tile_image 48 | 49 | tiles, coordinates = tile_image(img, tile_size=100, step_size=100) 50 | ``` 51 | 52 | For more examples, check the [examples](examples/) folder. 53 | 54 | ### Streamlit Demo App 55 | You can run the demo app with the following command: 56 | ``` 57 | streamlit run demo/explore_tiling_output.py 58 | ``` 59 | And when you open http://localhost:8501 in your browser, you should see the following: 60 | 61 | drawing 62 | 63 | # Benchmarks 64 | 65 | **Benchmarked on HP Laptop with specs**: AMD Ryzen 5 PRO 6650U; 6 cores; 12 threads; 2.9 GHz 66 | 67 | | Dataset | Source | Formats (images/labels) | Number of images | tile_size | step_size | tiles generated | plakakia performance | 68 | | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | 69 | | Solar Panels v2 | [RoboFlow](https://universe.roboflow.com/roboflow-100/solar-panels-taxvb/dataset/2) | jpg/COCO | 112 | 150 | 50 | 3.075 | 1,11 sec | 70 | | Traffic Signs | [Kaggle](https://www.kaggle.com/datasets/valentynsichkar/traffic-signs-dataset-in-yolo-format) | jpg/YOLO | 741 | 300 | 200 | 1.695 | 2,8 sec | 71 | | Hard Hat Workers v2 | [RoboFlow](https://public.roboflow.com/object-detection/hard-hat-workers/2) | jpg/YOLO | 5.269 | 100 | 50 | 21.678 | 6,94 sec| 72 | | Microsoft COCO dataset | [RoboFlow](https://public.roboflow.com/object-detection/microsoft-coco-subset) | jpg/YOLO | 121.408 | 200 | 150 | 177.039 | 3 min 4 sec| 73 | 74 | # TODO list 75 | 76 | ☑️ ~~Fix reading of classes from annotations (create a 'mapper' dictionary to map classes to numerical values).~~ 77 | ☑️ ~~Read settings from a file (e.g. json).~~ 78 | ☑️ ~~Removing all tiles with duplicate bounding boxes (that appear in other tiles).~~ 79 | ☑️ ~~Support other annotation formats (e.g. coco).~~ (only input for now) 80 | ☑️ ~~Provide tiling functionality without any labels needed.~~ 81 | ☑️ ~~Add support for segmentation tasks (tile both input images and masks).~~ 82 | ☑️ ~~Add a demo app for the users to be able to see the tiling applied on an image.~~ 83 | ⬜️ Add less strict (flexible) duplicate removal methods to avoid missing bounding boxes. 84 | ⬜️ Consider bounding boxes in tiles if they *partially* belong to one. 85 | ⬜️ Support reading annotations from a dataframe/csv file. 86 | ⬜️ Make tiles with multidimensional data offline with config file (e.g. hdf5 hyperspectral images). 87 | 88 | 89 | # Want to contribute? 90 | If you want to contribute to this project, please check the [CONTRIBUTING.md](CONTRIBUTING.md) file. 91 | -------------------------------------------------------------------------------- /tests/test_utils_tiling.py: -------------------------------------------------------------------------------- 1 | # content of test_sysexit.py 2 | import os 3 | import sys 4 | 5 | sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) 6 | 7 | 8 | import json 9 | import tempfile 10 | from pathlib import Path 11 | from tempfile import NamedTemporaryFile 12 | 13 | import numpy as np 14 | import pandas as pd 15 | import pytest 16 | import yaml 17 | from lxml import etree as ET 18 | 19 | from plakakia.settings import Settings 20 | from plakakia.annotations import (read_coco_coordinates_from_json, 21 | read_pascalvoc_coordinates_from_xml, 22 | read_yolo_coordinates_from_txt) 23 | from plakakia.tiling import add_border, tile_image 24 | 25 | # Read the settings from the config.yaml file 26 | with open('plakakia/config_example.yaml', 'r') as f: 27 | config = yaml.load(f, Loader=yaml.FullLoader) 28 | 29 | # Create a settings object 30 | settings = Settings(**config) 31 | 32 | def test_add_border(): 33 | """ 34 | Test function for the add_border() function. 35 | 36 | Creates a 10x10 numpy array with random values and adds a border of one pixel width to each side using 37 | the add_border() function. The resulting array should have a shape of (12, 12). This test checks whether 38 | the resulting shape is as expected. 39 | """ 40 | 41 | tmp = np.random.randn(10, 10) 42 | settings.pad_size = 1 43 | tmp = add_border(tmp, settings=settings) 44 | assert tmp.shape == (12, 12) 45 | 46 | import os 47 | import xml.etree.ElementTree as ET 48 | 49 | 50 | def create_sample_xml(): 51 | """ 52 | Creates a sample XML file with two objects. 53 | 54 | Returns: 55 | None 56 | """ 57 | root = ET.Element("annotation") 58 | 59 | obj1 = ET.SubElement(root, "object") 60 | name1 = ET.SubElement(obj1, "name") 61 | name1.text = "cat" 62 | bbox1 = ET.SubElement(obj1, "bndbox") 63 | xmin1 = ET.SubElement(bbox1, "xmin") 64 | xmin1.text = "10" 65 | ymin1 = ET.SubElement(bbox1, "ymin") 66 | ymin1.text = "20" 67 | xmax1 = ET.SubElement(bbox1, "xmax") 68 | xmax1.text = "30" 69 | ymax1 = ET.SubElement(bbox1, "ymax") 70 | ymax1.text = "40" 71 | 72 | obj2 = ET.SubElement(root, "object") 73 | name2 = ET.SubElement(obj2, "name") 74 | name2.text = "car" 75 | bbox2 = ET.SubElement(obj2, "bndbox") 76 | xmin2 = ET.SubElement(bbox2, "xmin") 77 | xmin2.text = "50" 78 | ymin2 = ET.SubElement(bbox2, "ymin") 79 | ymin2.text = "60" 80 | xmax2 = ET.SubElement(bbox2, "xmax") 81 | xmax2.text = "80" 82 | ymax2 = ET.SubElement(bbox2, "ymax") 83 | ymax2.text = "90" 84 | 85 | tree = ET.ElementTree(root) 86 | tree.write("sample.xml") 87 | 88 | def test_read_pascalvoc_coordinates_from_xml(): 89 | """ 90 | Tests the function read_pascalvoc_coordinates_from_xml by creating a sample XML file, 91 | reading it, and comparing the results with expected values. The XML file contains two 92 | objects: a cat and a car. The function is tested with a settings object that maps the 93 | "cat" class to "animal" and the "car" class to "vehicle". 