{ "cells": [ { "cell_type": "markdown", "id": "3c00934d-0f14-4c1a-a563-94d627b377e5", "metadata": {}, "source": [ "## Notebook to visualize a dataset\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "b15dfdc1-5054-40bc-9631-4c7426eecfd9", "metadata": {}, "outputs": [], "source": [ "from notebook_imports import *\n", "\n", "import torch\n", "from torch.utils.data import DataLoader\n", "\n", "from mattport.nerf.dataset.image_dataset import ImageDataset, collate_batch\n", "from mattport.nerf.dataset.collate import CollateIterDataset, collate_batch_size_one\n", "from mattport.nerf.dataset.utils import get_dataset_inputs_dict\n", "from mattport.nerf.field_modules.ray_generator import RayGenerator\n", "from mattport.structures.rays import RayBundle\n", "from mattport.utils.io import get_absolute_path\n", "from mattport.viewer.plotly import get_line_segments_from_lines\n", "from mattport.structures.cameras import get_camera_model\n", "\n", "\n", "from hydra import compose, initialize\n", "from omegaconf import open_dict\n", "import pprint\n", "from tqdm import tqdm\n", "import random" ] }, { "cell_type": "code", "execution_count": 2, "id": "012bf657", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "import goat\n", "vis = goat.vis_utils.get_vis()" ] }, { "cell_type": "code", "execution_count": 3, "id": "71f46c79", "metadata": {}, "outputs": [], "source": [ "goat.vis_utils.show_box_test(vis)" ] }, { "cell_type": "code", "execution_count": 4, "id": "7fe7478d", "metadata": {}, "outputs": [], "source": [ "with initialize(config_path=\"../configs\"):\n", " # cfg = compose(config_name=\"default.yaml\")\n", " cfg = compose(config_name=\"default.yaml\", overrides=[\"data/dataset=friends_TBBT-big_living_room\"])" ] }, { "cell_type": "code", "execution_count": 5, "id": "ad63e53a", "metadata": {}, "outputs": [], "source": [ "import os\n", "from mattport.nerf.dataset.colmap_utils import read_cameras_binary, read_images_binary, read_pointsTD_binary" ] }, { "cell_type": "code", "execution_count": 13, "id": "01c42863", "metadata": {}, "outputs": [], "source": [ "from mattport.nerf.dataset.friends import load_friends_data" ] }, { "cell_type": "code", "execution_count": null, "id": "b097e5eb", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 40, "id": "c5516443", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2930.31056439 960. 544. ]\n", "[4466.94830951 1024. 574. ]\n" ] } ], "source": [ "dataset_inputs = load_friends_data(\"/shared/ethanweber/cartoons/data/colmap/smith_house_pair_manual_after\", include_point_cloud=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "eba07414", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 24, "id": "8ff323ed", "metadata": {}, "outputs": [], "source": [ "imdata = read_images_binary(os.path.join(\"/shared/ethanweber/mattport/data/friends/TBBT-big_living_room\", \"colmap\", \"images.bin\"))" ] }, { "cell_type": "code", "execution_count": 41, "id": "10539dc6", "metadata": {}, "outputs": [], "source": [ "# cfg.data.dataset.data_directory = get_absolute_path(cfg.data.dataset.data_directory)\n", "# dataset_inputs = get_dataset_inputs_dict(**cfg.data.dataset, splits=[\"train\"])[\"train\"]" ] }, { "cell_type": "code", "execution_count": null, "id": "0b010534", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 46, "id": "e95073a9", "metadata": {}, "outputs": [], "source": [ "import goat.threedvis.meshcat.geometry as g\n", "import numpy as np\n", "point_cloud = dataset_inputs.point_cloud\n", "size = 0.1\n", "vis[\"point_cloud\"].set_object(\n", " g.PointCloud(position=np.transpose(np.array(point_cloud.xyz)),\n", " color=np.transpose(np.array(point_cloud.rgb)) / 255.0, size=size)\n", ")" ] }, { "cell_type": "code", "execution_count": 47, "id": "aac97d4a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2/2 [00:00<00:00, 4.15it/s]\n" ] } ], "source": [ "# show dataset inputs with