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https://github.com/nerfstudio-project/nerfstudio.git
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forgot to add a notebook
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Ethan Weber committed
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3c00934d-0f14-4c1a-a563-94d627b377e5",
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"metadata": {},
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"source": [
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"## Notebook to visualize the viewer functionality\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": 1,
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"id": "b15dfdc1-5054-40bc-9631-4c7426eecfd9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from notebook_imports import *\n",
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"\n",
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"import time\n",
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"import torch\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"from pyrad.data.dataloader import TrainDataloader\n",
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"from pyrad.data.image_dataset import ImageDataset, PanopticImageDataset\n",
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"from pyrad.data.image_sampler import CacheImageSampler\n",
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"from pyrad.data.pixel_sampler import PixelSampler\n",
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"from pyrad.data.utils import DatasetInputs, get_dataset_inputs_from_dataset_config\n",
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"from pyrad.graphs.modules.ray_generator import RayGenerator\n",
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"from pyrad.graphs.modules.scene_colliders import SceneBoundsCollider, AABBBoxCollider\n",
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"from pyrad.cameras.cameras import get_camera\n",
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"from pyrad.cameras.camera_paths import InterpolatedCameraPath\n",
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"from pyrad.cameras.rays import RayBundle\n",
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"from pyrad.utils.io import get_absolute_path\n",
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"from pyrad.utils.plotly import get_line_segments_from_lines\n",
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"from pyrad.cameras.cameras import get_camera_model\n",
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"from pyrad.utils.misc import get_dict_to_torch, instantiate_from_dict_config\n",
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"\n",
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"# the new import\n",
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"from pyrad.viewer.backend import vis_utils\n",
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"\n",
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"\n",
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"from hydra import compose, initialize\n",
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"from omegaconf import open_dict\n",
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"import pprint\n",
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"from tqdm import tqdm\n",
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"import random"
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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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"id": "df82e083-e261-43e0-a1dc-05c6a59d16e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# establish the connection to the tcp backend, to talk to the visualizer\n",
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"# zmq_url should match the output of `python run_zmq_server.py`\n",
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"vis = vis_utils.get_vis(zmq_url=\"tcp://127.0.0.1:6000\")\n",
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"vis.delete()\n",
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"\n",
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"# draws a red box in the scene\n",
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"vis_utils.show_box_test(vis)"
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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": 3,
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"id": "7fe7478d",
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"metadata": {},
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"outputs": [],
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"source": [
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"with initialize(version_base=None, config_path=\"../configs\"):\n",
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" config = compose(config_name=\"graph_default.yaml\")"
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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": 4,
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"id": "642b0a25-0e88-4eac-b661-a57a74fb10fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset_inputs = get_dataset_inputs_from_dataset_config(**config.data.dataset_inputs_train, split=\"train\")"
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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": 5,
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"id": "938f090b-3b28-4fd6-82dc-d4b5c5917134",
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"metadata": {},
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"outputs": [],
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"source": [
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"# ImageDataset\n",
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"image_dataset_train = instantiate_from_dict_config(config.data.image_dataset_train, **dataset_inputs.as_dict())"
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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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"id": "5d014a6b-50fa-48ab-b135-0ab2b81c7529",
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"metadata": {},
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"outputs": [],
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"source": [
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"# grab 10 images and show their frustums\n",
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"indices = random.sample(range(len(image_dataset_train)), k=10)\n",
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"for idx in indices:\n",
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" image = image_dataset_train[idx][\"image\"]\n",
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" camera = get_camera(dataset_inputs.intrinsics[idx], dataset_inputs.camera_to_world[idx], None)\n",
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" pose = camera.get_camera_to_world().double().numpy()\n",
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" K = camera.get_intrinsics_matrix().double().numpy()\n",
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" vis_utils.draw_camera_frustum(\n",
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" vis,\n",
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" image=(image.double().numpy() * 255.0),\n",
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" pose=pose,\n",
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" K=K,\n",
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" height=1.0,\n",
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" name=\"{:06d}\".format(idx),\n",
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" displayed_focal_length=0.5,\n",
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" realistic=False,\n",
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" )"
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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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"id": "fd04703a-aa50-45e4-95fd-4d881ff18d61",
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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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"estimated_time: 3.3333333333333335\n",
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"time_elapsed: 3.5769083499908447\n"
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]
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}
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],
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"source": [
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"# move a camera around\n",
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"num_cameras = len(dataset_inputs.intrinsics)\n",
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"intrinsics = dataset_inputs.intrinsics\n",
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"camera_to_world = dataset_inputs.camera_to_world\n",
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"idx0, idx1 = random.sample(range(num_cameras), k=2)\n",
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"camera_a = get_camera(intrinsics[idx0], camera_to_world[idx0], None)\n",
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"camera_b = get_camera(intrinsics[idx1], camera_to_world[idx1], None)\n",
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"\n",
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"num_steps = 100\n",
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"fps = 30\n",
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"estimated_time = num_steps / fps\n",
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"print(\"estimated_time:\", estimated_time)\n",
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"\n",
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"camera_path = InterpolatedCameraPath(camera_a, camera_b)\n",
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"cameras = camera_path.get_path(steps=num_steps)\n",
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"\n",
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"start = time.time()\n",
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"for camera in cameras:\n",
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" vis_utils.set_camera(vis, camera)\n",
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" time.sleep(1 / fps)\n",
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"time_elapsed = time.time() - start\n",
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"print(\"time_elapsed:\", time_elapsed)"
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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": "f1cd7183-c514-4f87-a8cc-e14228228e24",
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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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"interpreter": {
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"hash": "4a749acc7d255f078aee52e0584cc77b3cb5aaed1b3a7407ec4262c1bf6cb526"
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.8.13"
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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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