94 | 95 | Returns: 96 | None 97 | """ 98 | create_sample_xml() 99 | settings.annotation_mapping = {"cat": "animal", "car": "vehicle"} 100 | boxes, classes = read_pascalvoc_coordinates_from_xml("sample.xml", settings=settings) 101 | 102 | assert boxes == [[10, 20, 30, 40], [50, 60, 80, 90]] 103 | assert classes == ["animal", "vehicle"] 104 | 105 | os.remove("sample.xml") # delete the sample.xml file once we're done with the test 106 | 107 | def test_read_pascalvoc_coordinates_from_xml_invalid_file(): 108 | """ 109 | Tests the function read_pascalvoc_coordinates_from_xml with an invalid file path, 110 | expecting a FileNotFoundError to be raised. 111 | 112 | Returns: 113 | None 114 | """ 115 | with pytest.raises(FileNotFoundError): 116 | read_pascalvoc_coordinates_from_xml('nonexistent.xml') 117 | 118 | def test_read_yolo_coordinates_from_txt(): 119 | """ 120 | Tests the function read_yolo_coordinates_from_txt by creating a temporary file with 121 | some YOLO-formatted data, reading it, and comparing the results with expected values. 122 | The YOLO data contains two objects: a bounding box around the center of the image 123 | with width and height equal to half the image size, and a smaller bounding box in 124 | the top-left corner. The function is tested with an image shape of (800, 600, 3) 125 | and no settings object. 126 | 127 | Returns: 128 | None 129 | """ 130 | # Create a temporary file with some data in YOLO format 131 | data = "0 0.5 0.5 0.5 0.5\n1 0.3 0.3 0.2 0.2\n" 132 | with tempfile.NamedTemporaryFile(mode='w', delete=False) as tmp_file: 133 | tmp_file.write(data) 134 | tmp_file.flush() 135 | path = tmp_file.name 136 | 137 | # Call the read_yolo_coordinates_from_txt function with the temporary file 138 | image_shape = (800, 600, 3) 139 | boxes, classes = read_yolo_coordinates_from_txt(path=path, image_shape=image_shape) 140 | 141 | # Check that the output is as expected 142 | expected_boxes = [[150, 200, 450, 600], [119, 160, 240, 320]] 143 | expected_classes = [0, 1] 144 | assert boxes == expected_boxes 145 | assert classes == expected_classes 146 | 147 | # Delete the temporary file 148 | os.remove(path) 149 | 150 | def test_read_yolo_coordinates_from_txt_invalid_file(): 151 | """ 152 | Tests the function read_yolo_coordinates_from_txt with an invalid file path, 153 | expecting a FileNotFoundError to be raised. 154 | 155 | Returns: 156 | None 157 | """ 158 | with pytest.raises(FileNotFoundError): 159 | read_yolo_coordinates_from_txt('nonexistent.txt') 160 | 161 | def test_read_yolo_coordinates_from_txt_invalid_image_shape(): 162 | """ 163 | Tests the function read_yolo_coordinates_from_txt with an invalid image shape, 164 | expecting a ValueError to be raised. 165 | 166 | Returns: 167 | None 168 | """ 169 | # Create a temporary file with some data in YOLO format 170 | data = "0 0.5 0.5 0.5 0.5\n1 0.3 0.3 0.2 0.2\n" 171 | with tempfile.NamedTemporaryFile(mode='w', delete=False) as tmp_file: 172 | tmp_file.write(data) 173 | tmp_file.flush() 174 | path = tmp_file.name 175 | 176 | # Call the read_yolo_coordinates_from_txt function with the temporary file 177 | image_shape = (800, 600) 178 | with pytest.raises(ValueError): 179 | read_yolo_coordinates_from_txt(path=path, image_shape=image_shape) 180 | 181 | # Delete the temporary file 182 | os.remove(path) 183 | 184 | def test_tile_image(): 185 | """ 186 | Tests the function tile_image by creating a random input image, tiling it, and 187 | comparing the results with expected values. The input image has a size of 512x512 188 | and the tiles have a size of 128x128 with a step size of 64. The output tiles and 189 | coordinates are checked for correctness. 190 | 191 | Returns: 192 | None 193 | """ 194 | 195 | # Create a random input image 196 | image = np.random.randint(0, 256, size=(512, 512, 3), dtype=np.uint8) 197 | 198 | # Call the tile_image function with the random image 199 | tile_size = 128 200 | step_size = 64 201 | 202 | tiles, coordinates = tile_image(image, tile_size=tile_size, step_size=step_size) 203 | 204 | # Check that the output shape and type is correct 205 | assert tiles.shape == (49, 128, 128, 3) 206 | assert coordinates.shape == (49, 4) 207 | assert tiles.dtype == image.dtype 208 | 209 | # Check that the coordinates correspond to the correct tiles 210 | for i, (x1, y1, x2, y2) in enumerate(coordinates): 211 | assert x2 - x1 == tile_size 212 | assert y2 - y1 == tile_size 213 | assert x1 % step_size == 0 214 | assert y1 % step_size == 0 215 | assert x2 <= image.shape[1] 216 | assert y2 <= image.shape[0] 217 | tile = tiles[i] 218 | assert tile.shape == (tile_size, tile_size, 3) 219 | assert np.array_equal(tile, image[y1:y2, x1:x2]) 220 | -------------------------------------------------------------------------------- /plakakia/settings.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from dataclasses import dataclass, field 3 | from logging.handlers import RotatingFileHandler 4 | from pathlib import Path 5 | 6 | import imagesize 7 | import psutil 8 | from tqdm import tqdm 9 | from .annotations import read_coco_coordinates_from_json 10 | 11 | # Create a dataclass for storing the settings 12 | @dataclass 13 | class Settings(): 14 | """ Settings for the tiling process. """ 15 | # Define the image input file extensions 16 | input_extension_images: str = 'jpg' 17 | # Define the image output file extension 18 | output_extension_images: str = 'jpg' 19 | # Set the annotation file extensions 20 | input_extension_annotations: str = 'txt' 21 | # Whether to pad the image with a border 22 | pad_image: bool = False 23 | # Size of the border to add to the image 24 | pad_size: int = 10 25 | # Size of the tile; only square tiles supported for now 26 | tile_size: int = 200 27 | # Step size to move the tile in a windowed manner 28 | step_size: int = 50 29 | # Check if bounding boxes are partially inside the tile 30 | check_partial: bool = False 31 | # Set the overlap threshold for the bounding boxes to be