diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..fcdf1fb --- /dev/null +++ b/.gitignore @@ -0,0 +1,98 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# 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 + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# pyenv +.python-version + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# VS Code +.vscode +!.vscode/settings.json +!.vscode/tasks.json +!.vscode/launch.json +!.vscode/extensions.json +*.code-workspace + +# Local History for Visual Studio Code +.history/ + +# Mac OS-specific storage files +.DS_Store +**/.DS_Store + +# Google Drive related +.tmp.driveupload +.tmp.drivedownload + +# Data +/data +*.mp4 + +# Misc +TODOs.txt + diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..f13a28e --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,20 @@ +cff-version: 1.2.0 +title: Stabilo +abstract: Reference frame video stabilization with or without user-provided masks. +message: If you use this software, please cite it as below. +type: software +authors: + - given-names: Robert + family-names: Fonod + email: robert.fonod@ieee.org + affiliation: EPFL + orcid: 'https://orcid.org/0000-0002-9434-3156' +identifiers: + - type: doi + value: XXX + description: Zenodo archive +repository-code: 'https://github.com/rfonod/stabilo' +url: 'https://github.com/rfonod/stabilo' +license: MIT +version: 0.1.0 +date-released: '2024-06-18' \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..4c38a57 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Robert Fonod + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..7cb8b3b --- /dev/null +++ b/README.md @@ -0,0 +1,134 @@ +# Stabilo + +![GitHub Release](https://img.shields.io/github/v/release/rfonod/stabilo?include_prereleases) [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) ![GitHub](https://img.shields.io/badge/Development-Active-brightgreen) + +🚧 **Development Notice** 🚧 + +> ⚠️ **IMPORTANT:** Stabilo is currently in its preliminary stages and under active development. Not all features are complete, and significant changes may occur. It is recommended for experimental use only. Please report any issues you encounter, and feel free to contribute to the project. + +Stabilo is a Python package for stabilizing video frames or tracked object trajectories in videos using homography or affine transformations, with optional user-provided masks that define areas to ignore during stabilization. It is optimized for use in environments where accurate, frame-by-frame alignment is crucial, such as video surveillance and research applications. For instance, Stabilo can be integrated with object detection and tracking algorithms to stabilize trajectories, enhancing the performance of such systems. See for example [this project](https://github.com/rfonod/geo-trax). The package offers flexibility through a variety of transformation and enhancement options, including the ability to handle different types of feature detection and masking techniques. + +## Features + +- **Video Stabilization**: Align video frames to a selected reference using homography or affine transformations. +- **Trajectory Stabilization**: Apply stabilization to object trajectories using homography or affine transformations. +- **User-Defined Masks**: Allow users to specify areas to ignore during stabilization, such as bounding boxes of moving objects or regions of no interest. +- **Wide Range of Algorithms**: Support for various feature detectors (e.g., ORB, (R)SIFT, BRISK, (A)KAZE), matchers (e.g., BF, FLANN), RANSAC algorithms, transformation types, and pre-processing options. +- **Customizable Parameters**: Fine-tune the stabilization process by adjusting parameters such as the number of keypoints, RANSAC and matching thresholds, and downsampling factors. +- **Visualization Tools**: Generate visualizations of the stabilization process, including frame-by-frame comparisons. +- **Threshold Analysis**: Analyze the relationship between detection thresholds and keypoint counts for BRISK, KAZE, and AKAZE feature detectors. + +
+🚀 Planned Enhancements + +- **Benchmarking and Tuning Tools**: Develop tools to benchmark and tune the performance of the stabilization algorithms. +- **Trajectory Stabilization Script**: Create a script to stabilize object trajectories in videos. +- **Custom Mask Encoding**: Support for more generic types of custom mask encodings. +- **Custom Reference Frame Selection**: Allow users to select a custom reference frame for stabilization. +- **GPU Acceleration**: Utilize GPU acceleration for faster processing. +- **Documentation**: Provide detailed documentation and examples for ease of use. +- **Unit Tests**: Implement comprehensive unit tests to ensure the stability and reliability of the package. +- **Deployment to PyPI**: Publish the package on PyPI for easy installation and distribution. + +
+ +## Installation + +First, create a **Python Virtual Environment** (Python >= 3.9) using e.g., [Miniconda3](https://docs.anaconda.com/free/miniconda/): +```bash +conda create -n stabilo python=3.9 -y +conda activate stabilo +``` + +Then, install the package using one of the following methods: + +### Option 1: Install from PyPI +You can install the package from PyPI (not available yet): + + + +```sh +pip install stabilo +``` + + + + +### Option 2: Install from Source (recommended) +You can install the package directly from the repository: +```sh +pip install git+https://github.com/rfonod/stabilo.git +``` + +### Option 3: Install from Local Source + +Clone the repository and install the package: + +```sh +git clone https://github.com/rfonod/stabilo.git +cd stabilo +pip install . +``` + +If you want the changes you make in the repo to be reflected in your install, use `pip install -e .` instead of `pip install .`. + +## Example Usage + +```python +from stabilo import Stabilizer + +# Create an instance of the Stabilizer class with default parameters +stabilizer = Stabilizer() + +# Set a reference frame with optional mask (e.g., bounding boxes) +stabilizer.set_ref_frame(ref_frame, ref_mask) + +# Stabilize any consecutive frame with optional mask +stabilizer.stabilize(frame, mask) + +# Get the stabilized (warped to the reference frame) frame +stabilized_frame = stabilizer.warp_cur_frame() + +# Get the transformed bounding boxes (if mask was provided) +stabilized_boxes = stabilizer.transform_cur_boxes() +``` + +## Additional Scripts + +Utility scripts are provided to demonstrate the functionality of the Stabilo package. These scripts can be found in the `scripts` directory. + +#### Stabilization Examples + +- `stabilize_video.py`: Demonstrates video stabilization relative to a reference frame. +- `stabilize_boxes.py`: Shows how to stabilize bounding boxes relative to a reference frame. + +#### Threshold Analysis + +The `find_threshold_models.py` script is designed to model the relationship between detection thresholds for BRISK, KAZE, and AKAZE feature detectors and their average keypoint counts. It outputs regression models, saves pertinent data, and generates plots for visual analysis. + +To run this script, install the optional dependencies `pip install .[extras]` (or `pip install '.[extras]'` if you use zsh). + +## Citing This Work + +If you use this project in your academic research, commercial products, or any published material, please acknowledge its use by citing it. For the correct citation, refer to the DOI badge above, which links to the appropriate version of the release on Zenodo. Ensure you reference the version you actually used. A formatted citation can also be obtained from the top right of the [GitHub repository](https://github.com/stabilo). + +```bibtex +@software{Fonod_Stabilo_2024, +author = {Fonod, Robert}, +license = {MIT}, +month = jun, +title = {{Stabilo}}, +url = {https://github.com/rfonod/stabilo}, +version = {0.1.0}, +year = {2024} +} +``` + +## Contributing + +Contributions are welcome! If you encounter any issues or have suggestions for improvements, please open a [GitHub Issue](https://github.com/rfonod/stabilo/issues) or submit a pull request. Your contributions are greatly appreciated! + + +## License + +This project is licensed under the MIT License, an [OSI-approved](https://opensource.org/licenses/MIT) open-source license, which allows for both academic and commercial use. By citing this project, you help support its development and acknowledge the effort that went into creating it. For more details, see the [LICENSE](LICENSE) file. Thank you! diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..a686a24 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,49 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "stabilo" +dynamic = ["version"] +description = "Reference frame video stabilization with optional user-provided masks." +authors = [ + { name = "Robert Fonod", email = "robert.fonod@ieee.org" }, +] +readme = "README.md" +license = { file = "LICENSE" } +requires-python = ">=3.9" +keywords = ["stabilo", "video-stabilization", "object-stabilization", "mask", "reference-frame", "computer-vision"] +classifiers = [ + "Development Status :: 3 - Alpha", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Topic :: Software Development", + "Topic :: Scientific/Engineering", +] + +dependencies = [ + "numpy>=1.26.4,<2.0", + "opencv-python>=4.6.0", + "pyyaml>=5.3.1", + "tqdm>=4.64.0", +] + +[project.optional-dependencies] +extras = [ + "matplotlib>=3.5.0" +] + +[tool.setuptools] +packages = { find = { where = ["."], include = ["stabilo", "stabilo.*"] } } +package-data = { "stabilo" = ["**/*.yaml", "**/*.txt"] } + +[tool.setuptools.dynamic] +version = { attr = "stabilo.__version__" } + +[project.urls] +homepage = "https://github.com/rfonod/stabilo/" +repository = "https://github.com/rfonod/stabilo/" +issues = "https://github.com/rfonod/stabilo/issues/" +releases = "https://github.com/rfonod/stabilo/releases" \ No newline at end of file diff --git a/scripts/find_thresholds/README.md b/scripts/find_thresholds/README.md new file mode 100644 index 0000000..1de2dc8 --- /dev/null +++ b/scripts/find_thresholds/README.md @@ -0,0 +1,32 @@ +# find_thresholds.py + +## Description +The `find_thresholds.py` script is designed to derive linear regression models for the BRISK, KAZE, and AKAZE feature detectors. These models aim to establish the correspondence between the average number of detected keypoints in an image and the threshold value applied by the detectors. The script employs a diverse image dataset with optional axis-aligned bounding boxes representing regions where features should not be detected. Additionally, histogram normalization using CLAHE may influence the models, leading to the creation of separate models for all combinations of mask usage and CLAHE application. The resulting threshold models play a crucial role in configuring the Stabilo module, enabling a fair comparison with other detectors like ORB, SIFT, and RSIFT. + +## Usage +``` +python find_thresholds.py --dataset-dir= + +Options +--dataset-dir=: Directory containing the diverse image dataset (default: scenes/). +``` + +## Dataset Requirements + +1. **Diverse Set of Images**: The dataset should include a diverse set of images representing various scenarios. +2. **Axis-Aligned Bounding Boxes (Optional)**: The images may come with axis-aligned bounding boxes representing regions where features should not be detected. + +## Script Functionality +- **Threshold Models**: Finds linear models for BRISK, KAZE, and AKAZE detectors, considering various combinations of detectors, mask usage, and CLAHE. +- **Data Generation**: Collects data by finding the average number of detected keypoints for different threshold values. +- **Model Fitting**: Fits linear models to the collected data, considering a specified range of keypoints. +- **Data Filtering**: Filters the collected data to fit the model only within a specified range of keypoints. +- **Data and Model Storage**: Saves raw and filtered data, as well as the linear models for further analysis. +- **Plotting**: Generates and saves plots illustrating the relationship between average keypoints and thresholds. + +## Important Notes +- The dataset should be carefully crafted to ensure a representative set of images with diverse content and scenarios. +- Axis-aligned bounding boxes can be provided to guide the feature detection process and create more meaningful threshold models. +- The generated models will be stored in the `models` directory, while the data and plots will be saved in the `results` and `plots` directories, respectively. + +Feel free to adapt the script and instructions based on your specific dataset and requirements. diff --git