diff --git a/.editorconfig b/.editorconfig index 7ec55f1..36831fd 100644 --- a/.editorconfig +++ b/.editorconfig @@ -1,21 +1,21 @@ -root = true - -[*] -charset = utf-8 -insert_final_newline = true -trim_trailing_whitespace = true -indent_style = space -indent_size = 4 -max_line_length = 88 - -[*.md] -indent_size = 2 - -[*.yaml] -indent_size = 2 - -[*.yml] -indent_size = 2 - -[Makefile] -indent_style = tab +root = true + +[*] +charset = utf-8 +insert_final_newline = true +trim_trailing_whitespace = true +indent_style = space +indent_size = 4 +max_line_length = 88 + +[*.md] +indent_size = 2 + +[*.yaml] +indent_size = 2 + +[*.yml] +indent_size = 2 + +[Makefile] +indent_style = tab diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 0122e04..783cb23 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,26 +1,26 @@ -# Description - -_Please include a summary of the change and which issue is fixed (if any). Please also -include relevant motivation and context. List any dependencies that are required for -this change._ - -Fixes # (issue) - -## Type of change - -- [ ] Documentation (non-breaking change that adds or improves the documentation) -- [ ] New feature (non-breaking change which adds functionality) -- [ ] Optimization (non-breaking, back-end change that speeds up the code) -- [ ] Bug fix (non-breaking change which fixes an issue) -- [ ] Breaking change (whatever its nature) - -## Key checklist - -- [ ] All tests pass (eg. `python -m pytest`) -- [ ] The documentation builds and looks OK (eg. `python -m sphinx -b html docs docs/build`) -- [ ] Pre-commit hooks run successfully (eg. `pre-commit run --all-files`) - -## Further checks - -- [ ] Code is commented, particularly in hard-to-understand areas -- [ ] Tests added or an issue has been opened to tackle that in the future. (Indicate issue here: # (issue)) +# Description + +_Please include a summary of the change and which issue is fixed (if any). Please also +include relevant motivation and context. List any dependencies that are required for +this change._ + +Fixes # (issue) + +## Type of change + +- [ ] Documentation (non-breaking change that adds or improves the documentation) +- [ ] New feature (non-breaking change which adds functionality) +- [ ] Optimization (non-breaking, back-end change that speeds up the code) +- [ ] Bug fix (non-breaking change which fixes an issue) +- [ ] Breaking change (whatever its nature) + +## Key checklist + +- [ ] All tests pass (eg. `python -m pytest`) +- [ ] The documentation builds and looks OK (eg. `python -m sphinx -b html docs docs/build`) +- [ ] Pre-commit hooks run successfully (eg. `pre-commit run --all-files`) + +## Further checks + +- [ ] Code is commented, particularly in hard-to-understand areas +- [ ] Tests added or an issue has been opened to tackle that in the future. (Indicate issue here: # (issue)) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 03ba474..29c6ee7 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -13,21 +13,36 @@ jobs: fail-fast: false matrix: os: [windows-latest, ubuntu-latest, macos-latest] - python-version: ["3.12"] + python-version: ["3.12.3"] steps: - uses: actions/checkout@v4 + - name: Install poetry + run: pipx install poetry==1.8.4 + - uses: actions/setup-python@v5 with: python-version: ${{matrix.python-version}} - - name: Install Poetry - uses: abatilo/actions-poetry@v3.0.0 - with: - poetry-version: 1.2.2 + cache: poetry + + - name: Show Python Version + run: python --version + + - name: Install system dependencies in Linux + if: runner.os == 'Linux' + shell: bash + run: | + sudo apt update + + # Without this, PySide6 gives an ImportError + sudo apt install libegl1 - name: Install dependencies run: poetry install + - name: Debug Poetry Environment + run: poetry env list + - name: Run tests run: poetry run pytest diff --git a/.gitignore b/.gitignore index b6e4761..e0c6d15 100644 --- a/.gitignore +++ b/.gitignore @@ -1,129 +1,131 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +data/ +datafile/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/.vscode/extensions.json b/.vscode/extensions.json deleted file mode 100644 index 04bd1a4..0000000 --- a/.vscode/extensions.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "recommendations": [ - "ms-python.mypy-type-checker", - "ms-python.python", - "editorconfig.editorconfig", - "esbenp.prettier-vscode", - "davidanson.vscode-markdownlint", - "charliermarsh.ruff" - ] -} diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 4ccee8d..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "[python]": { - "editor.formatOnSave": true, - "editor.defaultFormatter": "charliermarsh.ruff" - }, - "editor.rulers": [88], - - "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true -} diff --git a/LICENSE b/LICENSE index f288702..3877ae0 100644 --- a/LICENSE +++ b/LICENSE @@ -1,674 +1,674 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index 0368f8c..80c0c83 100644 --- a/README.md +++ b/README.md @@ -1,45 +1,45 @@ -# Bubble Analyser - -[Description for project.] - -This is a Python application that uses [poetry](https://python-poetry.org) for packaging -and dependency management. It also provides [pre-commit](https://pre-commit.com/) hooks -(for [ruff](https://pypi.org/project/ruff/) and -[mypy](https://mypy.readthedocs.io/en/stable/)) and automated tests using -[pytest](https://pytest.org/) and [GitHub Actions](https://github.com/features/actions). - -## For developers - -This is a Python application that uses [poetry](https://python-poetry.org) for packaging -and dependency management. It also provides [pre-commit](https://pre-commit.com/) hooks -for various linters and formatters and automated tests using -[pytest](https://pytest.org/) and [GitHub Actions](https://github.com/features/actions). -Pre-commit hooks are automatically kept updated with a dedicated GitHub Action. - -To get started: - -1. [Download and install Poetry](https://python-poetry.org/docs/#installation) following the instructions for your OS. -1. Clone this repository and make it your working directory -1. Set up the virtual environment: - - ```bash - poetry install - ``` - -1. Activate the virtual environment (alternatively, ensure any Python-related command is preceded by `poetry run`): - - ```bash - poetry shell - ``` - -1. Install the git hooks: - - ```bash - pre-commit install - ``` - -1. Run the main app: - - ```bash - python -m bubble_analyser - ``` +# Bubble Analyser + +[Description for project.] + +This is a Python application that uses [poetry](https://python-poetry.org) for packaging +and dependency management. It also provides [pre-commit](https://pre-commit.com/) hooks +(for [ruff](https://pypi.org/project/ruff/) and +[mypy](https://mypy.readthedocs.io/en/stable/)) and automated tests using +[pytest](https://pytest.org/) and [GitHub Actions](https://github.com/features/actions). + +## For developers + +This is a Python application that uses [poetry](https://python-poetry.org) for packaging +and dependency management. It also provides [pre-commit](https://pre-commit.com/) hooks +for various linters and formatters and automated tests using +[pytest](https://pytest.org/) and [GitHub Actions](https://github.com/features/actions). +Pre-commit hooks are automatically kept updated with a dedicated GitHub Action. + +To get started: + +1. [Download and install Poetry](https://python-poetry.org/docs/#installation) following the instructions for your OS. +1. Clone this repository and make it your working directory +1. Set up the virtual environment: + + ```bash + poetry install + ``` + +1. Activate the virtual environment (alternatively, ensure any Python-related command is preceded by `poetry run`): + + ```bash + poetry shell + ``` + +1. Install the git hooks: + + ```bash + pre-commit install + ``` + +1. Run the main app: + + ```bash + python -m bubble_analyser + ``` diff --git a/bubble_analyser/GUI_manual.py b/bubble_analyser/GUI_manual.py new file mode 100644 index 0000000..86c4fcb --- /dev/null +++ b/bubble_analyser/GUI_manual.py @@ -0,0 +1,1558 @@ +"""GUI Manual Module: A graphical user interface (GUI) for the Bubble Analyser. + +This module provides a graphical user interface (GUI) for the Bubble Analyser +application. It contains classes and functions for creating and managing the GUI, +including the main window, image processing, and data visualization. + +Author: Yiyang Guan +Date: 06-Oct-2024 + +Classes: + MplCanvas: A class for creating a Matplotlib figure within a PySide6 application. + MainWindow: The main window of the GUI application. + +""" + +import csv +import os +import sys +import time +from datetime import datetime +from pathlib import Path + +import numpy as np +import toml as tomllib +from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg as FigureCanvas +from matplotlib.figure import Figure +from numpy import typing as npt +from PySide6.QtCore import Qt, QThread, Signal +from PySide6.QtGui import QPixmap +from PySide6.QtWidgets import ( + QApplication, + QCheckBox, + QComboBox, + QDialog, + QFileDialog, + QFrame, + QGridLayout, + QHBoxLayout, + QLabel, + QLineEdit, + QListWidget, + QMainWindow, + QMessageBox, + QProgressBar, + QPushButton, + QSpinBox, + QTableWidget, + QTableWidgetItem, + QTabWidget, + QVBoxLayout, + QWidget, +) +from skimage import morphology + +from .calculate_px2mm import calculate_px2mm +from .config import Config +from .default import final_circles_filtering, run_watershed_segmentation +from .image_preprocess import image_preprocess +from .morphological_process import morphological_process +from .threshold import threshold, threshold_without_background + + +class WorkerThread(QThread): + """Thread to handle batch image processing.""" + + update_progress = Signal(int) # Signal to update the progress bar + processing_done = Signal() # Signal to indicate the processing is complete + + def __init__(self, main_window: "MainWindow") -> None: + """Initializes a WorkerThread instance. + + Args: + main_window (MainWindow): Reference to the MainWindow instance. + """ + super().__init__() + self.main_window = main_window # Reference to the MainWindow instance + + def run(self) -> None: + """Process all images in the list. + + This function is called when the WorkerThread is started and is responsible + for processing all images added to the image list. It iterates through the + list of images, applies the image processing algorithm to each image, and + stores the calculated properties in the main_window.all_properties list. + + The function also emits signals to update the progress bar and to indicate + that the processing is complete. + """ + # total_images = len(self.main_window.image_list_full_path) + + for idx, image_path in enumerate(self.main_window.image_list_full_path): + if image_path.lower().endswith((".png", ".jpg", ".jpeg", ".bmp", ".tiff")): + print("Current processing image:", image_path) + + imgThreshold, imgRGB = self.main_window.load_image_for_processing( + image_path + ) + + # Run the image processing algorithm + _, labels_before_filtering = self.main_window.run_processing( + imgThreshold, + imgRGB, + self.main_window.threshold_value, + self.main_window.element_size, + self.main_window.connectivity, + ) + + # Run the filtering algorithm + _, _, circle_properties = self.main_window.run_filtering( + imgRGB, + labels_before_filtering, + self.main_window.mm2px, + self.main_window.max_eccentricity, + self.main_window.min_solidity, + self.main_window.min_circularity, + ) + + for properties in circle_properties: + self.main_window.all_properties.append(properties) + + print("Circle properties for this image:", circle_properties) + + # Emit signal to update the progress bar + self.update_progress.emit(idx + 1) + + # Emit signal indicating the processing is done + self.processing_done.emit() + + +class MplCanvas(FigureCanvas): + """A class for creating a Matplotlib figure within a PySide6 application. + + Attributes: + fig: The Matplotlib figure. + axes: The axes of the figure. + """ + + def __init__( + self, parent: QMainWindow, width: float = 5, height: float = 4, dpi: float = 100 + ) -> None: + """The constructor for MplCanvas. + + Parameters: + parent: The parent widget. + width: The width of the figure in inches. + height: The height of the figure in inches. + dpi: The dots per inch of the figure. + """ + self.fig = Figure(figsize=(width, height), dpi=dpi) + self.axes = self.fig.add_subplot(111) + super().__init__(self.fig) + + +class MainWindow(QMainWindow): + """The main application window for the Bubble Analyser GUI. + + This class is responsible for loading the configuration parameters, setting up the + window title and geometry, and creating the main widgets, including the folder, + calibration, image processing, and results tabs. + + Attributes: + params (Config): The configuration parameters loaded from the TOML file. + img_resample_factor (float): The image resampling factor. + threshold_value (float): The threshold value for image processing. + element_size (int): The size of the morphological element. + connectivity (int): The connectivity for image processing. + max_eccentricity (float): The maximum eccentricity for feature detection. + min_solidity (float): The minimum solidity for feature detection. + + Methods: + load_toml: Loads the configuration parameters from the TOML file. + setup_folder_tab: Sets up the folder tab widget. + setup_calibration_tab: Sets up the calibration tab widget. + setup_image_processing_tab: Sets up the image processing tab widget. + setup_results_tab: Sets up the results tab widget. + """ + + def __init__(self) -> None: + """The constructor for the main window. + + Loads the configuration parameters from the TOML file, sets the window title and + geometry, and creates the main widgets, including the folder, calibration, image + processing and results tabs. + """ + super().__init__() + + self.params = self.load_toml("./bubble_analyser/config.toml") + self.img_resample_factor = self.params.resample + self.threshold_value = self.params.threshold_value + self.element_size = self.params.Morphological_element_size + self.connectivity = self.params.Connectivity + self.max_eccentricity = self.params.Max_Eccentricity + self.min_solidity = self.params.Min_Solidity + self.min_circularity = self.params.Min_Circularity + self.min_size = self.params.min_size + self.all_properties: list[dict[str, float]] = [] + + self.bknd_img_exist = False + self.calibration_confirmed = False + + self.setWindowTitle("Bubble Analyser") + self.setGeometry(100, 100, 1200, 800) + + # Create a Tab Widget + self.tabs = QTabWidget() + self.setCentralWidget(self.tabs) + + # Add Folder Tab + self.folder_tab = QWidget() + self.tabs.addTab(self.folder_tab, "Folder") + self.sample_images_confirmed = False + self.setup_folder_tab() + + # Add Calibration Tab + self.calibration_tab = QWidget() + self.tabs.addTab(self.calibration_tab, "Calibration") + self.bg_image_confirmed = False + self.px_res_confirmed = False + self.setup_calibration_tab() + + # Add Image Processing Tab + self.image_processing_tab = QWidget() + self.tabs.addTab(self.image_processing_tab, "Bubble detection and filtering") + self.setup_image_processing_tab() + + self.results_tab = QWidget() + self.tabs.addTab(self.results_tab, "Results") + self.setup_results_tab() + + def load_toml(self, file_path: str) -> Config: + """Load configuration parameters from a TOML file. + + This function reads the TOML configuration file from the specified path and + loads its contents into a dictionary. + + Args: + file_path: The file path of the TOML configuration file. + + Returns: + A dictionary containing the configuration parameters from the TOML file. + """ + toml_data = tomllib.load(file_path) + + return Config(**toml_data) + + def setup_folder_tab(self) -> None: + """Set up the folder tab. + + This function sets up the folder tab, which contains the following components: + 1. A text box for user to input the folder path. + 2. A button to select the folder. + 3. A button to confirm the folder selection. + 4. A list of images in the selected folder. + 5. An image preview section to show the selected image. + + When the user selects an image from the list, the image will be previewed in + the image preview section. + """ + layout = QVBoxLayout(self.folder_tab) + + # Top Part: Folder Selection + top_frame = QFrame() + top_layout = QHBoxLayout(top_frame) + self.folder_path_edit = QLineEdit() + select_folder_button = QPushButton("Select Folder") + confirm_folder_button = QPushButton("Confirm Folder") + select_folder_button.clicked.connect(self.select_folder) + confirm_folder_button.clicked.connect(self.confirm_folder_selection) + top_layout.addWidget(select_folder_button) + top_layout.addWidget(self.folder_path_edit) + top_layout.addWidget(confirm_folder_button) + + # Bottom Left: List of images in folder + bottom_left_frame = QFrame() + bottom_left_layout = QVBoxLayout(bottom_left_frame) + self.image_list = QListWidget() + self.image_list.clicked.connect(self.preview_image) + bottom_left_layout.addWidget(self.image_list) + + # Bottom Right: Image Preview + bottom_right_frame = QFrame() + bottom_right_layout = QVBoxLayout(bottom_right_frame) + self.image_preview = QLabel() + self.image_preview.setAlignment(Qt.AlignmentFlag.AlignCenter) + self.image_preview.setFixedSize( + 600, 600 + ) # Set a fixed size for the image preview + bottom_right_layout.addWidget(self.image_preview) + + # Split the bottom part into two sections + bottom_frame = QFrame() + bottom_layout = QHBoxLayout(bottom_frame) + bottom_layout.addWidget(bottom_left_frame) + bottom_layout.addWidget(bottom_right_frame) + + # Add top and bottom frames to the main layout + layout.addWidget(top_frame, 1) + layout.addWidget(bottom_frame, 6) + + def select_folder(self) -> None: + """Select a folder. + + Open a folder selection dialog and update the folder path edit + and image list if a valid folder is selected. If the sample images + have already been confirmed, display a warning message and do nothing. + """ + if self.sample_images_confirmed: + QMessageBox.warning( + self, + "Selection Locked", + "You have already confirmed the folder selection.", + ) + return + + folder_path = QFileDialog.getExistingDirectory(self, "Select Folder") + if folder_path: + self.folder_path_edit.setText(folder_path) + self.populate_image_list(folder_path) + + def confirm_folder_selection(self) -> None: + """Confirm the selection of folder. + + Confirm the folder selection and lock the folder path edit. If the + selection has already been confirmed, display a warning message and do + nothing. Otherwise, set the folder path edit to read-only, load the + images to process from the selected folder, and switch to the next tab. + """ + if not self.sample_images_confirmed: + self.sample_images_confirmed = True + # Lock the folder path edit and confirm the selection + self.folder_path_edit.setReadOnly(True) + self.load_images_to_process() + self.tabs.setCurrentIndex(self.tabs.currentIndex() + 1) + + else: + QMessageBox.warning( + self, + "Selection Locked", + "You have already confirmed the folder selection.", + ) + + def populate_image_list(self, folder_path: str) -> None: + """Popultate the image list with the names of images in the given folder path. + + Populate the image list with the names of images in the given folder path, + and store the full paths to the images in the image_list_full_path list. + + This function clears the image list, and then iterates over the files in the + given folder path. If a file has an extension matching a common image + format (e.g., .png, .jpg, .jpeg, .bmp, .tiff), it adds the file name to the + image list and the full path to the image_list_full_path list. + + The purpose of this function is to populate the image list in the GUI with + the names of images in the selected folder, so that the user can select + specific images to process. The full paths to the selected images are stored + in the image_list_full_path list, and are used later to load the images when + the user clicks the "Next" button. + """ + self.image_list.clear() + self.image_list_full_path: list[str] = [] + + for file_name in os.listdir(folder_path): + if file_name.lower().endswith((".png", ".jpg", ".jpeg", ".bmp", ".tiff")): + self.image_list.addItem(file_name) + self.image_list_full_path.append(os.path.join(folder_path, file_name)) + + print("self_image_list_full_path", self.image_list_full_path) + + def preview_image(self) -> None: + """Preview the currently selected image. + + Preview the currently selected image in the GUI. This function is + called when the user selects an image from the image list. It gets the + currently selected image, loads it as a QPixmap, and sets it to the + image preview label on the GUI. The image is scaled to fit the size of + the label while keeping the aspect ratio. + """ + self.selected_image = self.image_list.currentItem().text() + folder_path = self.folder_path_edit.text() + image_path = os.path.join(folder_path, self.selected_image) + pixmap = QPixmap(image_path) + + self.image_preview.setPixmap( + pixmap.scaled( + self.image_preview.size(), + Qt.AspectRatioMode.KeepAspectRatio, + ) + ) + + self.sample_image_preview.setPixmap( + pixmap.scaled( + self.sample_image_preview.size(), + Qt.AspectRatioMode.KeepAspectRatio, + ) + ) + + def load_images_to_process(self) -> None: + """Load images to process. + + Populate the image list with the names of images in the folder path + set in the folder path edit, and store the full paths to the images in + the image_list_full_path list. This function is called after the user + confirms the folder selection. The purpose of this function is to + populate the image list in the GUI with the names of images in the + selected folder, so that the user can select specific images to + process. The full paths to the selected images are stored