considered partially inside the tile 32 | partial_overlap_threshold: float = 0.8 33 | # Set the input directory for the images 34 | input_dir_images: str = 'input_images' 35 | # Set the input directory for the annotations 36 | input_dir_annotations: str = 'input_annotations' 37 | # Set the input annotation file format 38 | input_format_annotations: str = 'yolo' # 'yolo', 'pascal_voc', or 'coco' 39 | # Set the output directory for the images 40 | output_dir_images: str = 'output_images' 41 | # Set the output directory for the annotations 42 | output_dir_annotations: str = 'output_annotations' 43 | # Define the annotation file format 44 | output_format_annotations: str = 'yolo' # 'yolo' or 'pascal_voc' 45 | # Define a mapping for the annotation labels 46 | annotation_mapping: dict = field(default_factory=dict) 47 | # Whether to draw rectangels in the tile images 48 | draw_boxes: bool = False 49 | # Set a flag for logging output 50 | log: bool = False 51 | # Define a folder for saving the logs 52 | log_folder: str = 'logs' 53 | # Boolean flag for validating the settings 54 | validate_settings: bool = True 55 | # Define the number of threads to use 56 | num_workers: int = 1 57 | # Define a flag for removing duplicate tiles 58 | clear_duplicates: bool = False 59 | # Define dictionary for annotations and extension formats 60 | format_to_extension: dict = field(default_factory=dict) 61 | 62 | # Define the initialization method of this dataclass 63 | def __post_init__(self): 64 | self.format_to_extension = { 65 | 'yolo': 'txt', 66 | 'pascal_voc': 'xml', 67 | 'coco': 'json', 68 | 'segmentation': 'png', 69 | } 70 | assert Path(self.input_dir_images).exists(), \ 71 | f"{self.input_dir_images} image input directory does not exist." 72 | assert Path(self.input_dir_annotations).exists(), \ 73 | f"{self.input_dir_annotations} annotations directory does not exist." 74 | # Create the output directories for the images and annotations 75 | Path(self.output_dir_images).mkdir(parents=True, exist_ok=True) 76 | Path(self.output_dir_annotations).mkdir(parents=True, exist_ok=True) 77 | Path(self.log_folder).mkdir(parents=True, exist_ok=True) 78 | 79 | # Settings the annotations' file extension 80 | self.input_extension_annotations = self.format_to_extension[self.input_format_annotations] 81 | 82 | # Settings the annotations' output file extension 83 | self.output_extension_annotations = self.format_to_extension[self.output_format_annotations]\ 84 | if self.input_format_annotations != 'coco' else 'txt' 85 | 86 | # Get the list of images and annotations 87 | self.input_images = list(Path(self.input_dir_images).\ 88 | glob(f"*.{self.input_extension_images}")) 89 | self.input_images = [i.as_posix() for i in self.input_images] 90 | self.input_annotations = list(Path(self.input_dir_annotations).\ 91 | glob(f"*.{self.input_extension_annotations}")) 92 | input_annotations = [i.as_posix() for i in self.input_annotations] 93 | 94 | # Check if there are any images with the given extension 95 | assert self.input_images, f"No images found with extension: {self.input_extension_images}." 96 | # Check if there are any annotations with the given extension 97 | # Perform checks for 'yolo' and 'pascal_voc' formats 98 | if self.input_format_annotations in ['yolo', 'pascal_voc']: 99 | assert len(input_annotations), \ 100 | f"No annotations found with \'input' extension: {self.input_extension_annotations}." 101 | # Check if the number of annotations is equal to the number of images 102 | assert len(self.input_images) == len(input_annotations), \ 103 | "The number of images is not equal to the number of annotations." 104 | # Sort the images and annotations 105 | self.input_annotations.sort() 106 | self.input_images.sort() 107 | elif self.input_format_annotations == 'coco': 108 | self.input_annotations = ['' for i in range(len(self.input_images))] 109 | # Find the annotation json file in the input directory 110 | try: 111 | json_filepath = list(Path(self.input_dir_annotations).glob('*.json')) 112 | assert len(json_filepath) == 1, \ 113 | "More than one json files found in the input directory." 114 | json_filepath = json_filepath[0].as_posix() 115 | except AssertionError as err: 116 | print(err) 117 | exit() 118 | print(f"Reading the annotations from {json_filepath}") 119 | self.df_coco = read_coco_coordinates_from_json(json_filepath, self.input_dir_images) 120 | 121 | if self.validate_settings: 122 | # Calculate the minimum image dimension 123 | minimum_image_dim = float('inf') 124 | for img in tqdm(self.input_images, 125 | desc='Validating settings..', 126 | total=len(self.input_images)): 127 | width, height = imagesize.get(img) 128 | minimum_image_dim = min(minimum_image_dim, width, height) 129 | if self.input_format_annotations in ['yolo', 'pascal_voc']: 130 | # Check if there is an annotation for each image 131 | assert Path(img).stem in [Path(a).stem for a in self.input_annotations], \ 132 | f"No annotation found for image {img}." 133 | # Check if the tile size is smaller than the smallest image dimension 134 | assert minimum_image_dim >= self.tile_size, \ 135 | f"The tile size is larger than the smallest image dimension: {minimum_image_dim}. \n\ 136 | Try setting a smaller tile size." 137 | 138 | # Create the inverse mapping for the annotation labels 139 | self.inv_annotation_mapping = {v: k for k, v in self.annotation_mapping.items()} 140 | 141 | # Logging settings 142 | self.logger = logging.getLogger(__name__) 143 | self.logger.setLevel(logging.DEBUG) 144 | formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') 145 | # Create a rotating file handler with 5 files that have up to 5 MB size each 146 | file_handler = RotatingFileHandler(f'{self.log_folder}/app.log', 147 | maxBytes=5*1024*1024, 148 | backupCount=5) 149 | file_handler.setFormatter(formatter) 150 | self.logger.addHandler(file_handler) 151 | 152 | # Set check_partial to False by default 153 | self.check_partial = False 154 | 155 | # Set default value for pad_size 156 | self.pad_size = 10 157 | 158 | self.num_workers = psutil.cpu_count() if self.num_workers == -1 else self.num_workers 159 | 160 | if self.clear_duplicates: 161 | self.output_dir_duplicates = Path(self.output_dir_images).parent / "duplicates" 162 | Path(f"{self.output_dir_duplicates}").mkdir(parents=True, exist_ok=True) 163 | -------------------------------------------------------------------------------- /plakakia/annotations.