From 5868eabfb47e5fe704467313563c7a333c5565cb Mon Sep 17 00:00:00 2001 From: Nikolay Mayorov Date: Wed, 4 May 2022 10:33:25 +0500 Subject: [PATCH 1/5] Add .bz2 files into .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 719bc76..bb3c1a6 100644 --- a/.gitignore +++ b/.gitignore @@ -10,3 +10,4 @@ /docs/static/items /ipython/.ipynb_checkpoints .DS_Store +*.bz2 From fc2e94f7d4bd738172389f0eaec52c276a5fdd9e Mon Sep 17 00:00:00 2001 From: Nikolay Mayorov Date: Wed, 4 May 2022 10:35:25 +0500 Subject: [PATCH 2/5] Improve introduction on bundle adjustment --- ipython/bundle_adjustment.ipynb | 151 ++++++++++++++------------------ 1 file changed, 67 insertions(+), 84 deletions(-) diff --git a/ipython/bundle_adjustment.ipynb b/ipython/bundle_adjustment.ipynb index 4f84347..fbed733 100644 --- a/ipython/bundle_adjustment.ipynb +++ b/ipython/bundle_adjustment.ipynb @@ -24,14 +24,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "More precisely. We have a set of points in real world defined by their coordinates $(X, Y, Z)$ in some apriori chosen \"world coordinate frame\". We photograph these points by different cameras, which are characterized by their orientation and translation relative to the world coordinate frame and also by focal length and two radial distortion parameters (9 parameters in total). Then we precicely measure 2-D coordinates $(x, y)$ of the points projected by the cameras on images. Our task is to refine 3-D coordinates of original points as well as camera parameters, by minimizing the sum of squares of reprojecting errors." + "More precisely. We have a set of points in real world defined by their coordinates $(X, Y, Z)$ in some apriori chosen \"world coordinate frame\". We photograph these points by different cameras, which are characterized by their orientation and translation relative to the world coordinate frame and also by focal length and two radial distortion parameters (9 parameters in total). Then we precicely measure 2-D coordinates $(x, y)$ of the points projected by the cameras on images. Our task is to refine 3-D coordinates of original points as well as camera parameters by minimizing the sum of squares of reprojecting errors." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let $\\pmb{P} = (X, Y, Z)^T$ - a radius-vector of a point, $\\pmb{R}$ - a rotation matrix of a camera, $\\pmb{t}$ - a translation vector of a camera, $f$ - its focal distance, $k_1, k_2$ - its distortion parameters. Then the reprojecting is done as follows:\n", + "Let $\\pmb{P} = (X, Y, Z)^T$ - a radius-vector of a point, $\\pmb{R}$ - a rotation matrix of a camera, $\\pmb{t}$ - a translation vector of a camera, $f$ - its focal distance in pixels, $k_1, k_2$ - its distortion parameters. Then the reprojecting is done as follows:\n", "\n", "\\begin{align}\n", "\\pmb{Q} = \\pmb{R} \\pmb{P} + \\pmb{t} \\\\\n", @@ -44,7 +44,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The resulting vector $\\pmb{p}=(x, y)^T$ contains image coordinates of the original point. This model is called \"pinhole camera model\", a very good notes about this subject I found here http://www.comp.nus.edu.sg/~cs4243/lecture/camera.pdf" + "The resulting vector $\\pmb{p}=(x, y)^T$ contains image coordinates of the original point.\n", + "This model is called \"pinhole camera model\", you can read more about it here http://www.comp.nus.edu.sg/~cs4243/lecture/camera.pdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additional notes regarding the formulas above:\n", + " \n", + "1. $\\pmb{R}$ is the rotation matrix which projects from the world axes to the camera axes.\n", + "2. $\\pmb{t}$ is the translation vector from the camera origin to the world origin expressed in the camera axes.\n", + "3. The minus sign for $\\pmb{q}$ accounts for a particular camera axes convention: $x$ points right, $y$ points up and $z$ points backwards such that observed points have negative $z$ values. Usually $y$ and $z$ directions are chosen to be the opposite, but here we stick with the convention used in BAL dataset (see next).