a/scripts/find_thresholds/find_threshold_models.py b/scripts/find_thresholds/find_threshold_models.py new file mode 100644 index 0000000..b0eca19 --- /dev/null +++ b/scripts/find_thresholds/find_threshold_models.py @@ -0,0 +1,308 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Author: Robert Fonod (robert.fonod@ieee.org) + +""" +find_threshold_models.py - Find linear models for BRISK, KAZE, and AKAZE detectors + +Description: + This script develops linear regression models for feature detectors (BRISK, KAZE, AKAZE) based on their performance metrics, + specifically analyzing the relationship between detector thresholds and the average number of keypoints detected per image. + It processes images from a specified dataset directory, optionally applying a mask and CLAHE (Contrast Limited Adaptive Histogram Equalization) + for image preprocessing. The outcomes include saving the regression coefficients, generating plots to visualize relationships, + and saving raw and filtered data for extended analysis. + +Usage: + find_threshold_models.py [options] + +Options: + --dataset-dir, -dir : Directory containing the image dataset. [default: scenes] + --detectors, -d : List of detectors to analyze. Choices include 'brisk', 'kaze', 'akaze'. [default: brisk, kaze, akaze] + --mask-use, -m : Specify whether to use a mask during detection. Choices: True, False. [default: [True, False]] + --clahe-use, -c : Specify whether to apply CLAHE on images. Choices: True, False. [default: [True, False]] +""" + +import argparse +import cv2 +import numpy as np +from pathlib import Path +from tqdm import tqdm +import matplotlib.pyplot as plt +from typing import Union + +MASK_START = 1 # the column index where the mask values are stored in the .txt file +IMG_SUFFIXES = ['.jpg', '.jpeg', '.png', '.bmp'] # supported image formats +MASK_MARGIN_RATIO = 0.15 # the ratio of the bounding box width and height to add as a margin to the mask + +# model fitting parameters - the model is fitted only for the keypoints in the given range +MIN_KEYPOINTS_NUM_FIT = 1000 +MAX_KEYPOINTS_NUM_FIT = 10000 + + +def find_all_models(args): + """ + Find the linear models for all combinations of detectors, mask_use, and clahe. + """ + for detector in args.detectors: + for mask_on in args.mask_use: + for clahe_on in args.clahe_use: + if detector == 'brisk': + t_min, t_max, t_step = 65, 150, 5 + elif detector in ['akaze', 'kaze']: + t_min, t_max, t_step = 0.001, 0.025, 0.001 + threshold_settings = (t_min, t_max, t_step) + + print(f"Detector: {detector}, mask: {mask_on}, CLAHE: {clahe_on}") + find_linear_model(args.dataset_dir, detector, mask_on, clahe_on, threshold_settings) + + +def find_linear_model(dataset_dir: Path, detector_name: str, mask_on: bool, clahe_on: bool, threshold_settings: tuple): + """ + Find the linear model for the given data. + + Parameters: + - dataset_dir (Path): Path to the directory containing the dataset + - detector_name (str): Name of the detector + - mask_on (bool): Whether the mask was used + - clahe_on (bool): Whether CLAHE was used + - threshold_settings (tuple): (threshold_min, threshold_max, threshold_step) + """ + threshold_min, threshold_max, threshold_step = threshold_settings + + scenes_path = [s for s in dataset_dir.iterdir() if s.is_file() and s.suffix in IMG_SUFFIXES] + + data = [] + for scene_filepath in tqdm(scenes_path, desc="Loading scenes...", unit="scenes", leave=False): + scene, mask = load_image_and_mask(scene_filepath, mask_on) + if mask_on and mask is None: + continue + data.append((scene, mask)) + + if len(data) == 0: + print(f"No scenes found or no bounding boxes found for the scenes. Skipping {detector_name} detector for {mask_on} mask and {clahe_on} CLAHE.") + return + + clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8, 8)) if clahe_on else None + thresholds = np.arange(threshold_min, threshold_max + threshold_step, threshold_step) + print(f"Thresholds: {thresholds}") + + thresholds_arr, keypoints_arr = [], [] + + for threshold in tqdm(thresholds, desc="Finding # keypoints...", unit="threshold", leave=False): + if detector_name.lower() == "brisk": + detector = cv2.BRISK_create(thresh=int(threshold)) + elif detector_name.lower() == "kaze": + detector = cv2.KAZE_create(threshold=float(threshold)) + elif detector_name.lower() == "akaze": + detector = cv2.AKAZE_create(threshold=float(threshold)) + + keypoints_in_scene = [find_keypoints_for_threshold(detector, scene, mask, clahe) for scene, mask in data] + + keypoints_arr.append(np.mean(keypoints_in_scene)) + thresholds_arr.append(threshold) + + keypoints_arr = np.array(keypoints_arr) + thresholds_arr = np.array(thresholds_arr) + + save_data(thresholds_arr, keypoints_arr, detector_name, mask_on, clahe_on, True) + + interval_points = (keypoints_arr >= MIN_KEYPOINTS_NUM_FIT) & (keypoints_arr <= MAX_KEYPOINTS_NUM_FIT) + keypoints_arr = keypoints_arr[interval_points] + thresholds_arr = thresholds_arr[interval_points] + + save_data(thresholds_arr, keypoints_arr, detector_name, mask_on, clahe_on, False) + + model = fit_model(thresholds_arr, keypoints_arr) + + save_model(model, detector_name, mask_on, clahe_on) + + plot_and_save(thresholds_arr, keypoints_arr, model, detector_name, mask_on, clahe_on) + + +def find_keypoints_for_threshold(detector, scene: np.ndarray, mask: Union[np.ndarray, None] = None, clahe: Union[cv2.CLAHE, None] = None) -> int: + """ + Find the number of keypoints for the given threshold value. + + Parameters: + - detector: cv2.FeatureDetector - Detector to use + - scene (np.ndarray): Scene to find keypoints in + - mask (Union[np.ndarray, None]): Mask to use + - clahe (Union[cv2.CLAHE, None]): CLAHE object to use or None + + Returns: + - keypoints_num (int): Number of keypoints found + """ + scene = cv2.cvtColor(scene, cv2.COLOR_BGR2GRAY) + if clahe is not None: + scene = clahe.apply(scene) + kpts = detector.detectAndCompute(scene, mask)[0] + return len(kpts) + + +def load_image_and_mask(scene_path: Path, mask_on: bool) -> tuple[np.ndarray, Union[np.ndarray, None]]: + """ + Load the scene and the bounding boxes. + + Parameters: + - scene_path (Path): Path to the scene + - mask_on (bool): Whether the mask should be used + + Returns: + - scene (np.ndarray): Scene + - mask (Union[np.ndarray, None]): Mask or None + """ + scene = cv2.imread(str(scene_path)) + + mask = None + if mask_on: + boxes_path = scene_path.with_suffix('.txt') + if boxes_path.exists(): + boxes = np.loadtxt(str(boxes_path), delimiter=' ') + boxes = boxes[:, MASK_START:MASK_START + 4] + + h, w = scene.shape[:2] + + if np.max(boxes) <= 1: + boxes[:, 0] *= w + boxes[:, 1] *= h + boxes[:, 2] *= w + boxes[:, 3] *= h + + mask = create_mask(boxes, w, h) + else: + print(f"No bounding boxes found for {scene_path.name}") + + return scene, mask + + +def create_mask(boxes: np.ndarray, w: int, h: int) -> np.ndarray: + """ + Create a binary mask from the bounding boxes. + + Parameters: + - boxes (np.ndarray): Bounding boxes + - w (int): Width of the image + - h (int): Height of the image + + Returns: + - mask (np.ndarray): Binary mask + """ + mask = np.full((h, w), 255, dtype=np.uint8) + + for box in boxes: + xc, yc, wb, hb = box + + wb += wb * MASK_MARGIN_RATIO + hb += hb * MASK_MARGIN_RATIO + + x1 = max(0, min(w, int(xc - wb / 2))) + y1 = max(0, min(h, int(yc - hb / 2))) + x2 = max(0, min(w, int(xc + wb / 2))) + y2 = max(0, min(h, int(yc + hb / 2))) + + mask[y1:y2, x1:x2] = 0 + + return mask + + +def fit_model(thresholds_arr: np.ndarray, keypoints_arr: np.ndarray) -> np.ndarray: + """ + Fit the linear model to the given data. + + Parameters: + - thresholds_arr (np.ndarray): Threshold values + - keypoints_arr (np.ndarray): Number of keypoints for each threshold/scene value + + Returns: + - model (np.ndarray): Coefficients of the linear model + """ + model = np.polyfit(keypoints_arr, thresholds_arr, 1) + print(f"Model: {model}") + return model + + +def save_data(thresholds_arr: np.ndarray, keypoints_arr: np.ndarray, detector_name: str, mask_on: bool, clahe_on: bool, raw_data: bool): + """ + Save the data to a file. + + Parameters: + - thresholds_arr (np.ndarray): Threshold values + - keypoints_arr (np.ndarray): Number of keypoints for each threshold/scene value + - detector_name (str): Name of the detector + - mask_on (bool): Whether the mask was used + - clahe_on (bool): Whether CLAHE was used + - raw_data (bool): Whether the data is raw or filtered + """ + data_dir = Path(__file__).resolve().parent / 'results' / detector_name + data_dir.mkdir(parents=True, exist_ok=True) + + filename = f"data_{'RAW' if raw_data else 'FILTERED'}_mask_{mask_on}_clahe_{clahe_on}.txt" + filepath = data_dir / filename + + np.savetxt(str(filepath), np.column_stack((thresholds_arr, keypoints_arr)), delimiter=',', header="threshold, avg_num_keypoints", comments='') + + +def save_model(model: np.ndarray, detector_name: str, mask_on: bool, clahe_on: bool): + """ + Save the model to a file. + + Parameters: + - model (np.ndarray): Coefficients of the linear model + - detector_name (str): Name of the detector + - mask_on (bool): Whether the mask was used + - clahe_on (bool): Whether CLAHE was used + """ + model_dir = Path(__file__).resolve().parent / 'models' / detector_name + model_dir.mkdir(parents=True, exist_ok=True) + + filename = f"model_mask_{mask_on}_clahe_{clahe_on}.txt" + filepath = model_dir / filename + + np.savetxt(str(filepath), model) + + +def plot_and_save(thresholds_arr: np.ndarray, keypoints_arr: np.ndarray, model: np.ndarray, detector_name: str, mask_on: bool, clahe_on: bool): + """ + Plot the data and save the plot to a file. + + Parameters: + - thresholds_arr (np.ndarray): Threshold values + - keypoints_arr (np.ndarray): Number of keypoints for each threshold/scene value + - model (np.ndarray): Coefficients of the linear model + - detector_name (str): Name of the detector + - mask_on (bool): Whether the mask was used + - clahe_on (bool): Whether CLAHE was used + """ + plots_dir = Path(__file__).resolve().parent / 'plots' / detector_name + plots_dir.mkdir(parents=True, exist_ok=True) + + filename = f"plot_mask_{mask_on}_clahe_{clahe_on}.png" + filepath = plots_dir / filename + + plt.plot(keypoints_arr, thresholds_arr, 'o', label='data') + keypoints_fit = np.array([MIN_KEYPOINTS_NUM_FIT, MAX_KEYPOINTS_NUM_FIT]) + plt.plot(keypoints_fit, np.polyval(model, keypoints_fit), 'r-', label='model') + + plt.xlim(MIN_KEYPOINTS_NUM_FIT, MAX_KEYPOINTS_NUM_FIT) + plt.title(f"Thresholds for {detector_name} detector (Mask={mask_on}, CLAHE={clahe_on})") + plt.xlabel('Number of keypoints') + plt.ylabel(f'{detector_name} threshold value') + plt.legend() + plt.savefig(filepath) + plt.close() + + +def parse_args() -> argparse.Namespace: + """ + Parse the command line arguments. + """ + parser = argparse.ArgumentParser(description="Find brisk, kaze, or akaze threshold models for the given data") + parser.add_argument("--dataset-dir", "-dir", type=Path, default=Path('scenes'), help="Directory containing the dataset") + parser.add_argument("--detectors", "-d", type=str, nargs='+', default=['brisk', 'kaze', 'akaze'], help="Detectors to consider [default: brisk, kaze, akaze]") + parser.add_argument("--mask-use", "-m", type=bool, nargs='+', default=[True, False], help="Whether to use the mask [default: True False]") + parser.add_argument("--clahe-use", "-c", type=bool, nargs='+', default=[True, False], help="Whether to use CLAHE [default: True False]") + return parser.parse_args() + + +if __name__ == "__main__": + find_all_models(parse_args()) diff --git a/scripts/find_thresholds/models/AKAZE/model_mask_False_clahe_False.txt b/scripts/find_thresholds/models/AKAZE/model_mask_False_clahe_False.txt new file mode 100644 index 0000000..752c1d3 --- /dev/null +++ b/scripts/find_thresholds/models/AKAZE/model_mask_False_clahe_False.txt @@ -0,0 +1,2 @@ +-1.238671541205360271e-06 +1.399651010429193293e-02 diff --git a/scripts/find_thresholds/models/AKAZE/model_mask_False_clahe_True.txt b/scripts/find_thresholds/models/AKAZE/model_mask_False_clahe_True.txt new file mode 100644 index 0000000..c127ff0 --- /dev/null +++ b/scripts/find_thresholds/models/AKAZE/model_mask_False_clahe_True.txt @@ -0,0 +1,2 @@ +-1.433874243028384286e-06 +1.797921843899690300e-02 diff --git a/scripts/find_thresholds/models/AKAZE/model_mask_True_clahe_False.txt b/scripts/find_thresholds/models/AKAZE/model_mask_True_clahe_False.txt new file mode 100644 index 0000000..316edc0 --- /dev/null +++ b/scripts/find_thresholds/models/AKAZE/model_mask_True_clahe_False.txt @@ -0,0 +1,2 @@ +-1.007223706389076417e-06 +1.163307906351335880e-02 diff --git a/scripts/find_thresholds/models/AKAZE/model_mask_True_clahe_True.txt