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import xml.etree.ElementTree as ET 3 | import os 4 | import numpy as np 5 | from tqdm import tqdm 6 | from pathlib import Path 7 | import json 8 | import cv2 9 | 10 | def read_pascalvoc_coordinates_from_xml(filename=str, settings=None): 11 | ''' Read coordinates from PascalVOC xml file. ''' 12 | tree = ET.parse(filename) # type: ignore 13 | root = tree.getroot() 14 | 15 | boxes, classes = [], [] 16 | 17 | for obj in root.iter('object'): 18 | class_name = settings.annotation_mapping[obj.find('name').text] \ 19 | if isinstance(obj.find('name').text, str) else str(obj.find('name').text) 20 | 21 | # Get bounding box coordinates 22 | xmlbox = obj.find('bndbox') 23 | x_1 = int(float(xmlbox.find('xmin').text)) # type: ignore 24 | y_1 = int(float(xmlbox.find('ymin').text)) # type: ignore 25 | x_2 = int(float(xmlbox.find('xmax').text)) # type: ignore 26 | y_2 = int(float(xmlbox.find('ymax').text)) # type: ignore 27 | 28 | # Append to boxes and classes lists 29 | boxes.append([x_1, y_1, x_2, y_2]) 30 | classes.append(class_name) 31 | 32 | return boxes, classes 33 | 34 | def read_yolo_coordinates_from_txt(path=None, image_shape=()): 35 | """ Read coordinates from YOLO txt file. """ 36 | with open(path, mode='r', encoding="utf-8") as file: # type: ignore 37 | lines = file.readlines() 38 | 39 | image_height, image_width, _ = image_shape 40 | 41 | # Transform yolo_x, yolo_y, yolo_width, yolo_height to x_1, y_1, x_2, y_2 42 | boxes, classes = [], [] 43 | 44 | for line in lines: 45 | line = line.strip() 46 | if line == '': 47 | continue 48 | line = line.split(' ') 49 | class_id = int(line[0]) 50 | classes.append(class_id) 51 | 52 | x = float(line[1]) 53 | y = float(line[2]) 54 | w = float(line[3]) 55 | h = float(line[4]) 56 | x_1 = int((x - w/2) * image_width) 57 | y_1 = int((y - h/2) * image_height) 58 | x_2 = int((x + w/2) * image_width) 59 | y_2 = int((y + h/2) * image_height) 60 | boxes.append([x_1, y_1, x_2, y_2]) 61 | return boxes, classes 62 | 63 | def read_coco_coordinates_from_json(filename, dir_images) -> pd.DataFrame: 64 | """Read coordinates from COCO format json file.""" 65 | with open(filename, 'r', encoding='utf-8') as f: 66 | data = json.load(f) 67 | 68 | df_anns = pd.DataFrame(data['annotations']) 69 | df_ims = pd.DataFrame(data['images']) 70 | boxes = [] 71 | for annotation in data['annotations']: 72 | x, y, w, h = annotation['bbox'] 73 | x_1, y_1 = int(x), int(y) 74 | x_2, y_2 = int(x+w), int(y+h) 75 | boxes.append([x_1, y_1, x_2, y_2]) 76 | 77 | df_merged = pd.merge(df_anns, df_ims, left_on='image_id', right_on='id') 78 | df_merged['boxes'] = boxes 79 | df_merged['file_name'] = df_merged['file_name'].apply(lambda x: (Path(dir_images) / x).as_posix()) 80 | 81 | return df_merged 82 | 83 | def read_coordinates_from_annotations(im_path=None, 84 | ant_path=None, 85 | image_shape=None, 86 | settings=None) -> tuple: 87 | """ Read coordinates from annotations. """ 88 | if settings.input_format_annotations == 'yolo': 89 | boxes, classes = read_yolo_coordinates_from_txt(ant_path, image_shape) 90 | elif settings.input_format_annotations == 'pascal_voc': 91 | boxes, classes = read_pascalvoc_coordinates_from_xml(ant_path, settings) 92 | elif settings.input_format_annotations == 'coco': 93 | boxes = settings.df_coco.query("file_name == @im_path").boxes.tolist() 94 | classes = settings.df_coco.query("file_name == @im_path").category_id.tolist() 95 | else: 96 | raise ValueError(f"Annotation format {settings.input_format_annotations} not supported") 97 | 98 | box_classes = [int(i) for i in classes] 99 | 100 | return np.array(boxes), box_classes 101 | 102 | def export_yolo_annotation_from_csv(filename=None, output_dir=None) -> None: 103 | """ Export YOLO annotation from csv file. """ 104 | csv_filename = f"df_{filename}.csv" 105 | dataframe = pd.read_csv(f"{csv_filename}") 106 | dataframe = dataframe[dataframe.user_verification].copy().reset_index() 107 | dataframe['prediction_verified'] = np.random.randint(10,size=len(dataframe)).tolist() 108 | dataframe[['prediction_verified','yolo_x','yolo_y','yolo_width','yolo_height']]\ 109 | .to_csv(f"{output_dir}/{filename}.txt", index=False, header=False, sep=' ') 110 | 111 | def save_yolo_annotations_from_df(dataframe, 112 | filename=None, 113 | settings=None, 114 | disable_progress_bar=True) -> None: 115 | """ 116 | Save YOLO annotations from a dataframe containing the tile coordinates and the bounding boxes. 