\n", + "4. The components of $\\pmb{p}$ are pixel coordinates with $(0, 0)^T$ being the image center. Usually the pixel coordinates origin is chosen to be at the top left corner of an image and the image center parameters $c_x, c_y$ are considered or estimated. But here we stick with the convention used in BAL dataset where center parameters are not explicitly modeled." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Camera position $\\pmb{t}'$ and rotation $\\pmb{R}'$ relative to the world frame can be computed from $\\pmb{R}, \\pmb{t}$ as follows:\n", + "\n", + "\\begin{align}\n", + "\\pmb{t}' = -\\pmb{R}^T t \\\\\n", + "\\pmb{R}' = \\pmb{R}^T\n", + "\\end{align}" ] }, { @@ -58,15 +83,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's start solving some real bundle adjusment problem. We'll take a problem from http://grail.cs.washington.edu/projects/bal/." + "Now let's solve a real bundle adjusment problem from BAL dataset: http://grail.cs.washington.edu/projects/bal/." ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function" @@ -75,9 +98,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import urllib\n", @@ -96,9 +117,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "BASE_URL = \"http://grail.cs.washington.edu/projects/bal/data/ladybug/\"\n", @@ -109,9 +128,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "if not os.path.isfile(FILE_NAME):\n", @@ -128,9 +145,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def read_bal_data(file_name):\n", @@ -164,9 +179,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "camera_params, points_3d, camera_indices, point_indices, points_2d = read_bal_data(FILE_NAME)" @@ -190,9 +203,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -235,9 +246,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def rotate(points, rot_vecs):\n", @@ -259,9 +268,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def project(points, camera_params):\n", @@ -281,9 +288,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def fun(params, n_cameras, n_points, camera_indices, point_indices, points_2d):\n", @@ -307,9 +312,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from scipy.sparse import lil_matrix" @@ -318,9 +321,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def bundle_adjustment_sparsity(n_cameras, n_points, camera_indices, point_indices):\n", @@ -350,9 +351,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -362,9 +361,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "x0 = np.hstack((camera_params.ravel(), points_3d.ravel()))" @@ -373,9 +370,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "f0 = fun(x0, n_cameras, n_points, camera_indices, point_indices, points_2d)" @@ -384,14 +379,12 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 16, @@ -400,9 +393,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIRaoGAghhFigYiCEEGLh/wHjTnp/SG4rlgAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -416,9 +409,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "A = bundle_adjustment_sparsity(n_cameras, n_points, camera_indices, point_indices)" @@ -427,9 +418,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import time\n", @@ -439,9 +428,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -455,12 +442,12 @@ " 4 7 1.3695e+04 4.67e+02 1.32e+02 2.51e+04 \n", " 5 8 1.3481e+04 2.14e+02 2.24e+02 1.54e+04 \n", " 6 9 1.3436e+04 4.55e+01 3.18e+02 2.73e+04 \n", - " 7 10 1.3422e+04 1.37e+01 6.84e+01 2.20e+03 \n", - " 8 11 1.3418e+04 3.70e+00 1.28e+02 7.88e+03 \n", - " 9 12 1.3414e+04 4.19e+00 2.64e+01 6.24e+02 \n", - " 10 13 1.3412e+04 1.88e+00 7.55e+01 2.61e+03 \n", - " 11 14 1.3410e+04 2.09e+00 1.77e+01 4.97e+02 \n", - " 12 15 1.3409e+04 1.04e+00 4.02e+01 1.32e+03 \n", + " 7 10 1.3422e+04 1.37e+01 6.81e+01 2.19e+03 \n", + " 8 11 1.3418e+04 3.69e+00 1.27e+02 7.86e+03 \n", + " 9 12 1.3414e+04 4.18e+00 2.64e+01 6.25e+02 \n", + " 10 13 1.3412e+04 1.88e+00 7.51e+01 2.60e+03 \n", + " 11 14 1.3410e+04 2.08e+00 1.78e+01 4.98e+02 \n", + " 12 15 1.3409e+04 1.04e+00 4.00e+01 1.32e+03 \n", "`ftol` termination condition is satisfied.\n", "Function evaluations 15, initial cost 8.5091e+05, final cost 1.3409e+04, first-order optimality 1.32e+03.