From ddb0da5249005492d2580b29f4a6c9066cc22b42 Mon Sep 17 00:00:00 2001 From: natesimon Date: Sun, 11 Feb 2024 23:33:21 -0500 Subject: [PATCH] MonoNav: Code Release! --- .gitignore | 178 ++++++++ .gitmodules | 3 + LICENSE | 21 + README.md | 184 ++++++++ ZoeDepth | 1 + collect_dataset.py | 105 +++++ config.yml | 41 ++ .../crazyflie_frame-000000.pose.txt | 4 + .../crazyflie_frame-000001.pose.txt | 4 + .../crazyflie_frame-000002.pose.txt | 4 + .../crazyflie_frame-000003.pose.txt | 4 + .../crazyflie_frame-000004.pose.txt | 4 + .../crazyflie_frame-000005.pose.txt | 4 + .../crazyflie_frame-000006.pose.txt | 4 + .../crazyflie_frame-000007.pose.txt | 4 + .../crazyflie_frame-000008.pose.txt | 4 + .../crazyflie_frame-000009.pose.txt | 4 + .../crazyflie_frame-000010.pose.txt | 4 + .../crazyflie_frame-000011.pose.txt | 4 + .../crazyflie_frame-000012.pose.txt | 4 + .../crazyflie_frame-000013.pose.txt | 4 + .../crazyflie_frame-000014.pose.txt | 4 + .../crazyflie_frame-000015.pose.txt | 4 + 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mononav_cf.py create mode 100644 simulate.py create mode 100644 utils/calibration/calibrate.py create mode 100644 utils/calibration/demo_intrinsics.json create mode 100644 utils/calibration/take_pictures.py create mode 100644 utils/calibration/transform.py create mode 100644 utils/generate_primitives.py create mode 100644 utils/reconstruction.gif create mode 100644 utils/test_camera.py create mode 100644 utils/trajlib/traj-00.npz create mode 100644 utils/trajlib/traj-01.npz create mode 100644 utils/trajlib/traj-02.npz create mode 100644 utils/trajlib/traj-03.npz create mode 100644 utils/trajlib/traj-04.npz create mode 100644 utils/trajlib/traj-05.npz create mode 100644 utils/trajlib/traj-06.npz create mode 100644 utils/trajlib/visualization.png create mode 100644 utils/utils.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3489a2c --- /dev/null +++ b/.gitignore @@ -0,0 +1,178 @@ +# Project files +# Ignore everything in the /data/ folder +/data/* +# Except the data folder itself +!/data/ +# And the demo_hallway folder and its contents +!/data/demo_hallway/ +/data/demo_hallway/* +!/data/demo_hallway/crazyflie-poses/ +!/data/demo_hallway/crazyflie-rgb-images/ + +/utils/calibration/calibration_pictures/* +/utils/calibration/calibration_output/* +/utils/calibration/flydrone_calibration_pictures/* +/utils/calibration/output/* +/utils/calibration/undistort_output/* + +# Byte-compiled / optimized / DLL files +cache/* +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +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/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..554dcd6 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "ZoeDepth"] + path = ZoeDepth + url = https://github.com/isl-org/ZoeDepth.git diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..d2bb0dd --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Nate Simon + +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. diff --git a/README.md b/README.md new file mode 100644 index 0000000..7612e87 --- /dev/null +++ b/README.md @@ -0,0 +1,184 @@ +# MonoNav: MAV Navigation via Monocular
Depth Estimation and Reconstruction + +**Authors**: [Nathaniel Simon](https://natesimon.github.io/) and [Anirudha Majumdar](https://irom-lab.princeton.edu/majumdar/) + +_[Intelligent Robot Motion Lab](https://irom-lab.princeton.edu/), Princeton University_ + +[Project Page](https://natesimon.github.io/mononav) | [Paper (arXiv)](https://arxiv.org/pdf/2311.14100.pdf) | [Video](https://www.youtube.com/watch?v=msWLSfOmTpI) | + +--- + +MonoNav is a monocular navigation stack that uses RGB images and camera poses to generate a 3D reconstruction, enabling the use of conventional planning techniques. MonoNav leverages pre-trained depth-estimation ([ZoeDepth](https://github.com/isl-org/ZoeDepth)) and off-the-shelf fusion ([Open3D](https://github.com/isl-org/Open3D)) to generate a real-time 3D reconstruction of the environment. At each planning step, MonoNav selects from a library of motion primitives to navigate collision-free towards the goal. While the robot executes each motion primitive, new images and poses are integrated into the reconstruction. In our [paper](https://arxiv.org/pdf/2311.14100.pdf), we demonstrate MonoNav on a 37 gram micro aerial vehicle (MAV) navigating hallways at 0.5 m/s (see [project page](https://natesimon.github.io/mononav) and [video](https://www.youtube.com/watch?v=msWLSfOmTpI)). + +## Overview + + + +This repository contains code to run the following: +**MonoNav pipeline** ([`mononav_cf.py`](mononav_cf.py)): Monocular hallway navigation using a Crazyflie + FPV camera, as seen in our paper. We encourage you to adapt this script to other vehicles / scenes! + +We also offer scripts that break MonoNav into sub-parts, which can be run independently & offline: +1. **Data collection pipeline** ([`collect_dataset.py`](collect_dataset.py)): Collect images and poses from your own camera / robot. +2. **Depth estimation pipeline** ([`estimate_depth.py`](estimate_depth.py)): Estimate depths from RGB images using ZoeDepth. +3. **Fusion pipeline** ([`fuse_depth.py`](fuse_depth.py)): Fuse the estimated depth images and camera poses into a 3D reconstruction. +4. **Simulate MonoNav** ([`simulate.py`](simulate.py)): Step through the 3D reconstruction and visualize the motion primitives chosen by the MonoNav planner. This is a useful way to replay and debug MonoNav trials. + +These scripts (run in sequence) form a demo, which we highly recommend before adapting MonoNav for your system. We include a sample dataset ([`data/demo_hallway`](data/demo_hallway)), so no robot is needed to run the demo! + +In addition, we include the following resources in the `/utils/` directory: +- [`utils/test_camera.py`](utils/test_camera.py) to test your camera, +- [`utils/generate_primitives.py`](utils/generate_primitives.py) to generate and visualize new motion primitives, +- [`utils/calibration/take_pictures.py`](utils/calibration/take_pictures.py) to take pictures of a calibration target, +- [`utils/calibration/calibrate.py`](utils/calibration/calibrate.py) to calibrate the camera with OpenCV and save the camera intrinsics to file, +- [`utils/calibration/transform.py`](utils/calibration/transform.py) to test the undistortion and transformation pipeline. + +We hope you enjoy MonoNav! + +## Installation and Configuration + +Clone the repository and its submodules (ZoeDepth): + +``` +git clone --recurse-submodules https://github.com/natesimon/MonoNav.git +``` + +Install the dependencies from `environment.yml` using [mamba](https://github.com/mamba-org/mamba) (fastest): +```bash +mamba env create -n mononav --file environment.yml +mamba activate mononav +``` +or conda : + +```bash +conda env create -n mononav --file environment.yml +conda activate mononav +``` +**Note:** If the installation gets stuck at `Solving environment: ...