117 | """ 118 | # Compute the coordinates of the center of the box and the width and height of the box 119 | x_1, y_1, x_2, y_2 = dataframe.box_x1, dataframe.box_y1, dataframe.box_x2, dataframe.box_y2 120 | image_width = dataframe['tile_x2'] - dataframe['tile_x1'] 121 | image_height = dataframe['tile_y2'] - dataframe['tile_y1'] 122 | dataframe['yolo_x'] = (x_1 + x_2) / 2 / image_width 123 | dataframe['yolo_y'] = (y_1 + y_2) / 2 / image_height 124 | dataframe['yolo_w'] = (x_2 - x_1) / image_width 125 | dataframe['yolo_h'] = (y_2 - y_1) / image_height 126 | 127 | group = dataframe.groupby(['tile_x1', 'tile_y1', 'tile_x2', 'tile_y2']) 128 | output_dir = settings.output_dir_annotations 129 | 130 | for i, sub in tqdm(group, 131 | desc='Saving YOLO annotations', 132 | disable=disable_progress_bar, 133 | total=len(group.count())): 134 | file_name = f"tile_{filename}_{i[0]}_{i[1]}_{i[2]}_{i[3]}.txt" 135 | with open(Path(output_dir) / file_name, mode="a+", encoding="utf-8") as file: 136 | for _, row in sub.iterrows(): 137 | file.write(f"{int(row['box_class'])} {row['yolo_x']} {row['yolo_y']} {row['yolo_w']} {row['yolo_h']}\n") 138 | 139 | def save_to_pascal_voc_from_df(dataframe, 140 | filename=None, 141 | settings=None, 142 | disable_progress_bar=True) -> None: 143 | """ 144 | Saves a dataframe containing bounding box information in Pascal VOC format. 145 | """ 146 | # fix this function 147 | 148 | f_n = filename 149 | 150 | # Group by tile and iterate over groups 151 | group = dataframe.groupby(["tile_x1", "tile_y1", "tile_x2", "tile_y2"]) 152 | for _, tile_df in tqdm(group, 153 | desc="Saving Pascal VOC annotations", 154 | disable=disable_progress_bar, 155 | total=len(group.count())): 156 | # Create the XML structure 157 | tile_x1 = tile_df["tile_x1"].iloc[0] 158 | tile_y1 = tile_df["tile_y1"].iloc[0] 159 | tile_x2 = tile_df["tile_x2"].iloc[0] 160 | tile_y2 = tile_df["tile_y2"].iloc[0] 161 | 162 | tile_name = f"tile_{f_n}_{tile_x1}_{tile_y1}_{tile_x2}_{tile_y2}" 163 | assert 'at' not in str(tile_name), "Something went wrong with the tile name." 164 | 165 | annotation = ET.Element("annotation") 166 | folder = ET.SubElement(annotation, "folder") 167 | folder.text = settings.output_dir_annotations 168 | filename = ET.SubElement(annotation, "filename") 169 | filename.text = f"{tile_name}.{settings.output_extension_images}" 170 | size = ET.SubElement(annotation, "size") 171 | width = ET.SubElement(size, "width") 172 | width.text = str(np.abs(tile_x2 - tile_x1)) 173 | height = ET.SubElement(size, "height") 174 | height.text = str(np.abs(tile_y2 - tile_y1)) 175 | depth = ET.SubElement(size, "depth") 176 | depth.text = "3" 177 | 178 | # Iterate over rows in the group and add bounding box information 179 | for _, row in tile_df.iterrows(): 180 | obj = ET.SubElement(annotation, "object") 181 | name = ET.SubElement(obj, "name") 182 | name.text = str(row["box_class"]) 183 | bndbox = ET.SubElement(obj, "bndbox") 184 | xmin = ET.SubElement(bndbox, "xmin") 185 | xmin.text = str(row["box_x1"]) 186 | ymin = ET.SubElement(bndbox, "ymin") 187 | ymin.text = str(row["box_y1"]) 188 | xmax = ET.SubElement(bndbox, "xmax") 189 | xmax.text = str(row["box_x2"]) 190 | ymax = ET.SubElement(bndbox, "ymax") 191 | ymax.text = str(row["box_y2"]) 192 | 193 | # Write the XML to a file 194 | xml_tile_name = f"{tile_name}.xml" 195 | output_path = os.path.join(settings.output_dir_annotations, xml_tile_name) 196 | tree = ET.ElementTree(annotation) 197 | tree.write(output_path, 198 | encoding="utf-8", 199 | xml_declaration=True, 200 | short_empty_elements=False, 201 | method="xml") 202 | 203 | def save_annotations(dataframe=None, filename=None, settings=None, disable_progress_bar=True) -> None: 204 | """ Save the annotations in the format specified in the settings. """ 205 | if settings.output_format_annotations == 'yolo': 206 | save_yolo_annotations_from_df(dataframe, 207 | filename=filename, 208 | settings=settings, 209 | disable_progress_bar=disable_progress_bar) 210 | elif settings.output_format_annotations == 'pascal_voc': 211 | save_to_pascal_voc_from_df(dataframe, 212 | filename=filename, 213 | settings=settings, 214 | disable_progress_bar=disable_progress_bar) 215 | else: 216 | raise ValueError("The output format of the annotations is not valid.") 217 | 218 | def convert_yolo_to_xyxy(yolo_x, 219 | yolo_y, 220 | yolo_w, 221 | yolo_h, 222 | image_width, 223 | image_height): 224 | """ Convert YOLO format to XYXY format. """ 225 | x_1 = int((yolo_x - yolo_w/2) * image_width) 226 | y_1 = int((yolo_y - yolo_h/2) * image_height) 227 | x_2 = int((yolo_x + yolo_w/2) * image_width) 228 | y_2 = int((yolo_y + yolo_h/2) * image_height) 229 | return x_1, y_1, x_2, y_2 230 | 231 | def save_image_tiles(filename=None, tiles=None, coordinates=None, settings=None, prefix=None): 232 | """ Save the mask tiles. """ 233 | 234 | if prefix == 'mask': 235 | output_dir = settings.output_dir_annotations 236 | extension = settings.output_extension_images 237 | elif prefix in ['tile', 'image']: 238 | output_dir = settings.output_dir_images 239 | extension = settings.output_extension_images 240 | else: 241 | raise ValueError(f"The prefix is not valid. The only accepted values are 'mask', 'tile' and 'image'.") 242 | 243 | for i, (tile, tile_coord) in tqdm(enumerate(zip(tiles, coordinates)), 244 | desc="Saving mask tiles", 245 | total=len(tiles)): 246 | # Save the tile 247 | file_path = f"{prefix}_{filename}_{tile_coord[0]}_{tile_coord[1]}_{tile_coord[2]}_{tile_coord[3]}.{extension}" 248 | save_path = Path(output_dir) / Path(file_path) 249 | 250 | cv2.imwrite(save_path.as_posix(), tile) 251 | -------------------------------------------------------------------------------- /plakakia/tiling.