\n" ] @@ -476,15 +463,13 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimization took 33 seconds\n" + "Optimization took 21 seconds\n" ] } ], @@ -509,14 +494,12 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 21, @@ -525,9 +508,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nROTCxEBERC5MDERE5MLEQERELv8PE6d3QGfYlB4AAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -548,7 +531,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -562,9 +545,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.8.11" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From 0b9643e724f2846e3911efddb4043e029a33bec7 Mon Sep 17 00:00:00 2001 From: Nikolay Mayorov Date: Wed, 4 May 2022 11:08:07 +0500 Subject: [PATCH 3/5] Clarify relation between cameras and images in bundle adjustment --- ipython/bundle_adjustment.ipynb | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/ipython/bundle_adjustment.ipynb b/ipython/bundle_adjustment.ipynb index fbed733..f1691b7 100644 --- a/ipython/bundle_adjustment.ipynb +++ b/ipython/bundle_adjustment.ipynb @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -191,18 +191,23 @@ "source": [ "Here we have numpy arrays: \n", "\n", - "1. `camera_params` with shape `(n_cameras, 9)` contains initial estimates of parameters for all cameras. First 3 components in each row form a rotation vector (https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula), next 3 components form a translation vector, then a focal distance and two distortion parameters.\n", + "1. `camera_params` with shape `(n_cameras, 9)` contains initial estimates of parameters for all cameras. First 3 components in each row form a rotation vector (https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula) corresponding to $\\pmb{R}$, next 3 components form a translation vector $\\pmb{t}$, then a focal distance and two distortion parameters.\n", "2. `points_3d` with shape `(n_points, 3)` contains initial estimates of point coordinates in the world frame.\n", - "3. `camera_ind` with shape `(n_observations,)` contains indices of cameras (from 0 to `n_cameras - 1`) involved in each observation.\n", - "4. `point_ind` with shape `(n_observations,)` contatins indices of points (from 0 to `n_points - 1`) involved in each observation.\n", + "3. `camera_indices` with shape `(n_observations,)` contains indices of cameras (from 0 to `n_cameras - 1`) involved in each observation.\n", + "4. `point_indices` with shape `(n_observations,)` contatins indices of points (from 0 to `n_points - 1`) involved in each observation.\n", "5. `points_2d` with shape `(n_observations, 2)` contains measured 2-D coordinates of points projected on images in each observations.\n", "\n", - "And the numbers are:" + "In this dataset each image is taken from a different camera meaning that each camera has its own rotation, translation and optical parameters. \n", + "Another typical scenario is when one camera is used to take images from different vantages, in this case we would estimate many rotation and translation parameters and one set of optical parameters.\n", + "\n", + "So in this dataset there is a one-to-one correspondence between cameras and images, such that you can think of \"camera index\" as of \"image index\".\n", + "\n", + "Let's output the problem's dimenstions:" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "metadata": {}, "outputs": [ { From 473a6411469fd85480749af30fa99f42f79ce44d Mon Sep 17 00:00:00 2001 From: Nikolay Mayorov Date: Wed, 4 May 2022 11:56:36 +0500 Subject: [PATCH 4/5] Slightly rework the core part of bundle adjustment example --- ipython/bundle_adjustment.ipynb | 196 +++++++++++++++----------------- 1 file changed, 90 insertions(+), 106 deletions(-) diff --git a/ipython/bundle_adjustment.ipynb b/ipython/bundle_adjustment.ipynb index f1691b7..0a84deb 100644 --- a/ipython/bundle_adjustment.ipynb +++ b/ipython/bundle_adjustment.ipynb @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -207,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -245,7 +245,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now define the function which returns a vector of residuals. We use numpy vectorized computations:" + "Now define the function which returns a vector of residuals. \n", + "To create a rotation transform we use `from_rotvec` method of `Rotation` class availble in `scipy.spatial.transform`." ] }, { @@ -254,20 +255,7 @@ "metadata": {}, "outputs": [], "source": [ - "def rotate(points, rot_vecs):\n", - " \"\"\"Rotate points by given rotation vectors.