in the + image_list_full_path list, and are used later to load the images when + the user clicks the "Next" button. + """ + folder_path = self.folder_path_edit.text() + if os.path.exists(folder_path): + self.populate_image_list(folder_path) + + def setup_calibration_tab(self) -> None: + """Set up the calibration tab. + + Set up the calibration tab, which contains the following components: + + 1. A text box for user to input the name of the image for pixel + resolution calibration. + 2. A button to select the image for pixel resolution calibration. + 3. A label to preview the selected image. + 4. A text box for user to input the name of the background image. + 5. A button to select the background image. + 6. A label to preview the selected background image. + 7. A button to confirm the calibration and background image. + + When the user selects an image from the list, the image will be + previewed in the image preview section. When the user clicks the + "Confirm" button, the image will be processed and the pixel-to-mm + ratio will be calculated and stored in the "px_mm" attribute of the + MainWindow object. The background image will be stored in the + "bknd_img" attribute of the MainWindow object. The tab will then be + switched to the next tab. + """ + layout = QGridLayout(self.calibration_tab) + + # Create top frame + top_frame = QFrame() + top_frame_layout = QHBoxLayout() + top_frame.setLayout(top_frame_layout) + + # Pixel Resolution Calibration + px_res_frame = QFrame() + px_res_layout = QVBoxLayout(px_res_frame) + self.px_res_image_name = QLineEdit() + self.px_res_image_name.setText("Choose your ruler image from local") + px_res_confirm_button = QPushButton("Confirm") + px_res_confirm_button.clicked.connect(self.process_calibration_image) + px_res_select_button = QPushButton( + "Select other image for resolution calibration" + ) + px_res_select_button.clicked.connect(self.select_px_res_image) + self.px_res_image_preview = QLabel() + self.px_res_image_preview.setAlignment(Qt.AlignmentFlag.AlignCenter) + self.px_res_image_preview.setFixedSize(400, 300) + + px_res_layout.addWidget(QLabel("Step 1: Pixel resolution calibration")) + px_res_layout.addWidget(QLabel("Image name")) + px_res_layout.addWidget(self.px_res_image_name) + px_res_layout.addWidget(px_res_confirm_button) + px_res_layout.addWidget(px_res_select_button) + px_res_layout.addWidget(self.px_res_image_preview) + px_res_layout.addStretch() + + # Background Correction Image + bg_corr_frame = QFrame() + bg_corr_layout = QVBoxLayout(bg_corr_frame) + self.bg_corr_image_name = QLineEdit() + self.bg_corr_image_name.setText("Choose your background image from local") + # bg_corr_confirm_button = QPushButton("Confirm") + # bg_corr_confirm_button.clicked.connect(self.confirm_bg_corr_image) + bg_corr_select_button = QPushButton( + "Select other image for background correction" + ) + bg_corr_select_button.clicked.connect(self.select_bg_corr_image) + self.bg_corr_image_preview = QLabel() + self.bg_corr_image_preview.setAlignment(Qt.AlignmentFlag.AlignCenter) + self.bg_corr_image_preview.setFixedSize(400, 300) + + bg_corr_layout.addWidget(QLabel("Step 2: Background correction image")) + bg_corr_layout.addWidget(QLabel("Image name")) + bg_corr_layout.addWidget(self.bg_corr_image_name) + # bg_corr_layout.addWidget(bg_corr_confirm_button) + bg_corr_layout.addWidget(bg_corr_select_button) + bg_corr_layout.addWidget(self.bg_corr_image_preview) + bg_corr_layout.addStretch() + + # Adding Pixel Resolution and Background Correction frames to top layout + top_frame_layout.addWidget(px_res_frame) + top_frame_layout.addWidget(bg_corr_frame) + + # Create bottom frame for manual calibration input + bottom_frame = QFrame() + bottom_frame_layout = QVBoxLayout() + bottom_frame.setLayout(bottom_frame_layout) + + manualcalibration_frame = QFrame() + manualcalibration_layout = QHBoxLayout(manualcalibration_frame) + manualcalibration_layout.addWidget(QLabel("or:")) + self.manual_px_mm_input = QLineEdit() + self.manual_px_mm_input.setPlaceholderText("Calibrate manually") + manualcalibration_layout.addWidget(self.manual_px_mm_input) + manualcalibration_layout.addWidget(QLabel("px/mm")) + + confirm_px_mm_button = QPushButton("Confirm calibration and background image") + confirm_px_mm_button.clicked.connect( + self.confirm_calibration + ) # Connect confirm button to the handler + + bottom_frame_layout.addWidget(manualcalibration_frame) + bottom_frame_layout.addWidget(confirm_px_mm_button) + + # Add frames to main layout + layout.addWidget(top_frame, 0, 0, 1, 2) + layout.addWidget(bottom_frame, 1, 0, 1, 2) + + def confirm_bg_corr_image(self) -> None: + """Confirm the background correction image and lock the input.""" + if not self.calibration_confirmed: + self.bg_image_confirmed = True + self.bg_corr_image_name.setReadOnly(True) # Lock the input field + else: + QMessageBox.warning( + self, + "Selection Locked", + "You have already confirmed the background selection.", + ) + + def select_px_res_image(self) -> None: + """Open a file dialog to select an image for pixel resolution calibration. + + This function will lock if the calibration has already been confirmed. + + If a valid image path is selected, the image name will be displayed in the + corresponding text box and the image will be previewed in the image preview + section. The image is scaled to fit the size of the label while keeping the + aspect ratio. + """ + if self.calibration_confirmed: + QMessageBox.warning( + self, + "Selection Locked", + "You have already confirmed the pixel resolution.", + ) + return + image_path, _ = QFileDialog.getOpenFileName( + self, + "Select Image for Resolution Calibration", + "", + "Image Files (*.png *.jpg *.bmp)", + ) + + if image_path: + self.px_res_image_name.setText(image_path) + pixmap = QPixmap(image_path) + self.px_res_image_preview.setPixmap( + pixmap.scaled(self.px_res_image_preview.size()) + ) + + def process_calibration_image(self) -> None: + """Process the selected image for pixel resolution calibration. + + If a valid image path is selected, calculate the pixel-to-mm ratio and + display it in the manual calibration text box. If the image path is not + valid, display a status bar message. The function will lock if the + calibration has already been confirmed. + + Args: + None. + + Returns: + None. + """ + if self.calibration_confirmed: + QMessageBox.warning( + self, + "Selection Locked", + "You have already confirmed the pixel resolution.", + ) + + return + + image_path: Path = Path(self.px_res_image_name.text()) + if image_path and os.path.exists(image_path): + QMessageBox.information( + self, + "Calibration", + "Drag a line of 1cm in the next window, then Press Q to confirm.", + ) + __, mm2px = calculate_px2mm( + image_path, img_resample=0.5 + ) # Use the stored full path + self.manual_px_mm_input.setText(f"{mm2px:.3f}") + else: + self.statusBar().showMessage( + "Image file does not exist or not selected.", 5000 + ) + + def select_bg_corr_image(self) -> None: + """Open a file dialog to select an image for background correction. + + If the background correction has already been confirmed, display a warning + message and do nothing. Otherwise, open a file dialog to select an image. + If a valid image path is selected, display the image in the background + correction image preview section, and set the path to the background + correction image name text box. The background image existence flag is + also set to True. + + Args: + None. + + Returns: + None. + """ + if self.calibration_confirmed: + QMessageBox.warning( + self, + "Selection Locked", + "You have already confirmed the background selection.", + ) + return + + image_path, _ = QFileDialog.getOpenFileName( + self, + "Select Image for Background Correction", + "", + "Image Files (*.png *.jpg *.bmp)", + ) + if image_path: + self.bg_corr_image_name.setText(image_path) + pixmap = QPixmap(image_path) + self.bg_corr_image_preview.setPixmap( + pixmap.scaled(self.bg_corr_image_preview.size()) + ) + self.bknd_img_exist = True + + def confirm_calibration(self) -> None: + """Confirm the manual calibration and lock the input.""" + if not self.calibration_confirmed: + self.calibration_confirmed = True + self.px2mm = float( + self.manual_px_mm_input.text() + ) # Store the pixel to mm ratio + self.mm2px = 1 / self.px2mm + self.manual_px_mm_input.setReadOnly(True) # Lock the input field + self.tabs.setCurrentIndex(self.tabs.currentIndex() + 1) + else: + QMessageBox.warning( + self, + "Selection Locked", + "You have already confirmed the calibration process.", + ) + + def setup_image_processing_tab(self) -> None: + """Set up the image processing tab, which contains the following components. + + Set up the image processing tab, which contains the following components: + 1. A box to select the image processing algorithm. + 2. A table to display and edit the processing parameters. + 3. A button to confirm the parameter settings and preview a sample image. + 4. A button to batch process the sample images. + 5. A section to display the sample image preview. + 6. A section to display the processed image preview, including before and after + filtering. + + When the user selects an algorithm, the parameters will be loaded and displayed + in the table. When the user confirms the parameter settings and previews a + sample image, the sample image will be processed using the selected algorithm + and displayed in the preview section. + When the user clicks the "Batch process images" button, the algorithm will be + applied to all the images in the selected folder and the results will be saved + in a new folder. + """ + layout = QGridLayout(self.image_processing_tab) + + # ----------- First Column: Sample Image Preview ----------- + + first_column_frame = QFrame() + first_column_layout = QVBoxLayout(first_column_frame) + + # Sample Image Preview Canvas + self.sample_image_preview = QLabel("Sample image preview") + self.sample_image_preview.setAlignment(Qt.AlignmentFlag.AlignCenter) + self.sample_image_preview.setFixedSize(400, 300) # Adjust size as needed + + prev_button = QPushButton("< Prev. Img") + next_button = QPushButton("Next Img >") + prev_button.clicked.connect(lambda: self.update_sample_image("prev")) + next_button.clicked.connect(lambda: self.update_sample_image("next")) + + first_column_layout.addWidget(QLabel("Step 1: Select image and preview")) + first_column_layout.addWidget(self.sample_image_preview) + + # Prev/Next Buttons + first_column_buttons_layout = QHBoxLayout() + first_column_buttons_layout.addWidget(prev_button) + first_column_buttons_layout.addWidget(next_button) + first_column_layout.addLayout(first_column_buttons_layout) + + # ----------- Second Column: Processed Image Before Filtering and Sandbox ---- + + second_column_frame = QFrame() + second_column_layout = QVBoxLayout(second_column_frame) + + # Processed Image Before Filtering Canvas + self.label_before_filtering = MplCanvas(self, width=5, height=4, dpi=100) + second_column_layout.addWidget(QLabel("Processed Image_Before Filtering")) + second_column_layout.addWidget(self.label_before_filtering) + + # Parameter sandbox for img_resample_factor, threshold_value, element_size + sandbox1_label = QLabel("Step 2: Adjust parameters before filtering") + self.param_sandbox1 = QTableWidget(4, 2) + self.param_sandbox1.setHorizontalHeaderLabels(["Parameter", "Value"]) + + self.param_sandbox1.setItem(0, 0, QTableWidgetItem("img_resample_factor")) + self.param_sandbox1.setItem( + 0, 1, QTableWidgetItem(str(self.img_resample_factor)) + ) + + self.param_sandbox1.setItem(1, 0, QTableWidgetItem("threshold_value")) + self.param_sandbox1.setItem(1, 1, QTableWidgetItem(str(self.threshold_value))) + + self.param_sandbox1.setItem(2, 0, QTableWidgetItem("element_size")) + self.param_sandbox1.setItem(2, 1, QTableWidgetItem(str(self.element_size))) + + self.param_sandbox1.setItem(3, 0, QTableWidgetItem("connectivity")) + self.param_sandbox1.setItem(3, 1, QTableWidgetItem(str(self.connectivity))) + + # Confirm button for this sandbox + preview_button1 = QPushButton("Confirm parameter and preview") + preview_button1.clicked.connect(self.confirm_parameter_before_filtering) + + second_column_layout.addWidget(sandbox1_label) + second_column_layout.addWidget(self.param_sandbox1) + second_column_layout.addWidget(preview_button1) + + # ----------- Third Column: Processed Image After Filtering and Sandbox ------ + + third_column_frame = QFrame() + third_column_layout = QVBoxLayout(third_column_frame) + + # Processed Image After Filtering Canvas + self.processed_image_preview = MplCanvas(self, width=5, height=4, dpi=100) + third_column_layout.addWidget(QLabel("Processed Image_After Filtering")) + third_column_layout.addWidget(self.processed_image_preview) + + # Parameter sandbox for max_eccentricity, min_circularity, + # min_solidity, min_size + sandbox2_label = QLabel("Step 3: Adjust parameters for circle properties") + self.param_sandbox2 = QTableWidget(4, 2) + self.param_sandbox2.setHorizontalHeaderLabels(["Parameter", "Value"]) + + self.param_sandbox2.setItem(0, 0, QTableWidgetItem("max_eccentricity")) + self.param_sandbox2.setItem(0, 1, QTableWidgetItem(str(self.max_eccentricity))) + + self.param_sandbox2.setItem(1, 0, QTableWidgetItem("min_circularity")) + self.param_sandbox2.setItem(1, 1, QTableWidgetItem(str(self.min_circularity))) + + self.param_sandbox2.setItem(2, 0, QTableWidgetItem("min_solidity")) + self.param_sandbox2.setItem(2, 1, QTableWidgetItem(str(self.min_solidity))) + + self.param_sandbox2.setItem(3, 0, QTableWidgetItem("min_size")) + self.param_sandbox2.setItem(3, 1, QTableWidgetItem(str(self.min_size))) + + # Confirm and Batch Process buttons for this sandbox + preview_button2 = QPushButton("Confirm parameter and preview") + batch_process_button = QPushButton("Batch process images") + preview_button2.clicked.connect(self.confirm_parameter_after_filtering) + batch_process_button.clicked.connect(self.ask_if_batch) + + third_column_layout.addWidget(sandbox2_label) + third_column_layout.addWidget(self.param_sandbox2) + third_column_layout.addWidget(preview_button2) + third_column_layout.addWidget(batch_process_button) + + # Add the columns to the main layout + layout.addWidget(first_column_frame, 0, 0) + layout.addWidget(second_column_frame, 0, 1) + layout.addWidget(third_column_frame, 0, 2) + + def confirm_parameter_before_filtering(self) -> None: + """Confirm the parameters for processing and preview the processed image. + + This function is called when the user confirms the parameters for processing + and previews the processed image. It checks the validity of the parameters, + processes the image using the selected algorithm and parameters, and displays + the processed image on the right side of the window. + """ + self.check_parameters() # Check the validity of the parameters + + # Processing the image and displaying it on the right side + selected_image = self.selected_image + folder_path = self.folder_path_edit.text() + image_path = os.path.join(folder_path, selected_image) + + imgThreshold, self.imgRGB = self.load_image_for_processing(image_path) + preview_processed_image, self.labels_before_filtering = self.run_processing( + imgThreshold, + self.imgRGB, + self.threshold_value, + self.element_size, + self.connectivity, + ) + self.label_before_filtering.axes.clear() + self.label_before_filtering.axes.imshow(preview_processed_image) + self.label_before_filtering.draw() + + def confirm_parameter_after_filtering(self) -> None: + """Confirm the parameters for filtering and preview the filtered image.""" + self.check_parameters() # Check the validity of the parameters + + # Processing the image and displaying it on the right side + preview_processed_image, labels_after_filtering, _ = self.run_filtering( + self.imgRGB, + self.labels_before_filtering, + self.mm2px, + self.max_eccentricity, + self.min_solidity, + self.min_circularity, + ) + + self.processed_image_preview.axes.clear() + self.processed_image_preview.axes.imshow(preview_processed_image) + self.processed_image_preview.draw() + + def ask_if_batch(self) -> None: + """Function to handle the batch processing of all images in the folder.""" + # Confirm dialog + confirm_dialog = QMessageBox() + confirm_dialog.setWindowTitle("Batch Processing Confirmation") + confirm_dialog.setText( + "The parameters will be applied to all the images. Confirm to process." + ) + confirm_dialog.setStandardButtons( + QMessageBox.StandardButton.Ok | QMessageBox.StandardButton.Cancel + ) + + response = confirm_dialog.exec() + + if response == QMessageBox.StandardButton.Ok: + self.batch_process_images() + else: + print("Batch processing canceled.") + + def batch_process_images(self) -> None: + """Function to handle the batch processing of all images in the folder. + + The parameters set by the user will be applied to all the images in the folder. + The function processes each image one by one and stores the properties of all + images in the `all_properties` list. + + :return: None + """ + self.check_parameters() + + total_images = len(self.image_list_full_path) + self.show_progress_window(total_images) + + # Create a worker thread to handle the processing + self.worker_thread = WorkerThread(self) + + self.worker_thread.update_progress.connect(self.update_progress_bar) + # Connect signal to update progress bar + + self.worker_thread.processing_done.connect(self.on_processing_done) + # Connect signal for when processing is done + + self.worker_thread.start() # Start the worker thread + + def show_progress_window(self, num_images: int) -> None: + """Create and show a progress window with a loading bar.""" + self.progress_dialog = QDialog(self) + self.progress_dialog.setWindowTitle("Batch Processing in Progress") + self.progress_dialog.setFixedSize(400, 100) + + layout = QVBoxLayout(self.progress_dialog) + + self.progress_bar = QProgressBar(self.progress_dialog) + self.progress_bar.setRange(0, num_images) + self.progress_bar.setValue(0) # Start with 0 progress + + layout.addWidget(self.progress_bar) + + self.progress_dialog.setLayout(layout) + self.progress_dialog.show() + + def update_progress_bar(self, value: int) -> None: + """Update the progress bar value.""" + self.progress_bar.setValue(value) + + def on_processing_done(self) -> None: + """Handle the completion of image processing.""" + # Close the progress dialog + self.progress_dialog.close() + + # Automatically generate graph after batch processing + self.generate_histogram() + + # Switch to the final tab + self.tabs.setCurrentIndex(self.tabs.indexOf(self.results_tab)) + + def check_parameters(self) -> None: + """Check if parameters are valid numbers and calibration is confirmed. + + This function checks if the user has confirmed the calibration and if the + parameters in the table are valid numbers. If not, it displays a warning + message and returns without performing any action. + + Returns: + None + """ + if not self.calibration_confirmed: + QMessageBox.warning( + self, + "Process Locked", + "You have not yet confirmed the pixel resolution.", + ) + return + + try: + self.img_resample_factor = float(self.param_sandbox1.item(0, 1).text()) + self.threshold_value = float(self.param_sandbox1.item(1, 1).text()) + self.element_size = int(self.param_sandbox1.item(2, 1).text()) + self.connectivity = int(self.param_sandbox1.item(3, 1).text()) + self.max_eccentricity = float(self.param_sandbox2.item(0, 1).text()) + self.min_circularity = float(self.param_sandbox2.item(1, 1).text()) + self.min_solidity = float(self.param_sandbox2.item(2, 1).text()) + self.min_size = float(self.param_sandbox2.item(3, 1).text()) + + except (ValueError, TypeError): + QMessageBox.warning( + self, "Invalid Input", "Please ensure all parameters are valid numbers." + ) + return + + def load_image_for_processing( + self, image_path: str + ) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: + """Load and return the image, possibly applying some processing. + + This function loads an image using image_preprocess, applies background + subtraction and thresholding, and morphological processing using a disk + element of size Morphological_element_size. (If no background image is provided, + the function applies thresholding without background subtraction.) + + Args: + image_path (str): The path to the image to be processed. + + Returns: + tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: A tuple of two arrays, + where the first being the processed image and the second being the + original image in RGB format. + """ + start_time = time.perf_counter() + target_image_path: Path = Path(image_path) + target_img, imgRGB = image_preprocess( + target_image_path, self.img_resample_factor + ) + print("Time take for image_preprocess: ", time.perf_counter() - start_time) + start_time = time.perf_counter() + if self.bknd_img_exist: + bg_img_path: Path = Path(self.bg_corr_image_name.text()) + self.bknd_img, _ = image_preprocess(bg_img_path, self.img_resample_factor) + + imgThreshold = threshold(target_img, self.bknd_img, self.threshold_value) + print("Time take for threshold: ", time.perf_counter() - start_time) + else: + imgThreshold = threshold_without_background( + target_img, self.threshold_value + ) + print( + "Time take for threshold_without_background: ", + time.perf_counter() - start_time, + ) + start_time = time.perf_counter() + element_size = morphology.disk(self.params.Morphological_element_size) + + imgThreshold_new: npt.NDArray[np.int_] = morphological_process( + imgThreshold, element_size + ) + print("Time take for morphological_process: ", time.perf_counter() - start_time) + + return imgThreshold_new, imgRGB + + def run_processing( + self, + imgThreshold: npt.NDArray[np.int_], + imgRGB: npt.NDArray[np.int_], + threshold_value: float, + element_size: int, + connectivity: int, + ) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: + """Run the image processing algorithm on the preprocessed image. + + This function takes the preprocessed image, the original RGB image, the + conversion factor from millimeters to pixels, and several threshold values as + input. It then applies watershed segmentation to detect circular features in + the image. The detected features are then filtered based on their properties, + such as eccentricity, solidity, circularity, and size. + + Parameters: + imgThreshold (npt.NDArray[np.int_]): The preprocessed image after + thresholding. + imgRGB (npt.NDArray[np.int_]): The original image in RGB format. + threshold_value (float): The threshold value for background subtract. + element_size (int): The size of the morphological element for binary + operations. + connectivity (int): The connectivity of the morphological operations. + + Returns: + tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: A tuple of two arrays, + the first being the processed image and the second being the labeled + image before filtering. + """ + print("Threshold_value:", threshold_value) + print("element_size:", element_size) + print("connectivity:", connectivity) + + preview_processed_image, labels_before_filtering = run_watershed_segmentation( + imgThreshold, imgRGB, threshold_value, element_size, connectivity + ) + return (preview_processed_image, labels_before_filtering) + + def run_filtering( + self, + imgRGB: npt.NDArray[np.int_], + labels: npt.NDArray[np.int_], + mm2px: float, + max_eccentricity: float, + min_solidity: float, + min_circularity: float, + ) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_], list[dict[str, float]]]: + """Run the image processing algorithm on the target image. + + This function takes the preprocessed image, the labeled image before filtering, + the conversion factor from millimeters to pixels, and several threshold values + as input. It then applies watershed segmentation to detect circular features in + the image. The detected features are then filtered based on their properties, + such as eccentricity, solidity, circularity, and size. + + The function returns the processed image, the labeled image after filtering, and + the properties of the detected circular features. + + Parameters: + imgRGB (npt.NDArray[np.int_]): The preprocessed image in RGB format. + labels (npt.NDArray[np.int_]): The labeled image before filtering. + mm2px (float): The conversion factor from millimeters to pixels. + max_eccentricity (float): The maximum allowed eccentricity for circles. + min_solidity (float): The minimum allowed solidity for circles. + min_circularity (float): The minimum allowed circularity for circles. + + Returns: + tuple[npt.NDArray[np.int_], npt.NDArray[np.int_], list[dict[str, float]]]: + A tuple of three arrays, the first being the processed image, the second + being the labeled image after filtering, and the third being the + properties of the detected circular features. + """ + print("Max_eccentricity:", max_eccentricity) + print("Min_solidity:", min_solidity) + print("Min_circularity:", min_circularity) + + imgRGB_overlay, labels, circle_properties = final_circles_filtering( + imgRGB, labels, mm2px, max_eccentricity, min_solidity, min_circularity + ) + + return imgRGB_overlay, labels, circle_properties + + def update_sample_image(self, direction: str) -> None: + """Update the sample image preview based on user navigation (prev/next). + + This function updates the sample image preview by changing the currently + selected image in the image list. The direction parameter determines + whether the user is navigating to the previous or next image. The + function then calls the preview_image method to update the image preview. + """ + current_row = self.image_list.currentRow() + if direction == "prev": + if current_row > 0: + self.image_list.setCurrentRow(current_row - 1) + elif direction == "next": + if current_row < self.image_list.count() - 1: + self.image_list.setCurrentRow(current_row + 1) + self.preview_image() + + def setup_results_tab(self) -> None: + """Set up the results tab. + + This function sets up the results tab by creating the following widgets: + 1. A graph canvas for displaying the histogram. + 2. Controls for histogram options, including the type of histogram to + generate (by number or volume), checkboxes for PDF and CDF, the number + of bins, and the x-axis limits. + 3. A legend position and orientation dropdown. + 4. A descriptive size options section, which includes checkboxes for + displaying d32, dmean, and dxy, as well as input boxes for x and y + values for dxy. + 5. A save button that saves the graph and CSV data to a user-selected + folder. + + This function creates the layout of the results tab, including the graph + canvas, controls, and save button. The controls include the histogram type, + PDF/CDF checkboxes, number of bins, x-axis limits, legend position and + orientation dropdown, and descriptive size options. The save button is + connected to the save_results slot, which saves the graph and CSV data to + the user-selected folder. + """ + layout = QGridLayout(self.results_tab) + + # Create canvas for displaying the graph + self.histogram_canvas = MplCanvas(self, width=8, height=8, dpi=100) + + # Controls for histogram options + controls_frame = QFrame() + controls_layout = QVBoxLayout(controls_frame) + + # Histogram type + histogram_by_label = QLabel("Histogram by:") + self.histogram_by = QComboBox() + # self.histogram_by.addItems(["Number", "Volume"]) + self.histogram_by.addItems(["Number"]) + self.histogram_by.currentIndexChanged.connect( + self.generate_histogram + ) # Connect to auto-update + + # PDF/CDF Checkboxes + self.pdf_checkbox = QCheckBox("PDF") + self.cdf_checkbox = QCheckBox("CDF") + self.pdf_checkbox.stateChanged.connect( + self.generate_histogram + ) # Connect to auto-update + self.cdf_checkbox.stateChanged.connect( + self.generate_histogram + ) # Connect to auto-update + + # Number of bins + bins_label = QLabel("Number of bins:") + self.bins_spinbox = QSpinBox() + self.bins_spinbox.setValue(15) + self.bins_spinbox.setRange(1, 100) + self.bins_spinbox.valueChanged.connect( + self.generate_histogram + ) # Connect to auto-update + + # X-axis limits + x_axis_limits_label = QLabel("X-axis limits:") + self.min_x_axis_input = QLineEdit("0.0") + self.max_x_axis_input = QLineEdit("5.0") + self.min_x_axis_input.textChanged.connect( + self.generate_histogram + ) # Connect to auto-update + self.max_x_axis_input.textChanged.connect( + self.generate_histogram + ) # Connect to auto-update + + legend_label = QLabel("Legend settings:") + legend_frame = QFrame() + legend_layout = QGridLayout(legend_frame) + + # Legend position dropdown + legend_layout.addWidget(QLabel("Position:"), 0, 0) + self.legend_position_combobox = QComboBox() + self.legend_position_combobox.addItems( + ["North East", "North West", "South East", "South West"] + ) + self.legend_position_combobox.currentIndexChanged.connect( + self.generate_histogram + ) # Connect to auto-update + legend_layout.addWidget(self.legend_position_combobox, 0, 1) + + """ + # Legend orientation dropdown + legend_layout.addWidget(QLabel("Orientation:"), 1, 0) + self.legend_orientation_combobox = QComboBox() + self.legend_orientation_combobox.addItems(["Vertical", "Horizontal"]) + self.legend_orientation_combobox.currentIndexChanged.connect( + self.generate_histogram + ) # Connect to auto-update + legend_layout.addWidget(self.legend_orientation_combobox, 1, 1) + """ + + # Descriptive size options + # Descriptive Size Checkboxes Section + descriptive_frame = QFrame() + descriptive_layout = QGridLayout(descriptive_frame) + + self.d32_checkbox = QCheckBox("d32") + self.dmean_checkbox = QCheckBox("d mean") + self.dxy_checkbox = QCheckBox("dxy") + self.d32_checkbox.stateChanged.connect( + self.generate_histogram + ) # Connect to auto-update + self.dmean_checkbox.stateChanged.connect( + self.generate_histogram + ) # Connect to auto-update + self.dxy_checkbox.stateChanged.connect( + self.generate_histogram + ) # Connect to auto-update + + # Add input boxes for `x` and `y` values + self.dxy_x_input = QLineEdit() + self.dxy_x_input.setText("5") # Set default value for x + self.dxy_x_input.setMaximumWidth(40) + self.dxy_x_input.textChanged.connect( + self.generate_histogram + ) # Connect to auto-update + + self.dxy_y_input = QLineEdit() + self.dxy_y_input.setText("4") # Set default value for y + self.dxy_y_input.setMaximumWidth(40) + self.dxy_y_input.textChanged.connect( + self.generate_histogram + ) # Connect to auto-update + + # Add elements to the descriptive layout + descriptive_layout.addWidget(self.d32_checkbox, 0, 0) + descriptive_layout.addWidget(self.dmean_checkbox, 1, 0) + descriptive_layout.addWidget(self.dxy_checkbox, 2, 0) + descriptive_layout.addWidget(QLabel("x"), 2, 1) + descriptive_layout.addWidget(self.dxy_x_input, 2, 2) + descriptive_layout.addWidget(QLabel("y"), 2, 3) + descriptive_layout.addWidget(self.dxy_y_input, 2, 4) + + # Add Save button + save_frame = QFrame() + save_layout = QVBoxLayout(save_frame) + + # Folder selection box with button + folder_selection_frame = QFrame() + folder_selection_layout = QHBoxLayout(folder_selection_frame) + self.save_folder_edit = QLineEdit() + self.save_folder_edit.setPlaceholderText("No folder selected") + self.save_folder_edit.setReadOnly(True) + self.save_folder_edit.setMaximumWidth(300) + + select_folder_button = QPushButton("Select Folder") + select_folder_button.clicked.connect(self.select_save_folder) + + folder_selection_layout.addWidget(self.save_folder_edit) + folder_selection_layout.addWidget(select_folder_button) + + # Graph filename input row + graph_frame = QFrame() + graph_filename_layout = QHBoxLayout(graph_frame) + current_date = datetime.now().strftime("%Y%m%d") + + self.graph_filename_edit = QLineEdit( + current_date + ) # Default name as current date + graph_filename_label = QLabel(".png") + graph_filename_layout.addWidget(QLabel("Graph Filename:")) + graph_filename_layout.addWidget(self.graph_filename_edit) + graph_filename_layout.addWidget(graph_filename_label) + + # CSV filename input row + csv_filename_frame = QFrame() + csv_filename_layout = QHBoxLayout(csv_filename_frame) + self.csv_filename_edit = QLineEdit(current_date) # Default name as current date + csv_filename_label = QLabel(".csv") + csv_filename_layout.addWidget(QLabel("CSV Filename:")) + csv_filename_layout.addWidget(self.csv_filename_edit) + csv_filename_layout.addWidget(csv_filename_label) + + # Save button + save_button = QPushButton("Save graph and data") + save_button.clicked.connect(self.save_results) + + # Add folder selection and save button to the layout + save_layout.addWidget(folder_selection_frame) + save_layout.addWidget(graph_frame) + save_layout.addWidget(csv_filename_frame) + save_layout.addWidget(save_button) + + # Assemble controls layout + controls_layout.addWidget(histogram_by_label) + controls_layout.addWidget(self.histogram_by) + controls_layout.addWidget(self.pdf_checkbox) + controls_layout.addWidget(self.cdf_checkbox) + controls_layout.addWidget(bins_label) + controls_layout.addWidget(self.bins_spinbox) + controls_layout.addWidget(x_axis_limits_label) + controls_layout.addWidget(self.min_x_axis_input) + controls_layout.addWidget(self.max_x_axis_input) + controls_layout.addWidget(legend_label) + controls_layout.addWidget(legend_frame) + controls_layout.addWidget(descriptive_frame) + controls_layout.addWidget(save_frame) + + # Place graph and controls in the layout + layout.addWidget(self.histogram_canvas, 0, 0) + layout.addWidget(controls_frame, 0, 1) + + # Button to generate graph + # generate_button = QPushButton("Generate Graph") + # generate_button.clicked.connect(self.generate_histogram) + # layout.addWidget(generate_button, 1, 0, 1, 2) + + # Add a label to display the descriptive sizes + self.descriptive_size_label = QLabel("") + layout.addWidget(self.descriptive_size_label, 2, 0, 1, 2) + + def select_save_folder(self) -> None: + """Opens a QFileDialog to select a folder for saving.""" + folder_path = QFileDialog.getExistingDirectory(self, "Select Folder to Save") + if folder_path: + self.save_folder_edit.setText(folder_path) + + def save_results(self) -> None: + """Saves histogram and data to the selected folder.""" + folder_path = self.save_folder_edit.text() + if folder_path == "" or not os.path.exists(folder_path): + QMessageBox.warning( + self, "No Folder Selected", "Please select a folder to save the files." + ) + return + + # Get the user-specified filenames + graph_filename = self.graph_filename_edit.text() + csv_filename = self.csv_filename_edit.text() + + if not graph_filename or not csv_filename: + QMessageBox.warning( + self, + "Filename Missing", + "Please provide filenames for both graph and CSV files.", + ) + return + + # Set file paths + graph_path = os.path.join(folder_path, f"{graph_filename}.png") + csv_path = os.path.join(folder_path, f"{csv_filename}.csv") + + # Assuming `self.histogram_canvas` is a matplotlib canvas + self.histogram_canvas.fig.savefig(graph_path) + + headers = [ + "area", + "equivalent_diameter", + "eccentricity", + "solidity", + "circularity", + "surface_diameter", + ] + # Save the CSV data + rows = [] + for circle in self.all_properties: + rows.append( + [ + circle["area"], + circle["equivalent_diameter"], + circle["eccentricity"], + circle["solidity"], + circle["circularity"], + circle["surface_diameter"], + ] + ) + + # Write the data into a CSV file + with open(csv_path, mode="w", newline="") as data_file: + writer = csv.writer(data_file) + + # Write the header + writer.writerow(headers) + + # Write the rows of data + writer.writerows(rows) + + QMessageBox.information( + self, "Save Successful", f"Files saved to {folder_path}" + ) + return + + def generate_histogram(self) -> None: + """Generate a histogram of equivalent diameters of all detected bubbles. + + This function takes the following steps: + 1. Collect all equivalent diameters from the properties of the detected bubbles + 2. Plot histogram of the equivalent diameters + 3. Calculate descriptive sizes (d32, dmean, dxy) + 4. Update descriptive size label + 5. Optionally add CDF and/or PDF to the histogram + 6. Optionally add vertical lines for the descriptive sizes to the histogram + 7. Add legend to the graph + 8. Redraw the canvas + + :return: None + """ + # Get settings + num_bins = self.bins_spinbox.value() + show_pdf = self.pdf_checkbox.isChecked() + show_cdf = self.cdf_checkbox.isChecked() + show_d32 = self.d32_checkbox.isChecked() + show_dmean = self.dmean_checkbox.isChecked() + show_dxy = self.dxy_checkbox.isChecked() + + # Collect all equivalent diameters from the properties + equivalent_diameters_list: list[float] = [] + + for circle in self.all_properties: + equivalent_diameters_list.append(circle["equivalent_diameter"]) + equivalent_diameters_array = np.array(equivalent_diameters_list) + + x_min = float(np.min(equivalent_diameters_array)) + x_max = float(np.max(equivalent_diameters_array)) + + # Clear current graph + self.histogram_canvas.axes.set_xlabel("") + self.histogram_canvas.axes.set_ylabel("") + self.histogram_canvas.axes.clear() + try: + if self.histogram_canvas.axes2: + self.histogram_canvas.axes2.clear() + self.histogram_canvas.axes2.set_ylabel("") + self.histogram_canvas.axes2.set_yticklabels([]) + self.histogram_canvas.axes2.set_yticks([]) + del self.histogram_canvas.axes2 + except AttributeError: + pass + + # Plot histogram + counts, bins, patches = self.histogram_canvas.axes.hist( + equivalent_diameters_array, bins=num_bins, range=(x_min, x_max) + ) + # Set graph labels + self.histogram_canvas.axes.set_xlabel("Equivalent diameter [mm]") + self.histogram_canvas.axes.set_ylabel("Count [#]") + + # Calculate descriptive sizes + d32, d_mean, dxy = self.calculate_descriptive_sizes(equivalent_diameters_array) + + # Update descriptive size label + desc_text = ( + f"Results:\nd32 = {d32:.2f} mm\ndmean = {d_mean:.2f} mm\ndxy = {dxy:.2f} mm" + ) + self.descriptive_size_label.setText(desc_text) + + # Optionally add CDF + if show_pdf or show_cdf: + self.histogram_canvas.axes2 = self.histogram_canvas.axes.twinx() + self.histogram_canvas.axes2.set_ylabel("Probability [%]") + + if show_cdf: + cdf = np.cumsum(counts) / np.sum(counts) * 100 + self.histogram_canvas.axes2.plot( + bins[:-1], cdf, "r-", marker="o", label="CDF" + ) + + if show_pdf: + pdf = counts / np.sum(counts) * 100 + self.histogram_canvas.axes2.plot( + bins[:-1], pdf, "b-", marker="o", label="PDF" + ) + + if show_d32: + self.histogram_canvas.axes.axvline( + x=d32, color="r", linestyle="-", label="d32" + ) + + if show_dmean: + self.histogram_canvas.axes.axvline( + x=d_mean, color="g", linestyle="--", label="dmean" + ) + + if show_dxy: + self.histogram_canvas.axes.axvline( + x=dxy, color="b", linestyle="--", label="dxy" + ) + + # Apply Legend Options + legend_position = self.legend_position_combobox.currentText() + # legend_orientation = self.legend_orientation_combobox.currentText() + + legend_location_map = { + "North East": "upper right", + "North West": "upper left", + "South East": "lower right", + "South West": "lower left", + } + + print("legend_position:", legend_position) + print(legend_location_map.get(legend_position, "upper right")) + + # Add legend to the graph + if show_cdf or show_pdf or show_d32 or show_dmean or show_dxy: + lines1, labels1 = self.histogram_canvas.axes.get_legend_handles_labels() + if show_cdf or show_pdf: + lines2, labels2 = ( + self.histogram_canvas.axes2.get_legend_handles_labels() + ) + self.histogram_canvas.axes.legend( + lines1 + lines2, + labels1 + labels2, + loc=legend_location_map.get(legend_position, "upper right"), + ) + # else: + # legend = self.histogram_canvas.axes.legend( + # lines1, + # labels1, + # loc=legend_location_map.get(legend_position, "upper right"), + # ) + + # if legend_orientation == "Horizontal": + # legend.set_bbox_to_anchor( + # (1, 1) + # ) # Set orientation of the legend to horizontal if selected + + # Redraw the canvas + self.histogram_canvas.draw() + + return + + def calculate_descriptive_sizes( + self, equivalent_diameters: npt.NDArray[np.float64] + ) -> tuple[float, float, float]: + """Calculate d32, d mean, and dxy based on the equivalent diameters.""" + dxy_x_power: int = int(self.dxy_x_input.text()) + dxy_y_power: int = int(self.dxy_y_input.text()) + d32: float = np.sum(equivalent_diameters**3) / np.sum(equivalent_diameters**2) + + # d32, Sauter diameter, should be calculated based on the area, and volume + # diameter of a circle, which is unkown right now + + d_mean: float = float(np.mean(equivalent_diameters)) + dxy: float = np.sum(equivalent_diameters**dxy_x_power) / np.sum( + equivalent_diameters**dxy_y_power + ) + + return d32, d_mean, dxy + + +def main() -> None: + """Start the GUI application. + + This function initializes the PySide6 application and displays the main window. + """ + app = QApplication(sys.argv) + window = MainWindow() + window.show() + sys.exit(app.exec()) + + +if __name__ == "__main__": + main() + +# GUI - Jump generated graph after processing & every choice of additional elements +# Detection and filtering - Seperate process from filtering +# Store last previewed images + +# Finishe bubble analyser first*** +# Fix problems and bugs before meeting +# Make sure it works on different environments +# and make it ***executable*** + + +# Close the project - +# Relation between variographics and froth properties (stability) +# variographic feature -> mean size? (-> d32) +# air recovery diff --git a/bubble_analyser/__init__.py b/bubble_analyser/__init__.py index c945828..ad35123 100644 --- a/bubble_analyser/__init__.py +++ b/bubble_analyser/__init__.py @@ -1,5 +1,5 @@ -"""The main module for Bubble Analyser.""" - -from importlib.metadata import version - -__version__ = version(__name__) +"""The main module for Bubble Analyser.""" + +from importlib.metadata import version + +__version__ = version(__name__) diff --git a/bubble_analyser/__main__.py b/bubble_analyser/__main__.py index fd29cb7..a67731e 100644 --- a/bubble_analyser/__main__.py +++ b/bubble_analyser/__main__.py @@ -1,6 +1,7 @@ -"""The entry point for the Bubble Analyser program.""" - -from .default import default - -if __name__ == "__main__": - default() +"""The entry point for the Bubble Analyser program.""" + +if __name__ == "__main__": + from .GUI_manual import main as gui_main + + gui_main() + # default_main() diff --git a/bubble_analyser/background_subtraction_threshold.py b/bubble_analyser/background_subtraction_threshold.py index 12b84ac..e374752 100644 --- a/bubble_analyser/background_subtraction_threshold.py +++ b/bubble_analyser/background_subtraction_threshold.py @@ -1,107 +1,106 @@ -"""Background Subtraction and Thresholding: Isolate objects from backgrounds. - -This module provides tools for image preprocessing including grayscale conversion, -background subtraction, and thresholding. It is designed to handle images where objects -of interest need to be isolated from their backgrounds for further analysis. - -Functions: - convert_grayscale(image): Converts a color image to grayscale. - - background_subtraction(target_img, background_img): Subtracts the background image - from the target image to highlight differences. - - threshold(difference_img, threshold_value): Applies a binary threshold to an image - to create a binary mask. - - background_subtraction_threshold(target_img, background_img, threshold_value): - Combines background subtraction and thresholding to isolate objects of interest in - an image. - -These functions are used to preprocess images for applications such as object detection, -where isolating the changes between images or from a background is necessary. Each -function is designed to be modular, allowing them to be used independently or in -sequence depending on the requirements