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import random 3 | from pathlib import Path 4 | 5 | import cv2 6 | import matplotlib.pyplot as plt 7 | import numpy as np 8 | import pandas as pd 9 | from tqdm import tqdm 10 | 11 | from .annotations import * 12 | from .images import * 13 | 14 | logger = logging.getLogger(__name__) 15 | 16 | def tile_image(image, tile_size=250, step_size=50): 17 | """ Tile an image into overlapping tiles. """ 18 | 19 | # Check if the image is grayscale (1 channel) 20 | if len(image.shape) == 2: 21 | image = image[..., np.newaxis] # Add a third dimension for compatibility 22 | 23 | # Compute the number of rows and columns of tiles 24 | rows = (image.shape[0] - tile_size) // step_size + 1 25 | cols = (image.shape[1] - tile_size) // step_size + 1 26 | 27 | # Compute the shape and strides of the tile view 28 | tile_shape = (rows, cols, tile_size, tile_size, image.shape[2]) 29 | tile_strides = (step_size * image.strides[0], step_size * image.strides[1], *image.strides) 30 | 31 | # Create a view of the input image with the tile shape and strides 32 | tile_view = np.lib.stride_tricks.as_strided(image, shape=tile_shape, strides=tile_strides) 33 | 34 | # Reshape the tile view to a flat array of tiles 35 | tiles = tile_view.reshape(-1, tile_size, tile_size, image.shape[2]) 36 | 37 | # Compute the corresponding tile indices and pixel coordinates 38 | indices = np.arange(tiles.shape[0]) 39 | i, j = np.unravel_index(indices, (rows, cols)) 40 | x_1 = j * step_size 41 | y_1 = i * step_size 42 | x_2 = x_1 + tile_size 43 | y_2 = y_1 + tile_size 44 | 45 | # Stack the tile indices and coordinates into a single array 46 | coordinates = np.stack((x_1, y_1, x_2, y_2), axis=-1) 47 | 48 | # If the input was grayscale, convert the tiles to grayscale 49 | if len(image.shape) == 2: 50 | tiles = tiles[..., 0] 51 | 52 | return tiles, coordinates 53 | 54 | def get_boxes_inside_tiles(bboxes, 55 | tile_coordinates, 56 | settings): 57 | """ Get the bounding boxes that are inside the tiles. """ 58 | 59 | partial_boxes=settings.check_partial 60 | overlap_threshold=settings.partial_overlap_threshold 61 | 62 | boxes_inside_tiles = [[] for _ in range(len(tile_coordinates))] 63 | 64 | for i, tile_coord in enumerate(tile_coordinates): 65 | if partial_boxes: 66 | # Create a boolean mask indicating which boxes partially overlap with the tile 67 | mask = is_partial_square_inside_array(bboxes, 68 | tile_coord, 69 | overlap_threshold=overlap_threshold) 70 | else: 71 | # Create a boolean mask indicating which boxes are completely inside the tile 72 | mask = is_square_inside_array(bboxes, tile_coord) 73 | 74 | # Add the boxes that satisfy the condition to the corresponding tile 75 | boxes_inside_tiles[i] = bboxes[mask].tolist() 76 | 77 | return boxes_inside_tiles 78 | 79 | def is_partial_square_inside_array(bboxes, tile_coord, overlap_threshold=None): 80 | """ Check if a square is partially inside an array. """ 81 | # Compute the coordinates of the intersection between the box and the tile 82 | x_1 = np.maximum(bboxes[:, 0], tile_coord[0]) 83 | y_1 = np.maximum(bboxes[:, 1], tile_coord[1]) 84 | x_2 = np.minimum(bboxes[:, 2], tile_coord[2]) 85 | y_2 = np.minimum(bboxes[:, 3], tile_coord[3]) 86 | 87 | # Compute the areas of the intersection and the box 88 | intersection_area = (x_2 - x_1) * (y_2 - y_1) 89 | box_area = (bboxes[:, 2] - bboxes[:, 0]) * \ 90 | (bboxes[:, 3] - bboxes[:, 1]) 91 | 92 | # Compute the overlap between the box and the tile 93 | overlap = intersection_area / box_area 94 | 95 | # Return a boolean mask indicating which boxes have overlap above the threshold 96 | return overlap > overlap_threshold 97 | 98 | def is_square_inside_array(bboxes, tile_coord): 99 | """ Check if a square is completely inside an array. """ 100 | # Return a boolean mask indicating which boxes are inside the tile 101 | return np.logical_and.reduce(( 102 | bboxes[:, 0] >= tile_coord[0], 103 | bboxes[:, 1] >= tile_coord[1], 104 | bboxes[:, 2] <= tile_coord[2], 105 | bboxes[:, 3] <= tile_coord[3] 106 | )) 107 | 108 | def save_boxes(tiles=np.array([]), 109 | filename=None, 110 | coordinates=np.array([]), 111 | boxes_in_tiles=[], 112 | box_classes=[], 113 | settings=None, 114 | disable_progress_bar=True): 115 | ''' 116 | Save the tiles with the boxes drawn on them. 117 | ''' 118 | 119 | # Initialize an array to store the class and coordinates of the boxes and the tile coordinates 120 | results = np.zeros((0, 13), dtype=np.int32) 121 | 122 | # Loop through each tile and save it with a name that includes the tile coordinates 123 | for i, (tile, tile_coord, boxes) in tqdm(enumerate(zip(tiles, coordinates, boxes_in_tiles)), 124 | desc="Saving tiles", 125 | total=len(tiles), 126 | disable=disable_progress_bar): 127 | # Save boxes only if there are boxes in the tile 128 | if len(boxes_in_tiles[i]) == 0: 129 | continue 130 | 131 | # Draw the boxes on the tile with yellow borders 132 | for b, box in enumerate(boxes): 133 | new_x1 = box[0] - tile_coord[0] 134 | new_y1 = box[1] - tile_coord[1] 135 | new_x2 = box[2] - tile_coord[0] 136 | new_y2 = box[3] - tile_coord[1] 137 | 138 | if settings.draw_boxes: 139 | cv2.rectangle(tile, (new_x1, new_y1), (new_x2, new_y2), (0, 255, 255), 2) 140 | # Add the class on top of the rectangle 141 | cv2.putText(tile, 142 | str(box_classes[b]), 143 | (new_x1, new_y1), 144 | cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2) 145 | 146 | # Stack the class+coordinates of the boxes w/ results array and tile coordinates 147 | results = np.vstack((results, 148 | [tile_coord[0], tile_coord[1], tile_coord[2], tile_coord[3], 149 | box_classes[b], 150 | new_x1, new_y1, new_x2, new_y2, 151 | box[0], box[1], box[2], box[3]])) 152 | 153 | # Save the tile with the tile coordinates in the filename 154 | extension = settings.output_extension_images 155 | output_dir = settings.output_dir_images 156 | file_path = f"tile_{filename}_{tile_coord[0]}_{tile_coord[1]}_{tile_coord[2]}_{tile_coord[3]}.{extension}" 157 | save_path = Path(output_dir) / Path(file_path) 158 | 159 | cv2.imwrite(save_path.as_posix(), tile) 160 | 161 | # Create a dataframe with the results 162 | results_df = pd.DataFrame(results, 163 | columns=['tile_x1','tile_y1','tile_x2','tile_y2', 164 | 'box_class', 165 | 'box_x1','box_y1','box_x2','box_y2', 166 | 'old_box_x1','old_box_y1','old_box_x2','old_box_y2']) 167 | 168 | # Save the dataframe as a parquet file 169 | if settings.clear_duplicates: 170 | results_df.to_parquet(Path(settings.output_dir_duplicates) / f"tile_{filename}.parquet", 171 | index=False) 172 | 173 | return results_df 174 | 175 | def plot_example_tile_with_yolo_annotation(settings=None): 176 | """ Plot an example tile with the corresponding YOLO annotation. """ 177 | 178 | # Get all image files from the tiles folder 179 | tile_imagepaths = list(Path(settings.output_dir_images).glob('*.