b/scripts/find_thresholds/models/AKAZE/model_mask_True_clahe_True.txt new file mode 100644 index 0000000..14a44ad --- /dev/null +++ b/scripts/find_thresholds/models/AKAZE/model_mask_True_clahe_True.txt @@ -0,0 +1,2 @@ +-1.420484216983836093e-06 +1.574875889009140148e-02 diff --git a/scripts/find_thresholds/models/BRISK/model_mask_False_clahe_False.txt b/scripts/find_thresholds/models/BRISK/model_mask_False_clahe_False.txt new file mode 100644 index 0000000..ed170e0 --- /dev/null +++ b/scripts/find_thresholds/models/BRISK/model_mask_False_clahe_False.txt @@ -0,0 +1,2 @@ +-5.697760528375401222e-03 +1.362927870041904157e+02 diff --git a/scripts/find_thresholds/models/BRISK/model_mask_False_clahe_True.txt b/scripts/find_thresholds/models/BRISK/model_mask_False_clahe_True.txt new file mode 100644 index 0000000..3c1ed25 --- /dev/null +++ b/scripts/find_thresholds/models/BRISK/model_mask_False_clahe_True.txt @@ -0,0 +1,2 @@ +-6.082518482010841263e-03 +1.538229586731700920e+02 diff --git a/scripts/find_thresholds/models/BRISK/model_mask_True_clahe_False.txt b/scripts/find_thresholds/models/BRISK/model_mask_True_clahe_False.txt new file mode 100644 index 0000000..454d899 --- /dev/null +++ b/scripts/find_thresholds/models/BRISK/model_mask_True_clahe_False.txt @@ -0,0 +1,2 @@ +-5.026162052230478440e-03 +1.261693929838898498e+02 diff --git a/scripts/find_thresholds/models/BRISK/model_mask_True_clahe_True.txt b/scripts/find_thresholds/models/BRISK/model_mask_True_clahe_True.txt new file mode 100644 index 0000000..25fa86c --- /dev/null +++ b/scripts/find_thresholds/models/BRISK/model_mask_True_clahe_True.txt @@ -0,0 +1,2 @@ +-5.684927998372675539e-03 +1.451210374444453635e+02 diff --git a/scripts/find_thresholds/models/KAZE/model_mask_False_clahe_False.txt b/scripts/find_thresholds/models/KAZE/model_mask_False_clahe_False.txt new file mode 100644 index 0000000..52a51eb --- /dev/null +++ b/scripts/find_thresholds/models/KAZE/model_mask_False_clahe_False.txt @@ -0,0 +1,2 @@ +-1.342082518515861633e-06 +1.445548677509021600e-02 diff --git a/scripts/find_thresholds/models/KAZE/model_mask_False_clahe_True.txt b/scripts/find_thresholds/models/KAZE/model_mask_False_clahe_True.txt new file mode 100644 index 0000000..56e659d --- /dev/null +++ b/scripts/find_thresholds/models/KAZE/model_mask_False_clahe_True.txt @@ -0,0 +1,2 @@ +-1.471458874258221078e-06 +1.783699945834927095e-02 diff --git a/scripts/find_thresholds/models/KAZE/model_mask_True_clahe_False.txt b/scripts/find_thresholds/models/KAZE/model_mask_True_clahe_False.txt new file mode 100644 index 0000000..c27314c --- /dev/null +++ b/scripts/find_thresholds/models/KAZE/model_mask_True_clahe_False.txt @@ -0,0 +1,2 @@ +-9.680610223378339583e-07 +1.147348454716152349e-02 diff --git a/scripts/find_thresholds/models/KAZE/model_mask_True_clahe_True.txt b/scripts/find_thresholds/models/KAZE/model_mask_True_clahe_True.txt new file mode 100644 index 0000000..a0aa9fa --- /dev/null +++ b/scripts/find_thresholds/models/KAZE/model_mask_True_clahe_True.txt @@ -0,0 +1,2 @@ +-1.350186975845170620e-06 +1.509247010983386615e-02 diff --git a/scripts/find_thresholds/plots/AKAZE/plot_mask_False_clahe_False.png b/scripts/find_thresholds/plots/AKAZE/plot_mask_False_clahe_False.png new file mode 100644 index 0000000..2131857 Binary files /dev/null and b/scripts/find_thresholds/plots/AKAZE/plot_mask_False_clahe_False.png differ diff --git a/scripts/find_thresholds/plots/AKAZE/plot_mask_False_clahe_True.png b/scripts/find_thresholds/plots/AKAZE/plot_mask_False_clahe_True.png new file mode 100644 index 0000000..97de79c Binary files /dev/null and b/scripts/find_thresholds/plots/AKAZE/plot_mask_False_clahe_True.png differ diff --git a/scripts/find_thresholds/plots/AKAZE/plot_mask_True_clahe_False.png b/scripts/find_thresholds/plots/AKAZE/plot_mask_True_clahe_False.png new file mode 100644 index 0000000..4c39955 Binary files /dev/null and b/scripts/find_thresholds/plots/AKAZE/plot_mask_True_clahe_False.png differ diff --git a/scripts/find_thresholds/plots/AKAZE/plot_mask_True_clahe_True.png b/scripts/find_thresholds/plots/AKAZE/plot_mask_True_clahe_True.png new file mode 100644 index 0000000..c341c13 Binary files /dev/null and b/scripts/find_thresholds/plots/AKAZE/plot_mask_True_clahe_True.png differ diff --git a/scripts/find_thresholds/plots/BRISK/plot_mask_False_clahe_False.png b/scripts/find_thresholds/plots/BRISK/plot_mask_False_clahe_False.png new file mode 100644 index 0000000..cc08aca Binary files /dev/null and b/scripts/find_thresholds/plots/BRISK/plot_mask_False_clahe_False.png differ diff --git a/scripts/find_thresholds/plots/BRISK/plot_mask_False_clahe_True.png b/scripts/find_thresholds/plots/BRISK/plot_mask_False_clahe_True.png new file mode 100644 index 0000000..86fff2e Binary files /dev/null and b/scripts/find_thresholds/plots/BRISK/plot_mask_False_clahe_True.png differ diff --git a/scripts/find_thresholds/plots/BRISK/plot_mask_True_clahe_False.png b/scripts/find_thresholds/plots/BRISK/plot_mask_True_clahe_False.png new file mode 100644 index 0000000..9ea6c99 Binary files /dev/null and b/scripts/find_thresholds/plots/BRISK/plot_mask_True_clahe_False.png differ diff --git a/scripts/find_thresholds/plots/BRISK/plot_mask_True_clahe_True.png b/scripts/find_thresholds/plots/BRISK/plot_mask_True_clahe_True.png new file mode 100644 index 0000000..c1b6ffb Binary files /dev/null and b/scripts/find_thresholds/plots/BRISK/plot_mask_True_clahe_True.png differ diff --git a/scripts/find_thresholds/plots/KAZE/plot_mask_False_clahe_False.png b/scripts/find_thresholds/plots/KAZE/plot_mask_False_clahe_False.png new file mode 100644 index 0000000..9592006 Binary files /dev/null and b/scripts/find_thresholds/plots/KAZE/plot_mask_False_clahe_False.png differ diff --git a/scripts/find_thresholds/plots/KAZE/plot_mask_False_clahe_True.png b/scripts/find_thresholds/plots/KAZE/plot_mask_False_clahe_True.png new file mode 100644 index 0000000..494196d Binary files /dev/null and b/scripts/find_thresholds/plots/KAZE/plot_mask_False_clahe_True.png differ diff --git a/scripts/find_thresholds/plots/KAZE/plot_mask_True_clahe_False.png b/scripts/find_thresholds/plots/KAZE/plot_mask_True_clahe_False.png new file mode 100644 index 0000000..8c19e5d Binary files /dev/null and b/scripts/find_thresholds/plots/KAZE/plot_mask_True_clahe_False.png differ diff --git a/scripts/find_thresholds/plots/KAZE/plot_mask_True_clahe_True.png b/scripts/find_thresholds/plots/KAZE/plot_mask_True_clahe_True.png new file mode 100644 index 0000000..5247be5 Binary files /dev/null and b/scripts/find_thresholds/plots/KAZE/plot_mask_True_clahe_True.png differ diff --git a/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_False_clahe_False.txt b/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_False_clahe_False.txt new file mode 100644 index 0000000..61485d6 --- /dev/null +++ b/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_False_clahe_False.txt @@ -0,0 +1,11 @@ +threshold, avg_num_keypoints +5.000000000000000104e-03,8.287275862068965580e+03 +6.000000000000000125e-03,6.410862068965517210e+03 +7.000000000000000146e-03,5.035448275862068840e+03 +8.000000000000000167e-03,4.036379310344827445e+03 +9.000000000000001055e-03,3.262620689655172555e+03 +1.000000000000000021e-02,2.665413793103448370e+03 +1.099999999999999936e-02,2.171724137931034420e+03 +1.200000000000000025e-02,1.774034482758620697e+03 +1.300000000000000114e-02,1.457172413793103487e+03 +1.400000000000000029e-02,1.200137931034482790e+03 diff --git a/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_False_clahe_True.txt b/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_False_clahe_True.txt new file mode 100644 index 0000000..e10cd4f --- /dev/null +++ b/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_False_clahe_True.txt @@ -0,0 +1,15 @@ +threshold, avg_num_keypoints +6.000000000000000125e-03,9.912103448275862320e+03 +7.000000000000000146e-03,8.014965517241379530e+03 +8.000000000000000167e-03,6.545068965517240940e+03 +9.000000000000001055e-03,5.396620689655172100e+03 +1.000000000000000021e-02,4.489758620689654890e+03 +1.099999999999999936e-02,3.757965517241379530e+03 +1.200000000000000025e-02,3.171655172413793025e+03 +1.300000000000000114e-02,2.687344827586206975e+03 +1.400000000000000029e-02,2.284413793103448370e+03 +1.499999999999999944e-02,1.943724137931034420e+03 +1.600000000000000033e-02,1.656068965517241395e+03 +1.700000000000000122e-02,1.406793103448275815e+03 +1.800000000000000211e-02,1.200793103448275815e+03 +1.899999999999999953e-02,1.030482758620689765e+03 diff --git a/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_True_clahe_False.txt b/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_True_clahe_False.txt new file mode 100644 index 0000000..9095c06 --- /dev/null +++ b/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_True_clahe_False.txt @@ -0,0 +1,10 @@ +threshold, avg_num_keypoints +4.000000000000000083e-03,8.699241379310344200e+03 +5.000000000000000104e-03,6.353793103448276270e+03 +6.000000000000000125e-03,4.738275862068965580e+03 +7.000000000000000146e-03,3.584344827586206975e+03 +8.000000000000000167e-03,2.776172413793103260e+03 +9.000000000000001055e-03,2.171551724137931160e+03 +1.000000000000000021e-02,1.717172413793103487e+03 +1.099999999999999936e-02,1.355793103448275815e+03 +1.200000000000000025e-02,1.066862068965517210e+03 diff --git a/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_True_clahe_True.txt b/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_True_clahe_True.txt new file mode 100644 index 0000000..6411d21 --- /dev/null +++ b/scripts/find_thresholds/results/AKAZE/data_FILTERED_mask_True_clahe_True.txt @@ -0,0 +1,12 @@ +threshold, avg_num_keypoints +6.000000000000000125e-03,7.893000000000000000e+03 +7.000000000000000146e-03,6.225793103448276270e+03 +8.000000000000000167e-03,4.957689655172413950e+03 +9.000000000000001055e-03,3.987000000000000000e+03 +1.000000000000000021e-02,3.237310344827586050e+03 +1.099999999999999936e-02,2.647482758620689765e+03 +1.200000000000000025e-02,2.190517241379310235e+03 +1.300000000000000114e-02,1.817310344827586277e+03 +1.400000000000000029e-02,1.514586206896551630e+03 +1.499999999999999944e-02,1.258275862068965580e+03 +1.600000000000000033e-02,1.044655172413793025e+03 diff --git a/scripts/find_thresholds/results/AKAZE/data_RAW_mask_False_clahe_False.txt b/scripts/find_thresholds/results/AKAZE/data_RAW_mask_False_clahe_False.txt new file mode 100644 index 0000000..767438a --- /dev/null +++ b/scripts/find_thresholds/results/AKAZE/data_RAW_mask_False_clahe_False.txt @@ -0,0 +1,26 @@ +threshold, avg_num_keypoints +1.000000000000000021e-03,3.614558620689655072e+04 +2.000000000000000042e-03,2.167882758620689492e+04 +3.000000000000000062e-03,1.494296551724137862e+04 +4.000000000000000083e-03,1.095737931034482790e+04 +5.000000000000000104e-03,8.287275862068965580e+03 +6.000000000000000125e-03,6.410862068965517210e+03 +7.000000000000000146e-03,5.035448275862068840e+03 +8.000000000000000167e-03,4.036379310344827445e+03 +9.000000000000001055e-03,3.262620689655172555e+03 +1.000000000000000021e-02,2.665413793103448370e+03 +1.099999999999999936e-02,2.171724137931034420e+03 +1.200000000000000025e-02,1.774034482758620697e+03 +1.300000000000000114e-02,1.457172413793103487e+03 +1.400000000000000029e-02,1.200137931034482790e+03 +1.499999999999999944e-02,9.944482758620689538e+02 +1.600000000000000033e-02,8.254827586206896513e+02 +1.700000000000000122e-02,6.934137931034482563e+02 +1.800000000000000211e-02,5.839655172413793025e+02 +1.899999999999999953e-02,4.955517241379310462e+02 +2.000000000000000042e-02,4.237586206896551744e+02 +2.100000000000000130e-02,3.643448275862069181e+02 +2.199999999999999872e-02,3.147931034482758719e+02 +2.299999999999999961e-02,2.746896551724137794e+02 +2.400000000000000050e-02,2.405517241379310462e+02 +2.500000000000000139e-02,2.098620689655172384e+02 diff --git a/scripts/find_thresholds/results/AKAZE/data_RAW_mask_False_clahe_True.txt b/scripts/find_thresholds/results/AKAZE/data_RAW_mask_False_clahe_True.txt new file mode 100644 index 0000000..aac4e00 --- /dev/null +++ b/scripts/find_thresholds/results/AKAZE/data_RAW_mask_False_clahe_True.txt @@ -0,0 +1,26 @@ +threshold, avg_num_keypoints +1.000000000000000021e-03,4.769513793103448552e+04 +2.000000000000000042e-03,2.981179310344827536e+04 +3.000000000000000062e-03,2.122437931034482608e+04 +4.000000000000000083e-03,1.599458620689655254e+04 +5.000000000000000104e-03,1.246444827586206884e+04 +6.000000000000000125e-03,9.912103448275862320e+03 +7.000000000000000146e-03,8.014965517241379530e+03 +8.000000000000000167e-03,6.545068965517240940e+03 +9.000000000000001055e-03,5.396620689655172100e+03 +1.000000000000000021e-02,4.489758620689654890e+03 +1.099999999999999936e-02,3.757965517241379530e+03 +1.200000000000000025e-02,3.171655172413793025e+03 +1.300000000000000114e-02,2.687344827586206975e+03 +1.400000000000000029e-02,2.284413793103448370e+03 +1.499999999999999944e-02,1.943724137931034420e+03 +1.600000000000000033e-02,1.656068965517241395e+03 +1.700000000000000122e-02,1.406793103448275815e+03 +1.800000000000000211e-02,1.200793103448275815e+03 +1.899999999999999953e-02,1.030482758620689765e+03 +2.000000000000000042e-02,8.823793103448275588e+02 +2.100000000000000130e-02,7.580000000000000000e+02 +2.199999999999999872e-02,6.545517241379310462e+02 +2.299999999999999961e-02,5.653448275862068613e+02 +2.400000000000000050e-02,4.930000000000000000e+02 +2.500000000000000139e-02,4.311034482758620925e+02 diff --git a/scripts/find_thresholds/results/AKAZE/data_RAW_mask_True_clahe_False.txt b/scripts/find_thresholds/results/AKAZE/data_RAW_mask_True_clahe_False.txt new file mode 100644 index 0000000..044c26a --- /dev/null +++ b/scripts/find_thresholds/results/AKAZE/data_RAW_mask_True_clahe_False.txt @@ -0,0 +1,26 @@ +threshold, avg_num_keypoints +1.000000000000000021e-03,3.165410344827586232e+04 +2.000000000000000042e-03,1.834189655172413768e+04 +3.000000000000000062e-03,1.225706896551724094e+04 +4.000000000000000083e-03,8.699241379310344200e+03 +5.000000000000000104e-03,6.353793103448276270e+03 +6.000000000000000125e-03,4.738275862068965580e+03 +7.000000000000000146e-03,3.584344827586206975e+03 +8.000000000000000167e-03,2.776172413793103260e+03 +9.000000000000001055e-03,2.171551724137931160e+03 +1.000000000000000021e-02,1.717172413793103487e+03 +1.099999999999999936e-02,1.355793103448275815e+03 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b/scripts/find_thresholds/results/AKAZE/data_RAW_mask_True_clahe_True.txt @@ -0,0 +1,26 @@ +threshold, avg_num_keypoints +1.000000000000000021e-03,4.272786206896551448e+04 +2.000000000000000042e-03,2.601206896551724276e+04 +3.000000000000000062e-03,1.810713793103448188e+04 +4.000000000000000083e-03,1.334762068965517210e+04 +5.000000000000000104e-03,1.016613793103448188e+04 +6.000000000000000125e-03,7.893000000000000000e+03 +7.000000000000000146e-03,6.225793103448276270e+03 +8.000000000000000167e-03,4.957689655172413950e+03 +9.000000000000001055e-03,3.987000000000000000e+03 +1.000000000000000021e-02,3.237310344827586050e+03 +1.099999999999999936e-02,2.647482758620689765e+03 +1.200000000000000025e-02,2.190517241379310235e+03 +1.300000000000000114e-02,1.817310344827586277e+03 +1.400000000000000029e-02,1.514586206896551630e+03 +1.499999999999999944e-02,1.258275862068965580e+03 +1.600000000000000033e-02,1.044655172413793025e+03 +1.700000000000000122e-02,8.631724137931034875e+02 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0000000..1bed87d --- /dev/null +++ b/scripts/find_thresholds/results/AKAZE/not_aggregated/data_FILTERED_mask_False_clahe_True.txt @@ -0,0 +1,382 @@ +threshold, avg_num_keypoints +4.000000000000000083e-03,9.685000000000000000e+03 +5.000000000000000104e-03,9.363000000000000000e+03 +5.000000000000000104e-03,8.370000000000000000e+03 +5.000000000000000104e-03,7.224000000000000000e+03 +5.000000000000000104e-03,8.139000000000000000e+03 +5.000000000000000104e-03,8.146000000000000000e+03 +6.000000000000000125e-03,9.174000000000000000e+03 +6.000000000000000125e-03,7.772000000000000000e+03 +6.000000000000000125e-03,7.459000000000000000e+03 +6.000000000000000125e-03,9.410000000000000000e+03 +6.000000000000000125e-03,6.753000000000000000e+03 +6.000000000000000125e-03,8.588000000000000000e+03 +6.000000000000000125e-03,9.959000000000000000e+03 +6.000000000000000125e-03,7.644000000000000000e+03 +6.000000000000000125e-03,9.151000000000000000e+03 +6.000000000000000125e-03,8.387000000000000000e+03 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a/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_False_clahe_False.txt b/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_False_clahe_False.txt new file mode 100644 index 0000000..a511bb0 --- /dev/null +++ b/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_False_clahe_False.txt @@ -0,0 +1,11 @@ +threshold, avg_num_keypoints +9.000000000000000000e+01,8.813172413793103260e+03 +9.500000000000000000e+01,7.380689655172413950e+03 +1.000000000000000000e+02,6.117724137931034420e+03 +1.050000000000000000e+02,5.011862068965517210e+03 +1.100000000000000000e+02,4.056689655172413950e+03 +1.150000000000000000e+02,3.230862068965517210e+03 +1.200000000000000000e+02,2.533379310344827445e+03 +1.250000000000000000e+02,1.956103448275862092e+03 +1.300000000000000000e+02,1.498965517241379303e+03 +1.350000000000000000e+02,1.158689655172413723e+03 diff --git a/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_False_clahe_True.txt b/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_False_clahe_True.txt new file mode 100644 index 0000000..bc3da0a --- /dev/null +++ b/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_False_clahe_True.txt @@ -0,0 +1,11 @@ +threshold, avg_num_keypoints +1.050000000000000000e+02,8.669310344827586960e+03 +1.100000000000000000e+02,7.323827586206896740e+03 +1.150000000000000000e+02,6.147103448275862320e+03 +1.200000000000000000e+02,5.116931034482759060e+03 +1.250000000000000000e+02,4.224310344827586050e+03 +1.300000000000000000e+02,3.463137931034482790e+03 +1.350000000000000000e+02,2.816965517241379530e+03 +1.400000000000000000e+02,2.264758620689655345e+03 +1.450000000000000000e+02,1.809000000000000000e+03 +1.500000000000000000e+02,1.441068965517241395e+03 diff --git a/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_True_clahe_False.txt b/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_True_clahe_False.txt new file mode 100644 index 0000000..cbed3ba --- /dev/null +++ b/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_True_clahe_False.txt @@ -0,0 +1,11 @@ +threshold, avg_num_keypoints +8.000000000000000000e+01,9.979206896551724640e+03 +8.500000000000000000e+01,8.311689655172413040e+03 +9.000000000000000000e+01,6.898724137931034420e+03 +9.500000000000000000e+01,5.672448275862068840e+03 +1.000000000000000000e+02,4.600586206896551630e+03 +1.050000000000000000e+02,3.673862068965517210e+03 +1.100000000000000000e+02,2.887034482758620470e+03 +1.150000000000000000e+02,2.214034482758620470e+03 +1.200000000000000000e+02,1.652275862068965580e+03 +1.250000000000000000e+02,1.202517241379310235e+03 diff --git a/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_True_clahe_True.txt b/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_True_clahe_True.txt new file mode 100644 index 0000000..991ed7a --- /dev/null +++ b/scripts/find_thresholds/results/BRISK/data_FILTERED_mask_True_clahe_True.txt @@ -0,0 +1,12 @@ +threshold, 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b/scripts/find_thresholds/results/BRISK/data_RAW_mask_False_clahe_True.txt new file mode 100644 index 0000000..3e47871 --- /dev/null +++ b/scripts/find_thresholds/results/BRISK/data_RAW_mask_False_clahe_True.txt @@ -0,0 +1,19 @@ +threshold, avg_num_keypoints +6.500000000000000000e+01,3.001682758620689492e+04 +7.000000000000000000e+01,2.567686206896551812e+04 +7.500000000000000000e+01,2.201444827586206884e+04 +8.000000000000000000e+01,1.889231034482758696e+04 +8.500000000000000000e+01,1.624017241379310326e+04 +9.000000000000000000e+01,1.393544827586206884e+04 +9.500000000000000000e+01,1.193065517241379348e+04 +1.000000000000000000e+02,1.020175862068965580e+04 +1.050000000000000000e+02,8.669310344827586960e+03 +1.100000000000000000e+02,7.323827586206896740e+03 +1.150000000000000000e+02,6.147103448275862320e+03 +1.200000000000000000e+02,5.116931034482759060e+03 +1.250000000000000000e+02,4.224310344827586050e+03 +1.300000000000000000e+02,3.463137931034482790e+03 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b/scripts/find_thresholds/results/BRISK/not_aggregated/data_FILTERED_mask_False_clahe_False.txt new file mode 100644 index 0000000..97bd684 --- /dev/null +++ b/scripts/find_thresholds/results/BRISK/not_aggregated/data_FILTERED_mask_False_clahe_False.txt @@ -0,0 +1,289 @@ +threshold, avg_num_keypoints +7.000000000000000000e+01,9.909000000000000000e+03 +7.500000000000000000e+01,9.734000000000000000e+03 +7.500000000000000000e+01,8.601000000000000000e+03 +8.000000000000000000e+01,9.110000000000000000e+03 +8.000000000000000000e+01,8.533000000000000000e+03 +8.000000000000000000e+01,8.234000000000000000e+03 +8.000000000000000000e+01,9.532000000000000000e+03 +8.000000000000000000e+01,8.811000000000000000e+03 +8.000000000000000000e+01,9.147000000000000000e+03 +8.000000000000000000e+01,7.419000000000000000e+03 +8.500000000000000000e+01,9.050000000000000000e+03 +8.500000000000000000e+01,7.115000000000000000e+03 +8.500000000000000000e+01,8.779000000000000000e+03 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a/scripts/find_thresholds/results/KAZE/data_RAW_mask_False_clahe_True.txt b/scripts/find_thresholds/results/KAZE/data_RAW_mask_False_clahe_True.txt new file mode 100644 index 0000000..666870d --- /dev/null +++ b/scripts/find_thresholds/results/KAZE/data_RAW_mask_False_clahe_True.txt @@ -0,0 +1,26 @@ +threshold, avg_num_keypoints +1.000000000000000021e-03,5.288710344827586232e+04 +2.000000000000000042e-03,3.238420689655172464e+04 +3.000000000000000062e-03,2.243934482758620652e+04 +4.000000000000000083e-03,1.644127586206896376e+04 +5.000000000000000104e-03,1.246910344827586232e+04 +6.000000000000000125e-03,9.672241379310344200e+03 +7.000000000000000146e-03,7.658965517241379530e+03 +8.000000000000000167e-03,6.175931034482759060e+03 +9.000000000000001055e-03,5.051344827586206520e+03 +1.000000000000000021e-02,4.192448275862068840e+03 +1.099999999999999936e-02,3.506448275862068840e+03 +1.200000000000000025e-02,2.956275862068965580e+03 +1.300000000000000114e-02,2.510655172413793025e+03 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a/scripts/find_thresholds/results/KAZE/not_aggregated/data_FILTERED_mask_False_clahe_False.txt b/scripts/find_thresholds/results/KAZE/not_aggregated/data_FILTERED_mask_False_clahe_False.txt new file mode 100644 index 0000000..5acc109 --- /dev/null +++ b/scripts/find_thresholds/results/KAZE/not_aggregated/data_FILTERED_mask_False_clahe_False.txt @@ -0,0 +1,307 @@ +threshold, avg_num_keypoints +4.000000000000000083e-03,7.116000000000000000e+03 +4.000000000000000083e-03,9.069000000000000000e+03 +4.000000000000000083e-03,8.057000000000000000e+03 +4.000000000000000083e-03,9.940000000000000000e+03 +4.000000000000000083e-03,7.337000000000000000e+03 +4.000000000000000083e-03,7.156000000000000000e+03 +4.000000000000000083e-03,6.962000000000000000e+03 +4.000000000000000083e-03,8.038000000000000000e+03 +4.000000000000000083e-03,8.590000000000000000e+03 +5.000000000000000104e-03,8.673000000000000000e+03 +5.000000000000000104e-03,8.030000000000000000e+03 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+2.500000000000000139e-02,1.950000000000000000e+02 diff --git a/scripts/stabilize_boxes.py b/scripts/stabilize_boxes.py new file mode 100644 index 0000000..ed69a38 --- /dev/null +++ b/scripts/stabilize_boxes.py @@ -0,0 +1 @@ +# To be done \ No newline at end of file diff --git a/scripts/stabilize_video.py b/scripts/stabilize_video.py new file mode 100644 index 0000000..0035d94 --- /dev/null +++ b/scripts/stabilize_video.py @@ -0,0 +1,361 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Author: Robert Fonod (robert.fonod@ieee.org) + +""" +stabilize_video.py - Video Stabilization Script + +Description: + This script enhances video stability using the 'stabilo' library, applying a reference frame stabilization method + that's particularly effective for videos requiring accurate trajectory analysis, such as drone footage. It supports + various feature detectors and matchers, allows visualization during processing, and offers extensive customization + through command-line options or a configuration file. + +Usage: + python stabilize_video.py [options] + +Arguments: + input : Path to the input video file. + +Script Options: + --output OUTPUT : Path to the output stabilized video file (default: input_stab_detector_matcher_transformation-type.suffix). + --viz : Visualize the transformation process. + --save : Save the stabilized video. + --save-viz : Save the visualization of the transformation process. + --no-mask : Do not use a mask for stabilization. + --mask-path MASK_PATH : Path to a mask file (default: input with .txt extension). + --mask-start MASK_START : Start column index of YOLO format masks (default: 2). Column 0 reserved for frame number. + --mask-encoding MASK_ENCODING : Bounding box format (default: 'yolo'). Choices: 'yolo', 'pascal', 'coco'. + --no-lines : Do not draw lines between points. + --no-boxes : Do not draw bounding boxes. + --custom-config : Path to a custom stabilo configuration file that overrides the CLI arguments. + +Stabilo Configuration: + --detector-name DETECTOR_NAME : Type of detector (default: orb). Choices: 'orb', 'sift', 'rsift', 'brisk', 'kaze', 'akaze'. + --matcher-name MATCHER_NAME : Type of matcher (default: bf). Choices: 'bf', 'flann'. + --filter-type FILTER_TYPE : Type of match filter (default: ratio). Choices: 'none', 'ratio', 'distance'. + --transformation-type TRANSFORMATION_TYPE : Type of transformation (default: projective). Choices: 'projective', 'affine'. + --clahe : Apply CLAHE to grayscale images. + --downsample-ratio DOWNSAMPLE_RATIO : Downsample ratio for the input video (default: 0.5). + --max-features MAX_FEATURES : Maximum number of features to detect (default: 2000). + --ref-multiplier REF_MULTIPLIER : Multiplier for max features in reference frame (default: 2). + --filter-ratio FILTER_RATIO : Filter ratio for the match filter (default: 0.9). + --ransac-method RANSAC_METHOD : RANSAC method (default: 38 (MAGSAC++)). + --ransac-epipolar-threshold RANSAC_EPIPOLAR_THRESHOLD : RANSAC epipolar threshold (default: 2.0). + --ransac-max-iter RANSAC_MAX_ITER : RANSAC maximum iterations (default: 5000). + --ransac-confidence RANSAC_CONFIDENCE : RANSAC confidence (default: 0.999999). + --mask-margin-ratio MASK_MARGIN_RATIO : Mask margin ratio (default: 0.15). + +Examples: + 1. Visualize the stabilization process: + python stabilize_video.py path/to/video/video.mp4 --viz + + 2. Save a stabilized video using a custom detector and matcher: + python stabilize_video.py path/to/video/video.mp4 --detector-name sift --matcher-name flann --save + + 3. Apply stabilization without a mask and visualize the process: + python stabilize_video.py path/to/video/video.mp4 --no-mask --viz + + 4. Customize output path and enable visualization save: + python stabilize_video.py path/to/video/video.mp4 --output path/to/output-video/output.mp4 --save-viz + + 5. Use a custom mask file and specify start column index for bounding boxes: + python stabilize_video.py path/to/video/video.mp4 --mask-path path/to/mask/mask.txt --mask-start 1 --viz + + 6. Apply stabilization with a custom configuration file: + python stabilize_video.py path/to/video/video.mp4 --custom-config path/to/config/config.yaml + +Notes: + - Press 'q' during visualization to quit (with --viz). +""" + +# ToDo: +# implement custom reference frame selection through CLI + +import sys +import cv2 +import numpy as np +import argparse +import platform +from tqdm import tqdm +from pathlib import Path +from stabilo import Stabilizer +from stabilo.utils import load_config, detect_delimiter + +MACOS, LINUX, WINDOWS = (platform.system() == x for x in ['Darwin', 'Linux', 'Windows']) # environment booleans +COLOURS = np.random.randint(0, 256, (100, 3)) # used to draw points on the frame + +def stabilize_video(cli_args): + """ + Stabilize a video using the stabilo library. + """ + args, kwargs = get_args_and_kwargs(cli_args) + stabilizer = Stabilizer(**kwargs) + + tracks = None if args.no_mask else load_bounding_boxes(args) + reader, writer, writer_viz, pbar, w, h = initialize_streams(args, stabilizer.get_basic_info()) + + try: + frame_num = 0 + while reader.isOpened(): + flag, frame = reader.read() + if not flag: + break + + boxes = None if args.no_mask or tracks is None else tracks[tracks[:, 0].astype(int) == frame_num, 1:] + + if frame_num == 0: + stabilizer.set_ref_frame(frame, boxes) + stabilized_frame = frame + stabilized_boxes = boxes + else: + stabilizer.stabilize(frame, boxes) + stabilized_frame = stabilizer.warp_cur_frame() + stabilized_boxes = stabilizer.transform_cur_boxes() + + if writer is not None and stabilized_frame is not None: + writer.write(stabilized_frame) + + if args.viz or args.save_viz: + imgs = visualize_frame(stabilizer, frame.copy(), stabilized_frame.copy(), boxes, stabilized_boxes, frame_num, args) + if args.viz: + cv2.imshow('Stabilization Process Visualization', imgs) + if cv2.waitKey(0) & 0xFF == ord('q'): + break + if writer_viz is not None: + imgs = cv2.resize(imgs, (w, h)) + writer_viz.write(imgs) + + frame_num += 1 + pbar.update(1) + + except KeyboardInterrupt: + print('Interrupted by user.') + finally: + close_streams(args, reader, writer, writer_viz, pbar, frame_num) + +def initialize_streams(args, kwargs): + """ + Initialize video reader, video writer, and progress bar. + """ + if not args.input.exists(): + print(f'File {args.input} not found.') + sys.exit(1) + + reader = cv2.VideoCapture(str(args.input)) + if not reader.isOpened(): + print(f'Failed to open {args.input}.') + sys.exit(1) + + w = int(reader.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(reader.get(cv2.CAP_PROP_FRAME_HEIGHT)) + frame_count = int(reader.get(cv2.CAP_PROP_FRAME_COUNT)) + + writer, writer_viz = None, None + if args.output is None: + suffix = 'mp4' if MACOS else 'avi' if WINDOWS else 'mp4' + output = args.input.parent / f"{args.input.stem}_stab_{kwargs['detector_name']}_{kwargs['matcher_name']}_{kwargs['transformation_type']}{'' if kwargs['mask_use'] else '_no_mask'}.{suffix}" + else: + output = args.output + suffix = output.suffix[1:] + + fps = reader.get(cv2.CAP_PROP_FPS) # int() might be required, floats might produce error in MP4 codec + fourcc = 'avc1' if MACOS else 'WMV2' if WINDOWS else 'mp4v' + + if args.save: + if output.exists(): + print(f'File {output} already exists. Overwriting it.') + writer = cv2.VideoWriter(str(output), cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) + + if args.save_viz: + output_viz = output.parent / f'{output.stem}_VIZ.{suffix}' + if output_viz.exists(): + print(f'File {output_viz} already exists. Overwriting it.') + writer_viz = cv2.VideoWriter(str(output_viz), cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) + + pbar = tqdm(total=frame_count, desc=f'Stabilizing {args.input}', unit='frames', leave=True, colour='green') + + return reader, writer, writer_viz, pbar, w, h + +def close_streams(args, reader, writer, writer_viz, pbar, frame_num): + """ + Close video reader, video writer, and progress bar. + """ + reader.release() + if writer is not None: + writer.release() + if writer_viz is not None: + writer_viz.release() + if args.viz: + cv2.destroyAllWindows() + pbar.total = frame_num + pbar.refresh() + pbar.close() + +def load_bounding_boxes(args): + """ + Read the bounding boxes from a .txt file. + """ + tracks_path = args.mask_path or args.input.with_suffix('.txt') + if not tracks_path.exists(): + print(f'File {tracks_path} not found. Make sure you have bounding boxes available for {args.input}, otherwise run with --no-mask.') + sys.exit(1) + + delimiter = detect_delimiter(tracks_path) + tracks = np.loadtxt(tracks_path, delimiter=delimiter, usecols=[0, *range(args.mask_start, args.mask_start + 4)]) + + if args.mask_encoding == 'pascal': + tracks[:, 1] = (tracks[:, 1] + tracks[:, 3]) / 2 + tracks[:, 2] = (tracks[:, 2] + tracks[:, 4]) / 2 + tracks[:, 3] -= tracks[:, 1] + tracks[:, 4] -= tracks[:, 2] + elif args.mask_encoding == 'coco': + tracks[:, 1] += tracks[:, 3] / 2 + tracks[:, 2] += tracks[:, 4] / 2 + + return tracks + +def visualize_frame(stabilizer, frame, stabilized_frame, boxes, stabilized_boxes, frame_num, args): + """ + Visualize the stabilization process. + """ + + def draw_mask(img, mask): + if mask is not None: + img = cv2.bitwise_and(img, img, mask=mask) + return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + + def draw_points(img, points): + if points is not None: + for i, pt in enumerate(points): + x, y = pt.ravel() + cv2.circle(img, (int(x), int(y)), 9, COLOURS[i % 100].tolist(), 6) + return img + + def draw_lines(img, ref_pts, cur_pts): + best_match_count = 'N/A' + if ref_pts is not None and cur_pts is not None: + for i, (pt1, pt2) in enumerate(zip(ref_pts, cur_pts)): + x1, y1 = pt1.ravel() + x2, y2 = pt2.ravel() + color = [0, 255, 0] if stabilizer.cur_inliers[i] else [0, 0, 255] + cv2.line(img, (int(x1), int(y1)), (ref_frame.shape[1] + int(x2), int(y2)), color, 1, cv2.LINE_AA) + best_match_count = i + 1 if 'i' in locals() else 'N/A' + return img, best_match_count + + def draw_boxes(img, boxes): + if boxes is not None: + for box in boxes: + x, y, w, h = box + cv2.rectangle(img, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (0, 255, 0), 2, cv2.LINE_AA) + return img + + def draw_text(img, text, font=cv2.FONT_HERSHEY_PLAIN, pos=(0, 0), font_scale=6, font_thickness=4, text_color=(0, 255, 0), text_color_bg=(0, 0, 0)): + x, y = pos + text_size, _ = cv2.getTextSize(text, font, font_scale, font_thickness) + cv2.rectangle(img, pos, (x + text_size[0], y + text_size[1]), text_color_bg, -1) + cv2.putText(img, text, (x, y + text_size[1] + font_scale - 1), font, font_scale, text_color, font_thickness) + + ref_frame = stabilizer.ref_frame_gray + ref_frame = draw_mask(ref_frame, stabilizer.ref_mask) + ref_frame = draw_points(ref_frame, stabilizer.ref_pts) + draw_text(ref_frame, "Reference frame (=0)") + + cur_frame = stabilizer.cur_frame_gray if stabilizer.cur_frame_gray is not None else np.full(stabilizer.ref_frame_gray.shape, 0, dtype=np.uint8) + cur_frame = draw_mask(cur_frame, stabilizer.cur_mask) + cur_frame = draw_points(cur_frame, stabilizer.cur_pts) + draw_text(cur_frame, f"Frame {frame_num}") + + imgs_upper = np.hstack((ref_frame, cur_frame)) + if not args.no_lines: + imgs_upper, best_match_count = draw_lines(imgs_upper, stabilizer.ref_pts, stabilizer.cur_pts) + inliers_count = stabilizer.cur_inliers_count if stabilizer.cur_inliers_count is not None else 'N/A' + draw_text(imgs_upper, f"Number of matched points after pre-filtering: {best_match_count}", pos=(ref_frame.shape[1] // 2, 10), font_scale=4, text_color=(255, 0, 255)) + draw_text(imgs_upper, f"Number of inliers after RANSAC: {inliers_count}", pos=(ref_frame.shape[1] // 2, 80), font_scale=4, text_color=(255, 0, 255)) + + if not args.no_boxes: + frame = draw_boxes(frame, boxes) + stabilized_frame = draw_boxes(stabilized_frame, stabilized_boxes) + draw_text(frame, f'Original video frame {frame_num}') + draw_text(stabilized_frame, f'Stabilized video frame {frame_num}') + + imgs_lower = np.hstack((stabilized_frame, frame if stabilizer.cur_frame is not None else np.zeros_like(stabilized_frame))) + draw_text(imgs_lower, "Press 'q' to quit.", pos=(imgs_lower.shape[1] - 600, imgs_lower.shape[0] - 80), font_scale=4, text_color=(0, 0, 255)) + + return np.vstack((imgs_upper, imgs_lower)) + +def drop_none_values(kwargs): + """ + Drop None values from a dictionary. + """ + return {k: v for k, v in kwargs.items() if v is not None} + +def get_args_and_kwargs(cli_args): + """ + Get the arguments and keyword arguments from the CLI arguments. + """ + kwargs = vars(cli_args) + + args = argparse.Namespace() + args.input = kwargs.pop('input') + args.output = kwargs.pop('output') + args.mask_path = kwargs.pop('mask_path') + args.no_mask = kwargs.pop('no_mask') + args.mask_start = kwargs.pop('mask_start') + args.mask_encoding = kwargs.pop('mask_encoding') + args.save = kwargs.pop('save') + args.viz = kwargs.pop('viz') + args.save_viz = kwargs.pop('save_viz') + args.no_lines = kwargs.pop('no_lines') + args.no_boxes = kwargs.pop('no_boxes') + args.custom_config = kwargs.pop('custom_config') + + if args.custom_config is not None: + kwargs.update(load_config(args.custom_config)) + else: + kwargs = drop_none_values(kwargs) + kwargs['mask_use'] = not args.no_mask + kwargs['viz'] = args.viz or args.save_viz + + return args, kwargs + +def parse_args(): + """ + Parse the command-line arguments. + """ + parser = argparse.ArgumentParser(description="Stabilize a video using the stabilo library.") + + # script options + parser.add_argument("input", type=Path, help="input video filepath") + parser.add_argument("--output", "-o", type=Path, help="output video(s) filepath [if not provided, output will be saved in the same directory as input]") + parser.add_argument("--viz", "-v", action="store_true", help="visualize the transformation process [default: False]") + parser.add_argument("--save", "-s", action="store_true", help="save the stabilized video [default: False]") + parser.add_argument("--save-viz", "-sv", action="store_true", help="save the visualization of the transformation process [default: False]") + parser.add_argument("--no-mask", "-nm", action="store_true", help="do not use masks for stabilization [default: False]") + parser.add_argument("--mask-path", "-mp", type=Path, help="custom mask filepath [default: input with .txt extension]") + parser.add_argument("--mask-start", "-ms", type=int, default=2, help="start column index