Added examples to site
Frank Dellaert
parent
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commit
3e8a29ae13
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# Examples
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This section contains python examples in interactive Python notebooks (`*.ipynb`). Python notebooks with an <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> button near the top can be opened in your browser, where you can run the files yourself and make edits to play with and understand GTSAM.
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@ -1074,6 +1074,11 @@ class PinholeCamera {
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static gtsam::Point2 Project(const gtsam::Point3& cameraPoint);
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pair<gtsam::Point2, bool> projectSafe(const gtsam::Point3& pw) const;
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gtsam::Point2 project(const gtsam::Point3& point);
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gtsam::Point2 project(const gtsam::Point3& point,
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Eigen::Ref<Eigen::MatrixXd> Dpose);
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gtsam::Point2 project(const gtsam::Point3& point,
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Eigen::Ref<Eigen::MatrixXd> Dpose,
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Eigen::Ref<Eigen::MatrixXd> Dpoint);
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gtsam::Point2 project(const gtsam::Point3& point,
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Eigen::Ref<Eigen::MatrixXd> Dpose,
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Eigen::Ref<Eigen::MatrixXd> Dpoint,
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5
myst.yml
5
myst.yml
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@ -8,7 +8,7 @@ project:
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toc:
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- file: README.md
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- file: INSTALL.md
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- file: ./gtsam/user_guide.md
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- file: ./doc/user_guide.md
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children:
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- file: ./gtsam/geometry/geometry.md
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children:
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@ -16,6 +16,9 @@ project:
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- file: ./gtsam/nonlinear/nonlinear.md
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children:
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- pattern: ./gtsam/nonlinear/doc/*
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- file: ./doc/examples.md
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children:
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- pattern: ./python/gtsam/examples/*.ipynb
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site:
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nav:
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- title: Getting started
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@ -0,0 +1,188 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Camera Resectioning Example\n",
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"\n",
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"This is a 1:1 transcription of CameraResectioning.cpp, but using custom factors."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"remove-cell"
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]
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},
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"source": [
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"GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n",
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"Atlanta, Georgia 30332-0415\n",
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"All Rights Reserved\n",
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"\n",
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"Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n",
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"\n",
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"See LICENSE for the license information"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/borglab/gtsam/blob/develop/python/examples/CameraResectioning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"remove-cell"
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],
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"vscode": {
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"languageId": "markdown"
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}
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},
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"outputs": [],
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"source": [
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"%pip install --quiet gtsam-develop"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from gtsam import Cal3_S2, CustomFactor, LevenbergMarquardtOptimizer, KeyVector\n",
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"from gtsam import NonlinearFactor, NonlinearFactorGraph\n",
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"from gtsam import PinholeCameraCal3_S2, Point2, Point3, Pose3, Rot3, Values\n",
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"from gtsam.noiseModel import Base as SharedNoiseModel, Diagonal\n",
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"from gtsam.symbol_shorthand import X"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def resectioning_factor(\n",
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" model: SharedNoiseModel,\n",
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" key: int,\n",
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" calib: Cal3_S2,\n",
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" p: Point2,\n",
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" P: Point3,\n",
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") -> NonlinearFactor:\n",
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"\n",
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" def error_func(this: CustomFactor, v: Values, H: list[np.ndarray]) -> np.ndarray:\n",
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" pose = v.atPose3(this.keys()[0])\n",
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" camera = PinholeCameraCal3_S2(pose, calib)\n",
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" if H is None:\n",
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" return camera.project(P) - p\n",
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" Dpose = np.zeros((2, 6), order=\"F\")\n",
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" result = camera.project(P, Dpose) - p\n",
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" H[0] = Dpose\n",
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" return result\n",
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"\n",
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" return CustomFactor(model, KeyVector([key]), error_func)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Assumptions:\n",
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"- Camera: $f = 1$, Image: $100\\times100$, center: $50, 50.0$\n",
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"- Pose (ground truth): $(X_w, -Y_w, -Z_w, [0,0,2.0]^T)$\n",
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"- Known landmarks: $(10,10,0), (-10,10,0), (-10,-10,0), (10,-10,0)$\n",
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"- Perfect measurements: $(55,45), (45,45), (45,55), (55,55)$\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Final result:\n",
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"\n",
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"Values with 1 values:\n",
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"Value x1: (gtsam::Pose3)\n",
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"R: [\n",
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"\t1, 0, 0;\n",
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"\t0, -1, 0;\n",
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"\t0, 0, -1\n",
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"]\n",
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"t: 0 0 2\n",
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"\n"
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]
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}
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],
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"source": [
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"# Create camera intrinsic parameters\n",
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"calibration = Cal3_S2(1, 1, 0, 50, 50)\n",
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"\n",
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"# 1. create graph\n",
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"graph = NonlinearFactorGraph()\n",
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"\n",
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"# 2. add factors to the graph\n",
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"measurement_noise = Diagonal.Sigmas(np.array([0.5, 0.5]))\n",
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"graph.add(\n",
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" resectioning_factor(\n",
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" measurement_noise, X(1), calibration, Point2(55, 45), Point3(10, 10, 0)\n",
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" )\n",
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")\n",
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"graph.add(\n",
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" resectioning_factor(\n",
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" measurement_noise, X(1), calibration, Point2(45, 45), Point3(-10, 10, 0)\n",
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" )\n",
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")\n",
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"graph.add(\n",
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" resectioning_factor(\n",
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" measurement_noise, X(1), calibration, Point2(45, 55), Point3(-10, -10, 0)\n",
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" )\n",
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")\n",
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"graph.add(\n",
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" resectioning_factor(\n",
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" measurement_noise, X(1), calibration, Point2(55, 55), Point3(10, -10, 0)\n",
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" )\n",
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")\n",
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"\n",
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"# 3. Create an initial estimate for the camera pose\n",
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"initial: Values = Values()\n",
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"initial.insert(X(1), Pose3(Rot3(1, 0, 0, 0, -1, 0, 0, 0, -1), Point3(0, 0, 1)))\n",
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"\n",
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"# 4. Optimize the graph using Levenberg-Marquardt\n",
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"result: Values = LevenbergMarquardtOptimizer(graph, initial).optimize()\n",
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"result.print(\"Final result:\\n\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "py312",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -1,85 +0,0 @@
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# pylint: disable=consider-using-from-import,invalid-name,no-name-in-module,no-member,missing-function-docstring
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"""
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This is a 1:1 transcription of CameraResectioning.cpp.
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"""
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import numpy as np
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from gtsam import Cal3_S2, CustomFactor, LevenbergMarquardtOptimizer, KeyVector
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from gtsam import NonlinearFactor, NonlinearFactorGraph
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from gtsam import PinholeCameraCal3_S2, Point2, Point3, Pose3, Rot3, Values
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from gtsam.noiseModel import Base as SharedNoiseModel, Diagonal
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from gtsam.symbol_shorthand import X
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def resectioning_factor(
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model: SharedNoiseModel,
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key: int,
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calib: Cal3_S2,
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p: Point2,
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P: Point3,
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) -> NonlinearFactor:
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def error_func(this: CustomFactor, v: Values, H: list[np.ndarray]) -> np.ndarray:
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pose = v.atPose3(this.keys()[0])
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camera = PinholeCameraCal3_S2(pose, calib)
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if H is None:
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return camera.project(P) - p
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Dpose = np.zeros((2, 6), order="F")
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Dpoint = np.zeros((2, 3), order="F")
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Dcal = np.zeros((2, 5), order="F")
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result = camera.project(P, Dpose, Dpoint, Dcal) - p
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H[0] = Dpose
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return result
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return CustomFactor(model, KeyVector([key]), error_func)
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def main() -> None:
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"""
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Camera: f = 1, Image: 100x100, center: 50, 50.0
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Pose (ground truth): (Xw, -Yw, -Zw, [0,0,2.0]')
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Known landmarks:
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3D Points: (10,10,0) (-10,10,0) (-10,-10,0) (10,-10,0)
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Perfect measurements:
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2D Point: (55,45) (45,45) (45,55) (55,55)
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"""
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# read camera intrinsic parameters
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calib = Cal3_S2(1, 1, 0, 50, 50)
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# 1. create graph
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graph = NonlinearFactorGraph()
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# 2. add factors to the graph
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measurement_noise = Diagonal.Sigmas(np.array([0.5, 0.5]))
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graph.add(
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resectioning_factor(
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measurement_noise, X(1), calib, Point2(55, 45), Point3(10, 10, 0)
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)
