From cdfdea99b4b5cb266306bc80e4c032e40cf39df9 Mon Sep 17 00:00:00 2001 From: ltriess Date: Sat, 3 Oct 2020 14:36:02 +0200 Subject: [PATCH] Add tutorial to construct spherical depth projections from raw lidar point clouds --- .../pandar64_channel_distribution.csv | 65 +++++ .../raw_depth_projection.ipynb | 225 ++++++++++++++++++ 2 files changed, 290 insertions(+) create mode 100644 tutorials/raw_depth_projection/pandar64_channel_distribution.csv create mode 100644 tutorials/raw_depth_projection/raw_depth_projection.ipynb diff --git a/tutorials/raw_depth_projection/pandar64_channel_distribution.csv b/tutorials/raw_depth_projection/pandar64_channel_distribution.csv new file mode 100644 index 0000000..0f76e0b --- /dev/null +++ b/tutorials/raw_depth_projection/pandar64_channel_distribution.csv @@ -0,0 +1,65 @@ +channel,horizontal_angle_offset,vertical_angle +1,-1.042,14.882 +2,-1.042,11.032 +3,-1.042,8.059 +4,-1.042,5.057 +5,-1.042,3.040 +6,-1.042,2.028 +7,1.042,1.860 +8,3.125,1.688 +9,5.208,1.522 +10,-5.208,1.351 +11,-3.125,1.184 +12,-1.042,1.013 +13,1.042,0.846 +14,3.125,0.675 +15,5.208,0.508 +16,-5.208,0.337 +17,-3.125,0.169 +18,-1.042,0.000 +19,1.042,-0.169 +20,3.125,-0.337 +21,5.208,-0.508 +22,-5.208,-0.675 +23,-3.125,-0.845 +24,-1.042,-1.013 +25,1.042,-1.184 +26,3.125,-1.351 +27,5.208,-1.522 +28,-5.208,-1.688 +29,-3.125,-1.860 +30,-1.042,-2.028 +31,1.042,-2.198 +32,3.125,-2.365 +33,5.208,-2.536 +34,-5.208,-2.700 +35,-3.125,-2.873 +36,-1.042,-3.040 +37,1.042,-3.210 +38,3.125,-3.375 +39,5.208,-3.548 +40,-5.208,-3.712 +41,-3.125,-3.884 +42,-1.042,-4.050 +43,1.042,-4.221 +44,3.125,-4.385 +45,5.208,-4.558 +46,-5.208,-4.720 +47,-3.125,-4.892 +48,-1.042,-5.057 +49,1.042,-5.229 +50,3.125,-5.391 +51,5.208,-5.565 +52,-5.208,-5.726 +53,-3.125,-5.898 +54,-1.042,-6.061 +55,-1.042,-7.063 +56,-1.042,-8.059 +57,-1.042,-9.060 +58,-1.042,-9.885 +59,-1.042,-11.032 +60,-1.042,-12.006 +61,-1.042,-12.974 +62,-1.042,-13.930 +63,-1.042,-18.889 +64,-1.042,-24.897 \ No newline at end of file diff --git a/tutorials/raw_depth_projection/raw_depth_projection.ipynb b/tutorials/raw_depth_projection/raw_depth_projection.ipynb new file mode 100644 index 0000000..47ce1f8 --- /dev/null +++ b/tutorials/raw_depth_projection/raw_depth_projection.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Raw Depth Projection Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### This tutorial shows how to do a cylindrical depth projection of the 3D LiDAR point clouds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Download the raw point cloud data\n", + "You can find the link to download the raw data for the LiDAR point clouds [here](https://github.com/scaleapi/pandaset-devkit/issues/67#issuecomment-674403708).\n", + "In the following we define `/data/pandaset` as the location of the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Import required python modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "import gzip\n", + "import pickle\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Load the raw sensor data\n", + "The description of the raw sensor data is provided in [Data.Instructions.pdf](https://github.com/scaleapi/pandaset-devkit/files/5078794/PandaSet.Raw.Data.Instructions.pdf).\n", + "The data directly provides the laser and column id for each measured point." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " laser_id column_id elevation azimuth_col azimuth_col_corrected \\\n", + "0 8 37793 1.510 354.850006 360.058014 \n", + "1 14 37793 0.496 354.850006 360.058014 \n", + "2 20 37793 -0.520 354.850006 360.058014 \n", + "3 26 37793 -1.534 354.850006 360.058014 \n", + "4 32 37793 -2.548 354.850006 360.058014 \n", + "... ... ... ... ... ... \n", + "106881 13 39644 0.663 5.050000 8.175000 \n", + "106882 18 39644 -0.181 5.050000 6.092000 \n", + "106883 39 39644 -3.724 5.050000 -0.158000 \n", + "106884 45 39644 -4.732 5.050000 -0.158000 \n", + "106885 51 39644 -5.738 5.050000 -0.158000 \n", + "\n", + " distance intensity \n", + "0 84.695999 39 \n", + "1 103.564003 9 \n", + "2 61.416000 56 \n", + "3 45.952000 3 \n", + "4 14.436000 0 \n", + "... ... ... \n", + "106881 174.084000 10 \n", + "106882 50.796001 156 \n", + "106883 23.176001 2 \n", + "106884 19.440001 1 \n", + "106885 16.832001 2 \n", + "\n", + "[106886 rows x 7 columns]\n" + ] + } + ], + "source": [ + "file = \"/data/pandaset/001/lidar/00.pkl.gz\"\n", + "with gzip.open(file, \"rb\") as fin:\n", + " data = pickle.load(fin)\n", + "\n", + "num_rows = 64 # the number of lasers\n", + "num_columns = int(360 / 0.2) # horizontal field of view / horizontal angular resolution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Load the horizontal angle offets\n", + "The column ids of the raw data are those collected at the same motor rotation angle. This is not the same horizontal angle, due to horizontal angle offsets in the mounting position of the lasers.\n", + "With the data provided in the `csv` file, taken from the Pandar64 User Manual, we can correct the column ids to sort the points accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"pandar64_channel_distribution.csv\", \"r\") as fin:\n", + " reader = csv.DictReader(fin)\n", + " horizontal_angle_offset = np.array([float(r[\"horizontal_angle_offset\"]) for r in reader])\n", + "\n", + "column_shift = (num_columns / 360 * horizontal_angle_offset).astype(np.int64)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. Retrieve the laser and column ids\n", + "The column ids are given in absolute values, therefore we have to substract the smallest value to obtain the indices relative to the current frame. Due to the angle offets, the span from minimum to maximum value is exactly 1852 per frame instead of 1800. This is since the maximum offset in both positive and negative is 26 `>> 2 * 26 = 52`. With the offsets we can compute the correct column ids." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "rows_list = data[\"laser_id\"].values\n", + "\n", + "cols_list = data[\"column_id\"].values\n", + "cols_list -= np.min(cols_list)\n", + "cols_list = np.mod(cols_list + column_shift[rows_list] + num_columns, num_columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. Create the depth projection\n", + "First, we initialize an empty image. All locations without measurements will have the value `-1`. Then we scatter the point cloud into the image." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 1800)\n", + "[[ -1. -1. -1. ... -1. 16.24 16.304]\n", + " [ -1. -1. -1. ... 42.98 43.068 43.112]\n", + " [101.408 101.288 101.24 ... 101.504 101.456 101.4 ]\n", + " ...\n", + " [ 8.08 8.076 8.064 ... 8.096 8.084 8.072]\n", + " [ -1. -1. -1. ... -1. -1. -1. ]\n", + " [ 1.008 0.964 0.956 ... -1. 1.032 0.908]]\n" + ] + } + ], + "source": [ + "depth_img = np.full((num_rows, num_columns), fill_value=-1, dtype=np.float32)\n", + "depth_img[rows_list, cols_list] = data[\"distance\"].values" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20, 4))\n", + "plt.imshow(depth_img, vmin=0.5, vmax=80)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}