three.js\n", "# goat.vis_utils.show_box_test(vis)\n", "num_images_to_show = 200\n", "for idx in tqdm(list(range(len(dataset_inputs.image_filenames)))[:num_images_to_show]):\n", " image = imageio.imread(dataset_inputs.image_filenames[idx])[:,:,:3]\n", "\n", " num_intrinsics_params = len(dataset_inputs.intrinsics[idx])\n", " camera_class = get_camera_model(num_intrinsics_params)\n", " camera_to_world = dataset_inputs.camera_to_world[idx]\n", " camera = camera_class(*dataset_inputs.intrinsics[idx].tolist(), camera_to_world=camera_to_world)\n", " K = camera.get_intrinsics_matrix()\n", "\n", " name = \"train/{:06d}\".format(idx)\n", " # height = 0.5\n", " displayed_focal_length = 0.5\n", " goat.vis_utils.draw_camera_frustum(vis, \n", " name=name, \n", " image=image, \n", " K=K.numpy(), \n", " pose=camera_to_world.double().numpy(),\n", " height=3.0,\n", " realistic=False,\n", " displayed_focal_length=0.0\n", " )\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "001bdf3b-60a0-4bb2-9f88-ae36f2036c80", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/10 [00:03" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "media.show_image(image_dataset.get_image(10))" ] }, { "cell_type": "code", "execution_count": 10, "id": "b90f4a78-4b8b-4615-a127-7d7579874765", "metadata": {}, "outputs": [], "source": [ "def show_batch(batch):\n", " # set the color of the sampled rays\n", " c, y, x = [i.flatten() for i in torch.split(batch[\"local_indices\"], 1, dim=-1)]\n", " batch[\"image\"][c,y,x] = 1.0\n", "\n", " # batch[\"image\"] is num_images, h, w, 3\n", " images = torch.split(batch[\"image\"], 1, dim=0)\n", " image_list = [image[0] for image in images]\n", " image = torch.cat(image_list, dim=1) # cat along the width dimension\n", "\n", " # the white pixels are rays\n", " media.show_image((image*255).to(torch.uint8))\n", "\n", "def sample_and_show_batch():\n", " batch = next(iter(dataloader))\n", " show_batch(batch)\n", " return batch" ] }, { "cell_type": "code", "execution_count": 11, "id": "09e625f7-fa1f-4b3f-acd4-bddfcdf7cd9f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch = sample_and_show_batch()" ] }, { "cell_type": "code", "execution_count": 12, "id": "fd8cf48a-823e-4395-99ca-eda4c2026440", "metadata": {}, "outputs": [], "source": [ "ray_indices = batch[\"indices\"]\n", "ray_bundle = ray_generator(ray_indices)" ] }, { "cell_type": "code", "execution_count": 23, "id": "646107c0-f979-408a-941f-98b2ad3e07f7", "metadata": {}, "outputs": [], "source": [ "skip = 1\n", "size = 8\n", "data = []\n", "data += [go.Scatter3d(\n", " x=ray_generator.camera_to_world[::skip,0,3],\n", " y=ray_generator.camera_to_world[::skip,1,3],\n", " z=ray_generator.camera_to_world[::skip,2,3],\n", " mode='markers',\n", " name=\"origins\",\n", " marker=dict(color='rgba(0, 0, 0, 1)', size=size)\n", ")]" ] }, { "cell_type": "code", "execution_count": 24, "id": "c73ba9bd-2902-4c46-b592-bc87ac8c71f6", "metadata": {}, "outputs": [], "source": [ "near_plane = cfg.graph.network.near_plane\n", "far_plane = cfg.graph.network.far_plane\n", "lines = torch.stack([ray_bundle.origins + ray_bundle.directions * near_plane,\n", " ray_bundle.origins + ray_bundle.directions * far_plane\n", " ], dim=1).tolist() # (num_rays, 2, 3)\n", "lines = torch.tensor(random.sample(lines, k=100))" ] }, { "cell_type": "code", "execution_count": 25, "id": "991df6a2-efe3-4412-ba41-9a244e2e90cc", "metadata": {}, "outputs": [], "source": [ "data += get_line_segments_from_lines(lines)" ] }, { "cell_type": "code", "execution_count": 26, "id": "0b2404d8-ca9e-4630-9f67-7c68d19458cb", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "rgba(0, 0, 0, 1)", "size": 8 }, "mode": "markers", "name": "origins", "type": "scatter3d", "x": [ -2.620957851409912, -2.3065438270568848, -1.3201767206192017, 3.2671120166778564, 0.3710319399833679, 0.4049864411354065, 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