\n", - " \n", - " Rodrigues' rotation formula is used.\n", - " \"\"\"\n", - " theta = np.linalg.norm(rot_vecs, axis=1)[:, np.newaxis]\n", - " with np.errstate(invalid='ignore'):\n", - " v = rot_vecs / theta\n", - " v = np.nan_to_num(v)\n", - " dot = np.sum(points * v, axis=1)[:, np.newaxis]\n", - " cos_theta = np.cos(theta)\n", - " sin_theta = np.sin(theta)\n", - "\n", - " return cos_theta * points + sin_theta * np.cross(v, points) + dot * (1 - cos_theta) * v" + "from scipy.spatial.transform import Rotation" ] }, { @@ -278,9 +266,9 @@ "source": [ "def project(points, camera_params):\n", " \"\"\"Convert 3-D points to 2-D by projecting onto images.\"\"\"\n", - " points_proj = rotate(points, camera_params[:, :3])\n", - " points_proj += camera_params[:, 3:6]\n", - " points_proj = -points_proj[:, :2] / points_proj[:, 2, np.newaxis]\n", + " rotation = Rotation.from_rotvec(camera_params[:, :3])\n", + " points_camera = rotation.apply(points) + camera_params[:, 3:6]\n", + " points_proj = -points_camera[:, :2] / points_camera[:, 2, np.newaxis]\n", " f = camera_params[:, 6]\n", " k1 = camera_params[:, 7]\n", " k2 = camera_params[:, 8]\n", @@ -350,7 +338,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we are ready to run optimization. Let's visualize residuals evaluated with the initial parameters." + "Now we are ready to run optimization. \n", + "We stack the camera parameters and the 3D point coordinates in a 1-dimensional vector, because it is expected by `least_squares` function.," ] }, { @@ -358,71 +347,38 @@ "execution_count": 13, "metadata": {}, "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], "source": [ "x0 = np.hstack((camera_params.ravel(), points_3d.ravel()))" ] }, { - "cell_type": "code", - "execution_count": 15, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "f0 = fun(x0, n_cameras, n_points, camera_indices, point_indices, points_2d)" + "Compute sparsity structure:" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "plt.plot(f0)" + "A = bundle_adjustment_sparsity(n_cameras, n_points, camera_indices, point_indices)" ] }, { - "cell_type": "code", - "execution_count": 17, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "A = bundle_adjustment_sparsity(n_cameras, n_points, camera_indices, point_indices)" + "And finally run the optimization with `least_squares` function. \n", + "\n", + "We set `scaling='jac'` to automatically scale the variables and equalize their influence on the cost function. This is necessary because we have 5 kind of parameters (rotation, translation, focal length, distortion and 3D coordinates) of completely different nature." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -432,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -446,15 +402,15 @@ " 3 5 1.4163e+04 1.91e+03 2.86e+02 1.21e+05 \n", " 4 7 1.3695e+04 4.67e+02 1.32e+02 2.51e+04 \n", " 5 8 1.3481e+04 2.14e+02 2.24e+02 1.54e+04 \n", - " 6 9 1.3436e+04 4.55e+01 3.18e+02 2.73e+04 \n", - " 7 10 1.3422e+04 1.37e+01 6.81e+01 2.19e+03 \n", - " 8 11 1.3418e+04 3.69e+00 1.27e+02 7.86e+03 \n", - " 9 12 1.3414e+04 4.18e+00 2.64e+01 6.25e+02 \n", - " 10 13 1.3412e+04 1.88e+00 7.51e+01 2.60e+03 \n", - " 11 14 1.3410e+04 2.08e+00 1.78e+01 4.98e+02 \n", - " 12 15 1.3409e+04 1.04e+00 4.00e+01 1.32e+03 \n", + " 6 9 1.3436e+04 4.54e+01 3.18e+02 2.73e+04 \n", + " 7 10 1.3422e+04 1.38e+01 6.79e+01 2.19e+03 \n", + " 8 11 1.3418e+04 3.72e+00 1.31e+02 8.07e+03 \n", + " 9 12 1.3414e+04 4.30e+00 2.62e+01 6.11e+02 \n", + " 10 13 1.3412e+04 1.89e+00 7.68e+01 2.66e+03 \n", + " 11 14 1.3410e+04 2.12e+00 1.76e+01 5.06e+02 \n", + " 12 15 1.3409e+04 1.02e+00 3.98e+01 1.30e+03 \n", "`ftol` termination condition is satisfied.\n", - "Function evaluations 15, initial cost 8.5091e+05, final cost 1.3409e+04, first-order optimality 1.32e+03.\n" + "Function evaluations 15, initial cost 8.5091e+05, final cost 1.3409e+04, first-order optimality 1.30e+03.