320 lines
8.6 KiB
Text
320 lines
8.6 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 58,
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"id": "4e107ea1-d6d3-4bc2-b2fc-32d6f283447e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import trimesh\n",
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"import pysdf\n",
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"import k3d\n",
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"import numpy as np\n",
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"import scipy\n",
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"from ortools.init.python import init\n",
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"from ortools.linear_solver import pywraplp"
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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": 155,
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"id": "1f25c5c1-97e5-4d27-8a5c-5b50d2c931bc",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Note: pysdf chokes on FreeCad .obj files imported through trimesh.\n",
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"\n",
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"mesh = trimesh.load('/tmp/feedthrough1_cut2-PrimaryMeshbracket.stl')"
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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": 206,
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"id": "efed4eb5-0779-4be1-9849-9816dfd81c5d",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/jaseg/proj/ihsm-secondary-mesh/venv/lib/python3.12/site-packages/traittypes/traittypes.py:97: UserWarning: Given trait value dtype \"int64\" does not match required type \"float32\". A coerced copy has been created.\n",
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" warnings.warn(\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "bda3e2161d89497ca8ee788334884892",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Output()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"line = np.array([\n",
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" (3, 0, -13),\n",
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" (23, 0, -13),\n",
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" (23, 0, -2),\n",
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" (40, 0, -2),\n",
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" (40, 0, -13),\n",
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" (55, 0, -13),\n",
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" (55, 0, -2),\n",
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" (75, 0, -2),\n",
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" (85, 0, -8)])\n",
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"\n",
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"plot = k3d.plot()\n",
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"kmesh = k3d.mesh(mesh.vertices, mesh.faces, color=0xdc8add, opacity=0.8)\n",
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"plot += kmesh\n",
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"plot += k3d.label('Start', position=line[0])\n",
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"plot += k3d.label('End', position=line[-1])\n",
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"plot += k3d.line(line)\n",
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"plot.display()"
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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": 180,
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"id": "4215b9e5-4533-451a-94be-0fc62ade9b34",
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"metadata": {},
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"outputs": [],
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"source": [
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"f = pysdf.SDF(mesh.vertices, mesh.faces)\n",
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"\n",
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"xs = np.linspace(-10, 90, 2000, dtype=np.float32)\n",
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"ys = np.linspace(-10, 10, 40, dtype=np.float32)\n",
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"zs = np.linspace(-14, 0, 32, dtype=np.float32)\n",
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"coords = np.array(np.meshgrid(xs, ys, zs, indexing='ij')).T.reshape(-1, 3)\n",
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"sdf_vol = f(coords)"
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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": 181,
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"id": "6b955c31-639d-49a8-983d-744bf9648a38",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "3fb11fc88976448990c5ee91669cfa77",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Output()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plot = k3d.plot()\n",
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"plot += k3d.points(positions=coords,\n",
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" attribute=sdf_vol,\n",
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" point_size=0.1,\n",
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" opacity=0.2,\n",
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" shader='flat')\n",
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"plot.display()"
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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": 183,
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"id": "d3a63bbe-a827-4fe9-8806-50081d844702",
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"metadata": {},
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"outputs": [],
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"source": [
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"def space_to_cloud(x, y, z):\n",
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" return min(len(xs), max(0, round((x+10)/100 * 2000))),\\\n",
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" min(len(ys), max(0, round((y+10)/20 * 40))),\\\n",
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" min(len(zs), max(0, round((z+14)/14 * 32)))\n",
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" \n",
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"def cloud_to_space(x, y, z):\n",
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" return np.vstack([x/2000 * 100 - 10,\n",
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" y/40 * 20 - 10,\n",
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" z/32 * 14 - 14]).T\n",
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" \n",
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"line_interp = np.vstack([np.interp(np.linspace(0, 1, 100), np.linspace(0, 1, len(line)), np.array(line)[:,i]) for i in range(3)]).T\n",
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"line_cloud = np.array([space_to_cloud(x, y, z) for x, y, z in line_interp], dtype=int)"
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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": 184,
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"id": "ef4b7b64-70ba-4e26-90c1-240be0bb48ce",
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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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"Objective: 34.75516\n"