`, we recommend updating your system, re-installing conda / miniconda, or using mamba. + +**Tested on:** (release / driver / GPU) +- Ubuntu 22.04 / NVIDIA 535 / RTX 4090 +- Ubuntu 22.04 / NVIDIA 535 / Titan RTX +- Ubuntu 20.04 / NVIDIA 530 / Titan Xp +- Ubuntu 18.04 / NVIDIA 470 / Titan Xp + +If you do not have access to GPU, set `device = "CPU:0"` in `config.yml`. This will reduce the speed of both depth estimation and fusion, and may not be fast enough for real-time operations. + +## Demo: Out of the Box! + +We include a demo dataset ([`data/demo_hallway`](data/demo_hallway)) to try MonoNav out of the box - no robot needed! From a series of ([occasionally noisy](data/demo_hallway/crazyflie-rgb-images/crazyflie_frame-000030.rgb.jpg)) images and poses, we will transform the images, estimate depth, and fuse them into a [3D reconstuction](utils/reconstruction.gif). +1. The dataset includes just RGB images and camera poses from a Crazyflie (see [Hardware](#mononav-hardware)): + ``` + ├── + │ ├── # camera poses + │ ├── # raw camera images + ``` +1. Set the dataset path in `config.yml`. By default, `data_dir: 'data/demo_hallway`, but make sure to change this if you want to process your own dataset. + ``` + data_dir: 'data/demo_hallway' # change to whichever directory you want to process + ``` +1. To demonstrate ZoeDepth, run `python estimate_depth.py`. This reads in the crazyflie images and transforms them to match the camera intrinsics used in the ZoeDepth training dataset. This is crucial for depth estimation accuracy (see [Calibration](#camera-calibration) for more details). The transformed images are saved in `` and used to estimate depth. The estimated depths are saved as numpy arrays and colormaps (for visualization) in ``. After running, take a look at the resulting images and note the loss of peripheral information as the raw images are undistorted. +1. To demonstrate fusion, run: `python fuse_depth.py`. This script reads in the (transformed) images, poses, and depths, and integrates them using Open3D's TSDF Fusion. After completion, a reconstruction should be displayed with coordinate frames to mark the camera poses throughout the run. The reconstruction is saved to file as a VoxelBlockGrid (`vbg.npz`) and pointcloud (`pointcloud.ply` - which can be opened using MeshLab). +1. Next, run `python simulate.py`. This loads the reconstruction (`vbg.npz`) and executes the MonoNav planner. The planner is executed at each of the camera poses, and does the following: + 1. visualizes (in black) the available motion primitives in the trajectory library (`utils/trajlib`), + 1. chooses a motion primitive according to the planner: `choose_primitive()` in `utils/utils.py` selects the primitive that makes the most progress towards `goal_position` while remaining `min_dist2obs` from all obstacles in the reconstruction, + 1. paints the chosen primitive green. +`simulate.py` is useful for debugging and de-briefing, and also to anticipate how changes in the trajectory library or planner affect performance. For example, by changing `min_dist2obs` in`config.yml`, it is possible to see how increasing/decreasing the distance threshold to obstacles affects planner performance. + +1. Finally, try changing the motion primitives to see how they affect planner performance! To modify and generate the trajectory library, open `utils/generate_primitives.py`. Try changing `num_trajectories` from `7` to `11`, and run `generate_primitives.py.` This will display the new motion primitives and update the trajectory library. Note that each motion primitive is defined by a set of gentle turns left, right, or straight. An "extension" segment is added to the primitive (but not flown) to encourage foresight in the planner. See our paper for more details. Feel free to re-run `simulate.py` to try out the new primitives! + +The tutorial should result in the additional files added to `data/demo_hallway`: +``` +├── +│ ├── # images transformed to match kinect intrinsics +│ ├── # estimated depth (.npy for fusion and .jpg for visualization) +│ ├── vbg.npz / pointcloud.ply # reconstructions generated by fuse_depth.py +``` +If you are unable to execute the full tutorial but want to reference the output, you can download it from [Google Drive](https://drive.google.com/file/d/1r9KgkOoOsSP_7FARzuB4NcOaF94TS1HF/view?usp=sharing). If you have made it through the tutorials, you can try MonoNav on your own dataset! + +## Collect your own Dataset + +To run MonoNav on your own dataset, there are two crucial steps: +1. Collect your own dataset (RGB images and poses). We provide [`collect_dataset.py`](collect_dataset.py) which works for the Crazyflie, but you may have to modify it for your system. Ensure that you are transforming the pose (rotation + translation) into the Open3D frame correctly, and saving it in homogeneous form. See `get_crazyflie_pose` in `utils/utils.py` for reference. Make sure to update `data_dir` in `config.yml` to point to your collected dataset. +2. Provide the camera intrinsics. This is crucial for the image transformation step and can affect depth estimation quality dramatically. We recommend that you follow our provided calibration sequence to automatically generate a `.json` of camera intrinsics and distortion coefficients. `config.yml` should then be updated to point to the intrinsics json file path: `camera_calibration_path: 'utils/calibration/intrinsics.json'`. + +With those steps complete, you can run `estimate_depth.py`, `fuse_depth.py`, and `simulate.py` to reconstruct and try MonoNav on your own dataset! + +## Camera Calibration +A key aspect to MonoNav is using a pre-trained depth estimation network on a different camera than the one used during training. The micro FPV camera ([Wolfwhoop WT05](https://a.co/d/bhTwelB)) that we use has significant barrel distortion (fish-eye), and thus the images must be first undistorted to better match the camera intrinsics used to collect the training data. To maintain the metric depth estimation accuracy of the model, we must transform the input image to match the intrinsics of the training dataset. ZoeDepth is trained on [NYU-Depth-v2](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html), which used the Microsoft [Kinect](https://en.wikipedia.org/wiki/Kinect). + +The `transform_image()` function in `utils/utils.py` performs the transformation: resizing the image and undistorting it to match the Kinect's intrinsics. + +In `utils/calibration`, we provide scripts to generate `intrinsics.json` for your own camera. Steps to calibrate: + +1. Make a chessboard calibration target. +1. `take_pictures.py`: Take many pictures (recommended: 80+) of the chessboard by pressing the spacebar. Saves them to `utils/calibration/calibration_pictures/`. +1. `calibrate.py`: Based on the [OpenCV sample](https://github.com/opencv/opencv/blob/4.x/samples/python/calibrate.py).You need to provide several arguments, including the structure and dimensions of your chessboard target. Example: + ``` + MonoNav/utils/calibration$ python calibrate.py -w 6 -h 8 -t chessboard --square_size=35 ./calibration_pictures/frame*.jpg + + ``` + The intrinsics are printed and saved to `utils/calibration/intrinsics.json`. +1. `transform.py`: This script loads the intrinsics from `intrinsics.json` and transforms your `calibration_pictures` to the Kinect's dimensions (640x480) and intrinsics. This operation may involve resizing your image. The transformed images are saved in `utils/calibration/transform_output` and should be inspected. +1. Finally, we recommend re-running calibration on `transform_output` to ensure that the intrinsics match the Kinect. + ``` + MonoNav/utils/calibration$ python calibrate.py -w 6 -h 8 -t chessboard --square_size=35 ./transform_output/frame*.jpg + + ``` + `transform.py` will save the intrinsics of the transformed images to `check_intrinsics.json`, which should roughly match those of the Kinect: + ``` + [[525. 0. 319.5] + [ 0. 525. 239.5] + [ 0. 0. 1. ]] + ``` + +## MonoNav: Hardware +To run MonoNav as shown in our paper, you need a monocular robot with pose (position & orientation) estimation. We used the [Crazyflie 2.1](https://www.bitcraze.io/products/crazyflie-2-1/) micro aerial vehicle modified with an FPV camera. Our hardware setup follows closely the one used in Princeton's [Introduction to Robotics](https://irom-lab.princeton.edu/intro-to-robotics/) course. If you are using the Crazyflie, we recommend that you follow the [Bitcraze tutorials](https://www.bitcraze.io/documentation/tutorials/getting-started-with-crazyflie-2-x/) to ensure that the vehicle flies and commmunicates properly. + +**List of parts:** +- [Crazyflie 2.1](https://www.bitcraze.io/products/crazyflie-2-1/) micro aerial vehicle, +- [Flowdeck v2](https://www.bitcraze.io/products/flow-deck-v2/) for position and velocity estimation (pose), +- 5.8 GHz micro FPV camera (e.g., [Wolfwhoop WT05](https://a.co/d/bhTwelB)), +- Custom PCB with [long pins](https://store.bitcraze.io/collections/spare-parts-crazyflie-2-0/products/male-long-deck-connector), to attach the camera to the Crazyflie, +- [Crazyradio PA](https://store.bitcraze.io/collections/kits/products/crazyradio-pa) for communication with the vehicle, +- 5.8 GHz video receiver (e.g., [Skydroid](https://a.co/d/9lO7Md8)). + +## Running `mononav_cf.py` + +The `mononav_cf.py` ("cf" for "crazyflie") script performs the image transformation, depth estimation, fusion, and planning process simultaneously for goal-directed obstacle avoidance. If `FLY_CRAZYFLIE: True`, the Crazyflie will takeoff, if `False`, the pipeline will execute without the Crazyflie starting its motors (useful for testing). + +After takeoff, the Crazyflie can be controlled manually by the following key commands: +``` +w: choose MIDDLE primitive (typically FORWARD) +a: choose FIRST primitive (typically LEFT) +d: choose LAST primitive (typically RIGHT) +c: end control (stop and land) +q: end control immediately (EMERGENCY stop and land) +g: start MonoNav +``` + +Manual control is an excellent way to check that the pipeline is working, as it should produce a sensible reconstruction after landing. As mentioned in the paper, it is HIGHLY RECOMMENDED to manually fly forward 3x (press `w, w, w`) before starting MonoNav (press `g`). This is due to the narrow field of view of the transformed images, which discards peripheral information; to make an informed decision, the planner needs information collected 3 primitives ago. + +After MonoNav is started, the Crazyflie will choose and execute primitives according to the planner. If collision seems imminent, you can manually choose a primitive (by `w, a, d`) or stop the planner (`c` or `q`). During the run, a `crazyflie_trajectories.csv` log is produced, which includes the `frame_number` and `time_elapsed` during replanning, as well as the `chosen_traj_idx`. + +## Future Work + +MonoNav is a "work in progress" - there are many exciting directions for future work! If you find any bugs or implement any exciting features, please submit a pull request as we'd love to continue improving the system. Once you get MonoNav running on your robot, send us a video! We'd love to see it. + +**Areas of future work in the pipeline:** +- Run MonoNav on new platforms and in new settings! +- Integrate with ROS for easier interoperability on new systems. +- Integrate improved depth estimation pipelines, which might take in multiple frames or metadata to improve the depth estimate. +- Improve the planner to treat space as unsafe UNTIL explored, which could prevent crashes into occluded obstacles. (The current planner treats unseen space as free.) +- Improve the motion primitive control from open-loop to closed-loop, for more reliable and accurate primitive tracking. + +## Citation +``` +@inproceedings{simon2023mononav, + author = {Nathaniel Simon and Anirudha Majumdar}, + title = {{MonoNav: MAV Navigation via Monocular Depth Estimation and Reconstruction}}, + booktitle = {Symposium on Experimental Robotics (ISER)}, + year = {2023}, + url = {https://arxiv.org/abs/2311.14100} +} +``` + diff --git a/ZoeDepth b/ZoeDepth new file mode 160000 index 0000000..edb6daf --- /dev/null +++ b/ZoeDepth @@ -0,0 +1 @@ +Subproject commit edb6daf45458569e24f50250ef1ed08c015f17a7 diff --git a/collect_dataset.py b/collect_dataset.py new file mode 100644 index 0000000..2ec74a3 --- /dev/null +++ b/collect_dataset.py @@ -0,0 +1,105 @@ +""" + __ __ _ _ + | \/ | ___ _ __ ___ | \ | | __ ___ __ + | |\/| |/ _ \| '_ \ / _ \| \| |/ _` \ \ / / + | | | | (_) | | | | (_) | |\ | (_| |\ V / + |_| |_|\___/|_| |_|\___/|_| \_|\__,_| \_/ +Copyright (c) 2023 Nate Simon +License: MIT +Authors: Nate Simon and Anirudha Majumdar, Princeton University +Project Page: https://natesimon.github.io/mononav + +The purpose of this script is to collect a custom dataset for the MonoNav demo. +This script saves images and poses from a Crazyflie to file. +Those images and poses can then be used in the demo (depth estimation, fusion, and simulated planning). + +""" +import cv2 +import numpy as np +import time +import os +import time + +# For Craziflie logging +import logging +import cflib.crtp +from cflib.crazyflie import Crazyflie +from cflib.crazyflie.log import LogConfig +from cflib.crazyflie.syncCrazyflie import SyncCrazyflie +from cflib.utils import uri_helper + +# sys.path.append('/home/nsimon/Documents/MonoNav/') +from utils.utils import reset_estimator, VideoCapture, get_crazyflie_pose, load_config +''' +This file collects the following synchronized images: +- Crazyflie (front-facing camera) +- Crazyflie pose +- Timestamps +''' +# LOAD CONFIG +CONFIG_PATH = "config.yml" +config = load_config("config.yml") + +URI = uri_helper.uri_from_env(default=config["radio_uri"]) +logging.basicConfig(level=logging.ERROR) +height = config["height"] +FLY_CRAZYFLIE = config["FLY_CRAZYFLIE"] +camera_num = config["camera_num"] + +# Make directories for data +save_dir = 'data/trial-' + time.strftime("%Y-%m-%d-%H-%M-%S") +os.mkdir(save_dir) if not os.path.exists(save_dir) else None + +crazyflie_img_dir = os.path.join(save_dir, "crazyflie-rgb-images") +crazyflie_pose_dir = os.path.join(save_dir, "crazyflie-poses") + +os.mkdir(crazyflie_img_dir) if not os.path.exists(crazyflie_img_dir) else None +os.mkdir(crazyflie_pose_dir) if not os.path.exists(crazyflie_pose_dir) else None + +print("Saving files to: " + save_dir) + +# Drone object +# Initialize the low-level drivers +cflib.crtp.init_drivers() + +# Set up log conf +logstate = LogConfig(name='state', period_in_ms=10) +logstate.add_variable('stateEstimate.x', 'float') +logstate.add_variable('stateEstimate.y', 'float') +logstate.add_variable('stateEstimate.z', 'float') +logstate.add_variable('stateEstimate.roll', 'float') +logstate.add_variable('stateEstimate.pitch', 'float') +logstate.add_variable('stateEstimate.yaw', 'float') + +with SyncCrazyflie(URI, cf=Crazyflie(rw_cache='./cache')) as scf: + cf = scf.cf + + reset_estimator(cf) + + # Camera Object + cap = VideoCapture(camera_num) + + # Initialize counter for images + frame_number = 0 + + while True: + + start_time = time.time() + + crazyflie_rgb = cap.read() + + # Save images and depth array + cv2.imwrite(crazyflie_img_dir + "/crazyflie_frame-%06d.rgb.jpg"%(frame_number), crazyflie_rgb) + camera_position = get_crazyflie_pose(scf, logstate) + np.savetxt(crazyflie_pose_dir + "/crazyflie_frame-%06d.pose.txt"%(frame_number), camera_position) + + # Update counter + frame_number += 1 + + # Show images + cv2.imshow('crazyflie', crazyflie_rgb) + if chr(cv2.waitKey(1)&255) == 'q': + break + time.sleep(0.1) +cap.cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git a/config.yml b/config.yml new file mode 100644 index 0000000..ee1cb73 --- /dev/null +++ b/config.yml @@ -0,0 +1,41 @@ +# Directory Settings +data_dir: 'data/demo_hallway' +save_dir_prefix: 'data/mononav-' +# Camera Settings +camera_num: 0 # camera number index (test with utils/test_camera.py), try (-1, 0, 1, 2, ...) +camera_source: 'crazyflie' # used for naming files +camera_calibration_path: 'utils/calibration/demo_intrinsics.json' # camera calibration file (for undistortion) + +# ZoeDepth Settings +zoedepth_mode: "eval" # "eval" or "infer" - see ZoeDepth documentation for other settings +num_prezoe_frames: 5 # how many times to run ZoeDepth before starting (the first few inferences are much slower) + +# Reconstruction settings +filterYvals: True #this helps filter out the ground / ceiling (for planar navigation) +filterWeights: True # this helps filter out low-weight voxels +filterTSDF: True +weight_threshold: 3. # determine the weight threshold for filtering (higher threshold -> fewer points) +VoxelBlockGrid: + depth_scale: 1000.0 # 1000.0 is default (mm to m), scale factor for depth values during integration + depth_max: 7.0 # max depth to include in the reconstruction + trunc_voxel_multiplier: 8.0 + device: "CUDA:0" # "CUDA:0" (faster) or "CPU:0" (if no GPU is available) + +# Planning Settings +trajlib_dir: 'utils/trajlib/' # where to save & retrieve the trajectory library (motion primitives) +# comment out goal position for undirected exploration +goal_position: # goal position in meters + - -10.0 # +X RIGHT + - -1.0 # +Y DOWN (HEIGHT) + - 10.0 # +Z FORWARD +min_dist2obs: 0.5 # minimum acceptable distance from obstacles, meters (for primitive selection) +min_dist2goal: 1.0 # termination condition for reaching the goal, meters + + +# Crazyflie Settings +radio_uri: 'radio://0/4/2M/E7E7E7E7E7' # update the drone ID# to match your vehicle, check using cfclient +height: 1.0 # crazyflie flight altitude, meters +FLY_CRAZYFLIE: True # whether to actually fly the crazyflie +# Control gain settings (TUNED FOR CRAZYFLIE) +yvel_gain: 0.015 # yvel = -yawrate*yvel_gain +yawrate_gain: 1.25 # yawrate = yawrate*yawrate_gain \ No newline at end of file diff --git a/data/demo_hallway/crazyflie-poses/crazyflie_frame-000000.pose.txt b/data/demo_hallway/crazyflie-poses/crazyflie_frame-000000.pose.txt new file mode 100644 index 0000000..4efe1d2 --- /dev/null +++ b/data/demo_hallway/crazyflie-poses/crazyflie_frame-000000.pose.txt @@ -0,0 +1,4 @@ +9.973858499659662424e-01 -4.017458997433130674e-03 7.214794737808424840e-02 -1.190161481499671936e-01 +4.290575044141858169e-03 9.999842035743383706e-01 -3.630918284212129865e-03 -1.066727876663208008e+00 +-7.213222063306701237e-02 3.930982701562352015e-03 9.973873320438466639e-01 2.025074213743209839e-01 +0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 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nvidia + - conda-forge +dependencies: + - cuda=11.7.1 + - h5py=3.7.0 + - matplotlib=3.6.2 + - matplotlib-base=3.6.2 + - numpy=1.24.1 + - opencv=4.6.0 + - pip=22.3.1 + - python=3.9.7 + - pytorch=1.13.1 + - pytorch-cuda=11.7 + - pytorch-mutex=1.0 + - scipy=1.10.0 + - torchaudio=0.13.1 + - torchvision=0.14.1 + - ffmpeg=5.1.2 + - pip: + - huggingface-hub==0.11.1 + - timm==0.6.12 + - tqdm==4.64.1 + - wandb==0.13.9 + - cflib==0.1.23 # crazyflie + - cfclient==2023.6 # crazyflie + - open3d==0.18.0 # mononav + - Pillow==9.4.0 # mononav + - pynput==1.7.6 # mononav + - PyYAML==5.4.1 # mononav + - trimesh==3.22.1 # mononav + diff --git a/estimate_depth.py b/estimate_depth.py new file mode 100644 index 0000000..be8c945 --- /dev/null +++ b/estimate_depth.py @@ -0,0 +1,92 @@ +""" + __ __ _ _ + | \/ | ___ _ __ ___ | \ | | __ ___ __ + | |\/| |/ _ \| '_ \ / _ \| \| |/ _` \ \ / / + | | | | (_) | | | | (_) | |\ | (_| |\ V / + |_| |_|\___/|_| |_|\___/|_| \_|\__,_| \_/ +Copyright (c) 2023 Nate Simon +License: MIT +Authors: Nate Simon and Anirudha Majumdar, Princeton University +Project Page: https://natesimon.github.io/mononav + +This script reads in a stream of RGB images, transforms them to the Kinect intrinsics, +and estimates metric depth using ZoeDepth. + +The following are saved to file: +│ ├── # images transformed to match kinect intrinsics +│ ├── # estimated depth (.npy for fusion and .jpg for visualization) + +""" + +import time +import os +import sys +import torch +import cv2 + +# Add ZoeDepth to path +sys.path.insert(0, "ZoeDepth") +from zoedepth.models.builder import build_model +from zoedepth.utils.config import get_config + +import open3d as o3d +from PIL import Image +import numpy as np + +from utils.utils import compute_depth, load_config, get_calibration_values, transform_image + +"""" +This script runs a depth estimation model on a directory of RGB images and saves the depth images. +""" + +# LOAD CONFIG +CONFIG_PATH = "config.yml" +config = load_config("config.yml") +data_dir = config["data_dir"] # parent directory to look for RGB images, and save depth images +camera_source = config["camera_source"] # what camera was used for the RGB images? +print("Loading" + camera_source + "images from: ", data_dir, ".") + +# Set & create directories for images +rgb_dir = os.path.join(data_dir, camera_source + "-rgb-images") +kinect_img_dir = os.path.join(data_dir, "kinect-rgb-images") +os.mkdir(kinect_img_dir) if not os.path.exists(kinect_img_dir) else None +kinect_depth_dir = os.path.join(data_dir, "kinect-depth-images") +os.mkdir(kinect_depth_dir) if not os.path.exists(kinect_depth_dir) else None +print("Saving Depth images to: ", kinect_depth_dir) + +# Load the calibration values +camera_calibration_path = config["camera_calibration_path"] +mtx, dist = get_calibration_values(camera_calibration_path) +# Kinect intrinsic matrix +kinect = o3d.camera.PinholeCameraIntrinsic(o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault) + +# Load the ZoeDepth model +conf = get_config("zoedepth", config["zoedepth_mode"]) # NOTE: "eval" runs slightly slower, but is stated to be more metrically accurate +model_zoe = build_model(conf) +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +print("Device is: ", DEVICE) +zoe = model_zoe.to(DEVICE) + +# Figure out how many images are in folder by counting .jpg files +end_frame = len([name for name in os.listdir(rgb_dir) if os.path.isfile(os.path.join(rgb_dir, name)) and name.endswith(".jpg")]) +end_frame = end_frame - 1 # Ignore last image since sometimes pose information is not saved for it + +start_time = time.time() + +for frame_number in range(0, end_frame): + print("Applying ZoeDepth to: %d/%d"%(frame_number+1,end_frame)) + filename = rgb_dir + "/" + camera_source + "_frame-%06d.rgb.jpg"%(frame_number) + # Read in image with Pillow and convert to RGB + crazyflie_rgb = Image.open(filename)#.convert("RGB") # load + # Resize, Undistort, and Warp image to kinect's dimensions and intrinsics + kinect_rgb = transform_image(np.asarray(crazyflie_rgb), mtx, dist, kinect) + kinect_rgb = cv2.cvtColor(kinect_rgb, cv2.COLOR_BGR2RGB) + # Compute depth + depth_numpy, depth_colormap = compute_depth(kinect_rgb, zoe) + # Save images + cv2.imwrite(kinect_img_dir + "/kinect_frame-%06d.rgb.jpg"%(frame_number), kinect_rgb) + cv2.imwrite(kinect_depth_dir + "/" + "kinect_frame-%06d.depth.jpg"%(frame_number), depth_colormap) + np.save(kinect_depth_dir + "/" + "kinect_frame-%06d.depth.npy"%(frame_number), depth_numpy) # saved in meters + +print("Time to compute depth for %d images: %f"%(end_frame, time.time()-start_time)) +# On Nvidia GeForce RTX 4090: 13.6 s for 80 images \ No newline at end of file diff --git a/fuse_depth.py b/fuse_depth.py new file mode 100644 index 0000000..ec76c72 --- /dev/null +++ b/fuse_depth.py @@ -0,0 +1,118 @@ +""" + __ __ _ _ + | \/ | ___ _ __ ___ | \ | | __ ___ __ + | |\/| |/ _ \| '_ \ / _ \| \| |/ _` \ \ / / + | | | | (_) | | | | (_) | |\ | (_| |\ V / + |_| |_|\___/|_| |_|\___/|_| \_|\__,_| \_/ +Copyright (c) 2023 Nate Simon +License: MIT +Authors: Nate Simon and Anirudha Majumdar, Princeton University +Project Page: https://natesimon.github.io/mononav + +The purpose of this script is to fuse depth images and poses into a 3D reconstruction. +Here, we use Open3D's tensor reconstruction system: the VoxelBlockGrid. + +After fusion, the reconstruction is visualized (in addition to the camera poses), and saved to file. + +""" + +import numpy as np +import time +import os + +import open3d as o3d +from PIL import Image +import numpy as np +import yaml +from utils.utils import * +##################################################################### + +addPose = True + +CONFIG_PATH = "config.yml" +with open(CONFIG_PATH, "r") as f: + config = yaml.safe_load(f) + +data_dir = config["data_dir"] # parent directory to look for RGB images, and save depth images + +source = "kinect" # meaning: crazyflie images have been undistorted to match kinect +rgb_dir = data_dir + "/" + source + "-rgb-images/" +depth_dir = data_dir + "/" + source + "-depth-images" +pose_dir = data_dir + "/crazyflie-poses/" +##################################################################### + +# Initialize TSDF VoxelBlockGrid +depth_scale = config["VoxelBlockGrid"]["depth_scale"] +depth_max = config["VoxelBlockGrid"]["depth_max"] +trunc_voxel_multiplier = config["VoxelBlockGrid"]["trunc_voxel_multiplier"] +weight_threshold = config["weight_threshold"] # for planning and visualization (!! important !!) +device = config["VoxelBlockGrid"]["device"] + +vbg = VoxelBlockGrid(depth_scale, depth_max, trunc_voxel_multiplier, o3d.core.Device(device)) + +##################################################################### + +poses = [] # for visualization +t_start = time.time() + +depth_files = [name for name in os.listdir(depth_dir) if os.path.isfile(os.path.join(depth_dir, name)) and name.endswith(".jpg")] +depth_files = sorted(depth_files) + +# Get last frame +first_frame = split_filename(depth_files[0]) +end_frame = split_filename(depth_files[-1]) + +for filename in depth_files: + # Get the frame number from the depth filename + frame_number = split_filename(filename) + print("Integrating frame %d/%d"%(frame_number,end_frame)) + # Get rbg_file + rgb_file = rgb_dir + source + "_frame-%06d.rgb.jpg"%(frame_number) + + # Read in camera pose + pose_file = data_dir + "/crazyflie-poses/crazyflie_frame-%06d.pose.txt"%(frame_number) + cam_pose = np.loadtxt(pose_file) + poses.append(cam_pose) + + # Get color image with Pillow and convert to RGB + color = Image.open(rgb_file).convert("RGB") # load + + # Integrate + depth_file = depth_dir + "/" + source + "_frame-%06d.depth.npy"%(frame_number) + depth_numpy = np.load(depth_file) # mm + vbg.integration_step(color, depth_numpy, cam_pose) + +##################################################################### +# Print out timing information +t_end = time.time() +print("Time taken (s): ", t_end - t_start) +print("FPS: ", end_frame/(t_end - t_start)) + +pcd = vbg.vbg.extract_point_cloud(weight_threshold) + +if addPose: + pose_lineset = get_poses_lineset(poses) + visualizer = o3d.visualization.Visualizer() + visualizer.create_window() + visualizer.add_geometry(pcd.to_legacy()) + visualizer.add_geometry(pose_lineset) + for pose in poses: + # Add coordinate frame ( The x, y, z axis will be rendered as red, green, and blue arrows respectively.) + coordinate_frame = o3d.geometry.TriangleMesh.create_coordinate_frame().scale(0.5, center=(0, 0, 0)) + visualizer.add_geometry(coordinate_frame.transform(pose)) + visualizer.run() + visualizer.destroy_window() +else: + o3d.visualization.draw([pcd]) + +##################################################################### + +npz_filename = os.path.join(data_dir, "vbg.npz") +ply_filename = os.path.join(data_dir, "pointcloud.ply") +print('Saving npz to {}...'.format(npz_filename)) +print('Saving ply to {}...'.format(ply_filename)) + +vbg.vbg.save(npz_filename) +o3d.io.write_point_cloud(ply_filename, pcd.to_legacy()) + +print('Saving finished') \ No newline at end of file diff --git a/mononav_cf.py b/mononav_cf.py new file mode 100644 index 0000000..f82d587 --- /dev/null +++ b/mononav_cf.py @@ -0,0 +1,311 @@ +""" + __ __ _ _ + | \/ | ___ _ __ ___ | \ | | __ ___ __ + | |\/| |/ _ \| '_ \ / _ \| \| |/ _` \ \ / / + | | | | (_) | | | | (_) | |\ | (_| |\ V / + |_| |_|\___/|_| |_|\___/|_| \_|\__,_| \_/ +Copyright (c) 2023 Nate Simon +License: MIT +Authors: Nate Simon and Anirudha Majumdar, Princeton University +Project Page: https://natesimon.github.io/mononav + +This script runs the MonoNav navigation pipeline on a Crazyflie micro aerial vehicle (MAV). +Essentially, the collect_dataset, estimate_depth, fuse_depth, and simulate scripts are combined +into a single script that: +- Collects synchronized images and poses from the Crazyflie, +- Estimates depth using ZoeDepth, +- Fuses depth images and poses into a 3D reconstruction, +- Chooses a motion primitive according to the planner, +- Executes the motion primitive while collecting & fusing new images, +- Repeats until the goal is reached or no primitive satisfies the obstacle avoidance constraint. + +This script is designed for the Crazyflie platform, and may require modification for your specific hardware! + +""" + +import cv2 +import torch +import numpy as np +import time +import os +import open3d as o3d +import sys +# Add path to ZoeDepth +sys.path.insert(0, "ZoeDepth") +from zoedepth.models.builder import build_model +from zoedepth.utils.config import get_config + +# For Craziflie logging +import logging +import time + +import cflib.crtp +from cflib.crazyflie import Crazyflie +from cflib.crazyflie.log import LogConfig +from cflib.crazyflie.syncCrazyflie import SyncCrazyflie +from cflib.utils import uri_helper + +# Keyboard control +from pynput import keyboard + +# helper functions +from utils.utils import * + +# GLOBAL VARIABLES +last_key_pressed = None # store the last key pressed +shouldStop = False + +# LOAD VALUES FROM CONFIG FILE +CONFIG_PATH = "config.yml" +config = load_config("config.yml") + +URI = uri_helper.uri_from_env(default=config["radio_uri"]) +logging.basicConfig(level=logging.ERROR) +height = config["height"] +FLY_CRAZYFLIE = config["FLY_CRAZYFLIE"] + +# Camera Settings for Undistortion +camera_num = config["camera_num"] +# Intrinsics for undistortion +camera_calibration_path = config["camera_calibration_path"] +mtx, dist = get_calibration_values(camera_calibration_path) # for the robot's camera +kinect = o3d.camera.PinholeCameraIntrinsic(o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault) # for the kinect + +# Initialize Zoedepth Model & Move to device +conf = get_config("zoedepth", config["zoedepth_mode"]) # NOTE: "eval" runs slightly slower, but is stated to be more metrically accurate +model_zoe = build_model(conf) +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +print("Device is: ", DEVICE) +zoe = model_zoe.to(DEVICE) + +# Initialize VoxelBlockGrid +depth_scale = config["VoxelBlockGrid"]["depth_scale"] +depth_max = config["VoxelBlockGrid"]["depth_max"] +trunc_voxel_multiplier = config["VoxelBlockGrid"]["trunc_voxel_multiplier"] +weight_threshold = config["weight_threshold"] # for planning and visualization (!! important !!) +device = config["VoxelBlockGrid"]["device"] +vbg = VoxelBlockGrid(depth_scale, depth_max, trunc_voxel_multiplier, o3d.core.Device(device)) + +# Initialize Trajectory Library (Motion Primitives) +trajlib_dir = config["trajlib_dir"] +traj_list = get_trajlist(trajlib_dir) +traj_linesets, period, forward_speed, amplitudes = get_traj_linesets(traj_list) +max_traj_idx = int(len(traj_list)/2) # set initial value to that of FORWARD flight (should be median value) +print("Initial trajectory chosen: %d out of %d"%(max_traj_idx, len(traj_list))) + +# Planning presets +filterYvals = config["filterYvals"] +filterWeights = config["filterWeights"] +filterTSDF = config["filterTSDF"] +if "goal_position" in config: + goal_position = np.array(config["goal_position"]).reshape(1, 3)#np.array([-5., -0.4, 10.0]).reshape(1, 3) # OpenCV frame: +X RIGHT, +Y DOWN, +Z FORWARD +else: + goal_position = None # non-directed exploration +print("Goal position: ", goal_position) +min_dist2obs = config["min_dist2obs"] +min_dist2goal = config["min_dist2goal"] + +# Make directories for data +time_string = time.strftime("%Y-%m-%d-%H-%M-%S") +save_dir = config["save_dir_prefix"] + time_string +print("Saving files to: " + save_dir) +npz_save_filename = save_dir + "/vbg.npz" + +crazyflie_img_dir = os.path.join(save_dir, "crazyflie-rgb-images") +crazyflie_pose_dir = os.path.join(save_dir, "crazyflie-poses") +kinect_img_dir = os.path.join(save_dir, "kinect-rgb-images") +kinect_depth_dir = os.path.join(save_dir, "kinect-depth-images") + +os.makedirs(crazyflie_img_dir, exist_ok=True) +os.makedirs(crazyflie_pose_dir, exist_ok=True) +os.makedirs(kinect_img_dir, exist_ok=True) +os.makedirs(kinect_depth_dir, exist_ok=True) + +# Save the run information to a csv +header = ["frame_number", "chosen_traj_idx", "time_elapsed"] +with open(save_dir + "/crazyflie_trajectories.csv", "w") as file: + file.write(",".join(header) + "\n") + +# key press callback function (for manual control) +def on_press(key): + global last_key_pressed + try: + last_key_pressed = key.char + except AttributeError: + last_key_pressed = key +# start keyboard listener +listener = keyboard.Listener(on_press=on_press) +listener.start() + +# MAIN MONONAV CONTROL LOOP +def main(): + global shouldStop + global last_key_pressed + global max_traj_idx + + # Run ZoeDepth a few times (the first inference is slow), and skip the first few frames + cap = VideoCapture(camera_num) + num_prezoe_frames = config["num_prezoe_frames"] + for i in range(0,num_prezoe_frames): + crazyflie_rgb = cap.read() + # COMPUTE DEPTH + start_time_test = time.time() + depth_numpy, depth_colormap = compute_depth(crazyflie_rgb, zoe) + print("TIME TO COMPUTE DEPTH:",time.time()-start_time_test) + cv2.imshow('frame', crazyflie_rgb) + cv2.waitKey(1) + cv2.destroyAllWindows() + + # CRAZYFLIE CONTROL + # Initialize the low-level drivers + cflib.crtp.init_drivers() + + with SyncCrazyflie(URI, cf=Crazyflie(rw_cache='./cache')) as scf: + cf = scf.cf + reset_estimator(cf) + # Set up log conf + logstate = LogConfig(name='state', period_in_ms=10) + logstate.add_variable('stateEstimate.x', 'float') + logstate.add_variable('stateEstimate.y', 'float') + logstate.add_variable('stateEstimate.z', 'float') + logstate.add_variable('stateEstimate.roll', 'float') + logstate.add_variable('stateEstimate.pitch', 'float') + logstate.add_variable('stateEstimate.yaw', 'float') + + # Initialize lists and frame counter. + frame_number = 0 + + start_flight_time = time.time() + if FLY_CRAZYFLIE: + print("Taking off.") + # Takeoff Sequence + for y in np.linspace(0, height, 21): + cf.commander.send_hover_setpoint(0, 0, 0, y) + time.sleep(0.1) + + for _ in range(20): + cf.commander.send_hover_setpoint(0, 0, 0, height) + time.sleep(0.1) + + ########################################## + print("Starting control.") + traj_counter = 0 # how many trajectory iterations have we done? + start_time = time.time() # seconds + + while not shouldStop: + cv2.imshow('frame', cap.read()) + cv2.waitKey(1) + print("shouldStop: ", shouldStop) + if last_key_pressed == 'a': + print("Pressed a. Going left.") + traj_index = 0 # left + elif last_key_pressed == 'w': + print("Pressed w. Going straight.") + traj_index = int(len(traj_list)/2) # straight + elif last_key_pressed == 'd': + print("Pressed d. Going right.") + traj_index = len(traj_list)-1 # right + elif last_key_pressed == 'g': + print("Pressed g. Using MonoNav.") + traj_index = max_traj_idx + elif last_key_pressed == 'c': #end control and land + print("Pressed c. Ending control.") + break + elif last_key_pressed == 'q': #end flight immediately + print("Pressed q. EMERGENCY STOP.") + cf.commander.send_stop_setpoint() + break + else: + print("Else: Staying put.") + start_time = time.time() + while time.time() - start_time < period: + if FLY_CRAZYFLIE: + cf.commander.send_hover_setpoint(0, 0, 0, height) + time.sleep(0.1) + continue + + # Save trajectory information + row = np.array([frame_number, int(max_traj_idx), time.time()-start_flight_time]) # time since start of flight + with open(save_dir + "/crazyflie_trajectories.csv", "a") as file: + np.savetxt(file, row.reshape(1, -1), delimiter=',', fmt='%s') + + # Fly the selected trajectory, as applicable. + start_time = time.time() + while time.time() - start_time < period: + # WARNING: This controller is tuned to work for the Crazyflie 2.1. + # You must check whether your robot follows the open-loop trajectory. + yawrate = -amplitudes[traj_index]*np.sin(np.pi/period*(time.time() - start_time))*180/np.pi # deg/s + yvel = -yawrate*config["yvel_gain"] + yawrate = yawrate*config["yawrate_gain"] + print("last_key_pressed = ", last_key_pressed) + if FLY_CRAZYFLIE: + cf.commander.send_hover_setpoint(forward_speed, yvel, yawrate, height) + # get camera capture and transform intrinsics + crazyflie_rgb = cap.read() + camera_position = get_crazyflie_pose(scf, logstate) # get camera position immediately + if goal_position is not None: + dist_to_goal = np.linalg.norm(camera_position[0:-1, -1]-goal_position[0]) + print("dist_to_goal: ", dist_to_goal) + if dist_to_goal < min_dist2goal: + print("Reached goal!") + shouldStop = True + last_key_pressed = 'q' + break + # Transform Crazyflie Image to Kinect Image + kinect_rgb = transform_image(np.asarray(crazyflie_rgb), mtx, dist, kinect) + kinect_bgr = cv2.cvtColor(kinect_rgb, cv2.COLOR_RGB2BGR) + # compute depth + depth_numpy, depth_colormap = compute_depth(kinect_rgb, zoe) + + # SAVE DATA TO FILE + cv2.imwrite(crazyflie_img_dir + "/crazyflie_frame-%06d.rgb.jpg"%(frame_number), crazyflie_rgb) + cv2.imwrite(kinect_img_dir + "/kinect_frame-%06d.rgb.jpg"%(frame_number), kinect_rgb) + cv2.imwrite(kinect_depth_dir + "/" + "kinect_frame-%06d.depth.jpg"%(frame_number), depth_colormap) + np.save(kinect_depth_dir + "/" + "kinect_frame-%06d.depth.npy"%(frame_number), depth_numpy) # saved in meters + np.savetxt(crazyflie_pose_dir + "/crazyflie_frame-%06d.pose.txt"%(frame_number), camera_position) + + # integrate the vbg (prefers bgr) + vbg.integration_step(kinect_bgr, depth_numpy, camera_position) + + frame_number += 1 + traj_counter += 1 + + # if crazyflie is not in "GO" (g) mode, reset to stopping mode + if last_key_pressed != 'g': + last_key_pressed = None + + shouldStop, max_traj_idx = choose_primitive(vbg.vbg, camera_position, traj_linesets, goal_position, min_dist2obs, filterYvals, filterWeights, filterTSDF, weight_threshold) + print("SELECTED max_traj_idx: ", max_traj_idx) + + # Exited while(!shouldStop); end control! + print("shouldStop: ", shouldStop) + print("Reached goal OR too close to obstacles.") + print("End control.") + + if FLY_CRAZYFLIE: + # Stopping sequence + for _ in range(10): + cf.commander.send_hover_setpoint(0, 0, 0, height) + time.sleep(0.1) + print("Landing.") + # Landing Sequence + for y in np.linspace(height, 0, 21): + cf.commander.send_hover_setpoint(0, 0, 0, y) + time.sleep(0.1) + + cf.commander.send_stop_setpoint() + + print("Releasing camera capture.") + cap.cap.release() + cv2.destroyAllWindows() + + # save and view vbg + print('Saving to {}...'.format(npz_save_filename)) + vbg.vbg.save(npz_save_filename) + print('Saving finished') + print("Visualize raw pointcloud.") + pcd = vbg.vbg.extract_point_cloud(weight_threshold) + o3d.visualization.draw([pcd.cpu()]) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/simulate.py b/simulate.py new file mode 100644 index 0000000..f7a7d71 --- /dev/null +++ b/simulate.py @@ -0,0 +1,95 @@ +""" + __ __ _ _ + | \/ | ___ _ __ ___ | \ | | __ ___ __ + | |\/| |/ _ \| '_ \ / _ \| \| |/ _` \ \ / / + | | | | (_) | | | | (_) | |\ | (_| |\ V / + |_| |_|\___/|_| |_|\___/|_| \_|\__,_| \_/ +Copyright (c) 2023 Nate Simon +License: MIT +Authors: Nate Simon and Anirudha Majumdar, Princeton University +Project Page: https://natesimon.github.io/mononav + +The purpose of this script is to step through the 3D reconstruction and to execute the MonoNav planner. +Steps: +1) load the reconstruction, poses, and trajectory library, +2) for each pose, choose the optimal motion primitive according to the planner, +3) visualize the reconstruction, poses, and motion primitives (both available and chosen). + +This script is a useful way to debug and debrief the planner, as well as to see how changes to the planner +and trajectory library affect the planning performance. + +""" + +import os +import open3d as o3d +import numpy as np +import copy + +from utils.utils import load_config, poses_from_posedir, get_poses_lineset, get_trajlist, get_traj_linesets, choose_primitive + +CONFIG_PATH = "config.yml" +config = load_config("config.yml") + +data_dir = config["data_dir"] # parent directory to look for RGB images, and save depth images +pose_dir = os.path.join(data_dir, "crazyflie-poses") +trajlib_dir = config["trajlib_dir"] + +# Load the VoxelBlockGrid from file. +files = [file for file in os.listdir(data_dir) if file.endswith('.npz')] +assert len(files) > 0, "No *.npz files found." +npz_filename = files[0] # if there are multiple files, change the index +print("Loading ", npz_filename, " with Open3D.") +vbg = o3d.t.geometry.VoxelBlockGrid.load(os.path.join(data_dir, npz_filename)).cpu() +pcd = vbg.extract_point_cloud(config["weight_threshold"]) + +# Planning presets +filterYvals = config["filterYvals"] +filterWeights = config["filterWeights"] +filterTSDF = config["filterTSDF"] +if "goal_position" in config: + goal_position = np.array(config["goal_position"]).reshape(1, 3) # OpenCV frame: +X RIGHT, +Y DOWN, +Z FORWARD +else: + goal_position = None +print("Goal position: ", goal_position) +min_dist2obs = config["min_dist2obs"] +weight_threshold = config["weight_threshold"] # for planning and visualization + +# Load poses from directory. +poses = poses_from_posedir(pose_dir) +# Get pose lineset +pose_lineset = get_poses_lineset(poses) + +# Load the trajectory linesets from the trajlib directory +traj_list = get_trajlist(trajlib_dir) +traj_linesets, period, forward_speed, amplitudes = get_traj_linesets(traj_list) + + +# Create the visualizer and add components +visualizer = o3d.visualization.Visualizer() +visualizer.create_window() +visualizer.add_geometry(pcd.to_legacy()) +visualizer.add_geometry(pose_lineset) + +# For each pose, compute the optimal motion primitive. +# Paint the optimal motion primitive green, and the rest black. +n = 5 # iterate over every n poses +for i in range(0, len(poses), n): + pose = poses[i] + shouldStop, max_traj_idx = choose_primitive(vbg, pose, traj_linesets, goal_position, min_dist2obs, filterYvals, filterWeights, filterTSDF, weight_threshold) + for traj_idx, traj_lineset in enumerate(traj_linesets): + traj_lineset_copy = copy.deepcopy(traj_lineset) + traj_lineset_copy.transform(pose) + + if traj_idx == max_traj_idx: + traj_lineset_copy.paint_uniform_color([0, 1, 0]) + else: + traj_lineset_copy.paint_uniform_color([0, 0, 0]) + + visualizer.add_geometry(traj_lineset_copy) + + # # (Optional) Uncomment to add coordinate frame, which may look busy. + # coordinate_frame = o3d.geometry.TriangleMesh.create_coordinate_frame().scale(0.5, center=(0, 0, 0)) + # visualizer.add_geometry(coordinate_frame.transform(pose)) + +visualizer.run() +visualizer.destroy_window() \ No newline at end of file diff --git a/utils/calibration/calibrate.py b/utils/calibration/calibrate.py new file mode 100644 index 0000000..6e0e0c8 --- /dev/null +++ b/utils/calibration/calibrate.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python + +''' +ADAPTED FROM OPENCV SAMPLES +https://github.com/opencv/opencv/blob/4.x/samples/python/calibrate.py +SAMPLE CALL: +python calibrate.py --debug ./calibration_output -w 6 -h 8 -t chessboard --square_size=35 ./calibration_pictures/frame*.jpg + + +SEE INSTRUCTIONS FROM OPENCV: +camera calibration for distorted images with chess board samples +reads distorted images, calculates the calibration and write undistorted images + +usage: + calibrate.py [--debug ] [-w ] [-h ] [-t ] [--square_size=] [] + +usage example: + calibrate.py -w 4 -h 6 -t chessboard --square_size=50 ../data/left*.jpg + +default values: + --debug: ./output/ + -w: 4 + -h: 6 + -t: chessboard + --square_size: 10 + --marker_size: 5 + --threads: 4 + +NOTE: Chessboard size is defined in inner corners. Charuco board size is defined in units, and has been removed from this sample. +''' + +import numpy as np +import cv2 as cv +import json +import os + +def splitfn(fn): + path, fn = os.path.split(fn) + name, ext = os.path.splitext(fn) + return path, name, ext + +def main(): + import sys + import getopt + + args, img_names = getopt.getopt(sys.argv[1:], 'w:h:t:', ['debug=','square_size=', 'threads=', ]) + args = dict(args) + args.setdefault('--debug', './output/') + args.setdefault('-w', 4) + args.setdefault('-h', 6) + args.setdefault('-t', 'chessboard') + args.setdefault('--square_size', 10) + args.setdefault('--threads', 4) + + assert img_names, 'Did you provide a path for images?' + + debug_dir = args.get('--debug') + if debug_dir and not os.path.isdir(debug_dir): + os.mkdir(debug_dir) + + height = int(args.get('-h')) + width = int(args.get('-w')) + pattern_type = str(args.get('-t')) + square_size = float(args.get('--square_size')) + + pattern_size = (width, height) + if pattern_type == 'chessboard': + pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32) + pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2) + pattern_points *= square_size + + obj_points = [] + img_points = [] + h, w = cv.imread(img_names[0], cv.IMREAD_GRAYSCALE).shape[:2] + + def processImage(fn): + print('processing %s... ' % fn) + img = cv.imread(fn, cv.IMREAD_GRAYSCALE) + if img is None: + print("Failed to load", fn) + return None + + assert w == img.shape[1] and h == img.shape[0], ("size: %d x %d ... " % (img.shape[1], img.shape[0])) + found = False + corners = 0 + if pattern_type == 'chessboard': + found, corners = cv.findChessboardCorners(img, pattern_size) + if found: + term = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1) + cv.cornerSubPix(img, corners, (5, 5), (-1, -1), term) + frame_img_points = corners.reshape(-1, 2) + frame_obj_points = pattern_points + else: + print("unknown pattern type", pattern_type) + return None + + if debug_dir: + vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR) + if pattern_type == 'chessboard': + cv.drawChessboardCorners(vis, pattern_size, corners, found) + _path, name, _ext = splitfn(fn) + outfile = os.path.join(debug_dir, name + '_board.png') + cv.imwrite(outfile, vis) + + if not found: + print('pattern not found') + return None + + print(' %s... OK' % fn) + return (frame_img_points, frame_obj_points) + + threads_num = int(args.get('--threads')) + if threads_num <= 1: + chessboards = [processImage(fn) for fn in img_names] + else: + print("Run with %d threads..." % threads_num) + from multiprocessing.dummy import Pool as ThreadPool + pool = ThreadPool(threads_num) + chessboards = pool.map(processImage, img_names) + + chessboards = [x for x in chessboards if x is not None] + for idx, (corners, pattern_points) in enumerate(chessboards): + if len(corners) < 4: + print("Not enough obj/img points for %d, skipping image!" % idx) + else: + img_points.append(corners) + obj_points.append(pattern_points) + + rms, camera_matrix, dist_coefs, _rvecs, _tvecs = cv.calibrateCamera(obj_points, img_points, (w, h), None, None) + newcameramtx, roi = cv.getOptimalNewCameraMatrix(camera_matrix, dist_coefs, (w, h), 1, (w, h)) + + print("\nRMS:", rms) + print("camera matrix:\n", camera_matrix) + print("distortion coefficients: ", dist_coefs.ravel()) + print("newcameramtx:\n", newcameramtx) + print("roi: ", roi) + + + data = { + "RMS": rms, + "CameraMatrix": camera_matrix.tolist(), + "DistortionCoefficients": dist_coefs.ravel().tolist(), + "NewCameraMatrix": newcameramtx.tolist(), + "ROI": roi + } + input_dirname = img_names[0].split('/')[1].split('.')[0] + if input_dirname == 'transform_output': + file_path = "check_intrinsics.json" + else: + file_path = "intrinsics.json" + + with open(file_path, "w") as json_file: + json.dump(data, json_file) + + # undistort the image with the calibration + print('') + for fn in img_names if debug_dir else []: + _path, name, _ext = splitfn(fn) + # img_found = os.path.join(debug_dir, name + '_board.png') + outfile = os.path.join(debug_dir, name + '_undistorted.png') + + img = cv.imread(fn) + if img is None: + continue + + dst = cv.undistort(img, camera_matrix, dist_coefs, None, newcameramtx) + + # # crop and save the image + # x, y, w, h = roi + # dst = dst[y:y+h, x:x+w] + + print('Undistorted image written to: %s' % outfile) + cv.imwrite(outfile, dst) + print('Done') + + +if __name__ == '__main__': + print(__doc__) + main() + cv.destroyAllWindows() + diff --git a/utils/calibration/demo_intrinsics.json b/utils/calibration/demo_intrinsics.json new file mode 100644 index 0000000..90491ab --- /dev/null +++ b/utils/calibration/demo_intrinsics.json @@ -0,0 +1,50 @@ +{ + "RMS": 0.33129824379597467, + "CameraMatrix": [ + [ + 210.24118591854983, + 0.0, + 318.51182284207556 + ], + [ + 0.0, + 211.4846805698751, + 252.48457990317036 + ], + [ + 0.0, + 0.0, + 1.0 + ] + ], + "DistortionCoefficients": [ + 0.19941623511989434, + -0.20736409267316344, + 0.0007040531507409564, + 0.0008060182732909919, + 0.047875485130274044 + ], + "NewCameraMatrix": [ + [ + 200.40865481935182, + 0.0, + 319.71648713379267 + ], + [ + 0.0, + 200.89201451631303, + 255.18999581949225 + ], + [ + 0.0, + 0.0, + 1.0 + ] + ], + "ROI": [ + 84, + 71, + 469, + 350 + ] +} \ No newline at end of file diff --git a/utils/calibration/take_pictures.py b/utils/calibration/take_pictures.py new file mode 100644 index 0000000..ac82c4d --- /dev/null +++ b/utils/calibration/take_pictures.py @@ -0,0 +1,52 @@ +""" +The purpose of this file is to take pictures of a calibration target. +Press "spacebar" to capture a frame and "q" to quit the program. +The calibration target should be a checkerboard pattern. +In calibrate_camera, the captured pictures will be used to get a calibration matrix. +""" +import cv2 +import os +import yaml + +# Read the camera number