of bounding boxes used as masks. Column 0 reserved for frame number [default: 2]") + parser.add_argument("--mask-encoding", "-me", type=str, default="yolo", choices=['yolo', 'pascal', 'coco'], help="bounding box encoding (all un-normalized) [default: yolo]") + parser.add_argument("--no-lines", "-nl", action="store_true", help="do not draw lines between matched feature points [default: False]") + parser.add_argument("--no-boxes", "-nb", action="store_true", help="do no draw bounding boxes on the (un-)stabilized videos [default: False]") + parser.add_argument("--custom-config", "-cc", type=Path, help="path to a custom stabilo configuration file that overrides the CLI arguments below [default: None]") + + # stabilo options (default values are set in the stabilo library) + parser.add_argument("--detector-name", "-dn", type=str, choices=['orb', 'sift', 'rsift', 'brisk', 'kaze', 'akaze'], help="detector type [default: orb]") + parser.add_argument("--matcher-name", "-mn", type=str, choices=['bf', 'flann'], help="matcher type [default: bf]") + parser.add_argument("--filter-type", "-ft", type=str, choices=['none', 'ratio', 'distance'], help="filter type for the match filter [default: ratio]") + parser.add_argument("--transformation-type", "-tt", type=str, choices=['projective', 'affine'], help="transformation type [default: projective]") + parser.add_argument("--clahe", "-c", action="store_true", help="apply CLAHE to grayscale images [default: False]") + parser.add_argument("--downsample-ratio", "-dr", type=float, help="downsample ratio for the input video [default: 0.5]") + parser.add_argument("--max-features", "-mf", type=int, help="maximum number of features to detect [default: 2000]") + parser.add_argument("--ref-multiplier", "-rm", type=float, help="multiplier for max features in reference frame (ref_multiplier x max_features) [default: 2]") + parser.add_argument("--filter-ratio", "-fr", type=float, help="filter ratio for the match filter [default: 0.9]") + parser.add_argument("--ransac-method", "-r", type=int, help="RANSAC method [default: 38 (MAGSAC++)]") + parser.add_argument("--ransac-epipolar-threshold", "-ret", type=float, help="RANSAC epipolar threshold [default: 2.0]") + parser.add_argument("--ransac-max-iter", "-rmi", type=int, help="RANSAC maximum iterations [default: 5000]") + parser.add_argument("--ransac-confidence", "-rc", type=float, help="RANSAC confidence [default: 0.999999]") + parser.add_argument("--mask-margin-ratio", "-mmr", type=float, help="mask margin ratio [default: 0.15]") + + return parser.parse_args() + +if __name__ == "__main__": + stabilize_video(parse_args()) diff --git a/stabilo/__init__.py b/stabilo/__init__.py new file mode 100644 index 0000000..b3f3b32 --- /dev/null +++ b/stabilo/__init__.py @@ -0,0 +1,4 @@ +from .stabilo import Stabilizer + +__version__ = '0.1.0' +__all__ = ['Stabilizer', '__version__'] diff --git a/stabilo/cfg/default.yaml b/stabilo/cfg/default.yaml new file mode 100644 index 0000000..2e4b89f --- /dev/null +++ b/stabilo/cfg/default.yaml @@ -0,0 +1,57 @@ +# This is the default configuration file (default.yaml) containing optimized parameters +# for the ORB detector, brute force (BF) matcher, ratio test, and projective transformation. +# This template serves as a starting point for users to customize according to their specific needs. +# +# The goal of this optimization was to balance high accuracy without compromising on speed, +# specifically tailored for high-altitude bird's-eye view (BeV) drone imagery analysis. +# +# The optimization involved a set of BeV drone scenes with associated vehicle masks captured +# from an altitude of approximately 150 meters. With the projective transformation fixed, +# we then optimized all the remaining parameters in a hierarchical manner. +# +# Details of the optimization process will be available soon at: +# https://github.com/rfonod/stabilo-benchmark +# +# The detailed description of each parameter is explained in the stabilo.py module. + + +# Image pre-processing related +clahe: false # Contrast Limited Adaptive Histogram Equalization (CLAHE); [true, false] +downsample_ratio: 0.5 # Downsample ratio for image resizing; (0.0, 1.0] + +# Feature detectors and descriptors related +detector_name: 'orb' # Feature detector name; ['orb', 'sift', 'rsift', 'brisk', 'kaze', 'akaze'] +max_features: 2000 # Maximum number of features to detect; [0, inf) +ref_multiplier: 2.0 # Reference multiplier for the number of features; (0.0, inf) + +# Feature matching and filtering related +matcher_name: 'bf' # Feature matcher name; ['bf', 'flann'] +filter_type: 'ratio' # Filter type; ['none', 'ratio', 'distance'] +filter_ratio: 0.9 # Ratio test threshold; (0.0, 1.0] + +# Transformation matrix calculation related +transformation_type: 'projective' # Transformation type; ['projective', 'affine'] +ransac_method: 38 # RANSAC method; [4: LMEDS, 8: RANSAC, 16: RHO, 32: DEGENSAC, 33: DEGENSAC (with different parameters), 35: LO-RANSAC, 36: GC-RANSAC, 37: PROSAC, 38: MAGSAC++] +ransac_epipolar_threshold: 2.0 # Epipolar threshold for RANSAC; (0.0, inf) +ransac_max_iter: 5000 # Maximum number of iterations for RANSAC; (0, inf] +ransac_confidence: 0.999999 # Confidence level for RANSAC; (0.0, 1.0] + +# Mask related +mask_use: true # Use mask for feature matching; [true, false] +mask_margin_ratio: 0.15 # Margin ratio for mask generation; [0.0, 1.0] + +# Default thresholds for BRISK, KAZE, and AKAZE (if threshold analysis not performed) +brisk_threshold: 130 # BRISK threshold; (0, 255] +kaze_threshold: 0.01 # KAZE threshold; (0.0, inf] +akaze_threshold: 0.01 # AKAZE threshold; (0.0, inf] + +# Computation related +gpu: false # Use GPU for computation; [true, false] + +# Miscellaneous +viz: false # Store features and matches for visualization; [true, false] +benchmark: false # Benchmark mode; [true, false] + +# Debug related +min_good_match_count_warning: 20 # Minimum good match count warning; [0, inf] +min_inliers_match_count_warning: 10 # Minimum inliers match count warning; [0, inf] \ No newline at end of file diff --git a/stabilo/stabilo.py b/stabilo/stabilo.py new file mode 100644 index 0000000..d0c3aa9 --- /dev/null +++ b/stabilo/stabilo.py @@ -0,0 +1,597 @@ +# -*- coding: utf-8 -*- +# Author: Robert Fonod (robert.fonod@ieee.org) + +""" +stabilo.py - Reference frame video stabilization with optional user-provided masks + +This module provides the Stabilizer class for video stabilization using feature matching +and transformation estimation. It leverages OpenCV for core functionalities. + +The class supports various feature detectors, matchers, filtering methods, and transformation types. +Fine-tuning these parameters allows customization for specific video stabilization needs. + +The parameters for the feature detectors, matchers, filtering methods, and transformations can be +fine-tuned to suit specific requirements, see https://github.com/rfonod/stabilo-benchmark. + +Key Features: + - Video or bounding box stabilization to a reference frame. + - Fine-tunable parameters for feature detectors, matchers, filtering methods, and transformations. + - Support for various feature detectors (e.g., ORB, SIFT) and matchers (e.g., BF, FLANN). + - Projective or affine transformations for frame stabilization. + - RANSAC-based algorithms for robust transformation matrix estimation. + - CLAHE and pre-processing options for contrast enhancement. + - Visualization and debugging features for keypoints, descriptors, and masks. + - GPU acceleration for improved performance (not implemented/tested yet). + +Usage: +1. Create an instance of the 'Stabilizer' class with desired parameter configurations. +2. Set a reference frame using the 'set_ref_frame' method. +3. Stabilize subsequent frames using the 'stabilize' method. +4. Access stabilized frames, bounding boxes, and transformation matrices using specific methods. + +**For detailed usage instructions, refer to the accompanying README file.** +""" + +import sys +import cv2 +import numpy as np +from pathlib import Path +from typing import Union +from .utils import timer, load_config, setup_logger, xywh2four, four2xywh + +# Configure logging +logger = setup_logger(__name__) + +# Define the root directory +ROOT = Path(__file__).resolve().parents[0] + +# Read the default parameters from a configuration file +cfg = load_config(ROOT / "cfg" / "default.yaml", logger) + +# Profiling flag +PROFILING = False + + +class Stabilizer: + """ + This class implements a video stabilizer. It uses feature matching to find the transformation + between the reference frame and the current frame, allowing stabilization of subsequent frames. + The transformation matrix can be used to transform the current frame to the reference frame or + to transform points from the current frame to the reference frame. + """ + + VALID_DETECTORS = ['orb', 'sift', 'rsift', 'brisk', 'kaze', 'akaze'] + VALID_MATCHERS = ['bf', 'flann'] + VALID_FILTER_TYPES = ['none', 'ratio', 'distance'] + VALID_TRANSFORMATION_TYPES = ['projective', 'affine'] + VALID_RANSAC_METHODS_DICT = { + 'cv2.LMEDS': cv2.LMEDS, # 4 - LMEDS + 'cv2.RANSAC': cv2.RANSAC, # 8 - RANSAC + 'cv2.RHO': cv2.RHO, # 16 - RHO + 'cv2.USAC_DEFAULT': cv2.USAC_DEFAULT, # 32 - DEGENSAC + 'cv2.USAC_PARALLEL': cv2.USAC_PARALLEL, # 33 - DEGENSAC (with different parameters) + 'cv2.USAC_FAST': cv2.USAC_FAST, # 35 - LO-RANSAC + 'cv2.USAC_ACCURATE': cv2.USAC_ACCURATE, # 36 - GC-RANSAC + 'cv2.USAC_PROSAC': cv2.USAC_PROSAC, # 37 - PROSAC + 'cv2.USAC_MAGSAC': cv2.USAC_MAGSAC # 38 - MAGSAC++ + } + + def __init__(self, **kwargs): + """ + Initialize the Stabilizer with user-provided or default configurations. + + Arguments: + - detector_name: str - feature detector to use (orb, sift, rsift, brisk, kaze, akaze) + - matcher_name: str - feature matcher to use (bf, flann) + - filter_type: str - filter type for the feature matcher (none, ratio, distance) + - transformation_type: str - transformation for stabilization (projective, affine) + - clahe: bool - use CLAHE for contrast enhancement + - downsample_ratio: float - down-sampling ratio for the frames (e.g., 0.5 for half the size) + - max_features: int - max number of features to detect (for BRISK, KAZE, and AKAZE this is an approximation) + - ref_multiplier: float - multiplier for max features in reference frame (ref_multiplier x max_features) + - mask_use: bool - use mask for feature detection + - filter_ratio: float - filtering ratio; Lowe's ratio for 'ratio' filter, distance threshold ratio for 'distance' filter + - ransac_method: int - method for RANSAC algorithm (see above for options) + - ransac_epipolar_threshold: float - threshold for RANSAC (e.g., 1.0) + - ransac_max_iter: int - max iterations for RANSAC (e.g., 2000) + - ransac_confidence: float - confidence for RANSAC (e.g., 0.999) + - brisk_threshold: int - threshold for BRISK detector (used only if 'max_features -> threshold' model is not available) + - kaze_threshold: float - threshold for KAZE detector (used only if 'max_features -> threshold' model is not available) + - akaze_threshold: float - threshold for AKAZE detector (used only if 'max_features -> threshold' model is not available) + - gpu: bool - use GPU acceleration (not fully implemented/tested yet) + - viz: bool - save some features for visualization (e.g., keypoints, descriptors, masks) + - benchmark: bool - different behavior for benchmarking purposes (e.g., re-use the last transformation if the current one is None) + - min_good_match_count_warning: int - min number of good matches to trigger a warning + - min_inliers_match_count_warning: int - min number of inliers to trigger a warning + """ + self._load_configuration(kwargs) + self._validate_arguments() + self._initialize_variables() + self._create_feature_detectors() + self._create_matcher() + self._create_transformer() + self._create_helpers() + + def _load_configuration(self, kwargs): + """ + Load configuration parameters, using defaults if not provided. + """ + for key, value in cfg.items(): + setattr(self, key, kwargs.get(key, value)) + + def _initialize_variables(self): + """ + Initialize internal variables for the Stabilizer. + """ + self.ref_frame, self.cur_frame = None, None + self.ref_frame_gray, self.cur_frame_gray = None, None + self.ref_boxes, self.cur_boxes = None, None + self.ref_mask, self.cur_mask = None, None + self.ref_kpts, self.cur_kpts = None, None + self.ref_desc, self.cur_desc = None, None + self.ref_pts, self.cur_pts = None, None + self.cur_trans_matrix, self.trans_matrix_last_known = None, None + self.cur_inliers, self.cur_inliers_count = None, None + self.h, self.w = None, None + + def _create_feature_detectors(self): + """ + Create feature detectors and descriptor extractors based on the provided configurations. + """ + self.detector_cur, self.detector_ref = self._create_detector(self.detector_name) + self.norm_type = self._get_norm_type() + + def _create_detector(self, detector_name: str): + """ + Create a feature detector based on the provided detector name. + """ + if detector_name == "orb": + return self._create_orb_detectors() + elif detector_name in ["sift", "rsift"]: + return self._create_sift_detectors() + elif detector_name == "brisk": + return self._create_brisk_detectors() + elif detector_name == "kaze": + return self._create_kaze_detectors() + elif detector_name == "akaze": + return self._create_akaze_detectors() + + def _create_orb_detectors(self): + """ + Create ORB detectors and descriptor extractors. + """ + return ( + cv2.cuda.ORB_create(self.max_features) if self.gpu else cv2.ORB_create(self.max_features), + cv2.cuda.ORB_create(round(self.ref_multiplier * self.max_features)) if self.gpu else cv2.ORB_create(round(self.ref_multiplier * self.max_features)) + ) + + def _create_sift_detectors(self): + """ + Create SIFT detectors and descriptor extractors. + """ + return ( + cv2.cuda.SIFT_create(self.max_features) if self.gpu else cv2.SIFT_create(self.max_features), # (enable_precise_upscale=True) + cv2.cuda.SIFT_create(round(self.ref_multiplier * self.max_features)) if self.gpu else cv2.SIFT_create(round(self.ref_multiplier * self.max_features)) + ) + + def _create_brisk_detectors(self): + """ + Create BRISK detectors and descriptor extractors. + """ + threshold_cur, threshold_ref = self._get_thresholds() + return ( + cv2.cuda.BRISK.create(thresh=round(threshold_cur)) if self.gpu else cv2.BRISK_create(thresh=round(threshold_cur)), + cv2.cuda.BRISK.create(thresh=round(threshold_ref)) if self.gpu else cv2.BRISK_create(thresh=round(threshold_ref)) + ) + + def _create_kaze_detectors(self): + """ + Create KAZE detectors and descriptor extractors. + """ + threshold_cur, threshold_ref = self._get_thresholds() + return ( + cv2.cuda.KAZE_create(threshold=threshold_cur) if self.gpu else cv2.KAZE_create(threshold=threshold_cur), + cv2.cuda.KAZE_create(threshold=threshold_ref) if self.gpu else cv2.KAZE_create(threshold=threshold_ref) + ) + + def _create_akaze_detectors(self): + """ + Create AKAZE detectors and descriptor extractors. + """ + threshold_cur, threshold_ref = self._get_thresholds() + return ( + cv2.cuda.AKAZE_create(threshold=threshold_cur) if self.gpu else cv2.AKAZE_create(threshold=threshold_cur), + cv2.cuda.AKAZE_create(threshold=threshold_ref) if self.gpu else cv2.AKAZE_create(threshold=threshold_ref) + ) + + def _get_thresholds(self): + """ + Get thresholds for BRISK, KAZE, and AKAZE based on precomputed models. + """ + detector_name = self.detector_name.upper() + threshold_model_filepath = ROOT / 'thresholds' / 'models' / f'{detector_name}' / f'model_mask_{self.mask_use}_clahe_{self.clahe}.txt' + if threshold_model_filepath.exists(): + model = np.loadtxt(str(threshold_model_filepath)) + threshold_cur = model[1] + model[0] * self.max_features + threshold_ref = model[1] + model[0] * self.max_features * self.ref_multiplier + if not self.benchmark: + logger.info(f"Using {detector_name} with threshold {threshold_ref} for the reference frame and {threshold_cur} for the current frame.") + else: + threshold_cur = self.brisk_threshold if detector_name == 'BRISK' else self.kaze_threshold if detector_name == 'KAZE' else self.akaze_threshold + threshold_ref = threshold_cur + if not self.benchmark: + logger.warning(f"No threshold analysis for {detector_name}. Using default threshold.") + return threshold_cur, threshold_ref + + def _get_norm_type(self): + """ + Get the norm type based on the detector name. + """ + if self.detector_name in ["orb", "brisk", "akaze"]: + return cv2.NORM_HAMMING # N.B.: if ORB is using WTA_K == 3 or 4, cv.NORM_HAMMING2 should be used + elif self.detector_name in ["sift", "rsift", "kaze"]: + return cv2.NORM_L2 + + def _create_matcher(self): + """ + Create the feature matcher based on the provided configurations. + """ + if self.matcher_name == "bf": + self.matcher = self._create_brute_force_matcher() + elif self.matcher_name == "flann": + self.matcher = self._create_flann_matcher() + + def _create_brute_force_matcher(self): + """ + Create a brute-force matcher. + """ + return cv2.cuda.DescriptorMatcher_createBFMatcher(self.norm_type, crossCheck=(True,False)[self.filter_type=='ratio']) if self.gpu else cv2.BFMatcher(self.norm_type, crossCheck=(True,False)[self.filter_type=='ratio']) + + def _create_flann_matcher(self): + """ + Create a FLANN-based matcher. + """ + if self.norm_type in [cv2.NORM_HAMMING, cv2.NORM_HAMMING2]: + index_params = dict(algorithm=6, table_number=6, key_size=12, multi_probe_level=1) + elif self.norm_type == cv2.NORM_L2: + index_params = dict(algorithm=0, trees=5) + search_params = dict(checks=100) + return cv2.cuda.DescriptorMatcher_createFlannBasedMatcher(index_params, search_params) if self.gpu else cv2.FlannBasedMatcher(index_params, search_params) + + def _create_transformer(self): + """ + Create the transformation matrix estimator based on the provided configurations. + """ + if self.transformation_type == 'projective': + self.transformer = cv2.findHomography + elif self.transformation_type == 'affine': + self.transformer = cv2.estimateAffinePartial2D + + def _create_helpers(self): + """ + Create helper objects for grayscale conversion, CLAHE, and resizing. + """ + self.grayscale_converter = cv2.cuda.cvtColor if self.gpu else cv2.cvtColor + self.claher = cv2.cuda.createCLAHE(clipLimit=1.0, tileGridSize=(8, 8)) if self.gpu else cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8, 8)) + self.resizer = cv2.cuda.resize if self.gpu else cv2.resize + + @timer(PROFILING) + def set_ref_frame(self, frame: np.ndarray, boxes: np.ndarray = None, box_format: str = 'xywh') -> None: + """ + Set the reference frame and object bounding boxes. + Calculate keypoints and descriptors for the reference frame. + """ + self.process_frame(frame, boxes, box_format, is_reference=True) + + @timer(PROFILING) + def stabilize(self, frame: np.ndarray, boxes: np.ndarray = None, box_format: str = 'xywh') -> None: + """ + This method takes an un-stabilized video frame and + calculates a transformation matrix that can transform + this frame or boxes to the reference frame coordinates. + """ + success = self.process_frame(frame, boxes, box_format, is_reference=False) + if success: + matches = self.get_matches(self.ref_desc, self.cur_desc) + if matches and self.ref_kpts is not None and self.cur_kpts is not None: + self.ref_pts = np.float32([self.ref_kpts[m.queryIdx].pt for m in matches]).reshape(-1, 2) + self.cur_pts = np.float32([self.cur_kpts[m.trainIdx].pt for m in matches]).reshape(-1, 2) + else: + self.ref_pts = [] + self.cur_pts = [] + self.calculate_transformation_matrix() + + def process_frame(self, frame: np.ndarray, boxes: np.ndarray = None, box_format: str = 'xywh', is_reference: bool = False) -> bool: + """ + Process the given frame and bounding boxes. + """ + if frame is None: + logger.error(f'{"Reference" if is_reference else "Current"} frame is invalid.') + sys.exit(1) + + if self.mask_use: + if boxes is None: + logger.warning(f'Mask is set to be used, but no bounding boxes were provided for the {"reference" if is_reference else "current"} frame.') + else: + boxes = None # if mask is not to be used, ignore the bounding boxes even if provided + + if is_reference: + self.h, self.w = frame.shape[:2] + self.ref_frame, self.ref_boxes, self.ref_box_format = frame, boxes, box_format + else: + self.cur_frame, self.cur_boxes, self.cur_box_format = frame, boxes, box_format + + mask = None if boxes is None else self.create_binary_mask(boxes, box_format) + kpts, desc, frame_gray = self.get_features_and_descriptors(frame, mask, is_reference) + + if is_reference: + self.ref_kpts, self.ref_desc, self.ref_frame_gray = kpts, desc, frame_gray + else: + self.cur_kpts, self.cur_desc, self.cur_frame_gray = kpts, desc, frame_gray + + if self.viz: + if is_reference: + self.ref_mask = mask + self.ref_pts = np.array([ref_kpt.pt for ref_kpt in self.ref_kpts], dtype=np.float32).reshape(-1, 2) if self.ref_kpts is not None else [] + else: + self.cur_mask = mask + + return True + + @timer(PROFILING) + def get_features_and_descriptors(self, frame: np.ndarray, mask: np.ndarray = None, ref_frame: bool = False) -> tuple: + """ + Get the features and descriptors for the given frame. + """ + if frame is None: + return None, None, None + + if self.gpu: + frame = cv2.cuda_GpuMat(frame) + mask = cv2.cuda_GpuMat(mask) if mask is not None else None + + frame = self.grayscale_converter(frame, cv2.COLOR_BGR2GRAY) + if self.clahe: + frame = self.claher.apply(frame) + + frame_gray = frame if self.viz else None + + if self.downsample_ratio != 1.0: + frame = self.resizer(frame, (0, 0), fx=self.downsample_ratio, fy=self.downsample_ratio) + mask = self.resizer(mask, (0, 0), fx=self.downsample_ratio, fy=self.downsample_ratio) if mask is not None else None + + try: + kpts, desc = (self.detector_ref if ref_frame else self.detector_cur).detectAndCompute(frame, mask) + except cv2.error as e: + logger.warning(f"Features and descriptors couldn't be found. \n Error: {e}") + return None, None, None + + if self.detector_name == 'rsift': + desc /= (desc.sum(axis=1, keepdims=True) + 1e-8) + desc = np.sqrt(desc) + + if self.downsample_ratio != 1.0: + for kpt in kpts: + kpt.pt = (kpt.pt[0] / self.downsample_ratio, kpt.pt[1] / self.downsample_ratio) + + if self.gpu: + kpts = self.detector_cur.convert(kpts) + desc = self.detector_cur.convert(desc) + frame_gray = frame.download(frame_gray) + + return kpts, desc, frame_gray + + + + @timer(PROFILING) + def get_matches(self, desc1: np.ndarray, desc2: np.ndarray) -> list: + """ + Match the given descriptors. + """ + if desc1 is None or desc2 is None: + logger.warning("One of the descriptors is invalid.") + return [] + + try: + if self.filter_type == 'none': + good_matches = self.matcher.match(desc1, desc2, None) + elif self.filter_type == 'distance': + matches = self.matcher.match(desc1, desc2, None) + matches = sorted(matches, key=lambda x: x.distance) + min_dist, max_dist = matches[0].distance, matches[-1].distance + good_thresh = min_dist + (max_dist - min_dist) * self.filter_ratio + good_matches = [m for m in matches if m.distance <= good_thresh] + elif self.filter_type == 'ratio': + matches = self.matcher.knnMatch(desc1, desc2, k=2) + good_matches = [] + for pair in matches: + if len(pair) == 2: + m, n = pair + if m.distance < self.filter_ratio*n.distance: + good_matches.append(m) + except cv2.error as e: + logger.error(f"Matches couldn't be found. \n Error: {e}") + return [] + + if len(good_matches) <= self.min_good_match_count_warning: + logger.warning(f'Only {len(good_matches)} good matches were found.') + + return list(good_matches) + + @timer(PROFILING) + def calculate_transformation_matrix(self) -> None: + """ + Estimate the transformation matrix using the current and reference points. + """ + if self.ref_pts is not None and self.cur_pts is not None and len(self.ref_pts) >= 4 and len(self.cur_pts) >= 4: + try: + self.cur_trans_matrix, inliers = self.transformer(self.cur_pts, self.ref_pts, maxIters=self.ransac_max_iter, + method=self.ransac_method, confidence=self.ransac_confidence, ransacReprojThreshold=self.ransac_epipolar_threshold) + except cv2.error as e: + logger.exception(f"Transformation matrix couldn't be calculated.