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Update test_calibration_optimize.ipynb
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Lines changed: 170 additions & 12 deletions

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‎tests/test_calibration_optimize.ipynb‎

Lines changed: 170 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -2,25 +2,23 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"# test calibration using scipy.optimize\n",
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"\n",
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"import numpy as np\n",
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"import scipy.optimize as opt"
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"# test calibration using scipy.optimize\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": 6,
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"import unittest\n",
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"\n",
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"import numpy as np\n",
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"import scipy.optimize as opt\n",
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"\n",
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"from openptv_python.calibration import (\n",
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" Calibration,\n",
@@ -31,12 +29,15 @@
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")\n",
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"from openptv_python.parameters import ControlPar, OrientPar, VolumePar, read_control_par\n",
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"from openptv_python.tracking_frame_buf import TargetArray\n",
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"from openptv_python.trafo import arr_metric_to_pixel"
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"from openptv_python.trafo import arr_metric_to_pixel\n",
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"from openptv_python.imgcoord import img_coord\n",
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"from openptv_python.trafo import pixel_to_metric\n",
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"from openptv_python.tracking_frame_buf import Target\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": 7,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -92,7 +93,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
@@ -103,7 +104,7 @@
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"External calibration using clicked points. ... ok\n",
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"\n",
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"----------------------------------------------------------------------\n",
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"Ran 1 test in 0.120s\n",
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"Ran 1 test in 0.014s\n",
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"\n",
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"OK\n"
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]
@@ -122,17 +123,174 @@
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{
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"data": {
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"text/plain": [
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"<unittest.main.TestProgram at 0x7f10eb2886a0>"
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"<unittest.main.TestProgram at 0x7f946fcd6e90>"
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]
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},
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"execution_count": 8,
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"unittest.main(argv=[''], verbosity=2, exit=False)"
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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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"control_file_name = \"testing_folder/corresp/control.par\"\n",
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"control = read_control_par(control_file_name)\n",
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"\n",
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"orient_par_file_name = \"testing_folder/corresp/orient.par\"\n",
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"orient_par = OrientPar().from_file(orient_par_file_name)\n",
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"\n",
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"cal = Calibration().from_file(\n",
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" \"testing_folder/calibration/cam1.tif.ori\",\n",
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" \"testing_folder/calibration/cam1.tif.addpar\",\n",
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")\n",
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"orig_cal = Calibration().from_file(\n",
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" \"testing_folder/calibration/cam1.tif.ori\",\n",
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" \"testing_folder/calibration/cam1.tif.addpar\")"
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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": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"ref_pts = np.array(\n",
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" [\n",
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" [-40.0, -25.0, 8.0],\n",
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" [40.0, -15.0, 0.0],\n",
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" [40.0, 15.0, 0.0],\n",
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" [40.0, 0.0, 8.0],\n",
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" ]\n",
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")\n",
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"\n",
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"# Fake the image points by back-projection\n",
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"targets = arr_metric_to_pixel(\n",
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" image_coordinates(ref_pts, cal, control.mm),\n",
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" control,\n",
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")\n",
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"\n",
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"cal.set_pos(np.array([0, 0, 100]))\n",
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"cal.set_angles(np.array([0, 0, 0]))\n",
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"\n",
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"# Jigg the fake detections to give raw_orient some challenge.\n",
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"targets[:, 1] -= 0.1"
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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": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"targs = [Target() for _ in targets]\n",
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"\n",
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"for ptx, pt in enumerate(targets):\n",
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" targs[ptx].x = pt[0]\n",
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" targs[ptx].y = pt[1]"
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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": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"def residual(calibration_array, ref_pts, targs, control, cc):\n",
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" # print(calibration_array)\n",
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" # print(ref_pts)\n",
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" # print(targs)\n",
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" # print(control)\n",
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" # print(calibration_array)\n",
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" \n",
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" c = Calibration()\n",
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" c.set_pos(calibration_array[:3])\n",
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" c.set_angles(calibration_array[3:])\n",
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" c.int_par.cc = cc\n",
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" c.update_rotation_matrix()\n",
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" \n",
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" \n",
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" # print(f\"{c.get_pos()=}\")\n",
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" \n",
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" residual = 0\n",
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" for i in range(len(targs)):\n",
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" xc, yc = pixel_to_metric(targs[i].x, targs[i].y, control)\n",
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" # print(f\"{xc=}, {yc=} mm\")\n",
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" \n",
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" xp, yp = img_coord(ref_pts[i], c, control.mm)\n",
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" # print(f\"{xp=}, {yp=} mm\")\n",
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" residual += ((xc - xp)**2 + (yc - yp)**2)\n",
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" \n",
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" # print(f\"{residual=}\")\n",
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" \n",
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" return residual"
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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": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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"x0 = np.hstack([cal.get_pos(), cal.get_angles()])\n",
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"cc = orig_cal.int_par.cc\n",
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"\n",
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"sol = opt.minimize(residual, x0, args=(ref_pts, targs, control, cc), method='Nelder-Mead', tol=1e-6)"
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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": 43,
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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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"sol.x=array([-3.79876331e+00, 3.77309709e+01, 4.03575316e+02, -8.61982784e-02,\n",
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" -6.33837589e-02, 5.14334685e-02])\n",
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"[-1.09061963e+02 -6.50148291e+01 -3.06884099e-01 1.52130822e-01\n",
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" -3.07664759e-01 -3.82423146e-03]\n"
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]
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}
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],
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"source": [
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"print(f\"{sol.x=}\")\n",
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"print(sol.x - np.hstack([orig_cal.get_pos(), orig_cal.get_angles()]))"
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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": 39,
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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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"93.58853214427855\n"
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]
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}
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],
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"source": [
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"print( residual(np.hstack([orig_cal.get_pos(), orig_cal.get_angles()]), ref_pts, targs, control, orig_cal.int_par.cc))"
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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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"source": []
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}
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],
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"metadata": {

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