of the task. -""" - -from typing import cast - -import cv2 -import numpy as np -from numpy import typing as npt - - -def convert_grayscale(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]: - """Converts an image to grayscale. - - Args: - image (npt.NDArray): The input image to be converted. - - Returns: - npt.NDArray: The converted image in grayscale. - """ - if len(image.shape) == 3: - image = cast(npt.NDArray[np.int_], cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)) - return image - - -def background_subtraction( - target_img: npt.NDArray[np.int_], background_img: npt.NDArray[np.int_] -) -> npt.NDArray[np.int_]: - """Performs background subtraction on two images. - - Args: - target_img (npt.NDArray): The target image where the objects of interest are - located. - background_img (npt.NDArray): The background image (without object of interest). - - Returns: - npt.NDArray: The difference image after background subtraction. - """ - difference_img = cv2.absdiff(target_img, background_img) - return cast(npt.NDArray[np.int_], difference_img) - - -def threshold( - difference_img: npt.NDArray[np.int_], threshold_value: float -) -> npt.NDArray[np.bool_]: - """Applies a binary threshold to the given difference image. - - Args: - difference_img (npt.NDArray): The input difference image to be thresholded. - threshold_value (int): The threshold value to apply to the difference image. - - Returns: - npt.NDArray: The thresholded image. - """ - _, thresholded_img = cv2.threshold( - difference_img, threshold_value, 255, cv2.THRESH_BINARY - ) - return cast(npt.NDArray[np.bool_], thresholded_img) - - -def background_subtraction_threshold( - target_img: npt.NDArray[np.int_], - background_img: npt.NDArray[np.int_], - threshold_value: float, -) -> npt.NDArray[np.bool_]: - """Perform background subtraction and apply thresholding. - - Args: - target_img: The target image where the objects of interest are located. - background_img: The background image (without objects of interest). - threshold_value: The threshold value to apply after background subtraction. - - Returns: - A binary image with the objects of interest isolated. - """ - # Ensure both images are in grayscale - target_img = convert_grayscale(target_img) - background_img = convert_grayscale(background_img) - - # Subtract the background image from the target image - difference_img = background_subtraction(target_img, background_img) - - # Apply a threshold to the difference image - thresholded_img = threshold(difference_img, threshold_value) - - return thresholded_img +"""Background Subtraction and Thresholding: Isolate objects from backgrounds. + +This module provides tools for image preprocessing including grayscale conversion, +background subtraction, and thresholding. It is designed to handle images where objects +of interest need to be isolated from their backgrounds for further analysis. + +Functions: + convert_grayscale(image): Converts a color image to grayscale. + + background_subtraction(target_img, background_img): Subtracts the background image + from the target image to highlight differences. + + threshold(difference_img, threshold_value): Applies a binary threshold to an image + to create a binary mask. + + background_subtraction_threshold(target_img, background_img, threshold_value): + Combines background subtraction and thresholding to isolate objects of interest in + an image. + +These functions are used to preprocess images for applications such as object detection, +where isolating the changes between images or from a background is necessary. Each +function is designed to be modular, allowing them to be used independently or in +sequence depending on the requirements of the task. +""" + +from typing import cast + +import cv2 +import numpy as np +from numpy import typing as npt + + +def convert_grayscale(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]: + """Converts an image to grayscale. + + Args: + image (npt.NDArray): The input image to be converted. + + Returns: + npt.NDArray: The converted image in grayscale. + """ + if len(image.shape) == 3: + image = cast(npt.NDArray[np.int_], cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)) + return image + + +def background_subtraction( + target_img: npt.NDArray[np.int_], background_img: npt.NDArray[np.int_] +) -> npt.NDArray[np.int_]: + """Performs background subtraction on two images. + + Args: + target_img (npt.NDArray): The target image where the objects of interest are + located. + background_img (npt.NDArray): The background image (without object of interest). + + Returns: + npt.NDArray: The difference image after background subtraction. + """ + difference_img = cv2.absdiff(target_img, background_img) + return cast(npt.NDArray[np.int_], difference_img) + + +def threshold( + difference_img: npt.NDArray[np.int_], threshold_value: float +) -> npt.NDArray[np.bool_]: + """Applies a binary threshold to the given difference image. + + Args: + difference_img (npt.NDArray): The input difference image to be thresholded. + threshold_value (int): The threshold value to apply to the difference image. + + Returns: + npt.NDArray: The thresholded image. + """ + _, thresholded_img = cv2.threshold( + difference_img, threshold_value, 255, cv2.THRESH_BINARY + ) + return cast(npt.NDArray[np.bool_], thresholded_img) + + +def background_subtraction_threshold( + target_img: npt.NDArray[np.int_], + background_img: npt.NDArray[np.int_], +) -> npt.NDArray[np.int_]: + """Perform background subtraction and apply thresholding. + + Args: + target_img: The target image where the objects of interest are located. + background_img: The background image (without objects of interest). + threshold_value: The threshold value to apply after background subtraction. + + Returns: + A binary image with the objects of interest isolated. + """ + # Ensure both images are in grayscale + target_img = convert_grayscale(target_img) + background_img = convert_grayscale(background_img) + + # Subtract the background image from the target image + difference_img = background_subtraction(target_img, background_img) + + # Apply a threshold to the difference image + # thresholded_img = threshold(difference_img, threshold_value) + + return difference_img diff --git a/bubble_analyser/calculate_circle_properties.py b/bubble_analyser/calculate_circle_properties.py index 9272c0a..aca37ed 100644 --- a/bubble_analyser/calculate_circle_properties.py +++ b/bubble_analyser/calculate_circle_properties.py @@ -1,83 +1,125 @@ -"""Calculate Circle Properties. - -This module contains functions for calculating geometric properties of regions -identified in an image. It is particularly focused on regions that are labeled in terms -of their circularity attributes. - -The `calculate_circle_properties` function evaluates the geometric features of labeled -regions within an image, which have been identified as separate entities, often through -a segmentation process. It measures various properties related to the shape and size of -the regions, adjusted to real-world dimensions using a provided pixel-to-centimeter -conversion ratio. - -Function: - calculate_circle_properties(labels, px2cm): Computes area, equivalent diameter, - eccentricity, solidity, and circularity for each labeled region. - -Each computed property is defined as follows: -- Area: Total area of the region converted from pixels to square centimeters. -- Equivalent diameter: Diameter of a circle with the equivalent area as the region, - provided in centimeters. -- Eccentricity: Measure of the deviation of the region from a perfect circle, where - 0 indicates a perfect circle and values closer to 1 indicate elongated shapes. -- Solidity: Ratio of the region's area to the area of its convex hull, indicating - the compactness of the shape. -- Circularity: A value that describes how closely the shape of the region approaches - that of a perfect circle, calculated from the area and the perimeter. - -The function returns a list of dictionaries, with each dictionary holding the properties -for a specific region, facilitating easy access and manipulation of these metrics in -subsequent analysis or reporting stages. -""" - -import numpy as np -from numpy import typing as npt -from skimage import measure - - -def calculate_circle_properties( - labels: npt.NDArray[np.int_], px2cm: float -) -> list[dict[str, float]]: - """Calculate geometric properties of labeled regions in an image. - - This function computes various properties that describe the "circularity" of regions - within the labeled image, such as area, equivalent diameter, eccentricity, solidity, - and circularity. These properties are calculated in centimeters based on the - provided pixel-to-centimeter ratio. - - Args: - labels: A labeled image where each distinct region (or "circle") is represented - by unique labels. - px2cm: The ratio of centimeters per pixel, used to convert measurements from - pixels to centimeters. - - Returns: - A list of dictionaries, each containing the following properties for a region: - - area: The area of the region in square centimeters. - - equivalent_diameter: The diameter of a circle with the same area as the region - , in centimeters. - - eccentricity: The eccentricity of the ellipse that has the same second-moments - as the region. - - solidity: The proportion of the pixels in the convex hull that are also in the - region. - - circularity: A measure of how close the shape is to a perfect circle, - calculated using the perimeter and area. - """ - properties = measure.regionprops(labels) - circle_properties = [] - for prop in properties: - area = prop.area * (px2cm**2) - equivalent_diameter = prop.equivalent_diameter * px2cm - eccentricity = prop.eccentricity - solidity = prop.solidity - circularity = (4 * np.pi * area) / (prop.perimeter * px2cm) ** 2 - circle_properties.append( - { - "area": area, - "equivalent_diameter": equivalent_diameter, - "eccentricity": eccentricity, - "solidity": solidity, - "circularity": circularity, - } - ) - return circle_properties +"""Calculate Circle Properties. + +This module contains functions for calculating geometric properties of regions +identified in an image. It is particularly focused on regions that are labeled in terms +of their circularity attributes. + +The `calculate_circle_properties` function evaluates the geometric features of labeled +regions within an image, which have been identified as separate entities, often through +a segmentation process. It measures various properties related to the shape and size of +the regions, adjusted to real-world dimensions using a provided pixel-to-centimeter +conversion ratio. + +Function: + calculate_circle_properties(labels, px2cm): Computes area, equivalent diameter, + eccentricity, solidity, and circularity for each labeled region. + +Each computed property is defined as follows: +- Area: Total area of the region converted from pixels to square centimeters. +- Equivalent diameter: Diameter of a circle with the equivalent area as the region, + provided in centimeters. +- Eccentricity: Measure of the deviation of the region from a perfect circle, where + 0 indicates a perfect circle and values closer to 1 indicate elongated shapes. +- Solidity: Ratio of the region's area to the area of its convex hull, indicating + the compactness of the shape. +- Circularity: A value that describes how closely the shape of the region approaches + that of a perfect circle, calculated from the area and the perimeter. + +The function returns a list of dictionaries, with each dictionary holding the properties +for a specific region, facilitating easy access and manipulation of these metrics in +subsequent analysis or reporting stages. +""" + +import numpy as np +from numpy import typing as npt +from skimage import measure + + +def calculate_circle_properties( + labels: npt.NDArray[np.int_], mm2px: float +) -> list[dict[str, float]]: + """Calculate geometric properties of regions identified in an image. + + Parameters: + labels (npt.NDArray[np.int_]): A labeled image where each distinct region is + represented by a unique label. + mm2px (float): The conversion factor from millimeters to pixels. + + Returns: + list[dict[str, float]]: A list of dictionaries containing the properties of + each region, including area, equivalent diameter, eccentricity, solidity, + circularity, and surface diameter. The area is given in square millimeters, + while the diameters are given in millimeters. + """ + properties = measure.regionprops(labels) + circle_properties = [] + for prop in properties: + if prop.label == 1: # Ignore the background, labeled as 1 + continue + + area = prop.area * (mm2px**2) + equivalent_diameter = prop.equivalent_diameter * mm2px + eccentricity = prop.eccentricity + solidity = prop.solidity + circularity = (4 * np.pi * area) / (prop.perimeter * mm2px) ** 2 + surface_diameter = 2 * np.sqrt(area / np.pi) + circle_properties.append( + { + "area": area, + "equivalent_diameter": equivalent_diameter, + "eccentricity": eccentricity, + "solidity": solidity, + "circularity": circularity, + "surface_diameter": surface_diameter, + } + ) + return circle_properties + + +def filter_circle_properties( + labels: npt.NDArray[np.int_], + px2mm: float, + max_eccentricity: float = 1.0, + min_solidity: float = 0.9, + min_circularity: float = 0.1, +) -> npt.NDArray[np.int_]: + """Filters out regions (circles) from the labeled image based on their properties. + + Args: + labels: A labeled image where each distinct region is represented by a unique + label. + px2mm: The pixel-to-mm conversion factor. + min_eccentricity: The minimum allowed eccentricity for circles. + max_eccentricity: The maximum allowed eccentricity for circles. + min_solidity: The minimum allowed solidity for circles. + max_solidity: The maximum allowed solidity for circles. + min_circularity: The minimum allowed circularity for circles. + max_circularity: The maximum allowed circularity for circles. + + Returns: + Updated labels array where regions not meeting the thresholds are removed. + """ + properties = measure.regionprops(labels) + new_labels = np.copy(labels) + + for prop in properties: + if prop.label == 1: # Ignore the background + continue + + # Calculate circle properties in mm + area = prop.area * (px2mm**2) + # equivalent_diameter = prop.equivalent_diameter * px2mm + eccentricity = prop.eccentricity + solidity = prop.solidity + circularity = (4 * np.pi * area) / (prop.perimeter * px2mm) ** 2 + + # Check if the circle properties meet the thresholds + if not ( + eccentricity <= max_eccentricity + and min_solidity <= solidity + and min_circularity <= circularity + ): + # Remove the region by setting it to 1 (background) + new_labels[new_labels == prop.label] = 1 + + return new_labels diff --git a/bubble_analyser/calculate_px2cm.py b/bubble_analyser/calculate_px2mm.py similarity index 91% rename from bubble_analyser/calculate_px2cm.py rename to bubble_analyser/calculate_px2mm.py index f0ad7fa..d182a3d 100644 --- a/bubble_analyser/calculate_px2cm.py +++ b/bubble_analyser/calculate_px2mm.py @@ -1,203 +1,205 @@ -"""Bubble Analyser: Image Processing for Circular Feature Detection. - -This module provides a suite of tools for image manipulation and measurement calibration -using computer vision techniques. It includes functions to resize images, draw on images -interactively, and calculate real-world measurements from pixels. - -The functions in this module utilize OpenCV and NumPy to perform tasks such as image -resizing, interactive line drawing for measurement marking, pixel distance calculations, -and conversionfrom pixel measurements to real-world units (e.g., centimeters). These -capabilities are particularly useful in applications where precision in spatial -measurements is required, such as in quality control, materials science, and medical -imaging. - -Key Functions: -- resize_to_target_width(image, target_width): Resizes an image to a specified target - width while maintaining the aspect ratio. -- draw_line(event, x, y, flags, param): A callback function that allows interactive line - drawing on an image displayed in an OpenCV window. -- get_pixel_distance(img): Displays an image and allows the user to draw a line, then - calculates the pixel distance between the endpoints of the line. -- get_cm_per_pixel(pixel_distance, scale_percent, img_resample): Calculates the - conversion ratio from pixels to centimeters, taking into account any image resizing - that has been applied. -- calculate_px2cm(image_path, img_resample): Orchestrates the process of loading an - image, resizing it, allowing the user to mark a measurement, and calculating a - pixel-to-centimeter conversion factor. - -Each function is designed to be modular, allowing for flexible integration into broader -image processing and analysis workflows. The module facilitates the extraction of -quantitative data from images, which can be critical for applications requiring detailed -spatial analysis. -""" - -from pathlib import Path -from typing import cast - -import cv2 -import numpy as np -import numpy.typing as npt - -from .image_preprocess import load_image - - -def resize_to_target_width( - image: npt.NDArray[np.int_], target_width: int = 1000 -) -> tuple[npt.NDArray[np.int_], float]: - """Resizes an image to a specified target width while maintaining the aspect ratio. - - Args: - image (npt.npt.NDArray[np.int_]): The input image to be resized. - target_width (int, optional): The desired width of the resized image. Defaults - to 1000. - - Returns: - npt.npt.NDArray[np.int_]: The resized image. - scale_percent: The scaling percentage applied to the image during resizing. - """ - # Scale down the image to a width of 1000 pixels, keeping the aspect ratio the same - scale_percent: float = ( - target_width / image.shape[1] - ) # Calculate the scale percent to make width 1000 - width: int = target_width # Set the new width to 1000 pixels - height: int = int( - image.shape[0] * scale_percent - ) # Adjust the height to maintain the aspect ratio - dim: tuple[int, int] = (width, height) # Define the new dimensions - image_resized = cv2.resize( - image, dim, interpolation=cv2.INTER_AREA - ) # Resize the image - - return cast(npt.NDArray[np.int_], image_resized), scale_percent - - -def draw_line(event: int, x: int, y: int, flags: int, param: dict[str, object]) -> None: - """Callback function to draw a line on the image. - - This function is used as a mouse callback to allow the user to draw a line on the - image. - - Args: - event: The type of mouse event (e.g., left button down, mouse move, left button - up). - x: The x-coordinate of the mouse event. - y: The y-coordinate of the mouse event. - flags: Any relevant flags passed by OpenCV. - param: A dictionary containing reference points and drawing state. - """ - refPt = cast(list[tuple[int, int]], param["refPt"]) - img = cast(npt.NDArray[np.uint8], param["img"]) - img_copy = cast(npt.NDArray[np.uint8], param["img_copy"]) - - if event == cv2.EVENT_LBUTTONDOWN: - refPt.append((x, y)) - param["drawing"] = True - - elif event == cv2.EVENT_MOUSEMOVE: - if param["drawing"]: - img_copy[:] = img[:] # Reset to the original image before drawing the line - cv2.line(img_copy, refPt[0], (x, y), (0, 255, 0), 2) - cv2.imshow("image", img_copy) - - elif event == cv2.EVENT_LBUTTONUP: - refPt.append((x, y)) - param["drawing"] = False - cv2.line(img, refPt[0], refPt[1], (0, 255, 0), 2) - cv2.imshow("image", img) - - -def get_pixel_distance(img: npt.NDArray[np.int_]) -> float: - """Display the image and allow the user to draw a line representing 1 cm. - - This function uses OpenCV to display the image and capture the user input - for drawing a line that represents 1 cm on the ruler. It calculates the - Euclidean distance between the two points of the line in pixels. - - Args: - img: The image on which the user will draw a line. - - Returns: - The distance in pixels between the two drawn points. Returns 0 if the - line was not drawn correctly. - """ - refPt: list[tuple[int, int]] = [] - drawing: bool = False - img_copy: npt.NDArray[np.int_] = img.copy() - - cv2.namedWindow("image") - cv2.setMouseCallback( - "image", - draw_line, # type: ignore - {"refPt": refPt, "drawing": drawing, "img": img, "img_copy": img_copy}, - ) - - print( - "Draw a line representing 1 cm according to the ruler's scaling in the image." - ) - - while True: - cv2.imshow("image", img_copy) - key: int = cv2.waitKey(1) & 0xFF - if key == ord("q"): - break - - cv2.destroyAllWindows() - - if len(refPt) == 2: - pixel_distance: float = np.sqrt( - (refPt[1][0] - refPt[0][0]) ** 2 + (refPt[1][1] - refPt[0][1]) ** 2 - ) - return pixel_distance - else: - print("Line was not drawn correctly.") - return 0.0 - - -def get_cm_per_pixel( - pixel_distance: float, scale_percent: float, img_resample: float -) -> float: - """Calculate the conversion ratio from pixels to centimeters. - - Args: - pixel_distance: The distance in pixels between the two drawn points. - scale_percent: The scaling percentage applied to the image during resizing. - img_resample: The resampling factor applied to the original target and - background image. - - Returns: - The conversion factor in centimeters per pixel, corrected for the resampling - applied to the original image. - """ - original_pixel_distance: float = pixel_distance / scale_percent - cm_per_pixel: float = 1.0 / original_pixel_distance - cm_per_pixel = cm_per_pixel / img_resample - return cm_per_pixel - - -def calculate_px2cm(image_path: Path, img_resample: float) -> float: - """Calculates the conversion factor from pixels to centimeters. - - This function reads an image of a ruler, allows the user to draw a line - correspondingnto 1 cm on the ruler, and calculates the pixel-to-centimeter - conversion factor. The image is scaled down for easier interaction, but the final - calculation