\n" ] } ], @@ -467,14 +423,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimization took 21 seconds\n" + "Optimization took 33 seconds\n" ] } ], @@ -486,52 +442,80 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Setting `scaling='jac'` was done to automatically scale the variables and equalize their influence on the cost function (clearly the camera parameters and coordinates of the points are very different entities). This option turned out to be crucial for successfull bundle adjustment." + "To assess the optimization efficiency let's plot the reprojection errors (residual values) before and after the optimization." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 18, "metadata": {}, + "outputs": [], "source": [ - "Now let's plot residuals at the found solution:" + "fun0 = fun(x0, n_cameras, n_points, camera_indices, point_indices, points_2d)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "plt.plot(res.fun)" + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" ] }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.subplot(211)\n", + "plt.plot(fun0)\n", + "plt.title(\"Reprojection errors before optimization, RMS = {:.1f} pixels\".format(np.mean(fun0**2) ** 0.5))\n", + "\n", + "plt.subplot(212)\n", + "plt.plot(res.fun)\n", + "plt.title(\"Reprojection errors after optimization, RMS = {:.1f} pixels\".format(np.mean(res.fun**2) ** 0.5))\n", + "\n", + "plt.tight_layout()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, { "cell_type": "markdown", - "metadata": {}, "source": [ - "We see much better picture of residuals now, with the mean being very close to zero. There are some spikes left. It can be explained by outliers in the data, or, possibly, the algorithm found a local minimum (very good one though) or didn't converged enough. Note that the algorithm worked with Jacobian finite difference aproximate, which can potentially block the progress near the minimum because of insufficient accuracy (but again, computing exact Jacobian for this problem is quite difficult)." - ] + "We see significant improvements of the reprojection errors distribution and their RMS which indicates a successful optimization.\n", + "\n", + "The remaining spikes can be explained by some sort of inconsistency in the input data or a compromised convergence of the algorithm. The convergence issues might be caused by finite difference approximation of the Jacobian." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Suggested excersises for the reader:\n", + "\n", + "1. Implement analytical Jacobian computation and see if it improves the final cost function and removes the spikes.\n", + "2. Try to reduce the final cost function by adjusting the algorithm's parameters.\n", + "3. Visualize 3D point clouds before and after optimization" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } } ], "metadata": { @@ -555,4 +539,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file From 9e136daac9df507db7bf114bf319e6ade9723c21 Mon Sep 17 00:00:00 2001 From: Nikolay Mayorov Date: Wed, 4 May 2022 12:21:23 +0500 Subject: [PATCH 5/5] Another adjustment of intro on bundle adjustment --- ipython/bundle_adjustment.ipynb | 73 +++++++++++++++++++++------------ 1 file changed, 46 insertions(+), 27 deletions(-) diff --git a/ipython/bundle_adjustment.ipynb b/ipython/bundle_adjustment.ipynb index 0a84deb..a7c0914 100644 --- a/ipython/bundle_adjustment.ipynb +++ b/ipython/bundle_adjustment.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A bundle adjusmtent problem arises in 3-D reconstruction and it can be formulated as follows (taken from https://en.wikipedia.org/wiki/Bundle_adjustment):\n", + "A bundle adjusmtent problem arises in 3D reconstruction and it can be formulated as follows (taken from https://en.wikipedia.org/wiki/Bundle_adjustment):\n", "\n", "> Given a set of images depicting a number of 3D points from different viewpoints, bundle adjustment can be defined as the problem of simultaneously refining the 3D coordinates describing the scene geometry as well as the parameters of the relative motion and the optical characteristics