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)
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graph.add(
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resectioning_factor(
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measurement_noise, X(1), calib, Point2(45, 45), Point3(-10, 10, 0)
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)
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)
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graph.add(
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resectioning_factor(
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measurement_noise, X(1), calib, Point2(45, 55), Point3(-10, -10, 0)
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)
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)
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graph.add(
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resectioning_factor(
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measurement_noise, X(1), calib, Point2(55, 55), Point3(10, -10, 0)
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)
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)
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# 3. Create an initial estimate for the camera pose
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initial: Values = Values()
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initial.insert(X(1), Pose3(Rot3(1, 0, 0, 0, -1, 0, 0, 0, -1), Point3(0, 0, 1)))
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# 4. Optimize the graph using Levenberg-Marquardt
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result: Values = LevenbergMarquardtOptimizer(graph, initial).optimize()
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result.print("Final result:\n")
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if __name__ == "__main__":
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main()
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"\n",
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"Implementing the example in [Fornasier et al, 2022, Overcoming Bias: Equivariant Filter Design for Biased Attitude Estimation with Online Calibration](https://arxiv.org/pdf/2209.12038).\n",
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"\n",
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"This notebook uses Alessandro Fornasier's equivariant filter code (https://github.com/aau-cns/ABC-EqF) converted to use GTSAM's libraries.\n",
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"Authors: Jennifer Oum & Darshan Rajasekaran\n",
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"This notebook uses [Alessandro Fornasier's equivariant filter code](https://github.com/aau-cns/ABC-EqF) converted to use GTSAM's libraries.\n",
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"\n",
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"We start by installing gtsam (GTSAM's python wrapper) and gtbook."
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"Authors: Jennifer Oum & Darshan Rajasekaran"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"remove-cell"
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]
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},
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"source": [
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"GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n",
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"Atlanta, Georgia 30332-0415\n",
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"All Rights Reserved\n",
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"\n",
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"Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n",
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"\n",
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"See LICENSE for the license information"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"metadata": {},
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"execution_count": null,
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"metadata": {
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"tags": [
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"remove-cell"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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@ -28,12 +48,13 @@
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}
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],
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"source": [
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"# We start by installing gtsam (GTSAM's python wrapper) and gtbook.\n",
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"%pip install --quiet gtsam gtbook"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -47,7 +68,7 @@
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"import gtsam\n",
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"from gtsam import findExampleDataFile, Rot3\n",
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"\n",
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"from EqF import *\n"
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"from EqF import *"
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]
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},
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{
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Range SLAM with iSAM\n",
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"\n",
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"A 2D Range SLAM example, with iSAM and smart range factors\n",
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"\n",
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"Author: Frank Dellaert"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"remove-cell"
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]
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},
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"source": [
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"GTSAM Copyright 2010-2022, Georgia Tech Research Corporation,\n",
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"Atlanta, Georgia 30332-0415\n",
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|
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@ -10,11 +25,30 @@
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"\n",
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"Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n",
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"\n",
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"See LICENSE for the license information\n",
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"\n",
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"A 2D Range SLAM example, with iSAM and smart range factors\n",
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"\n",
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"Author: Frank Dellaert"
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"See LICENSE for the license information"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/borglab/gtsam/blob/develop/python/gtsam/examples/RangeISAMExample_plaza2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"remove-cell"
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],
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"vscode": {
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"languageId": "markdown"
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}
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},
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"outputs": [],
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"source": [
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"%pip install --quiet gtsam-develop"
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]
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},
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||||
{
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|
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Reference in New Issue