accounts forthis scaling as well as the resample factor for target and - background images to ensure accuracy relative to the original image size. - - Args: - image_path (str): The path to the image file. - img_resample (float): The resampling factor applied to the original target and - background image. - - Returns: - float: The conversion factor in centimeters per pixel, corrected for the - resampling applied to the original image. - """ - image = load_image(image_path) - image, scale_percent = resize_to_target_width(image) - pixel_distance: float = get_pixel_distance(image) - if pixel_distance > 0: - cm_per_pixel: float = get_cm_per_pixel( - pixel_distance, scale_percent, img_resample - ) - print(f"Conversion factor: {cm_per_pixel} cm per pixel") - return cm_per_pixel +"""Bubble Analyser: Image Processing for Circular Feature Detection. + +This module provides a suite of tools for image manipulation and measurement calibration +using computer vision techniques. It includes functions to resize images, draw on images +interactively, and calculate real-world measurements from pixels. + +The functions in this module utilize OpenCV and NumPy to perform tasks such as image +resizing, interactive line drawing for measurement marking, pixel distance calculations, +and conversionfrom pixel measurements to real-world units (e.g., centimeters). These +capabilities are particularly useful in applications where precision in spatial +measurements is required, such as in quality control, materials science, and medical +imaging. + +Key Functions: +- resize_to_target_width(image, target_width): Resizes an image to a specified target + width while maintaining the aspect ratio. +- draw_line(event, x, y, flags, param): A callback function that allows interactive line + drawing on an image displayed in an OpenCV window. +- get_pixel_distance(img): Displays an image and allows the user to draw a line, then + calculates the pixel distance between the endpoints of the line. +- get_cm_per_pixel(pixel_distance, scale_percent, img_resample): Calculates the + conversion ratio from pixels to centimeters, taking into account any image resizing + that has been applied. +- calculate_px2cm(image_path, img_resample): Orchestrates the process of loading an + image, resizing it, allowing the user to mark a measurement, and calculating a + pixel-to-centimeter conversion factor. + +Each function is designed to be modular, allowing for flexible integration into broader +image processing and analysis workflows. The module facilitates the extraction of +quantitative data from images, which can be critical for applications requiring detailed +spatial analysis. +""" + +from pathlib import Path +from typing import cast + +import cv2 +import numpy as np +import numpy.typing as npt + +from .image_preprocess import load_image + + +def resize_to_target_width( + image: npt.NDArray[np.int_], target_width: int = 1000 +) -> tuple[npt.NDArray[np.int_], float]: + """Resizes an image to a specified target width while maintaining the aspect ratio. + + Args: + image (npt.npt.NDArray[np.int_]): The input image to be resized. + target_width (int, optional): The desired width of the resized image. Defaults + to 1000. + + Returns: + npt.npt.NDArray[np.int_]: The resized image. + scale_percent: The scaling percentage applied to the image during resizing. + """ + # Scale down the image to a width of 1000 pixels, keeping the aspect ratio the same + scale_percent: float = ( + target_width / image.shape[1] + ) # Calculate the scale percent to make width 1000 + width: int = target_width # Set the new width to 1000 pixels + height: int = int( + image.shape[0] * scale_percent + ) # Adjust the height to maintain the aspect ratio + dim: tuple[int, int] = (width, height) # Define the new dimensions + image_resized = cv2.resize( + image, dim, interpolation=cv2.INTER_AREA + ) # Resize the image + + return cast(npt.NDArray[np.int_], image_resized), scale_percent + + +def draw_line(event: int, x: int, y: int, flags: int, param: dict[str, object]) -> None: + """Callback function to draw a line on the image. + + This function is used as a mouse callback to allow the user to draw a line on the + image. + + Args: + event: The type of mouse event (e.g., left button down, mouse move, left button + up). + x: The x-coordinate of the mouse event. + y: The y-coordinate of the mouse event. + flags: Any relevant flags passed by OpenCV. + param: A dictionary containing reference points and drawing state. + """ + refPt = cast(list[tuple[int, int]], param["refPt"]) + img = cast(npt.NDArray[np.uint8], param["img"]) + img_copy = cast(npt.NDArray[np.uint8], param["img_copy"]) + + if event == cv2.EVENT_LBUTTONDOWN: + refPt.append((x, y)) + param["drawing"] = True + + elif event == cv2.EVENT_MOUSEMOVE: + if param["drawing"]: + img_copy[:] = img[:] # Reset to the original image before drawing the line + cv2.line(img_copy, refPt[0], (x, y), (0, 255, 0), 2) + cv2.imshow("image", img_copy) + + elif event == cv2.EVENT_LBUTTONUP: + refPt.append((x, y)) + param["drawing"] = False + cv2.line(img, refPt[0], refPt[1], (0, 255, 0), 2) + cv2.imshow("image", img) + + +def get_pixel_distance(img: npt.NDArray[np.int_]) -> float: + """Display the image and allow the user to draw a line representing 1 cm. + + This function uses OpenCV to display the image and capture the user input + for drawing a line that represents 1 cm on the ruler. It calculates the + Euclidean distance between the two points of the line in pixels. + + Args: + img: The image on which the user will draw a line. + + Returns: + The distance in pixels between the two drawn points. Returns 0 if the + line was not drawn correctly. + """ + refPt: list[tuple[int, int]] = [] + drawing: bool = False + img_copy: npt.NDArray[np.int_] = img.copy() + + cv2.namedWindow("image") + cv2.setMouseCallback( + "image", + draw_line, # type: ignore + {"refPt": refPt, "drawing": drawing, "img": img, "img_copy": img_copy}, + ) + + print( + "Draw a line representing 1 cm according to the ruler's scaling in the image." + ) + + while True: + cv2.imshow("image", img_copy) + key: int = cv2.waitKey(1) & 0xFF + if key == ord("q"): + break + + cv2.destroyAllWindows() + + if len(refPt) == 2: + pixel_distance: float = np.sqrt( + (refPt[1][0] - refPt[0][0]) ** 2 + (refPt[1][1] - refPt[0][1]) ** 2 + ) + return pixel_distance + else: + print("Line was not drawn correctly.") + return 0.0 + + +def get_mm_per_pixel( + pixel_distance: float, scale_percent: float, img_resample: float +) -> float: + """Calculate the conversion ratio from pixels to centimeters. + + Args: + pixel_distance: The distance in pixels between the two drawn points. + scale_percent: The scaling percentage applied to the image during resizing. + img_resample: The resampling factor applied to the original target and + background image. + + Returns: + The conversion factor in centimeters per pixel, corrected for the resampling + applied to the original image. + """ + original_pixel_distance: float = pixel_distance / scale_percent + mm_per_pixel: float = 10.0 / original_pixel_distance + mm_per_pixel = mm_per_pixel / img_resample + return mm_per_pixel + + +def calculate_px2mm(image_path: Path, img_resample: float) -> tuple[float, float]: + """Calculates the conversion factor from pixels to centimeters. + + This function reads an image of a ruler, allows the user to draw a line + correspondingnto 1 cm on the ruler, and calculates the pixel-to-centimeter + conversion factor. The image is scaled down for easier interaction, but the final + calculation accounts forthis scaling as well as the resample factor for target and + background images to ensure accuracy relative to the original image size. + + Args: + image_path (str): The path to the image file. + img_resample (float): The resampling factor applied to the original target and + background image. + + Returns: + float: The conversion factor in centimeters per pixel, corrected for the + resampling applied to the original image. + """ + image = load_image(image_path) + image, scale_percent = resize_to_target_width(image) + pixel_distance: float = get_pixel_distance(image) + if pixel_distance > 0: + mm_per_pixel: float = get_mm_per_pixel( + pixel_distance, scale_percent, img_resample + ) + print(f"Conversion factor: {mm_per_pixel} mm per pixel") + pixel_per_mm = 1 / mm_per_pixel + print(f"Conversion factor: {pixel_per_mm} pixels per mm") + return mm_per_pixel, pixel_per_mm diff --git a/bubble_analyser/config.py b/bubble_analyser/config.py index 9f6dbee..3556041 100644 --- a/bubble_analyser/config.py +++ b/bubble_analyser/config.py @@ -1,273 +1,301 @@ -"""This module defines the configuration parameters for the Bubble Analyser project. - -The `Config` class is a Pydantic model that validates and manages the configuration -parameters used in the image processing and analysis routines. These parameters -include morphological element sizes, connectivity, marker size, image resampling -factors, and more. The class also includes methods to validate the ranges of these -parameters, ensuring that they are logically consistent before being used in the -processing algorithms. - -Classes: - Config: A Pydantic model for storing and validating configuration parameters. - -Methods: - check_morphological_element_size_range: Validates the morphological element size - range. - check_connectivity_range: Validates the connectivity range. - check_marker_size_range: Validates the marker size range. - check_resample_range: Validates the resample range. - check_max_eccentricity_range: Validates the maximum eccentricity range. - check_min_solidity_range: Validates the minimum solidity range. - check_min_size_range: Validates the minimum size range. -""" - -from pathlib import Path - -import typing_extensions -from pydantic import ( - BaseModel, - PositiveFloat, - PositiveInt, - StrictBool, - StrictFloat, - model_validator, -) - - -class Config(BaseModel): # type: ignore - """A Pydantic model for storing and validating configuration parameters. - - The class contains parameters for image processing and analysis, such as - morphological element sizes, connectivity, marker size, image resampling - factors, maximum eccentricity, and more. The class also includes methods to - validate the ranges of these parameters, ensuring that they are logically - consistent before being used in the processing algorithms. - # Morphological element used for binary operations, e.g. opening, closing, etc. - Morphological_element_size: PositiveInt - Morphological_element_size_range: tuple[PositiveInt, PositiveInt] - - Attributes: - Morphological_element_size: PositiveInt - Morphological_element_size_range: tuple[PositiveInt, PositiveInt] - Connectivity: PositiveInt - Connectivity_range: tuple[PositiveInt, PositiveInt] - Marker_size: PositiveInt - Marker_size_range: tuple[PositiveInt, PositiveInt] - resample: PositiveFloat - resample_range: tuple[PositiveFloat, PositiveFloat] - Max_Eccentricity: PositiveFloat - Max_Eccentricity_range: tuple[PositiveFloat, PositiveFloat] - Min_Solidity: PositiveFloat - Min_Solidity_range: tuple[PositiveFloat, PositiveFloat] - min_size: StrictFloat - min_size_range: tuple[StrictFloat, StrictFloat] - px2mm: PositiveFloat - target_img_path: Path - background_img_path: Path - threshold_value: PositiveFloat - ruler_img_path: Path - do_batch: StrictBool - """ - - # Default PARAMETERS - - # Morphological element used for binary operations, e.g. opening, closing, etc. - Morphological_element_size: PositiveInt - Morphological_element_size_range: tuple[PositiveInt, PositiveInt] - - # Connectivity used, use 4 or 8 - Connectivity: PositiveInt - Connectivity_range: tuple[PositiveInt, PositiveInt] - - # Marker size for watershed segmentation - Marker_size: PositiveInt - Marker_size_range: tuple[PositiveInt, PositiveInt] - - # Images can be resampled to make processing faster - resample: PositiveFloat - resample_range: tuple[PositiveFloat, PositiveFloat] - - # Reject abnormal bubbles from quantification. E>0.85 or S<0.9 - Max_Eccentricity: PositiveFloat - Max_Eccentricity_range: tuple[PositiveFloat, PositiveFloat] - Min_Solidity: PositiveFloat - Min_Solidity_range: tuple[PositiveFloat, PositiveFloat] - - # Also ignore too small bubbles (equivalent diameter in mm) - min_size: StrictFloat - min_size_range: tuple[StrictFloat, StrictFloat] - - # User input Image resolution - px2mm: PositiveFloat - - # Path for Target image - target_img_path: Path - - # Path for Background image - background_img_path: Path - - # Threshold value for background subtraction - threshold_value: PositiveFloat - - # Path for Ruler image - ruler_img_path: Path - - # Batch processing flag - do_batch: StrictBool - - @model_validator(mode="after") - def check_morphological_element_size_range(self) -> typing_extensions.Self: - """Validates the morphological element size range. - - Ensures that the lower bound of the range is less than the upper bound. - If the bounds are in the wrong order, a ValueError is raised. - - Returns: - Self: The instance itself, for method chaining. - """ - low, high = self.Morphological_element_size_range - # Check if the lower bound is less than the upper bound - if low >= high: - # Raise a ValueError if the bounds are in the wrong order - raise ValueError( - "Limits for the Morphological_element_size_range are in the wrong order" - ) - return self - - @model_validator(mode="after") - def check_connectivity_range(self) -> typing_extensions.Self: - """Validates the connectivity range. - - Ensures that the lower bound of the range is less than the upper bound. - If the bounds are in the wrong order, a ValueError is raised. - - Returns: - Self: The instance itself, for method chaining. - """ - low, high = self.Connectivity_range - # Check if the lower bound is less than the upper bound - if low >= high: - # Raise a ValueError if the bounds are in the wrong order - raise ValueError("Limits for the Connectivity_range are in the wrong order") - return self - - @model_validator(mode="after") - def check_marker_size_range(self) -> typing_extensions.Self: - """Validates the marker size range. - - Ensures that the lower bound of the range is less than the upper bound. - If the bounds are in the wrong order, a ValueError is raised. - - Returns: - Self: The instance itself, for method chaining. - """ - # Get the lower and upper bounds of the marker size range - low, high = self.Marker_size_range - - # Check if the lower bound is less than the upper bound - if low >= high: - # Raise a ValueError if the bounds are in the wrong order - raise ValueError("Limits for the Marker_size_range are in the wrong order") - - # Return the instance itself for method chaining - return self - - @model_validator(mode="after") - def check_resample_range(self) -> typing_extensions.Self: - """Validates the resample range. - - Ensures that the lower bound of the range is less than the upper bound. - If the bounds are in the wrong order (lower bound >= upper bound), a ValueError - is raised. - - Returns: - Self: The instance itself, for method chaining. - - Raises: - ValueError: If the lower bound is greater than or equal to the upper bound. - """ - # Get the lower and upper bounds of the resample range - low, high = self.resample_range - - # Check if the lower bound is less than the upper bound - if low >= high: - # Raise a ValueError if the bounds are in the wrong order - raise ValueError("Limits for the resample_range are in the wrong order") - - # Return the instance itself for method chaining - return self - - @model_validator(mode="after") - def check_max_eccentricity_range(self) -> typing_extensions.Self: - """Validates the maximum eccentricity range. - - Ensures that the lower bound of the range is less than the upper bound. - If the bounds are in the wrong order (lower bound >= upper bound), a ValueError - is raised. - - Returns: - Self: The instance itself, for method chaining. - - Raises: - ValueError: If the lower bound is greater than or equal to the upper bound. - """ - # Get the lower and upper bounds of the maximum eccentricity range - low, high = self.Max_Eccentricity_range - - # Check if the lower bound is less than the upper bound - if low >= high: - # Raise a ValueError if the bounds are in the wrong order - raise ValueError( - "Limits for the Max_Eccentricity_range are in the wrong order" - ) - - # Return the instance itself for method chaining - return self - - @model_validator(mode="after") - def check_min_solidity_range(self) -> typing_extensions.Self: - """Validates the minimum solidity range. - - Ensures that the lower bound of the range is less than the upper bound. - If the bounds are in the wrong order (lower bound >= upper bound), a ValueError - is raised. - - Returns: - Self: The instance itself, for method chaining. - - Raises: - ValueError: If the lower bound is greater than or equal to the upper bound. - """ - # Get the lower and upper bounds of the minimum solidity range - low, high = self.Min_Solidity_range - - # Check if the lower bound is less than the upper bound - if low >= high: - # Raise a ValueError if the bounds are in the wrong order - raise ValueError("Limits for the Min_Solidity_range are in the wrong order") - - return self - - @model_validator(mode="after") - def check_min_size_range(self) -> typing_extensions.Self: - """Validates the minimum size range. - - Ensures that the lower bound of the range is less than the upper bound. - If the bounds are in the wrong order (lower bound >= upper bound), a ValueError - is raised. - - Returns: - Self: The instance itself, for method chaining. - - Raises: - ValueError: If the lower bound is greater than or equal to the upper bound. - """ - # Get the lower and upper bounds of the minimum size range - low, high = self.min_size_range - - # Check if the lower bound is less than the upper bound - if low >= high: - # Raise a ValueError if the bounds are in the wrong order - raise ValueError("Limits for the min_size_range are in the wrong order") - # Return the instance itself for method chaining - return self +"""This module defines the configuration parameters for the Bubble Analyser project. + +The `Config` class is a Pydantic model that validates and manages the configuration +parameters used in the image processing and analysis routines. These parameters +include morphological element sizes, connectivity, marker size, image resampling +factors, and more. The class also includes methods to validate the ranges of these +parameters, ensuring that they are logically consistent before being used in the +processing algorithms. + +Classes: + Config: A Pydantic model for storing and validating configuration parameters. + +Methods: + check_morphological_element_size_range: Validates the morphological element size + range. + check_connectivity_range: Validates the connectivity range. + check_marker_size_range: Validates the marker size range. + check_resample_range: Validates the resample range. + check_max_eccentricity_range: Validates the maximum eccentricity range. + check_min_solidity_range: Validates the minimum solidity range. + check_min_size_range: Validates the minimum size range. +""" + +from pathlib import Path + +import typing_extensions +from pydantic import ( + BaseModel, + PositiveFloat, + PositiveInt, + StrictBool, + StrictFloat, + model_validator, +) + + +class Config(BaseModel): # type: ignore + """A Pydantic model for storing and validating configuration parameters. + + The class contains parameters for image processing and analysis, such as + morphological element sizes, connectivity, marker size, image resampling + factors, maximum eccentricity, and more. The class also includes methods to + validate the ranges of these parameters, ensuring that they are logically + consistent before being used in the processing algorithms. + # Morphological element used for binary operations, e.g. opening, closing, etc. + Morphological_element_size: PositiveInt + Morphological_element_size_range: tuple[PositiveInt, PositiveInt] + + Attributes: + Morphological_element_size: PositiveInt + Morphological_element_size_range: tuple[PositiveInt, PositiveInt] + Connectivity: PositiveInt + Connectivity_range: tuple[PositiveInt, PositiveInt] + Marker_size: PositiveInt + Marker_size_range: tuple[PositiveInt, PositiveInt] + resample: PositiveFloat + resample_range: tuple[PositiveFloat, PositiveFloat] + Max_Eccentricity: PositiveFloat + Max_Eccentricity_range: tuple[PositiveFloat, PositiveFloat] + Min_Solidity: PositiveFloat + Min_Solidity_range: tuple[PositiveFloat, PositiveFloat] + min_size: StrictFloat + min_size_range: tuple[StrictFloat, StrictFloat] + px2mm: PositiveFloat + target_img_path: Path + background_img_path: Path + threshold_value: PositiveFloat + ruler_img_path: Path + do_batch: StrictBool + """ + + # Default PARAMETERS + + # Morphological element used for binary operations, e.g. opening, closing, etc. + Morphological_element_size: PositiveInt + Morphological_element_size_range: tuple[PositiveInt, PositiveInt] + + # Connectivity used, use 4 or 8 + Connectivity: PositiveInt + Connectivity_range: tuple[PositiveInt, PositiveInt] + + # Marker size for watershed segmentation + Marker_size: PositiveInt + Marker_size_range: tuple[PositiveInt, PositiveInt] + + # Images can be resampled to make processing faster + resample: PositiveFloat + resample_range: tuple[PositiveFloat, PositiveFloat] + + # Reject abnormal bubbles from quantification. E>0.85 or S<0.9 + Max_Eccentricity: PositiveFloat + Max_Eccentricity_range: tuple[PositiveFloat, PositiveFloat] + Min_Solidity: PositiveFloat + Min_Solidity_range: tuple[PositiveFloat, PositiveFloat] + Min_Circularity: PositiveFloat + Min_Circularity_range: tuple[PositiveFloat, PositiveFloat] + + # Also ignore too small bubbles (equivalent diameter in mm) + min_size: StrictFloat + min_size_range: tuple[StrictFloat, StrictFloat] + + # User input Image resolution + px2mm: PositiveFloat + + # Path for Target image + target_img_path: Path + + # Path for Background image + background_img_path: Path + + # Threshold value for background subtraction + threshold_value: PositiveFloat + + # Path for Ruler image + ruler_img_path: Path + + # Batch processing flag + do_batch: StrictBool + + @model_validator(mode="after") + def check_morphological_element_size_range(self) -> typing_extensions.Self: + """Validates the morphological element size range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order, a ValueError is raised. + + Returns: + Self: The instance itself, for method chaining. + """ + low, high = self.Morphological_element_size_range + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError( + "Limits for the Morphological_element_size_range are in the wrong order" + ) + return self + + @model_validator(mode="after") + def check_connectivity_range(self) -> typing_extensions.Self: + """Validates the connectivity range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order, a ValueError is raised. + + Returns: + Self: The instance itself, for method chaining. + """ + low, high = self.Connectivity_range + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError("Limits for the Connectivity_range are in the wrong order") + return self + + @model_validator(mode="after") + def check_marker_size_range(self) -> typing_extensions.Self: + """Validates the marker size range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order, a ValueError is raised. + + Returns: + Self: The instance itself, for method chaining. + """ + # Get the lower and upper bounds of the marker size range + low, high = self.Marker_size_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError("Limits for the Marker_size_range are in the wrong order") + + # Return the instance itself for method chaining + return self + + @model_validator(mode="after") + def check_resample_range(self) -> typing_extensions.Self: + """Validates the resample range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order (lower bound >= upper bound), a ValueError + is raised. + + Returns: + Self: The instance itself, for method chaining. + + Raises: + ValueError: If the lower bound is greater than or equal to the upper bound. + """ + # Get the lower and upper bounds of the resample range + low, high = self.resample_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError("Limits for the resample_range are in the wrong order") + + # Return the instance itself for method chaining + return self + + @model_validator(mode="after") + def check_max_eccentricity_range(self) -> typing_extensions.Self: + """Validates the maximum eccentricity range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order (lower bound >= upper bound), a ValueError + is raised. + + Returns: + Self: The instance itself, for method chaining. + + Raises: + ValueError: If the lower bound is greater than or equal to the upper bound. + """ + # Get the lower and upper bounds of the maximum eccentricity range + low, high = self.Max_Eccentricity_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError( + "Limits for the Max_Eccentricity_range are in the wrong order" + ) + + # Return the instance itself for method chaining + return self + + @model_validator(mode="after") + def check_min_solidity_range(self) -> typing_extensions.Self: + """Validates the minimum solidity range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order (lower bound >= upper bound), a ValueError + is raised. + + Returns: + Self: The instance itself, for method chaining. + + Raises: + ValueError: If the lower bound is greater than or equal to the upper bound. + """ + # Get the lower and upper bounds of the minimum solidity range + low, high = self.Min_Solidity_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError("Limits for the Min_Solidity_range are in the wrong order") + + return self + + @model_validator(mode="after") + def check_min_circularity_range(self) -> typing_extensions.Self: + """Validates the minimum circularity range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order (lower bound >= upper bound), a ValueError + is raised. + + Returns: + Self: The instance itself, for method chaining. + + Raises: + ValueError: If the lower bound is greater than or equal to the upper bound. + """ + # Get the lower and upper bounds of the minimum solidity range + low, high = self.Min_Circularity_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError( + "Limits for the Min_Circularity_range are in the wrong order" + ) + + return self + + @model_validator(mode="after") + def check_min_size_range(self) -> typing_extensions.Self: + """Validates the minimum size range. + + Ensures that the lower bound of the range is less than the upper bound. + If the bounds are in the wrong order (lower bound >= upper bound), a ValueError + is raised. + + Returns: + Self: The instance itself, for method chaining. + + Raises: + ValueError: If the lower bound is greater than or equal to the upper bound. + """ + # Get the lower and upper bounds of the minimum size range + low, high = self.min_size_range + + # Check if the lower bound is less than the upper bound + if low >= high: + # Raise a ValueError if the bounds are in the wrong order + raise ValueError("Limits for the min_size_range are in the wrong order") + # Return the instance itself for method chaining + return self diff --git a/bubble_analyser/config.toml b/bubble_analyser/config.toml index 3b48e61..283691e 100644 --- a/bubble_analyser/config.toml +++ b/bubble_analyser/config.toml @@ -1,48 +1,50 @@ -# Parameters are described by its name, value, and range. -# All three items separated by commas - -# Default PARAMETERS - -# Morphological element used for binary operations, e.g. opening, closing, etc. -Morphological_element_size = 5 -Morphological_element_size_range = [3, 10] - -# Connectivity used, use 4 or 8 -Connectivity = 8 -Connectivity_range = [4, 8] - -# Marker size for watershed segmentation -Marker_size = 10 -Marker_size_range = [2, 30] - -# Images can be resampled to make processing faster -resample = 0.5 -resample_range = [0.1, 1.0] - -# Reject abnormal bubbles from quantification. E>0.85 or S<0.9 -Max_Eccentricity = 0.85 -Max_Eccentricity_range = [0.1, 1.0] -Min_Solidity = 0.9 -Min_Solidity_range = [0.1, 1.0] - -# Also ignore too small bubbles (equivalent diameter in mm) -min_size = 0.1 -min_size_range = [0, 50] - -# Image resolution -px2mm = 1.0 - -# Target image -target_img_path = "./tests/sample_images/03.jpg" - -# Background image -background_img_path = "./tests/calibration_files/Background.png" - -# Threshold value for background subtraction -threshold_value = 0.3 - -# Ruler image -ruler_img_path = "./tests/calibration_files/Ruler.png" - -# Batch processing flag -do_batch = false +# Parameters are described by its name, value, and range. +# All three items separated by commas + +# Default PARAMETERS + +# Morphological element used for binary operations, e.g. opening, closing, etc. +Morphological_element_size = 5 +Morphological_element_size_range = [3, 10] + +# Connectivity used, use 4 or 8 +Connectivity = 8 +Connectivity_range = [4, 8] + +# Marker size for watershed segmentation +Marker_size = 10 +Marker_size_range = [2, 30] + +# Images can be resampled to make processing faster +resample = 0.5 +resample_range = [0.1, 1.0] + +# Reject abnormal bubbles from quantification. E>0.85 or S<0.9 +Max_Eccentricity = 0.85 +Max_Eccentricity_range = [0.1, 1.0] +Min_Solidity = 0.9 +Min_Solidity_range = [0.1, 1.0] +Min_Circularity = 0.1 +Min_Circularity_range = [0.1, 1.0] + +# Also ignore too small bubbles (equivalent diameter in mm) +min_size = 0.1 +min_size_range = [0, 50] + +# Image resolution +px2mm = 1.0 + +# Target image +target_img_path = "./tests/sample_images/03.jpg" + +# Background image +background_img_path = "./tests/calibration_files/Background.png" + +# Threshold value for background subtraction +threshold_value = 0.5 + +# Ruler image +ruler_img_path = "./tests/calibration_files/Ruler.png" + +# Batch processing flag +do_batch = false diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py index 6fba838..cd84ec7 100644 --- a/bubble_analyser/default.py +++ b/bubble_analyser/default.py @@ -1,246 +1,338 @@ -"""Bubble Analyser: Image Processing for Circular Feature Detection. - -This script is designed to process and analyze images to detect and evaluate circular -features, such as bubbles, using various image processing techniques. The script -integrates several modular functions for loading images, processing them, and -calculating properties of detected features in terms of real-world measurements. - -The key functionalities include: - -1. Loading configuration parameters from a TOML file, which govern the image processing - steps and parameters. -2. Loading and preprocessing images, including conversion to grayscale and resizing - based on a given resampling factor. -3. Calculating the conversion factor from pixels to centimeters using a reference ruler - image, ensuring that measurements of detected features are accurate and scalable. -4. Executing the image processing algorithm, which involves thresholding, morphological - processing, distance transformation, connected component labeling, and watershed - segmentation to isolate and analyze circular features. -5. Displaying and saving intermediary and final images, along with calculating and - printing properties such as equivalent diameter and area of the detected features. - -The script is structured to allow easy customization and extension, making it suitable -for a wide range of image analysis tasks that involve circular feature detection and -measurement. - -To run the script, simply execute the `default()` function, which orchestrates the -entire process from loading configurations and images to running the analysis and -displaying results. -""" - -from pprint import pprint - -import cv2 -import matplotlib.pyplot as plt -import numpy as np -import toml as tomllib -from numpy import typing as npt -from skimage import ( - color, - io, - morphology, - transform, -) - -from .calculate_circle_properties import calculate_circle_properties -from .calculate_px2cm import calculate_px2cm -from .config import Config -from .image_preprocess import image_preprocess -from .morphological_process import morphological_process -from .threshold import threshold - - -def load_image( - image_path: str, img_resample: float -) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: - """Read and preprocess the input image. - - This function loads an image from the specified path, resizes it according to the - given resampling factor, and converts it to grayscale if the image is in RGB format. - - Args: - image_path: The file path of the image to load. - img_resample: The factor by which the image will be resampled (e.g., 0.5 for - reducing the size by half). - - Returns: - A tuple containing: - - The preprocessed grayscale image (if the original was in RGB) or the original - grayscale image. - - The resized image in RGB format. - """ - # Read the input image - img = io.imread(image_path) - - imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - scale_percent = img_resample * 100 # percent of original size - width = int(imgRGB.shape[1] * scale_percent / 100) - height = int(imgRGB.shape[0] * scale_percent / 100) - dim = (width, height) - - # resize image - imgRGB = cv2.resize(imgRGB, dim, interpolation=cv2.INTER_AREA) - - img = transform.resize( - img, - (int(img.shape[0] * img_resample), int(img.shape[1] * img_resample)), - anti_aliasing=True, - ) - if img.ndim > 2: - img = color.rgb2gray(img) # Convert to grayscale if the image is in RGB - - return img, imgRGB - - -def load_toml(file_path: str) -> Config: - """Load configuration parameters from a TOML file. - - This function reads the TOML configuration file from the specified path and loads - its contents into a dictionary. - - Args: - file_path: The file path of the TOML configuration file. - - Returns: - A dictionary containing the configuration parameters from the TOML file. - """ - toml_data = tomllib.load(file_path) - - return Config(**toml_data) - - -def run_algorithm( - target_img: npt.NDArray[np.int_], - bknd_img: npt.NDArray[np.int_], - imgRGB: npt.NDArray[np.int_], - params: Config, - px2cm: float, - threshold_value: float, -) -> None: - """Execute the image processing algorithm on the target image. - - This function performs a series of image processing steps on the target image, - including thresholding, morphological processing, and watershed segmentation. - It then calculates properties of the detected circular features, such as equivalent - diameter and area, in centimeters using the provided pixel-to-centimeter ratio. - - Args: - target_img: The preprocessed target image. - bknd_img: The background image used for thresholding. - imgRGB: The resized target image in RGB format. - params: A dictionary of parameters loaded from the TOML file. - px2cm: The conversion factor between pixels and centimeters. - threshold_value: Threshold value for background subtraction - - Returns: - None. The function displays and saves various intermediary and final images, and - prints the properties of the detected circular features. - """ - # Extract parameters from the dictionary - element_size = morphology.disk( - params.Morphological_element_size - ) # Structuring element for morphological operations - - # Below are variables that might be used in the future coding - # connectivity = params.Connectivity # Neighborhood connectivity (4 or 8) - # marker_size = params.Marker_size # Marker size for watershed segmentation - # max_eccentricity = params.Max_Eccentricity # Maximum eccentricity threshold - # min_solidity = params.Min_Solidity # Minimum solidity threshold - # min_bubble_size = params.min_size # Minimum bubble size (in mm) - # do_batch = params.do_batch # Flag for batch processing - - # Display the original image - plt.figure() - plt.subplot(231) - plt.title("1. Original image") - plt.imshow(target_img, cmap="gray") - - # Apply thresholding and morphological processing - plt.subplot(232) - imgThreshold_ = threshold(target_img, bknd_img, threshold_value) - imgThreshold = morphological_process(imgThreshold_, element_size) - plt.title("2. Thresh&morph process") - plt.imshow(imgThreshold * 255, cmap="gray") - - # Apply distance transform - plt.subplot(233) - distTrans = cv2.distanceTransform(imgThreshold, cv2.DIST_L2, 5) - plt.title("3. Distance Transform") - plt.imshow(distTrans) - - # Apply thresholding to the distance transform - plt.subplot(234) - _, distThresh = cv2.threshold( - distTrans, 0.3 * distTrans.max(), 255, cv2.THRESH_BINARY - ) - plt.title("4. Threshold of distTrans") - plt.imshow(distThresh) - - # Apply connected component labeling - plt.subplot(235) - distThresh = distThresh.astype(np.uint8) - _, labels = cv2.connectedComponents(distThresh) - plt.title("5. Labels") - plt.imshow(labels) - - # Apply watershed segmentation - plt.figure() - plt.subplot(121) - labels = labels.astype(np.int32) - labels = cv2.watershed(imgRGB, labels).astype(np.int_) - plt.title("6. Final graph after watershed") - plt.imshow(labels) - - # Display the images - plt.show() - - # Calculate and print the circle properties - circle_properties = calculate_circle_properties(labels, px2cm) - pprint(circle_properties) - - -def default() -> None: - """Run the default image processing routine. - - This function loads the configuration parameters from the TOML file, calculates the - pixel-to-centimeter ratio using a reference ruler image, and then runs the image - processing algorithm on the target image to detect and analyze circular features. - - Args: - None. - - Returns: - None. The function orchestrates the loading of images, execution of the - algorithm, and display of results. - """ - # Load parameters from the TOML configuration file - params = load_toml("./bubble_analyser/config.toml") - - # Read path and image resample factor - ruler_img_path = params.ruler_img_path - target_img_path = params.target_img_path - bknd_img_path = params.background_img_path - img_resample_factor = params.resample - threshold_value = params.threshold_value - - # Calculate the pixel to cm ratio - px2cm = calculate_px2cm(ruler_img_path, img_resample_factor) - print(f"Pixel to cm ratio: {px2cm} cm/pixel") - - # Read the background and target image, resize and process into gray scale - bknd_img, _ = image_preprocess(bknd_img_path, img_resample_factor) - target_img, imgRGB = image_preprocess(target_img_path, img_resample_factor) - - # Run the default image processing algorithm - run_algorithm(target_img, bknd_img, imgRGB, params, px2cm, threshold_value) - - -if __name__ == "__main__": - default() - -# First background subtraction (optional) then otsu thresholding -# Let user define limitations based on the properties of the bubbles for filtering them -# Output the image that eliminate the bubbles being filtered out -# Table and Histogram -# Let user modify the parameters in UI -# Merge default branch +"""Bubble Analyser: Image Processing for Circular Feature Detection. + +This script is designed to process and analyze images to detect and evaluate circular +features, such as bubbles, using various image processing techniques. The script +integrates several modular functions for loading images, processing them, and +calculating properties of detected features in terms of real-world measurements. + +The key functionalities include: + +1. Loading configuration parameters from a TOML file, which govern the image processing + steps and parameters. +2. Loading and preprocessing images, including conversion to grayscale and resizing + based on a given resampling factor. +3. Calculating the conversion factor from pixels to centimeters using a reference ruler + image, ensuring that measurements of detected features are accurate and scalable. +4. Executing the image processing algorithm, which involves thresholding, morphological + processing, distance transformation, connected component labeling, and watershed + segmentation to isolate and analyze circular features. +5. Displaying and saving intermediary and final images, along with calculating and + printing properties such as equivalent diameter and area of the detected features. + +The script is structured to allow easy customization and extension, making it suitable +for a wide range of image analysis tasks that involve circular feature detection and +measurement. + +To run the script, simply execute the `default()` function, which orchestrates the +entire process from loading configurations and images to running the analysis and +displaying results. +""" + +import timeit +from pprint import pprint + +import cv2 +import numpy as np +import toml as tomllib +from numpy import typing as npt +from skimage import ( + color, + io, + morphology, + transform, +) + +from .calculate_circle_properties import ( + calculate_circle_properties, + filter_circle_properties, +) +from .calculate_px2mm import calculate_px2mm +from .config import Config +from .image_postprocess import overlay_labels_on_rgb +from .image_preprocess import image_preprocess +from .morphological_process import morphological_process +from .threshold import threshold + + +def load_image( + image_path: str, img_resample: float +) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: + """Read and preprocess the input image. + + This function loads an image from the specified path, resizes it according to the + given resampling factor, and converts it to grayscale if the image is in RGB format. + + Args: + image_path: The file path of the image to load. + img_resample: The factor by which the image will be resampled (e.g., 0.5 for + reducing the size by half). + + Returns: + A tuple containing: + - The preprocessed grayscale image (if the original was in RGB) or the original + grayscale image. + - The resized image in RGB format. + """ + # Read the input image + img = io.imread(image_path) + + imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + scale_percent = img_resample * 100 # percent of original size + width = int(imgRGB.shape[1] * scale_percent / 100) + height = int(imgRGB.shape[0] * scale_percent / 100) + dim = (width, height) + + # resize image + imgRGB = cv2.resize(imgRGB, dim, interpolation=cv2.INTER_AREA) + + img = transform.resize( + img, + (int(img.shape[0] * img_resample), int(img.shape[1] * img_resample)), + anti_aliasing=True, + ) + if img.ndim > 2: + img = color.rgb2gray(img) # Convert to grayscale if the image is in RGB + + return img, imgRGB + + +def load_toml(file_path: str) -> Config: + """Load configuration parameters from a TOML file. + + This function reads the TOML configuration file from the specified path and loads + its contents into a dictionary. + + Args: + file_path: The file path of the TOML configuration file. + + Returns: + A dictionary containing the configuration parameters from the TOML file. + """ + toml_data = tomllib.load(file_path) + + return Config(**toml_data) + + +def run_watershed_segmentation( + target_img: npt.NDArray[np.int_], + imgRGB: npt.NDArray[np.int_], + threshold_value: float = 0.3, + element_size: int = 5, + connectivity: int = 4, +) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: + """Run the image processing algorithm on the preprocessed image. + + This function takes the preprocessed image, the original RGB image, the conversion + factor from millimeters to pixels, and several threshold values as input. It then + applies watershed segmentation to detect circular features in the image. The + detected features are then filtered based on their properties, such as eccentricity, + solidity, circularity, and size. + + The function returns the processed image, the labeled image before filtering, the + properties of the detected