{ "cells": [ { "cell_type": "markdown", "id": "3c00934d-0f14-4c1a-a563-94d627b377e5", "metadata": {}, "source": [ "# Notebook to visualize a dataset\n", "\n", "Sanity check to ensure data is properly read\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", "\n", "from pyrad.data.dataloader import TrainDataloader\n", "from pyrad.data.utils import get_dataset_inputs_from_dataset_config\n", "from pyrad.graphs.modules.ray_generator import RayGenerator\n", "from pyrad.graphs.modules.scene_colliders import AABBBoxCollider\n", "from pyrad.utils.plotly import get_line_segments_from_lines\n", "from pyrad.utils.misc import instantiate_from_dict_config\n", "\n", "\n", "from hydra import compose, initialize\n", "from tqdm import tqdm\n", "import random" ] }, { "cell_type": "markdown", "id": "2cb2fd5f", "metadata": {}, "source": [ "## Load dataset" ] }, { "cell_type": "code", "execution_count": 25, "id": "7fe7478d", "metadata": {}, "outputs": [], "source": [ "CONFIG_NAME = \"graph_default.yaml\"\n", "DEVICE = \"cpu\"\n", "\n", "with initialize(version_base=None, config_path=\"../configs\"):\n", " config = compose(config_name=CONFIG_NAME)\n", "\n", "dataset_inputs = get_dataset_inputs_from_dataset_config(**config.data.dataset_inputs_train, split=\"train\")\n", "\n", "# ImageDataset\n", "image_dataset_train = instantiate_from_dict_config(config.data.image_dataset_train, **dataset_inputs.as_dict())\n", "# ImageSampler\n", "image_sampler_train = instantiate_from_dict_config(\n", " config.data.dataloader_train.image_sampler, dataset=image_dataset_train, device=DEVICE\n", ")\n", "# PixelSampler\n", "pixel_sampler_train = instantiate_from_dict_config(config.data.dataloader_train.pixel_sampler, keep_full_image=True)\n", "\n", "ray_generator = RayGenerator(dataset_inputs.intrinsics, dataset_inputs.camera_to_world)\n", "\n", "dataloader_train = TrainDataloader(image_sampler_train, pixel_sampler_train)\n", "iter_dataloader_train = iter(dataloader_train)\n", "\n", "\n", "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[\"full_image\"][c, y, x] = 0.0\n", " batch[\"full_image\"][c, y, x, 0] = 1.0\n", "\n", " # batch[\"image\"] is num_images, h, w, 3\n", " images = torch.split(batch[\"full_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 pink pixels are rays\n", " media.show_image((image * 255).to(torch.uint8))\n", "\n", "\n", "def sample_and_show_batch():\n", " ray_indices, batch = next(iter_dataloader_train)\n", " show_batch(batch)\n", " return batch" ] }, { "cell_type": "markdown", "id": "39349ea0", "metadata": {}, "source": [ "## Visualize Image\n" ] }, { "cell_type": "code", "execution_count": 28, "id": "19b85061-a1b0-458b-9d00-96cde6f37dca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Example image:\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Example Batch. Pink pixels denote the batch of rays:\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"Example image:\")\n", "media.show_image(image_dataset_train.get_image(10), height=200)\n", "print(\"Example Batch. Pink pixels denote the batch of rays:\")\n", "batch = sample_and_show_batch()" ] }, { "cell_type": "markdown", "id": "6eaab4db", "metadata": {}, "source": [ "## Visualize cameras and ray\n", "Camera origins denoted with black dots. 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ray_indices = batch[\"indices\"]\n", "ray_bundle = ray_generator(ray_indices)\n", "\n", "skip = 1\n", "size = 8\n", "data = []\n", "data += [\n", " 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", " )\n", "]\n", "\n", "scene_bounds_collider = AABBBoxCollider(dataset_inputs.scene_bounds)\n", "intersected_ray_bundle = scene_bounds_collider(ray_bundle)\n", "\n", "lines = torch.stack(\n", " [\n", " intersected_ray_bundle.origins + intersected_ray_bundle.directions * intersected_ray_bundle.nears[..., None],\n", " intersected_ray_bundle.origins + intersected_ray_bundle.directions * intersected_ray_bundle.fars[..., None],\n", " ],\n", " dim=1,\n", ").tolist() # (num_rays, 2, 3)\n", "lines = torch.tensor(random.sample(lines, k=100))\n", "\n", "data += get_line_segments_from_lines(lines)\n", "\n", "layout = go.Layout(\n", " autosize=False,\n", " width=1000,\n", " height=1000,\n", " margin=go.layout.Margin(l=50, r=50, b=100, t=100, pad=4),\n", " scene=go.layout.Scene(\n", " aspectmode=\"data\",\n", " camera=dict(up=dict(x=0, y=0, z=1), center=dict(x=0, y=0, z=0), eye=dict(x=1.25, y=1.25, z=1.25)),\n", " ),\n", ")\n", "fig = go.Figure(data=data, layout=layout)\n", "fig.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.12 ('pyrad')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "vscode": { "interpreter": { "hash": "41f62990c6e03ccb48eec29860b83c64e20fcd45204ae5014afab6809cd6f1f1" } } }, "nbformat": 4, "nbformat_minor": 5 }