{settings.output_extension_images}')) 180 | 181 | # Randomly select a tile from tile_imagepaths list 182 | im_selection = random.choice(tile_imagepaths) 183 | logger.info(im_selection) 184 | assert Path(im_selection).exists(), "does not exist" 185 | tile = cv2.imread(str(im_selection)) 186 | 187 | # Read the corresponding annotation file 188 | annotation_selection = Path(settings.output_dir_annotations) / f"{im_selection.stem}.txt" 189 | logger.info(annotation_selection) 190 | assert Path(annotation_selection).exists(), "does not exist" 191 | with open(annotation_selection, mode='r', encoding="utf-8") as file: 192 | lines = file.readlines() 193 | for line in lines: 194 | line = line.strip().split() 195 | yolo_x = float(line[1]) 196 | yolo_y = float(line[2]) 197 | yolo_w = float(line[3]) 198 | yolo_h = float(line[4]) 199 | x_1, y_1, x_2, y_2 = convert_yolo_to_xyxy(yolo_x, 200 | yolo_y, 201 | yolo_w, 202 | yolo_h, 203 | tile.shape[0], 204 | tile.shape[1]) 205 | cv2.rectangle(tile, (x_1, y_1), (x_2, y_2), (0, 255, 0), 2) 206 | 207 | # Plot the tile in RGB 208 | tile_rgb = cv2.cvtColor(tile, cv2.COLOR_BGR2RGB) 209 | plt.imshow(tile_rgb) 210 | plt.show() 211 | 212 | def process_tiles(t, input_im, input_annotation, settings=None): 213 | """ The main function to process a tile. """ 214 | # Get the file name 215 | file_name = Path(input_im).stem 216 | 217 | # Read the image 218 | im = read_input_image(im_fname=file_name, settings=settings) 219 | 220 | # Split the image into tiles and get the coordinates of the tiles 221 | tiles, coordinates = tile_image(im.copy(), tile_size=settings.tile_size, step_size=settings.step_size) 222 | 223 | if settings.input_format_annotations == "segmentation": 224 | # raise NotImplementedError("Segmentation annotations are not supported yet.") 225 | settings.output_format_annotations = "segmentation" 226 | # assert spatial dimensions of image and mask are the same 227 | mask = read_input_mask(im_fname=file_name, settings=settings) 228 | assert (im.shape[0]==mask.shape[0]) and (im.shape[1]==mask.shape[1]), "spatial dimensions of image and mask are not the same" 229 | # Split the mask into tiles 230 | mask_tiles, mask_coordinates = tile_image(mask.copy(), tile_size=settings.tile_size, step_size=settings.step_size) 231 | # Save the mask tiles in the output directory for masks 232 | save_image_tiles(filename=file_name, 233 | tiles=mask_tiles, 234 | coordinates=mask_coordinates, 235 | settings=settings, 236 | prefix="mask") 237 | 238 | # Save the image tiles in the output directory for images 239 | save_image_tiles(filename=file_name, 240 | tiles=tiles, 241 | coordinates=coordinates, 242 | settings=settings, 243 | prefix="tile") 244 | return t 245 | 246 | else: 247 | # Read the coordinates of the bounding boxes from the annotation files 248 | bboxes, box_classes = read_coordinates_from_annotations(im_path=input_im, 249 | ant_path=input_annotation, 250 | image_shape=im.shape, 251 | settings=settings) 252 | 253 | # Get the bounding boxes inside the tiles 254 | if bboxes.shape[0] > 0: 255 | boxes_in_tiles = get_boxes_inside_tiles(bboxes=bboxes, 256 | tile_coordinates=coordinates, 257 | settings=settings) 258 | else: 259 | return t 260 | 261 | # Generate the tiles with the bounding boxes 262 | df_results = save_boxes(filename=file_name, 263 | tiles=tiles, 264 | coordinates=coordinates, 265 | boxes_in_tiles=boxes_in_tiles, 266 | box_classes=box_classes, 267 | settings=settings) 268 | 269 | # Save the annotations in Pascal VOC format or YOLO format 270 | save_annotations(df_results, 271 | filename=file_name, 272 | settings=settings, 273 | disable_progress_bar=True) 274 | return t 275 | 276 | def clear_duplicates(settings): 277 | """ Clear the duplicate tiles. """ 278 | 279 | # Gather all the results from the different processes 280 | # since settings.duplicates is set to True 281 | # the results are saved in parquet files 282 | # in the settings.output_dir_duplicates folder 283 | all_subs = [] 284 | results = Path(settings.output_dir_duplicates).glob("*.parquet") 285 | for file in tqdm(results, desc="Gathering results for duplicate removal.."): 286 | sub = pd.read_parquet(file) 287 | sub['filename'] = file.stem 288 | all_subs.append(sub) 289 | # Delete the file 290 | file.unlink() 291 | results_df = pd.concat(all_subs, ignore_index=True) 292 | 293 | # Format the filename to match the format of the saved 294 | # images and annotations 295 | results_df['filename'] = results_df['filename']+"_"+ \ 296 | results_df['tile_x1'].astype(str)+"_"+ \ 297 | results_df['tile_y1'].astype(str)+"_"+ \ 298 | results_df['tile_x2'].astype(str)+"_"+ \ 299 | results_df['tile_y2'].astype(str) 300 | 301 | # Define two sets to store all unique filenames and 302 | # filenames without duplicates so that we only keep the latter. 303 | all_filenames = set(results_df['filename'].unique().tolist()) 304 | no_duplicates = set(results_df[ 305 | ~results_df[ 306 | ['old_box_x1', 'old_box_y1', 'old_box_x2', 'old_box_y2'] 307 | ].duplicated()].filename.tolist()) 308 | 309 | # Loop through all images and annotations and remove duplicates 310 | for filename in tqdm(all_filenames, desc="Removing duplicates..", total=len(all_filenames)): 311 | if filename in no_duplicates: 312 | continue 313 | else: 314 | annotation_file = Path(settings.output_dir_annotations) / \ 315 | f"{filename}.{settings.output_extension_annotations}" 316 | image_file = Path(settings.output_dir_images) / \ 317 | f"{filename}.{settings.output_extension_images}" 318 | annotation_file.unlink() 319 | image_file.unlink() 320 | -------------------------------------------------------------------------------- /tests/Test_outputs.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "b71cee3c-8b2c-406b-b690-4a26d3073e7b", 7 | "metadata": { 8 | "tags": [] 9 | }, 10 | "outputs": [], 11 | "source": [ 12 | "%reset -f\n", 13 | "%load_ext autoreload\n", 14 | "%autoreload 2\n", 15 | "%matplotlib inline" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 2, 21 | "id": "b458e039-6a97-4653-9e03-c6aa2ae307ab", 22 | "metadata": { 23 | "tags": [] 24 | }, 25 | "outputs": [ 26 | { 27 | "data": { 28 | "image/png": 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", 29 | "text/plain": [ 30 | "