circular features, and the labeled image after filtering. + + Parameters: + target_img (npt.NDArray[np.int_]): The preprocessed image after thresholding. + imgRGB (npt.NDArray[np.int_]): The original image in RGB format. + mm2px (float): The conversion factor from millimeters to pixels. + threshold_value (float, optional): The threshold value for background subtract. + Defaults to 0.3. + element_size (int, optional): The size of the morphological element for binary + operations. Defaults to 5. + connectivity (int, optional): The connectivity of the morphological operations. + Defaults to 4. + max_eccentricity (float, optional): The maximum eccentricity threshold for + filtering. Defaults to 1.0. + min_solidity (float, optional): The minimum solidity threshold for filtering. + Defaults to 0.9. + min_circularity (float, optional): The minimum circularity threshold for + filtering. Defaults to 0.1. + min_size (float, optional): The minimum size threshold for filtering in pixels. + Defaults to 0.1. + + Returns: + tuple[npt.NDArray[np.int_], npt.NDArray[np.int_], list[dict[str, float]], + npt.NDArray[np.int_]]: A tuple of four arrays, the first being the processed + image, the second being the labeled image before filtering, the third being + the properties of the detected circular features, and the fourth being the + labeled image after filtering. + """ + start_time = timeit.default_timer() + distTrans = cv2.distanceTransform(target_img, cv2.DIST_L2, element_size) + print(f"Distance transform time: {timeit.default_timer() - start_time:.4f} sec") + + start_time = timeit.default_timer() + # Apply thresholding to the distance transform - sure foreground area + _, distThresh = cv2.threshold( + distTrans, threshold_value * distTrans.max(), 255, cv2.THRESH_BINARY + ) + print(f"Thresholding time: {timeit.default_timer() - start_time:.4f} sec") + + start_time = timeit.default_timer() + sure_fg_initial = distThresh.copy() + + sure_bg = np.array( + cv2.dilate(target_img, np.ones((3, 3), np.uint8), iterations=3), dtype=np.uint8 + ) + sure_fg = np.array(sure_fg_initial, dtype=np.uint8) + + unknown = cv2.subtract(sure_bg, sure_fg) + + print( + f"Morphological operations time: {timeit.default_timer() - start_time:.4f} sec" + ) + + start_time = timeit.default_timer() + distThresh = distThresh.astype(np.uint8) + + _, labels = cv2.connectedComponents(sure_fg, connectivity) # type: ignore + labels = labels.astype(np.int32) + labels = labels + 1 + labels[unknown != 0] = 0 + print(f"Connected components time: {timeit.default_timer() - start_time:.4f} sec") + + start_time = timeit.default_timer() + labels_watershed = cv2.watershed(imgRGB, labels).astype(np.int_) + print(f"Watershed time: {timeit.default_timer() - start_time:.4f} sec") + + start_time = timeit.default_timer() + imgRGB_before_filtering = imgRGB.copy() + imgRGB_before_filtering = overlay_labels_on_rgb( + imgRGB_before_filtering, labels_watershed + ) + print( + f"Overlay labels before filtering time: \ + {timeit.default_timer() - start_time:.4f} sec" + ) + return imgRGB_before_filtering, labels_watershed + + +def final_circles_filtering( + imgRGB: npt.NDArray[np.int_], + labels: npt.NDArray[np.int_], + mm2px: float, + max_eccentricity: float, + min_solidity: float, + min_circularity: float, +) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_], list[dict[str, float]]]: + """Filter the circles in the image based on their properties. + + Args: + imgRGB (npt.NDArray[np.int_]): The image in RGB format. + labels (npt.NDArray[np.int_]): The labels of the circles in the image. + mm2px (float): The conversion factor from millimeters to pixels. + max_eccentricity (float): The maximum eccentricity threshold for filtering. + min_solidity (float): The minimum solidity threshold for filtering. + min_circularity (float): The minimum circularity threshold for filtering. + + Returns: + npt.NDArray[np.int_]: The filtered labels of the circles in the image. + """ + start_time = timeit.default_timer() + labels = filter_circle_properties( + labels, mm2px, max_eccentricity, min_solidity, min_circularity + ) + print(f"Filter properties time: {timeit.default_timer() - start_time:.4f} sec") + + start_time = timeit.default_timer() + circle_properties = calculate_circle_properties(labels, mm2px) + print(f"Calculate properties time: {timeit.default_timer() - start_time:.4f} sec") + pprint(circle_properties) + + start_time = timeit.default_timer() + imgRGB_overlay = overlay_labels_on_rgb(imgRGB, labels) + print(f"Overlay labels time: {timeit.default_timer() - start_time:.4f} sec") + + return imgRGB_overlay, labels, circle_properties + + +def pre_processing() -> ( + tuple[npt.NDArray[np.int_], npt.NDArray[np.int_], Config, float, float] +): + """Run the default image processing routine. + + This function loads the configuration parameters from the TOML file, calculates the + pixel-to-centimeter ratio using a reference ruler image, and then runs the image + processing algorithm on the target image to detect and analyze circular features. + + Args: + None. + + Returns: + None. The function orchestrates the loading of images, execution of the + algorithm, and display of results. + """ + # Load parameters from the TOML configuration file + params = load_toml("./bubble_analyser/config.toml") + + # Read path and image resample factor + ruler_img_path = params.ruler_img_path + target_img_path = params.target_img_path + bknd_img_path = params.background_img_path + img_resample_factor = params.resample + threshold_value = params.threshold_value + + # Calculate the pixel to mm ratio + mm2px, _ = calculate_px2mm(ruler_img_path, img_resample_factor) + print(f"Pixel to mm ratio: {mm2px} mm/pixel") + + # Read the background and target image, resize and process into gray scale + bknd_img, _ = image_preprocess(bknd_img_path, img_resample_factor) + target_img, imgRGB = image_preprocess(target_img_path, img_resample_factor) + + # Apply thresholding and morphological processing + imgThreshold = threshold(target_img, bknd_img, threshold_value) + element_size = morphology.disk(params.Morphological_element_size) + imgThreshold_new = morphological_process(imgThreshold, element_size) + + # plt.figure() + # plt.subplot(231) + # plt.title("1. Original image") + # plt.imshow(target_img, cmap="gray") + # plt.subplot(232) + # plt.title("2. Thresh process") + # plt.imshow(imgThreshold * 255, cmap="gray") + # plt.subplot(233) + # plt.title("3. morphological process") + # plt.imshow(imgThreshold * 255, cmap="gray") + # plt.show() + + # Run the default image processing algorithm + return imgThreshold_new, imgRGB, params, mm2px, threshold_value + + +def main() -> None: + """Run the default image processing routine. + + This function loads the configuration parameters from the TOML file, calculates the + pixel-to-centimeter ratio using a reference ruler image, and then runs the image + processing algorithm on the target image to detect and analyze circular features. + + Args: + None. + + Returns: + None. The function orchestrates the loading of images, execution of the + algorithm, and display of results. + """ + imgThreshold, imgRGB, params, px2mm, threshold_value = pre_processing() + # Run the default image processing algorithm + img_overlay, labels_watershed = run_watershed_segmentation( + imgThreshold, + imgRGB, + threshold_value, + element_size=params.Morphological_element_size, + connectivity=4, + ) + imgRGB_overlay, labels, circle_properties = final_circles_filtering( + imgRGB, + labels_watershed, + px2mm, + max_eccentricity=params.Max_Eccentricity, + min_solidity=params.Min_Solidity, + min_circularity=params.Min_Circularity, + ) + + +if __name__ == "__main__": + main() diff --git a/bubble_analyser/image_postprocess.py b/bubble_analyser/image_postprocess.py new file mode 100644 index 0000000..a4d51c0 --- /dev/null +++ b/bubble_analyser/image_postprocess.py @@ -0,0 +1,69 @@ +"""Functions that process the image after being watershed segmented. + +This module currently provides a single function, overlay_labels_on_rgb, which takes an +RGB image and a 2D array of labeled regions and combines them into a single image with +the labeled regions overlaid on the original image. The labeled regions are represented +with a unique color for each label, and the transparency of the overlay can be +controlled using the 'alpha' parameter. + +The function returns the resulting image as a 3D array in float format with range[0, 1]. +""" + +import cv2 +import numpy as np +from numpy import typing as npt + + +def overlay_labels_on_rgb( + imgRGB: npt.NDArray[np.int_], labels: npt.NDArray[np.int_], alpha: float = 0.5 +) -> npt.NDArray[np.int_]: + """Overlay labeled regions on an RGB image with a transparent color. + + Parameters + ---------- + imgRGB : ndarray + The RGB image to overlay the labeled regions on. + labels : ndarray + A 2D array of labeled regions, where each unique label is represented by a + distinct integer. + alpha : float, optional + The transparency of the overlay, with 0 being fully transparent and 1 being + fully opaque. Default is 0.5. + + Returns: + ------- + ndarray + The resulting image with the labeled regions overlaid on the original image. + """ + # Ensure imgRGB is in uint8 format + imgRGB = (imgRGB * 255.0).astype(np.uint8) if imgRGB.max() <= 1 else imgRGB + + unique_labels = np.unique(labels) + + # Convert the label image to BGR (OpenCV's color format is BGR, not RGB) + colored_labels = np.zeros_like(imgRGB) + + for label in unique_labels: + if label == 1: # Skip the background (assuming label 0 is background) + continue + # Create a mask for the current label + # Generate random hue (0-179 in OpenCV's HSV), max saturation, + # and max brightness + hue = np.random.randint(0, 179) + saturation = 255 # Max saturation + value = 255 # Max brightness + color_hsv = np.array( + [[[hue, saturation, value]]], dtype=np.uint8 + ) # HSV color format + color_bgr = cv2.cvtColor(color_hsv, cv2.COLOR_HSV2BGR)[0][ + 0 + ] # Convert HSV to BGR color + + # Create a mask for the current label + mask = labels == label + colored_labels[mask] = color_bgr + + # Blend the colored labels with the original image using transparency (alpha) + label_overlay = cv2.addWeighted(imgRGB, 1 - alpha, colored_labels, alpha, 0) + + return label_overlay # type: ignore diff --git a/bubble_analyser/image_preprocess.py b/bubble_analyser/image_preprocess.py index 005fb6c..d6d19ce 100644 --- a/bubble_analyser/image_preprocess.py +++ b/bubble_analyser/image_preprocess.py @@ -1,163 +1,184 @@ -"""Image Preprocessing Functions. - -This module provides a collection of functions for image loading, color space conversion -, and resizing. It supports the preprocessing steps required for image analysis tasks, -especially in contexts where images need to be adapted for algorithmic processing and -visualization. - -Key Functions: -- load_image(image_path): Loads an image from a specified path into a NumPy array. -- get_greyscale(image): Converts an RGB image to grayscale, facilitating algorithms - that require single-channel input. -- get_RGB(image): Converts an image from BGR (common in OpenCV) to RGB format, suitable - for consistent image display and processing. -- resize_for_RGB(image, img_resample_factor): Resizes an RGB image according to a - specified resampling factor, typically used to reduce the image size for faster - processing without losing significant detail. -- resize_for_original_image(image, img_resample_factor): Similar to resize_for_RGB but - uses skimage's transform for resizing, providing a high-quality downsampling suitable - for analytical purposes. -- image_preprocess(img_path, img_resample): Orchestrates the loading, converting, and - resizing of an image. It outputs both a grayscale version for processing and an RGB - version for visualization. - -These functions are designed to be modular and can be combined in different ways -depending on the specific requirements of the image processing task at hand. For example -, in a typical workflow for image analysis, an image might be loaded, converted to -grayscale for analysis, and also kept in RGB for result visualization. - -Usage: -These utilities are particularly useful in applications like computer vision and digital -image processing where preprocessing steps are crucial for subsequent analysis, such as -object detection, pattern recognition, and more. -""" - -from pathlib import Path -from typing import cast - -import cv2 -import numpy as np -from numpy import typing as npt -from skimage import ( - color, - io, - transform, -) - - -def load_image(image_path: Path) -> npt.NDArray[np.int_]: - """Read and preprocess the input image. - - Args: - image_path (str): The file path of the image to load. - - Returns: - npt.NDArray: The image read in ndarray format. - """ - # Read the input image - - img = io.imread(image_path) - - return img - - -def get_greyscale(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]: - """Converts an image to grayscale if it is in RGB format. - - Args: - image (npt.NDArray): The input image to be converted. - - Returns: - npt.NDArray: The grayscale image. - """ - if image.ndim > 2: - image = color.rgb2gray(image) # Convert to grayscale if the image is in RGB - return image - - -def get_RGB(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]: - """Converts an image from BGR color space to RGB color space. - - Args: - image (npt.NDArray): The input image in BGR format. - - Returns: - npt.NDArray: The converted image in RGB format. - """ - imgRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - return cast(npt.NDArray[np.int_], imgRGB) - - -def resize_for_RGB( - image: npt.NDArray[np.int_], img_resample_factor: float -) -> npt.NDArray[np.int_]: - """Resizes an image in RGB format based on the provided resampling factor. - - Args: - image (npt.NDArray): The input image in RGB format. - img_resample_factor (float): The factor by which the image will be resampled. - - Returns: - npt.NDArray: The resized image in RGB format. - """ - scale_percent = img_resample_factor * 100 # percent of original size - width = int(image.shape[1] * scale_percent / 100) - height = int(image.shape[0] * scale_percent / 100) - img_resample_dimension = (width, height) - - image_resized = cv2.resize( - image, img_resample_dimension, interpolation=cv2.INTER_AREA - ) - return cast(npt.NDArray[np.int_], image_resized) - - -def resize_for_original_image( - image: npt.NDArray[np.int_], img_resample_factor: float -) -> npt.NDArray[np.int_]: - """Resizes an image based on the provided resampling factor for original image. - - Args: - image (npt.NDArray): The input image to be resized. - img_resample_factor (float): The factor by which the image will be resampled. - - Returns: - npt.NDArray: The resized image. - """ - image = transform.resize( - image, - ( - int(image.shape[0] * img_resample_factor), - int(image.shape[1] * img_resample_factor), - ), - anti_aliasing=True, - ) - return image - - -def image_preprocess( - img_path: Path, img_resample: float -) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: - """Load an image, resizing it based on a provided resampling factor. - - The resized grayscale image (img) is for use in the following "default" watershed - algorithm as a target image. And the RGB image (imgRGB) is for the visualization of - the results. They are resized in different ways for different use cases (the output - format of the methods are differnt). - - Args: - img_path (str): The file path of the image to preprocess. - img_resample (float): The resampling factor to apply to the image. - - Returns: - tuple[npt.NDArray, npt.NDArray]: A tuple containing the resized grayscale image - and the resized RGB image. - """ - image = load_image(img_path) - image_RGB = get_RGB(image) - - image = resize_for_original_image(image, img_resample) - image = get_greyscale(image) - - image_RGB = resize_for_RGB(image_RGB, img_resample) - - return image, image_RGB +"""Image Preprocessing Functions. + +This module provides a collection of functions for image loading, color space conversion +, and resizing. It supports the preprocessing steps required for image analysis tasks, +especially in contexts where images need to be adapted for algorithmic processing and +visualization. + +Key Functions: +- load_image(image_path): Loads an image from a specified path into a NumPy array. +- get_greyscale(image): Converts an RGB image to grayscale, facilitating algorithms + that require single-channel input. +- get_RGB(image): Converts an image from BGR (common in OpenCV) to RGB format, suitable + for consistent image display and processing. +- resize_for_RGB(image, img_resample_factor): Resizes an RGB image according to a + specified resampling factor, typically used to reduce the image size for faster + processing without losing significant detail. +- resize_for_original_image(image, img_resample_factor): Similar to resize_for_RGB but + uses skimage's transform for resizing, providing a high-quality downsampling suitable + for analytical purposes. +- image_preprocess(img_path, img_resample): Orchestrates the loading, converting, and + resizing of an image. It outputs both a grayscale version for processing and an RGB + version for visualization. + +These functions are designed to be modular and can be combined in different ways +depending on the specific requirements of the image processing task at hand. For example +, in a typical workflow for image analysis, an image might be loaded, converted to +grayscale for analysis, and also kept in RGB for result visualization. + +Usage: +These utilities are particularly useful in applications like computer vision and digital +image processing where preprocessing steps are crucial for subsequent analysis, such as +object detection, pattern recognition, and more. +""" + +import time +from pathlib import Path +from typing import cast + +import cv2 +import numpy as np +from numpy import typing as npt +from skimage import ( + color, + io, +) + + +def load_image(image_path: Path) -> npt.NDArray[np.int_]: + """Read and preprocess the input image. + + Args: + image_path (str): The file path of the image to load. + + Returns: + npt.NDArray: The image read in ndarray format. + """ + # Read the input image + + img = io.imread(image_path) + + return img + + +def get_greyscale(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]: + """Converts an image to grayscale if it is in RGB format. + + Args: + image (npt.NDArray): The input image to be converted. + + Returns: + npt.NDArray: The grayscale image. + """ + if image.ndim > 2: + image = color.rgb2gray(image) # Convert to grayscale if the image is in RGB + return image + + +def get_RGB(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]: + """Converts an image from BGR color space to RGB color space. + + Args: + image (npt.NDArray): The input image in BGR format. + + Returns: + npt.NDArray: The converted image in RGB format. + """ + imgRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + return cast(npt.NDArray[np.int_], imgRGB) + + +def resize_for_RGB( + image: npt.NDArray[np.int_], img_resample_factor: float +) -> npt.NDArray[np.int_]: + """Resizes an image in RGB format based on the provided resampling factor. + + Args: + image (npt.NDArray): The input image in RGB format. + img_resample_factor (float): The factor by which the image will be resampled. + + Returns: + npt.NDArray: The resized image in RGB format. + """ + scale_percent = img_resample_factor * 100 # percent of original size + width = int(image.shape[1] * scale_percent / 100) + height = int(image.shape[0] * scale_percent / 100) + img_resample_dimension = (width, height) + + image_resized = cv2.resize( + image, img_resample_dimension, interpolation=cv2.INTER_AREA + ) + return cast(npt.NDArray[np.int_], image_resized) + + +def resize_for_original_image( + image: npt.NDArray[np.int_], img_resample_factor: float +) -> npt.NDArray[np.int_]: + """Resizes an image based on the provided resampling factor for original image. + + Args: + image (npt.NDArray): The input image to be resized. + img_resample_factor (float): The factor by which the image will be resampled. + + Returns: + npt.NDArray: The resized image. + """ + # image = transform.resize( + # image, + # ( + # int(image.shape[0] * img_resample_factor), + # int(image.shape[1] * img_resample_factor), + # ), + # anti_aliasing=True, + # ) + # return image + + resize_image: npt.NDArray[np.int_] = cv2.resize( + image, + (0, 0), + fx=img_resample_factor, + fy=img_resample_factor, + interpolation=cv2.INTER_AREA, + ) # type: ignore + return resize_image + + +def image_preprocess( + img_path: Path, img_resample: float +) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]: + """Load an image, resizing it based on a provided resampling factor. + + The resized grayscale image (img) is for use in the following "default" watershed + algorithm as a target image. And the RGB image (imgRGB) is for the visualization of + the results. They are resized in different ways for different use cases (the output + format of the methods are differnt). + + Args: + img_path (str): The file path of the image to preprocess. + img_resample (float): The resampling factor to apply to the image. + + Returns: + tuple[npt.NDArray, npt.NDArray]: A tuple containing the resized grayscale image + and the resized RGB image. + """ + start_time = time.perf_counter() + image = load_image(img_path) + print("Time used for load_image: ", time.perf_counter() - start_time) + + start_time = time.perf_counter() + image_RGB = get_RGB(image) + print("Time used for get_RGB: ", time.perf_counter() - start_time) + + start_time = time.perf_counter() + image = resize_for_original_image(image, img_resample) + print("Time used for resize_for_original_image: ", time.perf_counter() - start_time) + + start_time = time.perf_counter() + image = get_greyscale(image) + print("Time used for get_greyscale: ", time.perf_counter() - start_time) + + start_time = time.perf_counter() + image_RGB = resize_for_RGB(image_RGB, img_resample) + print("Time used for resize_for_RGB: ", time.perf_counter() - start_time) + + return image, image_RGB diff --git a/bubble_analyser/morphological_process.py b/bubble_analyser/morphological_process.py index e726869..5df6b78 100644 --- a/bubble_analyser/morphological_process.py +++ b/bubble_analyser/morphological_process.py @@ -1,57 +1,68 @@ -"""Morphological Processing Function for filling holes and clear borders. - -This module includes functions for advanced image processing using morphological -operations tailored to for filling holes and clear borders for further analysis. It -specifically focuses on refining the binary masks generated during image segmentation -processes. - -Function: -- morphological_process(target_img, element_size): Enhances a binary image by - applying morphological operations such as closing, hole filling, and border clearing. - -This function is particularly useful in contexts where binary images derived from -thresholding or other segmentation methods contain noise, small holes, or artifacts -that can interfere with further analysis. By using operations like closing to connect -nearby regions, filling holes to ensure that objects are solid, and clearing borders -to remove partial objects, this function prepares images for more reliable and robust -analysis. -""" - -import numpy as np -from numpy import typing as npt -from scipy import ndimage -from skimage import ( - morphology, - segmentation, -) - - -def morphological_process( - target_img: npt.NDArray[np.bool_], element_size: int -) -> npt.NDArray[np.int_]: - """Apply morphological operations to process the target image. - - This function performs a series of morphological operations on the input image, - including closing, filling holes, and clearing borders. These operations help in - refining the binary image by removing noise and filling gaps. - - Args: - target_img: A binary image (numpy array) where the regions of interest are - typically in white (True) and the background in black (False). - element_size: A structuring element used for morphological closing, typically a - disk-shaped array. - - - Returns: - A processed binary image (numpy array) where the regions of interest are more - defined, with filled holes and cleared borders. - """ - # Perform morphological closing and fill holes - image_processed = morphology.closing(target_img, element_size) - image_processed = ndimage.binary_fill_holes(image_processed) - image_processed = segmentation.clear_border(image_processed) - - image_processed = image_processed.astype(np.uint8) - # opening = cv2.morphologyEx(B,cv2.MORPH_OPEN,kernel, iterations = 2) - - return image_processed +"""Morphological Processing Function for filling holes and clear borders. + +This module includes functions for advanced image processing using morphological +operations tailored to for filling holes and clear borders for further analysis. It +specifically focuses on refining the binary masks generated during image segmentation +processes. + +Function: +- morphological_process(target_img, element_size): Enhances a binary image by + applying morphological operations such as closing, hole filling, and border clearing. + +This function is particularly useful in contexts where binary images derived from +thresholding or other segmentation methods contain noise, small holes, or artifacts +that can interfere with further analysis. By using operations like closing to connect +nearby regions, filling holes to ensure that objects are solid, and clearing borders +to remove partial objects, this function prepares images for more reliable and robust +analysis. +""" + +import time + +import cv2 +import numpy as np +from numpy import typing as npt +from scipy import ndimage +from skimage import ( + segmentation, +) + + +def morphological_process( + target_img: npt.NDArray[np.bool_], element_size: int +) -> npt.NDArray[np.int_]: + """Apply morphological operations to process the target image. + + This function performs a series of morphological operations on the input image, + including closing, filling holes, and clearing borders. These operations help in + refining the binary image by removing noise and filling gaps. + + Args: + target_img: A binary image (numpy array) where the regions of interest are + typically in white (True) and the background in black (False). + element_size: A structuring element used for morphological closing, typically a + disk-shaped array. + + + Returns: + A processed binary image (numpy array) where the regions of interest are more + defined, with filled holes and cleared borders. + """ + start_time = time.perf_counter() + + # image_processed = morphology.closing(target_img, element_size) + image_processed = cv2.morphologyEx( + target_img.astype(np.uint8), cv2.MORPH_CLOSE, element_size + ) # type: ignore + print("Time consumed for closing: ", time.perf_counter() - start_time) + start_time = time.perf_counter() + image_processed = ndimage.binary_fill_holes(image_processed) + print("Time consumed for filling holes: ", time.perf_counter() - start_time) + start_time = time.perf_counter() + image_processed = segmentation.clear_border(image_processed) + print("Time consumed for clearing borders: ", time.perf_counter() - start_time) + + image_processed = image_processed.astype(np.uint8) + # opening = cv2.morphologyEx(B,cv2.MORPH_OPEN,kernel, iterations = 2) + + return image_processed diff --git a/bubble_analyser/threshold.py b/bubble_analyser/threshold.py index 40ee358..890bdf3 100644 --- a/bubble_analyser/threshold.py +++ b/bubble_analyser/threshold.py @@ -1,101 +1,94 @@ -"""Main Thresholding Functions. - -This module provides functions for applying various thresholding techniques to images, -aimed at segmenting objects from their backgrounds. It includes implementations of -Otsu's method and a customizable thresholding approach that involves background -subtraction. - -Functions: -- otsu_threshold(target_img): Applies Otsu's method to a grayscale image to create - a binary mask, which is then inverted for consistency with other processing steps. -- select_threshold_method(): Provides an interactive prompt for the user to select - a thresholding method from available options, enhancing flexibility in choosing - the appropriate method for different scenarios. -- threshold(target_img, bknd_img, threshold_value): Applies the chosen threshold method - to the target image, supporting either Otsu's method or a custom threshold based on - background subtraction, determined by user input. - -These thresholding functions are adaptable to various use cases, from basic academic -projects to complex industrial applications requiring robust foreground-background -segmentation. They are particularly useful in workflows that require pre-processing -before detailed image analysis, such as feature detection or object classification. - -Usage: -The functions can be directly called with appropriate parameters, with `threshold` -function allowing for dynamic method selection based on runtime decisions. This -design ensures that users can select the most suitable thresholding technique -based on the specific characteristics of the images they are working with. -""" - -import numpy as np -from numpy import typing as npt -from skimage import ( - filters, -) - -from .background_subtraction_threshold import background_subtraction_threshold - - -def otsu_threshold(target_img: npt.NDArray[np.int_]) -> npt.NDArray[np.bool_]: - """Apply Otsu's thresholding to the target image and return an inverted binary mask. - - This function takes a target image and a background image. It applies Otsu's - thresholding to the target image to create a binary mask where the foreground - objects are separatedfrom the background. The binary mask is then inverted, so - the foreground becomes the background and vice versa, the inversion is for easier - processing in following morphologcal process. - - Args: - target_img: A grayscale image (2D array) representing the target image. - bknd_img: A grayscale image (2D array) representing the background image (not - used in this function). - - Returns: - A binary (inverted) image where the foreground and background are swapped. - """ - binary_image = target_img > filters.threshold_otsu( - target_img - ) # Binary image using Otsu's thresholding - return ~binary_image # Invert the binary image - - -def select_threshold_method() -> str: - """Selects a threshold method from a list of available options. - - Prompts the user to choose a threshold method from the list of options. - The options are displayed with their corresponding numbers, and the user - is asked to input the number of their chosen method. - - Returns: - str: The chosen threshold method. - """ - options = ["OTSU's method", "Background subtraction"] - print("Select a threshold method:") - for i, option in enumerate(options): - print(f"{i+1}. {option}") - choice = input("Enter the number of your choice: ") - return options[int(choice) - 1] - - -def threshold( - target_img: npt.NDArray[np.int_], - bknd_img: npt.NDArray[np.int_], - threshold_value: float, -) -> npt.NDArray[np.bool]: - """Applies a threshold to the target image based on the selected method. - - Args: - target_img (npt.NDArray): The target image to apply the threshold to. - bknd_img (npt.NDArray): The background image used for background subtraction. - threshold_value (float): The threshold value used for background subtraction. - - Returns: - npt.NDArray: The thresholded image. - """ - method = select_threshold_method() - if method == "OTSU's method": - return otsu_threshold(target_img) - elif method == "Background subtraction": - return background_subtraction_threshold(target_img, bknd_img, threshold_value) - else: - raise ValueError(f"Unsupported threshold method: {method}") +"""Main Thresholding Functions. + +This module provides functions for applying various thresholding techniques to images, +aimed at segmenting objects from their backgrounds. It includes implementations of +Otsu's method and a customizable thresholding approach that involves background +subtraction. + +Functions: +- otsu_threshold(target_img): Applies Otsu's method to a grayscale image to create + a binary mask, which is then inverted for consistency with other processing steps. +- select_threshold_method(): Provides an interactive prompt for the user to select + a thresholding method from available options, enhancing flexibility in choosing + the appropriate method for different scenarios. +- threshold(target_img, bknd_img, threshold_value): Applies the chosen threshold method + to the target image, supporting either Otsu's method or a custom threshold based on + background subtraction, determined by user input. + +These thresholding functions are adaptable to various use cases, from basic academic +projects to complex industrial applications requiring robust foreground-background +segmentation. They are particularly useful in workflows that require pre-processing +before detailed image analysis, such as feature detection or object classification. + +Usage: +The functions can be directly called with appropriate parameters, with `threshold` +function allowing for dynamic method selection based on runtime decisions. This +design ensures that users can select the most suitable thresholding technique +based on the specific characteristics of the images they are working with. +""" + +import numpy as np +from numpy import typing as npt +from skimage import ( + filters, +) + +from .background_subtraction_threshold import background_subtraction_threshold + + +def otsu_threshold(target_img: npt.NDArray[np.int_]) -> npt.NDArray[np.bool_]: + """Apply Otsu's thresholding to the target image and return an inverted binary mask. + + This function takes a target image and a background image. It applies Otsu's + thresholding to the target image to create a binary mask where the foreground + objects are separatedfrom the background. The binary mask is then inverted, so + the foreground becomes the background and vice versa, the inversion is for easier + processing in following morphologcal process. + + Args: + target_img: A grayscale image (2D array) representing the target image. + bknd_img: A grayscale image (2D array) representing the background image (not + used in this function). + + Returns: + A binary (inverted) image where the foreground and background are swapped. + """ + binary_image = target_img > filters.threshold_otsu( + target_img + ) # Binary image using Otsu's thresholding + + return binary_image + + +def threshold( + target_img: npt.NDArray[np.int_], + bknd_img: npt.NDArray[np.int_], + threshold_value: float, +) -> npt.NDArray[np.bool]: + """Applies a threshold to the target image based on the selected method. + + Args: + target_img (npt.NDArray): The target image to apply the threshold to. + bknd_img (npt.NDArray): The background image used for background subtraction. + threshold_value (float): The threshold value used for background subtraction. + + Returns: + npt.NDArray: The thresholded image. + """ + target_img = background_subtraction_threshold(target_img, bknd_img) + return otsu_threshold(target_img) + + +def threshold_without_background( + target_img: npt.NDArray[np.int_], threshold_value: float +) -> npt.NDArray[np.bool_]: + """Applies a threshold to the target image without background subtraction. + + Args: + target_img (npt.NDArray): The target image to apply the threshold to. + threshold_value (float): The threshold value used for background subtraction. + + Returns: + npt.NDArray: The thresholded image. + """ + return ~otsu_threshold(target_img) diff --git a/docs/index.md b/docs/index.md deleted file mode 100644 index c1e0f92..0000000 --- a/docs/index.md +++ /dev/null @@ -1,19 +0,0 @@ -# Welcome to MkDocs - -This is the documentation for Bubble Analyser - -For full documentation visit [mkdocs.org](https://www.mkdocs.org). - -## Commands - -* `mkdocs new [dir-name]` - Create a new project. -* `mkdocs serve` - Start the live-reloading docs server. -* `mkdocs build` - Build the documentation site. -* `mkdocs -h` - Print help message and exit. - -## Project layout - - mkdocs.yml # The configuration file. - docs/ - index.md # The documentation homepage. - ... # Other markdown pages, images and other files. diff --git a/poetry.lock b/poetry.lock index f0811bc..5dd5258 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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"zope.event", "zope.testing"] +testing = ["coverage[toml]", "zope.event", "zope.testing"] + [metadata] lock-version = "2.0" -python-versions = "^3.10" -content-hash = "0f151e30f9dff59463c7635ecb20c77817fdca510ad5fd7a9db13f05a23e79ae" +python-versions = ">=3.12, <3.13" +content-hash = "1851b118fee215e182911d8899deccc35f22502d7bf832e18bb9ff5d4b957d0a" diff --git a/pyproject.toml b/pyproject.toml index a35802e..49d2172 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,70 +1,73 @@ -[tool.poetry] -name = "bubble_analyser" -version = "0.1.0" -description = "[Description for project.]" -authors = [ - "Diego Alonso Álvarez ", - "Imperial College London RSE Team " -] - -[tool.poetry.dependencies] -python = "^3.10" -matplotlib = "^3.9.1.post1" -toml = "^0.10.2" -numpy = "^2.0.1" -scikit-image = "^0.24.0" -scipy = "^1.14.0" -pydantic = "^2.8.2" -pathlib = "^1.0.1" -typing-extensions = "^4.12.2" -opencv-python = "^4.10.0.84" - -[tool.poetry.group.docs.dependencies] -mkdocs = "^1.6.0" 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-[tool.ruff.lint.per-file-ignores] -"tests/*" = ["D100", "D104"] # Missing docstring in public module, Missing docstring in public package +[tool.poetry] +name = "bubble_analyser" +version = "0.1.0" +description = "[Description for project.]" +authors = [ + "Diego Alonso Álvarez ", + "Imperial College London RSE Team " +] + +[tool.poetry.dependencies] +python = ">=3.12, <3.13" +matplotlib = "^3.9.1.post1" +toml = "^0.10.2" +numpy = "^2.0.1" +scikit-image = "^0.24.0" +scipy = "^1.14.0" +pydantic = "^2.8.2" +pathlib = "^1.0.1" +typing-extensions = "^4.12.2" +pyside6 = "^6.7.2" +opencv-python = "^4.10.0.84" +datetime = "^5.5" +numba = "^0.60.0" + +[tool.poetry.group.docs.dependencies] +mkdocs = "^1.6.0" +mkdocs-material = "^9.5.29" + +[tool.poetry.group.dev.dependencies] +pytest = "^8.2" +pytest-cov = "^5.0.0" +pytest-mypy = "^0.10.0" +pytest-mock = "^3.7.0" +pre-commit = "^3.0.4" +ruff = "^0.5.2" +types-toml = "^0.10.8.20240310" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.mypy] +ignore_missing_imports = true +disallow_any_explicit = true +disallow_any_generics = true +warn_unreachable = true +warn_unused_ignores = false +disallow_untyped_defs = true +exclude = [".venv/"] + +[[tool.mypy.overrides]] +module = "tests.*" +disallow_untyped_defs = false + +[tool.pytest.ini_options] +addopts = "-v --mypy -p no:warnings --cov=bubble_analyser --cov-report=html --doctest-modules --ignore=bubble_analyser/__main__.py" + +[tool.ruff] +target-version = "py312" + +[tool.ruff.lint] +select = [ + "D", # pydocstyle + "E", # pycodestyle + "F", # Pyflakes + "I", # isort + "UP", # pyupgrade + "RUF" # ruff +] +pydocstyle.convention = "google" + +[tool.ruff.lint.per-file-ignores] +"tests/*" = ["D100", "D104"] # Missing docstring in public module, Missing docstring in public package diff --git a/tests/__init__.py b/tests/__init__.py index 76d8514..245b9c8 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,6 +1,6 @@ -"""Unit tests for MyProject.""" - -from logging import getLogger - -# Disable flake8 logger as it can be rather verbose -getLogger("flake8").propagate = False +"""Unit tests for MyProject.""" + +from logging import getLogger + +# Disable flake8 logger as it can be rather verbose +getLogger("flake8").propagate = False diff --git a/tests/calibration_files/Background.png b/tests/calibration_files/Background.png deleted file mode 100644 index c220a23..0000000 Binary files a/tests/calibration_files/Background.png and /dev/null differ diff --git a/tests/calibration_files/Ruler.png b/tests/calibration_files/Ruler.png deleted file mode 100644 index 20491eb..0000000 Binary files a/tests/calibration_files/Ruler.png and /dev/null differ diff --git a/tests/calibration_files/config.toml b/tests/calibration_files/config.toml deleted file mode 100644 index 18bed01..0000000 --- a/tests/calibration_files/config.toml +++ /dev/null @@ -1,5 +0,0 @@ -# Config file for bubble analyser - -[image_resolution] -value = 183.06589 -units = "px/mm" diff --git a/tests/sample_images/01.jpg b/tests/sample_images/01.jpg deleted file mode 100644 index 9d4da5f..0000000 Binary files a/tests/sample_images/01.jpg and /dev/null differ diff --git a/tests/sample_images/02.jpg b/tests/sample_images/02.jpg deleted file mode 100644 index eecd72c..0000000 Binary files a/tests/sample_images/02.jpg and /dev/null differ diff --git a/tests/sample_images/03.jpg b/tests/sample_images/03.jpg deleted file mode 100644 index 1e2d128..0000000 Binary files a/tests/sample_images/03.jpg and /dev/null differ diff --git a/tests/sample_images/04.jpg b/tests/sample_images/04.jpg deleted file mode 100644 index 51902ca..0000000 Binary files a/tests/sample_images/04.jpg and /dev/null differ diff --git a/tests/sample_images/05.jpg b/tests/sample_images/05.jpg deleted file mode 100644 index d650b28..0000000 Binary files a/tests/sample_images/05.jpg and /dev/null differ diff --git a/tests/sample_images/06.jpg b/tests/sample_images/06.jpg deleted file mode 100644 index dc6a1f8..0000000 Binary files a/tests/sample_images/06.jpg and /dev/null differ diff --git a/tests/sample_images/07.jpg b/tests/sample_images/07.jpg deleted file mode 100644 index 4423834..0000000 Binary files a/tests/sample_images/07.jpg and /dev/null differ diff --git a/tests/sample_images/08.jpg b/tests/sample_images/08.jpg deleted file mode 100644 index a095c2a..0000000 Binary files a/tests/sample_images/08.jpg and /dev/null differ diff --git a/tests/sample_images/09.jpg b/tests/sample_images/09.jpg deleted file mode 100644 index 1ebbb60..0000000 Binary files a/tests/sample_images/09.jpg and /dev/null differ diff --git a/tests/sample_images/10.JPG b/tests/sample_images/10.JPG deleted file mode 100644 index c9e08c5..0000000 Binary files a/tests/sample_images/10.JPG and /dev/null differ diff --git a/tests/sample_images/11.JPG b/tests/sample_images/11.JPG deleted file mode 100644 index 428cbe2..0000000 Binary files a/tests/sample_images/11.JPG and /dev/null differ diff --git a/tests/sample_images/12.JPG b/tests/sample_images/12.JPG deleted file mode 100644 index 9323544..0000000 Binary files a/tests/sample_images/12.JPG and /dev/null differ diff --git a/tests/sample_images/13.JPG b/tests/sample_images/13.JPG deleted file mode 100644 index d445a91..0000000 Binary files a/tests/sample_images/13.JPG and /dev/null differ diff --git a/tests/sample_images/14.JPG b/tests/sample_images/14.JPG deleted file mode 100644 index 9912376..0000000 Binary files a/tests/sample_images/14.JPG and /dev/null differ diff --git a/tests/sample_images/15.JPG b/tests/sample_images/15.JPG deleted file mode 100644 index 4d946a4..0000000 Binary files a/tests/sample_images/15.JPG and /dev/null differ diff --git a/tests/test_bubble_analyser.py b/tests/test_bubble_analyser.py index fc5a92c..68cd9a9 100644 --- a/tests/test_bubble_analyser.py +++ b/tests/test_bubble_analyser.py @@ -1,8 +1,8 @@ -"""Tests for the main module.""" - -from bubble_analyser import __version__ - - -def test_version(): - """Check that the version is acceptable.""" - assert __version__ == "0.1.0" +"""Tests for the main module.""" + +from bubble_analyser import __version__ + + +def test_version(): + """Check that the version is acceptable.""" + assert __version__ == "0.1.0"