\n Error: {e}") + self.cur_trans_matrix = np.eye(3) if self.benchmark else self.trans_matrix_last_known + inliers = np.full((len(self.cur_pts), 1), False, dtype=bool) + inliers_count = 'N/A' + if not self.benchmark: + logger.warning("Re-using the last known transformation matrix.") + else: + if self.cur_trans_matrix is not None: + self.trans_matrix_last_known = self.cur_trans_matrix + inliers_count = sum(inliers.ravel().tolist()) + if inliers_count <= self.min_inliers_match_count_warning: + logger.warning(f'Only {inliers_count} inliers points were used to estimate the transformation matrix.') + else: + logger.warning(f'Transformation matrix is None.') + self.cur_trans_matrix = np.eye(3) if self.benchmark else self.trans_matrix_last_known + inliers = np.full((len(self.cur_pts), 1), False, dtype=bool) + inliers_count = 'N/A' + if not self.benchmark: + logger.warning("Re-using the last known transformation matrix.") + else: + logger.warning(f'Not enough points to estimate the transformation matrix.') + self.cur_trans_matrix = np.eye(3) if self.benchmark else self.trans_matrix_last_known + if not self.benchmark: + logger.warning("Re-using the last known transformation matrix.") + inliers = np.full((len(self.cur_pts), 1), False, dtype=bool) + inliers_count = 'N/A' + + if self.viz: + self.cur_inliers = inliers + self.cur_inliers_count = inliers_count + + @timer(PROFILING) + def create_binary_mask(self, boxes: np.ndarray, box_format: str) -> np.ndarray: + """ + Create a mask from the given bounding boxes. + """ + if self.h is None or self.w is None: + logger.error("Reference frame is not set.") + sys.exit(1) + + if box_format == 'four': + boxes = four2xywh(boxes) + + mask = np.full((self.h, self.w), 255, dtype=np.uint8) + for box in boxes: + xc, yc, wb, hb = box + wb += wb * self.mask_margin_ratio + hb += hb * self.mask_margin_ratio + x1, y1, x2, y2 = int(xc - wb / 2), int(yc - hb / 2), int(xc + wb / 2), int(yc + hb / 2) + mask[max(0, y1):min(self.h, y2), max(0, x1):min(self.w, x2)] = 0 + + return mask + + @timer(PROFILING) + def warp_cur_frame(self) -> Union[np.ndarray, None]: + """ + Warp the current frame to the reference frame using the current transformation matrix. + """ + return self.warp_frame(self.cur_frame) + + def warp_frame(self, frame: np.ndarray) -> Union[np.ndarray, None]: + """ + Warp the given frame to the reference frame using the current transformation matrix. + """ + if frame is None: + return None + if self.w is None or self.h is None: + logger.error("Reference frame is not set.") + sys.exit(1) + if self.cur_trans_matrix is None: + logger.warning("Transformation matrix is None.") + return frame + if self.transformation_type == 'projective': + return cv2.warpPerspective(frame, self.cur_trans_matrix, (self.w, self.h)) + elif self.transformation_type == 'affine': + return cv2.warpAffine(frame, self.cur_trans_matrix, (self.w, self.h)) + + def transform_cur_boxes(self, out_box_format: str = 'xywh') -> Union[np.ndarray, None]: + """ + Warp the current bounding boxes to the reference frame using the current transformation matrix. + """ + return self.transform_boxes(self.cur_boxes, self.cur_trans_matrix, self.cur_box_format, out_box_format) + + def transform_boxes(self, boxes: np.ndarray, trans_matrix: np.ndarray, in_box_format: str = 'xywh', out_box_format: str = 'xywh') -> Union[np.ndarray, None]: + """ + Transform the provided bounding boxes using the provided transformation matrix. + """ + if boxes is None: + return None + if trans_matrix is None: + return boxes + + if in_box_format == 'xywh': + boxes = xywh2four(boxes) + + boxes = np.array([boxes]).reshape(-1, 1, 2) + if self.transformation_type == 'projective': + boxes = cv2.perspectiveTransform(boxes, trans_matrix) + elif self.transformation_type == 'affine': + boxes = cv2.transform(boxes, trans_matrix) + + if out_box_format == 'xywh': + return four2xywh(boxes.reshape(-1, 8)) + elif out_box_format == 'four': + return boxes.reshape(-1, 8) + + def get_cur_frame(self) -> Union[np.ndarray, None]: + """ + Get the current frame. + """ + return self.cur_frame + + def get_cur_boxes(self) -> Union[np.ndarray, None]: + """ + Get the current bounding boxes. + """ + return self.cur_boxes + + def get_cur_trans_matrix(self) -> Union[np.ndarray, None]: + """ + Get the current transformation matrix. + """ + return self.cur_trans_matrix + + def get_basic_info(self) -> dict: + """ + Get basic information about the Stabilizer. + """ + return { + 'detector_name': self.detector_name, + 'matcher_name': self.matcher_name, + 'filter_type': self.filter_type, + 'transformation_type': self.transformation_type, + 'clahe': self.clahe, + 'mask_use': self.mask_use, + } + + def _validate_arguments(self): + """ + Validate the arguments provided during the initialization of the Stabilizer class. + """ + if self.detector_name not in self.VALID_DETECTORS: + raise ValueError(f"Invalid detector: {self.detector_name}. Choose from {self.VALID_DETECTORS}") + if self.matcher_name not in self.VALID_MATCHERS: + raise ValueError(f"Invalid matcher: {self.matcher_name}. Choose from {self.VALID_MATCHERS}") + if self.filter_type not in self.VALID_FILTER_TYPES: + raise ValueError(f"Invalid filter type: {self.filter_type}. Choose from {self.VALID_FILTER_TYPES}") + if self.transformation_type not in self.VALID_TRANSFORMATION_TYPES: + raise ValueError(f"Invalid transformation type: {self.transformation_type}. Choose from {self.VALID_TRANSFORMATION_TYPES}") + if self.ransac_method not in self.VALID_RANSAC_METHODS_DICT.values(): + raise ValueError(f"Invalid RANSAC method: {self.ransac_method}. Choose from {self.VALID_RANSAC_METHODS_DICT.keys()}") + if not (0.0 < self.downsample_ratio <= 1.0): + raise ValueError("Invalid downsample_ratio. It should be in the range (0.0, 1.0]") + if not (0 < self.max_features) and isinstance(self.max_features, int): + raise ValueError("Invalid max_features. It should be greater than 0 and an integer") + if not (1 <= self.ref_multiplier): + raise ValueError("Invalid ref_multiplier. It should be greater than or equal to 1") + if not (0.0 < self.filter_ratio <= 1.0): + raise ValueError("Invalid filter_ratio. It should be in the range (0.0, 1.0]") + if not (0 < self.ransac_max_iter) and isinstance(self.ransac_max_iter, int): + raise ValueError("Invalid ransac_max_iter. It should be greater than 0 and an integer") + if not (0.0 < self.ransac_epipolar_threshold): + raise ValueError("Invalid ransac_epipolar_threshold. It should be greater than 0") + if not (0.0 < self.ransac_confidence <= 1.0): + raise ValueError("Invalid ransac_confidence. It should be in the range (0.0, 1.0]") + if self.gpu and not cv2.cuda.getCudaEnabledDeviceCount(): + raise ValueError("GPU is enabled but no CUDA-enabled device was found") \ No newline at end of file diff --git a/stabilo/thresholds/models/AKAZE/model_mask_False_clahe_False.txt b/stabilo/thresholds/models/AKAZE/model_mask_False_clahe_False.txt new file mode 100644 index 0000000..752c1d3 --- /dev/null +++ b/stabilo/thresholds/models/AKAZE/model_mask_False_clahe_False.txt @@ -0,0 +1,2 @@ +-1.238671541205360271e-06 +1.399651010429193293e-02 diff --git a/stabilo/thresholds/models/AKAZE/model_mask_False_clahe_True.txt b/stabilo/thresholds/models/AKAZE/model_mask_False_clahe_True.txt new file mode 100644 index 0000000..c127ff0 --- /dev/null +++ b/stabilo/thresholds/models/AKAZE/model_mask_False_clahe_True.txt @@ -0,0 +1,2 @@ +-1.433874243028384286e-06 +1.797921843899690300e-02 diff --git a/stabilo/thresholds/models/AKAZE/model_mask_True_clahe_False.txt b/stabilo/thresholds/models/AKAZE/model_mask_True_clahe_False.txt new file mode 100644 index 0000000..316edc0 --- /dev/null +++ b/stabilo/thresholds/models/AKAZE/model_mask_True_clahe_False.txt @@ -0,0 +1,2 @@ +-1.007223706389076417e-06 +1.163307906351335880e-02 diff --git a/stabilo/thresholds/models/AKAZE/model_mask_True_clahe_True.txt b/stabilo/thresholds/models/AKAZE/model_mask_True_clahe_True.txt new file mode 100644 index 0000000..14a44ad --- /dev/null +++ b/stabilo/thresholds/models/AKAZE/model_mask_True_clahe_True.txt @@ -0,0 +1,2 @@ +-1.420484216983836093e-06 +1.574875889009140148e-02 diff --git a/stabilo/thresholds/models/BRISK/model_mask_False_clahe_False.txt b/stabilo/thresholds/models/BRISK/model_mask_False_clahe_False.txt new file mode 100644 index 0000000..ed170e0 --- /dev/null +++ b/stabilo/thresholds/models/BRISK/model_mask_False_clahe_False.txt @@ -0,0 +1,2 @@ +-5.697760528375401222e-03 +1.362927870041904157e+02 diff --git a/stabilo/thresholds/models/BRISK/model_mask_False_clahe_True.txt b/stabilo/thresholds/models/BRISK/model_mask_False_clahe_True.txt new file mode 100644 index 0000000..3c1ed25 --- /dev/null +++ b/stabilo/thresholds/models/BRISK/model_mask_False_clahe_True.txt @@ -0,0 +1,2 @@ +-6.082518482010841263e-03 +1.538229586731700920e+02 diff --git a/stabilo/thresholds/models/BRISK/model_mask_True_clahe_False.txt b/stabilo/thresholds/models/BRISK/model_mask_True_clahe_False.txt new file mode 100644 index 0000000..454d899 --- /dev/null +++ b/stabilo/thresholds/models/BRISK/model_mask_True_clahe_False.txt @@ -0,0 +1,2 @@ +-5.026162052230478440e-03 +1.261693929838898498e+02 diff --git a/stabilo/thresholds/models/BRISK/model_mask_True_clahe_True.txt b/stabilo/thresholds/models/BRISK/model_mask_True_clahe_True.txt new file mode 100644 index 0000000..25fa86c --- /dev/null +++ b/stabilo/thresholds/models/BRISK/model_mask_True_clahe_True.txt @@ -0,0 +1,2 @@ +-5.684927998372675539e-03 +1.451210374444453635e+02 diff --git a/stabilo/thresholds/models/KAZE/model_mask_False_clahe_False.txt b/stabilo/thresholds/models/KAZE/model_mask_False_clahe_False.txt new file mode 100644 index 0000000..52a51eb --- /dev/null +++ b/stabilo/thresholds/models/KAZE/model_mask_False_clahe_False.txt @@ -0,0 +1,2 @@ +-1.342082518515861633e-06 +1.445548677509021600e-02 diff --git a/stabilo/thresholds/models/KAZE/model_mask_False_clahe_True.txt b/stabilo/thresholds/models/KAZE/model_mask_False_clahe_True.txt new file mode 100644 index 0000000..56e659d --- /dev/null +++ b/stabilo/thresholds/models/KAZE/model_mask_False_clahe_True.txt @@ -0,0 +1,2 @@ +-1.471458874258221078e-06 +1.783699945834927095e-02 diff --git a/stabilo/thresholds/models/KAZE/model_mask_True_clahe_False.txt b/stabilo/thresholds/models/KAZE/model_mask_True_clahe_False.txt new file mode 100644 index 0000000..c27314c --- /dev/null +++ b/stabilo/thresholds/models/KAZE/model_mask_True_clahe_False.txt @@ -0,0 +1,2 @@ +-9.680610223378339583e-07 +1.147348454716152349e-02 diff --git a/stabilo/thresholds/models/KAZE/model_mask_True_clahe_True.txt b/stabilo/thresholds/models/KAZE/model_mask_True_clahe_True.txt new file mode 100644 index 0000000..a0aa9fa --- /dev/null +++ b/stabilo/thresholds/models/KAZE/model_mask_True_clahe_True.txt @@ -0,0 +1,2 @@ +-1.350186975845170620e-06 +1.509247010983386615e-02 diff --git a/stabilo/utils.py b/stabilo/utils.py new file mode 100644 index 0000000..23d3a3f --- /dev/null +++ b/stabilo/utils.py @@ -0,0 +1,103 @@ +# -*- coding: utf-8 -*- +# Author: Robert Fonod (robert.fonod@ieee.org) + +import sys +import yaml +import time +import numpy as np +import logging + +def setup_logger(name: str, log_file: str = None, level: int = logging.INFO) -> logging.Logger: + """ + Setup the logger + """ + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + logger = logging.getLogger(name) + logger.setLevel(level) + + if log_file: + file_handler = logging.FileHandler(log_file) + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + + console_handler = logging.StreamHandler() + console_handler.setFormatter(formatter) + logger.addHandler(console_handler) + + return logger + +def timer(profiling: bool = False): + """ + Decorator function to measure the execution time of a function. + """ + def decorator(func): + def wrapper(*args, **kwargs): + if not profiling: + return func(*args, **kwargs) + start_time = time.time() + result = func(*args, **kwargs) + print(f"{func.__name__:<35} execution time: {1000*(time.time() - start_time):>10.2f} ms") + return result + return wrapper + return decorator + + +def load_config(cfg_filepath: str, logger: logging.Logger = None) -> dict: + """ + Load the configuration file + """ + try: + with open(cfg_filepath, 'r') as f: + config = yaml.safe_load(f) + except FileNotFoundError: + if logger is not None: + logger.error(f"Configuration file {cfg_filepath} not found.") + sys.exit(1) + return config + +def xywh2four(boxes: np.ndarray) -> np.ndarray: + """ + Convert bounding boxes from [xc, yc, w, h] to four point format [x1, y1, x2, y2, x3, y3, x4, y4]. + """ + x_c, y_c, w, h = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3] + + x1 = x_c - 0.5 * w + y1 = y_c - 0.5 * h + x2 = x_c + 0.5 * w + y2 = y_c - 0.5 * h + x3 = x_c + 0.5 * w + y3 = y_c + 0.5 * h + x4 = x_c - 0.5 * w + y4 = y_c + 0.5 * h + + return np.column_stack((x1, y1, x2, y2, x3, y3, x4, y4)) + +def four2xywh(boxes: np.ndarray) -> np.ndarray: + """ + Convert bounding boxes from four point format [x1, y1, x2, y2, x3, y3, x4, y4] to [xc, yc, w, h]. + """ + x1, y1, x2, y2, x3, y3, x4, y4 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3], boxes[:, 4], boxes[:, 5], boxes[:, 6], boxes[:, 7] + + x_c = 0.25 * (x1 + x2 + x3 + x4) + y_c = 0.25 * (y1 + y2 + y3 + y4) + w = 0.5 * (x2 - x1 + x3 - x4) + h = 0.5 * (y3 - y2 + y4 - y1) + + return np.column_stack((x_c, y_c, w, h)) + +def detect_delimiter(filepath: str, lines_to_check: int = 5) -> str: + """ + Detect the delimiter of a CSV file by reading a few lines + """ + delimiters = {',': 0, ' ': 0, '\t': 0} + with open(filepath, 'r') as file: + for _ in range(lines_to_check): + line = file.readline() + if not line: + break + delimiters[','] += line.count(',') + delimiters[' '] += line.count(' ') + delimiters['\t'] += line.count('\t') + + return max(delimiters, key=delimiters.get) +