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]
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}
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],
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"source": [
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"def objective(line_cloud):\n",
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" return np.sqrt((sdf_vol[line_cloud]**2).sum())\n",
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" \n",
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"print('Objective:', objective(line_cloud))"
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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": 215,
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"id": "a497cb6b-32dc-4b67-8a84-6a2a99e68fdb",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "e55c322c205d42b2ae2cc0d4354964ec",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Output()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"sdf_interp = scipy.interpolate.RegularGridInterpolator((xs, ys, zs), sdf_vol.reshape(len(zs), len(ys), len(xs)).swapaxes(0,2))\n",
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"\n",
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"plot = k3d.plot()\n",
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"sel = (coords[:,1]%-8 > -0.2)\n",
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"plot += k3d.points(positions=coords[sel],\n",
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" attribute=sdf_interp(coords[sel]),\n",
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" point_size=0.6,\n",
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" opacity=0.2,\n",
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" shader='flat')\n",
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"plot += k3d.line([cloud_to_space(x, y, z) for x, y, z in line_cloud])\n",
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"\n",
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"plot.display()"
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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": 198,
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"id": "b523f8fd-9119-4c9a-98bb-20f583182509",
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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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"0.002690871616336777 0.009143656777662025\n",
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"0.011834528393998803\n"
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]
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}
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],
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"source": [
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"def objective_space(idx, debug=False):\n",
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" idx = idx.reshape(-1, 3)\n",
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" idx = np.vstack([line[:1], idx, line[-1:]])\n",
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" deltas = idx[:-1] - idx[1:]\n",
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" dists = np.sqrt(deltas[:,0]**2 + deltas[:,1]**2 + deltas[:,2]**2)\n",
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" \n",
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" sdf_vals = sdf_interp(idx)\n",
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" sdf_comp = 1/(0.00001 + np.abs(sdf_vals + .1)).sum()\n",
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" dist_comp = 0.1* ((dists - (dists.sum()/len(dists)))**2).mean()\n",
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"\n",
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" if debug:\n",
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" print(sdf_comp, dist_comp)\n",
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"\n",
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" return sdf_comp + dist_comp\n",
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"\n",
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"print(objective_space(line_interp[1:-1], debug=True))"
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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": 200,
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"id": "a31cd866-093b-46f4-9c50-c83e0b84d3d5",
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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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" message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
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" success: True\n",
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" status: 0\n",
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" fun: 0.0020290044959345027\n",
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" x: [ 4.058e+00 3.529e-02 ... 1.216e-04 -7.654e+00]\n",
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" nit: 233\n",
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" jac: [-8.357e-06 -2.846e-06 ... 4.337e-11 1.758e-07]\n",
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" nfev: 86730\n",
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" njev: 294\n",
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" hess_inv: <294x294 LbfgsInvHessProduct with dtype=float64>\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "e4aec3152a484d59b060731442352a7b",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Output()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"bounds = np.tile([[-10, 90], [-10, 10], [-14, 0]], (len(line_interp)-2, 1))\n",
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"res = scipy.optimize.minimize(objective_space, x0=line_interp[1:-1].flatten(), tol=1e-8,\n",
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" bounds=bounds, method='L-BFGS-B', options=dict(maxiter=100000, maxfun=100000, maxls=40))\n",
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"print(repr(res))\n",
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"assert res.success\n",
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"\n",
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"plot = k3d.plot()\n",
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"plot += k3d.mesh(mesh.vertices, mesh.faces, color=0xdc8add, opacity=0.8)\n",
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"plot += k3d.line(res.x.reshape(-1, 3))\n",
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"plot.display()"
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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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"id": "ada64018-2fbf-44ce-8880-3680b0195894",
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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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"kernelspec": {
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"display_name": "ihsm-secmesh",
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"language": "python",
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"name": "ihsm-secmesh"
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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": "3.12.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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