" 31 | ] 32 | }, 33 | "metadata": {}, 34 | "output_type": "display_data" 35 | } 36 | ], 37 | "source": [ 38 | "import cv2\n", 39 | "import numpy as np\n", 40 | "import random\n", 41 | "import os\n", 42 | "import matplotlib.pyplot as plt\n", 43 | "import xml.etree.ElementTree as ET\n", 44 | "\n", 45 | "pascal_yolo = 'pascal'\n", 46 | "\n", 47 | "def read_pascal_voc_and_plot(image_path, annotations_path):\n", 48 | " # Read the image using OpenCV\n", 49 | " image = cv2.imread(image_path)\n", 50 | " if image is None:\n", 51 | " print(\"Error: Unable to read the image.\")\n", 52 | " return\n", 53 | "\n", 54 | " # Read the Pascal VOC annotations from the XML file\n", 55 | " tree = ET.parse(annotations_path)\n", 56 | " root = tree.getroot()\n", 57 | "\n", 58 | " annotations = []\n", 59 | " for obj in root.findall('object'):\n", 60 | " label = obj.find('name').text\n", 61 | " bbox = obj.find('bndbox')\n", 62 | " x_min = int(bbox.find('xmin').text)\n", 63 | " y_min = int(bbox.find('ymin').text)\n", 64 | " x_max = int(bbox.find('xmax').text)\n", 65 | " y_max = int(bbox.find('ymax').text)\n", 66 | "\n", 67 | " annotations.append((label, x_min, y_min, x_max, y_max))\n", 68 | "\n", 69 | " # Plot the image with bounding boxes\n", 70 | " for label, x_min, y_min, x_max, y_max in annotations:\n", 71 | " color = (0, 255, 0) # Green color for bounding boxes\n", 72 | " thickness = 2\n", 73 | " cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, thickness)\n", 74 | " cv2.putText(image, label, (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\n", 75 | "\n", 76 | " plt.imshow(image)\n", 77 | " plt.show()\n", 78 | "\n", 79 | "def read_and_plot_yolo_annotations(image_path, annotations_path):\n", 80 | " # Read the image using OpenCV\n", 81 | " image = cv2.imread(image_path)\n", 82 | " if image is None:\n", 83 | " print(\"Error: Unable to read the image.\")\n", 84 | " return\n", 85 | " \n", 86 | " # Read the YOLO annotations from the file\n", 87 | " with open(annotations_path, 'r') as f:\n", 88 | " lines = f.readlines()\n", 89 | "\n", 90 | " annotations = []\n", 91 | " for line in lines:\n", 92 | " line = line.strip().split()\n", 93 | " label = line[0]\n", 94 | " x_center = float(line[1])\n", 95 | " y_center = float(line[2])\n", 96 | " width = float(line[3])\n", 97 | " height = float(line[4])\n", 98 | "\n", 99 | " # Calculate the coordinates of the bounding box\n", 100 | " x_min = int((x_center - width / 2) * image.shape[1])\n", 101 | " y_min = int((y_center - height / 2) * image.shape[0])\n", 102 | " x_max = int((x_center + width / 2) * image.shape[1])\n", 103 | " y_max = int((y_center + height / 2) * image.shape[0])\n", 104 | "\n", 105 | " annotations.append((label, x_min, y_min, x_max, y_max))\n", 106 | "\n", 107 | " # Plot the image with bounding boxes\n", 108 | " for label, x_min, y_min, x_max, y_max in annotations:\n", 109 | " color = (0, 255, 0) # Green color for bounding boxes\n", 110 | " thickness = 2\n", 111 | " cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, thickness)\n", 112 | " cv2.putText(image, label, (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\n", 113 | "\n", 114 | " plt.imshow(image)\n", 115 | " plt.show()\n", 116 | "\n", 117 | "images = os.listdir('../output/images')\n", 118 | "image_path = os.path.join('../output/images', random.choice(images))\n", 119 | "\n", 120 | "plt.figure()\n", 121 | "if pascal_yolo == 'yolo':\n", 122 | " annotations_path = image_path.replace('images','annotations').replace('jpg','txt')\n", 123 | " assert os.path.isfile(annotations_path), \"File not found. Check if pascal_voc is set correctly.\"\n", 124 | " read_and_plot_yolo_annotations(image_path, annotations_path)\n", 125 | "elif pascal_yolo == 'pascal':\n", 126 | " annotations_path = image_path.replace('images','annotations').replace('jpg','xml')\n", 127 | " assert os.path.isfile(annotations_path), \"File not found. Check if pascal_voc is set correctly.\"\n", 128 | " read_pascal_voc_and_plot(image_path, annotations_path)\n", 129 | "else:\n", 130 | " raise ValueError(\"Wrong value for pascal_yolo\")" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": null, 136 | "id": "be311255-abad-410a-b5f7-9bda40f631f3", 137 | "metadata": {}, 138 | "outputs": [], 139 | "source": [] 140 | } 141 | ], 142 | "metadata": { 143 | "kernelspec": { 144 | "display_name": "Python [conda env:plakakia] *", 145 | "language": "python", 146 | "name": "conda-env-plakakia-py" 147 | }, 148 | "language_info": { 149 | "codemirror_mode": { 150 | "name": "ipython", 151 | "version": 3 152 | }, 153 | "file_extension": ".py", 154 | "mimetype": "text/x-python", 155 | "name": "python", 156 | "nbconvert_exporter": "python", 157 | "pygments_lexer": "ipython3", 158 | "version": "3.11.0" 159 | } 160 | }, 161 | "nbformat": 4, 162 | "nbformat_minor": 5 163 | } 164 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 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 General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------