mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-15 12:23:46 +08:00
273 lines
7.5 KiB
Plaintext
273 lines
7.5 KiB
Plaintext
{
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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 a dataset\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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"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 torch\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"from mattport.nerf.dataset.image_dataset import ImageDataset, collate_batch\n",
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"from mattport.nerf.dataset.collate import CollateIterDataset, collate_batch_size_one\n",
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"from mattport.nerf.dataset.utils import get_dataset_inputs_dict\n",
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"from mattport.nerf.field_modules.ray_generator import RayGenerator\n",
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"from mattport.structures.rays import RayBundle\n",
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"from mattport.utils.io import get_absolute_path\n",
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"from mattport.viewer.plotly import get_line_segments_from_lines\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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"\n",
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"import random\n",
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"from tqdm import tqdm"
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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": "001bdf3b-60a0-4bb2-9f88-ae36f2036c80",
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"metadata": {},
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"outputs": [],
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"source": [
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"with initialize(config_path=\"../configs\"):\n",
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" cfg = compose(config_name=\"default.yaml\")\n",
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" cfg.dataset.downscale_factor = 4\n",
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" \n",
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"cfg.dataset.data_directory = get_absolute_path(cfg.dataset.data_directory)\n",
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"dataset_inputs = get_dataset_inputs(**cfg.dataset)[\"train\"]\n",
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"image_dataset = ImageDataset(\n",
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" image_filenames=dataset_inputs.image_filenames, downscale_factor=dataset_inputs.downscale_factor\n",
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" )\n",
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"iter_dataset = CollateIterDataset(\n",
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" image_dataset,\n",
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" collate_fn=lambda batch_list: collate_batch(\n",
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" batch_list, cfg.dataloader.num_rays_per_batch, keep_full_image=True\n",
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" ),\n",
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" num_samples_to_collate=cfg.dataloader.num_images_to_sample_from,\n",
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" num_times_to_repeat=cfg.dataloader.num_times_to_repeat_images,\n",
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")\n",
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"dataloader = DataLoader(\n",
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" iter_dataset,\n",
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" batch_size=1,\n",
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" num_workers=cfg.dataloader.num_workers,\n",
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" collate_fn=collate_batch_size_one,\n",
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" pin_memory=True,\n",
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")\n",
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"dataloader_iter = iter(dataloader)\n",
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"\n",
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"ray_generator = RayGenerator(dataset_inputs.intrinsics, dataset_inputs.camera_to_world)\n",
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"\n",
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"num_batches = 10\n",
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"for _ in tqdm(range(num_batches)):\n",
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" batch = next(dataloader_iter)\n",
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" ray_bundle = ray_generator.forward(batch[\"indices\"])\n",
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" break"
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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": "19b85061-a1b0-458b-9d00-96cde6f37dca",
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"metadata": {},
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"outputs": [],
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"source": [
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"media.show_image(image_dataset.get_image(10))"
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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": "b90f4a78-4b8b-4615-a127-7d7579874765",
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"metadata": {},
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"outputs": [],
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"source": [
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"def show_batch(batch):\n",
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" # set the color of the sampled rays\n",
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" c, y, x = [i.flatten() for i in torch.split(batch[\"local_indices\"], 1, dim=-1)]\n",
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" batch[\"image\"][c,y,x] = 1.0\n",
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"\n",
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" # batch[\"image\"] is num_images, h, w, 3\n",
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" images = torch.split(batch[\"image\"], 1, dim=0)\n",
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" image_list = [image[0] for image in images]\n",
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" image = torch.cat(image_list, dim=1) # cat along the width dimension\n",
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"\n",
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" # the white pixels are rays\n",
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" media.show_image((image*255).to(torch.uint8))\n",
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"\n",
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"def sample_and_show_batch():\n",
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" batch = next(iter(dataloader))\n",
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" show_batch(batch)\n",
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" return batch"
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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": "09e625f7-fa1f-4b3f-acd4-bddfcdf7cd9f",
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"metadata": {},
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"outputs": [],
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"source": [
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"batch = sample_and_show_batch()"
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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": "fd8cf48a-823e-4395-99ca-eda4c2026440",
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"metadata": {},
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"outputs": [],
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"source": [
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"ray_indices = batch[\"indices\"]\n",
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"ray_bundle = ray_generator(ray_indices)"
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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": "646107c0-f979-408a-941f-98b2ad3e07f7",
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"metadata": {},
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"outputs": [],
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"source": [
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"skip = 1\n",
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"size = 8\n",
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"data = []\n",
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"data += [go.Scatter3d(\n",
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" x=ray_generator.camera_to_world[::skip,0,3],\n",
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" y=ray_generator.camera_to_world[::skip,1,3],\n",
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" z=ray_generator.camera_to_world[::skip,2,3],\n",
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" mode='markers',\n",
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" name=\"origins\",\n",
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" marker=dict(color='rgba(0, 0, 0, 1)', size=size)\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": null,
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"id": "c73ba9bd-2902-4c46-b592-bc87ac8c71f6",
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"metadata": {},
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"outputs": [],
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"source": [
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"near_plane = 1.0\n",
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"far_plane = 6.0\n",
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"lines = torch.stack([ray_bundle.origins + ray_bundle.directions * near_plane,\n",
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" ray_bundle.origins + ray_bundle.directions * far_plane\n",
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" ], dim=1).tolist() # (num_rays, 2, 3)\n",
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"lines = torch.tensor(random.sample(lines, k=100))"
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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": "991df6a2-efe3-4412-ba41-9a244e2e90cc",
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"metadata": {},
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"outputs": [],
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"source": [
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"data += get_line_segments_from_lines(lines)"
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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": "0b2404d8-ca9e-4630-9f67-7c68d19458cb",
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"metadata": {},
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"outputs": [],
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"source": [
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"layout = go.Layout(\n",
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" autosize=False,\n",
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" width=1000,\n",
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" height=1000,\n",
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" margin=go.layout.Margin(\n",
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" l=50,\n",
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" r=50,\n",
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" b=100,\n",
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" t=100,\n",
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" pad=4\n",
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" ),\n",
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" scene=go.layout.Scene(\n",
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" aspectmode=\"data\",\n",
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" camera=dict(\n",
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" up=dict(x=0, y=0, z=1),\n",
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" center=dict(x=0, y=0, z=0),\n",
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" eye=dict(x=1.25, y=1.25, z=1.25)\n",
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" )\n",
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" )\n",
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")\n",
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"fig = go.Figure(data=data, layout=layout)\n",
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"fig.show()"
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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": "7aa506fe-66a1-4284-ade4-8c90fde5476a",
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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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"cell_type": "code",
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"execution_count": null,
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"id": "f259edb0-4126-4930-99f4-b5574dbb2eea",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig.write_image(\"temp.png\")\n",
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"plotly_image = imageio.imread(\"temp.png\")\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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"id": "d1a11772-4692-4800-b446-cfdbe4ab92fa",
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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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"cell_type": "code",
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"execution_count": null,
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"id": "722f5370-3b0f-4ad1-87a1-df479ae85b60",
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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": "mattport",
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"language": "python",
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"name": "mattport"
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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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