from the config file +CONFIG_PATH = "../../config.yml" +with open(CONFIG_PATH, "r") as f: + config = yaml.safe_load(f) +camera_num = config["camera_num"] # you may have to try 0, 1, 2, ... (depending on # of cameras on system) + +# Receive the image +cap = cv2.VideoCapture(camera_num) + +# Define the directory where you want to save the captured images +calibration_pictures = "calibration_pictures" +print("Saving calibration images to: ", calibration_pictures) +# Create the directory if it doesn't exist +os.makedirs(calibration_pictures, exist_ok=True) + + +frame_count = 0 +print("Press space to capture frame. Press q to quit.") +while True: + # Capture a frame from the camera + ret, frame = cap.read() + # Display the frame + cv2.imshow('Press Space to Capture, q to quit', frame) + # Check for the spacebar key press (ASCII code 32) + key = cv2.waitKey(1) + if key == 32: # 32 is the ASCII code for spacebar + filename = os.path.join(calibration_pictures, f"frame-{frame_count:04d}.jpg") + + # Increment the frame count for the next captured frame + frame_count += 1 + + # Save the captured frame as a .jpg image + cv2.imwrite(filename, frame) + print(f"Image saved as {filename}") + + # Check for the 'q' key press to quit the program + elif key == ord('q'): + break + +# Release the camera and close all OpenCV windows +cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git a/utils/calibration/transform.py b/utils/calibration/transform.py new file mode 100644 index 0000000..f4dd384 --- /dev/null +++ b/utils/calibration/transform.py @@ -0,0 +1,49 @@ +""" +This script is used to transform the images from the robot's camera to the kinect's camera. +It uses the camera intrinsics from calibrate.py, and OpenCV's undistort function. +The transform_image function also resizes the image (and intrinsic matrix) appropriately +to match the kinect's dimensions and intrinsics. This is crucial for the performance of +the depth estimation model. +""" + +import os +import open3d as o3d +import numpy as np +import cv2 + +import os, sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from utils import load_config, get_calibration_values, transform_image + +# LOAD CONFIG +CONFIG_PATH = "../../config.yml" +config = load_config(CONFIG_PATH) + +# Load the calibration images from "calibration_pictures" +calibration_dir = "calibration_pictures" +print("Loading images from: ", calibration_dir) +calibration_files = [file for file in os.listdir(calibration_dir) if file.endswith('.jpg')] +calibration_files = sorted(calibration_files) + +# Load the calibration values +camera_calibration_path = "intrinsics.json"#os.path.join('../../',config["camera_calibration_path"]) +intrinsic_filename = os.path.basename(camera_calibration_path) +print("Loading intrinsics from: ", intrinsic_filename) +mtx, dist = get_calibration_values(camera_calibration_path) # for the robot's camera +# Kinect intrinsic matrix +kinect = o3d.camera.PinholeCameraIntrinsic(o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault) + + +# Set directories for images +transform_dir = "transform_output" +os.mkdir(transform_dir) if not os.path.exists(transform_dir) else None +print("Saving transformed images to: ", transform_dir) + + +for filename in calibration_files: + # Read in the image + img = cv2.imread(os.path.join(calibration_dir, filename)) + # transform + transformed_image = transform_image(np.asarray(img), mtx, dist, kinect) + # write image + cv2.imwrite(os.path.join(transform_dir, filename), transformed_image) \ No newline at end of file diff --git a/utils/generate_primitives.py b/utils/generate_primitives.py new file mode 100644 index 0000000..c5882fc --- /dev/null +++ b/utils/generate_primitives.py @@ -0,0 +1,128 @@ +""" + __ __ _ _ + | \/ | ___ _ __ ___ | \ | | __ ___ __ + | |\/| |/ _ \| '_ \ / _ \| \| |/ _` \ \ / / + | | | | (_) | | | | (_) | |\ | (_| |\ V / + |_| |_|\___/|_| |_|\___/|_| \_|\__,_| \_/ +Copyright (c) 2023 Nate Simon +License: MIT +Authors: Nate Simon and Anirudha Majumdar, Princeton University +Project Page: https://natesimon.github.io/mononav + +The purpose of this script is to generate a library of motion primitives (trajectories) +as described in the paper. Each motion primitive is defined by a sinusoidal yawrate profile, +which keeps the initial and final yawrates zero (for smooth chaining of primitives). +The trajectory library is visualized in trajlib/visualization.png. +By changing the trajectory constants and extension segment, you can generate a diverse variety of primitives. +If you change the trajlib_dir, be sure to update `trajlib_dir: 'utils/trajlib/'` in config.yml. +""" + +import os +import numpy as np +import matplotlib.pyplot as plt + +# Trajectory Constants +T = 1.0 # s, period of the primitive +V = 0.5 # m/s, forward speed +max_yawrate = 0.7 # rad/s +num_trajectories = 7 # number of trajectories should be ODD (e.g., 11) to ensure a straight line is included +num_commands = 65 # number of points in the trajectory +num_points = 8 # how many points should be in each primitive and each extension segment? (for primitive evaluation) + +# Extension segment - straight line at the end of the trajectory (to encourage foresight) +x_ext = np.linspace(0., 1.0, num_points) # extension segment in the body frame (x = forward) +y_ext = np.zeros_like(x_ext) +ext = np.vstack((x_ext, y_ext)) + +# Assertion statements +assert num_trajectories % 2 == 1, "num_trajectories should be odd" +assert max_yawrate > 0, "max_yawrate should be positive" + +# Derived quantities +omega = np.pi/T # 1/s, angular frequency +t = np.linspace(0,T,num_commands) # s, time vector +Avals = np.linspace(max_yawrate, -max_yawrate, num_trajectories) # rad/s, yawrate amplitude vector + + +# Create a subplot +fig, (ax2, ax1) = plt.subplots(2, 1)#, sharex=True) +fig.tight_layout() + +traj_num = 0 +trajlib_dir = './trajlib/' +# os.mkdir(trajlib_dir) if not os.path.exists(trajlib_dir) else None + +if os.path.exists(trajlib_dir): + # Delete the existing trajectory library\ + for filename in os.listdir(trajlib_dir): + file_path = os.path.join(trajlib_dir, filename) + os.remove(file_path) +else: + # Create the directory + os.mkdir(trajlib_dir) + +trajlist = [] + +# For each yawrate profile, determine the associate xdot, ydot commands (which are used for the Crazyflie) +# numerically integrate to obtain x(t) and y(t) (for collision avoidance) +for A in Avals: + yawrate = A*np.sin(omega*t) + psi = A*(1 - np.cos(omega*t)) + # Calculate xdot and ydot + xdot = V * np.cos(psi) + ydot = V * np.sin(psi) + # Numerical integration to obtain x(t) and y(t) + x = np.cumsum(xdot) * (t[1] - t[0]) + y = np.cumsum(ydot) * (t[1] - t[0]) + + # rotate and add the extension segment + yaw = psi[-1] + R = np.array([[np.cos(yaw), -np.sin(yaw)], + [np.sin(yaw), np.cos(yaw)]]) + ext_rot = np.matmul(R,ext) + np.array([[x[-1]],[y[-1]]]) + # sample every num_points for efficient primitive evaluation + sample = int(num_commands/num_points) + x_sample = np.hstack((x[::sample], ext_rot[0,:])) + y_sample = np.hstack((y[::sample], ext_rot[1,:])) + + trajectory = { + 'xvals': x, + 'yvals': y, + 'yawvals': psi, + 'x_sample': x_sample, + 'y_sample': y_sample, + 'yawrates': yawrate, + 'times': t, + 'period': T, + 'forward_speed': V, + 'amplitude': A + } + + # Save to file + np.savez(trajlib_dir + 'traj-%02d.npz'%(traj_num), **trajectory) + traj_num += 1 + + # Top plot: yawrate and psi + ax2.plot(t, yawrate, label=r'$\dot{\psi}_{max}=$ %.2f' % A) + + # Bottom plot + ax1.plot(x,y)#, label=r'$\dot{\psi}_{max}=$ %.2f' % A) + # Plot samples and distance + ax1.plot(x_sample, y_sample, 'o', color = 'red') # slice every other value + +ax1.set_title('Trajectory @ V = %.2f m/s' % V) +ax1.set_xlabel('X position (m)') +ax1.set_ylabel('Y position (m)') +ax1.axis('equal') +#ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5)) +ax2.set_xlabel('Time (s)') +ax2.set_ylabel('Yawrates rad/s') +ax2.set_title('Yawrates') +ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5)) + +# Suggested plotting adjustment +plt.subplots_adjust(0.125, 0.1, 0.75, 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