of the camera(s) employed to acquire the images, according to an optimality criterion involving the corresponding image projections of all points." ] @@ -24,14 +24,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "More precisely. We have a set of points in real world defined by their coordinates $(X, Y, Z)$ in some apriori chosen \"world coordinate frame\". We photograph these points by different cameras, which are characterized by their orientation and translation relative to the world coordinate frame and also by focal length and two radial distortion parameters (9 parameters in total). Then we precicely measure 2-D coordinates $(x, y)$ of the points projected by the cameras on images. Our task is to refine 3-D coordinates of original points as well as camera parameters by minimizing the sum of squares of reprojecting errors." + "More specifically. \n", + "We are given a set of points in real world defined by their coordinates $(X, Y, Z)$ in an apriori chosen world coordinate frame. \n", + "These points are photographed by different cameras, which are characterized by their orientation and translation relative to the world coordinate frame and also by focal length and two radial distortion parameters (9 parameters in total). \n", + "The corresponding 2D images coordinates $(x, y)$ of 3D points are measured.\n", + "The task is to refine 3D coordinates of the points as well as the camera parameters by minimizing the sum of squares of reprojecting errors, given an initial guess for all unknowns." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let $\\pmb{P} = (X, Y, Z)^T$ - a radius-vector of a point, $\\pmb{R}$ - a rotation matrix of a camera, $\\pmb{t}$ - a translation vector of a camera, $f$ - its focal distance in pixels, $k_1, k_2$ - its distortion parameters. Then the reprojecting is done as follows:\n", + "Let $\\pmb{P} = (X, Y, Z)^T$ - a radius-vector of a point, $\\pmb{R}$ - a rotation matrix of a camera, $\\pmb{t}$ - a translation vector of a camera, $f$ - its focal length in pixels, $k_1, k_2$ - its radial distortion parameters. Then the transformation by a camera is defined as follows:\n", "\n", "\\begin{align}\n", "\\pmb{Q} = \\pmb{R} \\pmb{P} + \\pmb{t} \\\\\n", @@ -72,6 +76,13 @@ "\\end{align}" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cost function to be minimized is the sum of squares of the reprojection errors: $F = \\frac{1}{2} \\sum_{i=1}^N \\lVert \\pmb{p}_i - \\tilde{\\pmb{p}}_i \\rVert^2$. Where $N$ is the total number of 2D observations, $\\pmb{p}_i$ is 2D coordinates of a point computed by the projection formulas from the estimated parameters, and $\\pmb{\\tilde{p}}_i$ is observed 2D coordinates." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -430,7 +441,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization took 33 seconds\n" + "Optimization took 27 seconds\n" ] } ], @@ -466,8 +477,24 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 20, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(10, 6))\n", "plt.subplot(211)\n", @@ -479,43 +506,35 @@ "plt.title(\"Reprojection errors after optimization, RMS = {:.1f} pixels\".format(np.mean(res.fun**2) ** 0.5))\n", "\n", "plt.tight_layout()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We see significant improvements of the reprojection errors distribution and their RMS which indicates a successful optimization.\n", "\n", "The remaining spikes can be explained by some sort of inconsistency in the input data or a compromised convergence of the algorithm. The convergence issues might be caused by finite difference approximation of the Jacobian." - ], + ] + }, + { + "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "Suggested excersises for the reader:\n", "\n", "1. Implement analytical Jacobian computation and see if it improves the final cost function and removes the spikes.\n", "2. Try to reduce the final cost function by adjusting the algorithm's parameters.\n", "3. Visualize 3D point clouds before and after optimization" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] } ], "metadata": { @@ -539,4 +558,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +}