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trap/test_trajectron_maps.ipynb
Ruben van de Ven 1033516712 First attempt to provide image map encoder
2024-12-27 11:28:16 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Trajectron seems to support providing a map for a scene. This might be a way to get better predictions, that actually stay on the pathways instead of go through buildings. However, by default it supports maps from NuScenes, but not images (even though some traces of that remain in the code.) More info about support in trajectron is in [issue #14](https://github.com/StanfordASL/Trajectron-plus-plus/issues/14) on their Github.\n",
"\n",
"This notebook is used to test my implementation to add map support to Trajectron."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CHANGELOG:\n",
"\n",
"* 2024-12-27 : Created\n",
" * Draw the map image\n",
" * Training _sometimes_ (randomly?) gives NaN matrixes since using map encoding.\n",
" * Call Image map and test if converted points of all tracks fall within realistic image bounds (e.g. no negative points)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"from trap.frame_emitter import Camera\n",
"from trap.utils import ImageMap\n",
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"calibration_path = Path(\"../DATASETS/hof3/calibration.json\")\n",
"homography_path = Path(\"../DATASETS/hof3/homography.json\")\n",
"\n",
"camera = Camera.from_paths(calibration_path, homography_path, 12)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f1e2b3c6050>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"image_path = Path(\"../DATASETS/hof3/map-undistorted-H.png\")\n",
"\n",
"imgmap = ImageMap(image_path, None, \"hof3-undistorted-H\")\n",
"\n",
"plt.imshow(cv2.imread(image_path))\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"\n",
"path = Path(\"EXPERIMENTS/raw/hof3/\")\n",
"calibration_path = Path(\"../DATASETS/hof3/calibration.json\")\n",
"homography_path = Path(\"../DATASETS/hof3/homography.json\")\n",
"\n",
"camera = Camera.from_paths(calibration_path, homography_path, 12)\n",
"\n",
"imgmap = ImageMap(image_path, None, \"hof3-undistorted\")\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"\n",
"img = imgmap.as_image()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f1e30811b70>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"img = np.flipud(img)\n",
"plt.imshow(img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from trap.tracker import TrackReader\n",
"\n",
"\n",
"reader = TrackReader(path, camera.fps, exclude_whitelisted = False, include_blacklisted=False)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"\n",
"from typing import List\n",
"from trap.frame_emitter import Track\n",
"from trap.tracker import FinalDisplacementFilter\n",
"\n",
"\n",
"tracks: List[Track] = [t for t in reader]\n",
"filter = FinalDisplacementFilter(2)\n",
"tracks = filter.apply(tracks, camera)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 12.247 6.8275]\n",
" [ 12.416 6.5942]\n",
" [ 12.528 6.5035]\n",
" [ 12.594 6.4782]\n",
" [ 12.631 6.477]\n",
" [ 12.66 6.424]\n",
" [ 12.706 6.369]\n",
" [ 12.785 6.2094]\n",
" [ 12.849 6.0079]\n",
" [ 12.919 5.7624]\n",
" [ 12.954 5.6717]\n",
" [ 12.979 5.6476]\n",
" [ 12.985 5.613]\n",
" [ 13.027 5.4535]\n",
" [ 13.072 5.2315]\n",
" [ 13.129 4.995]\n",
" [ 13.159 4.894]\n",
" [ 13.167 4.8371]\n",
" [ 13.163 4.8151]\n",
" [ 13.174 4.7545]\n",
" [ 13.2 4.5546]\n",
" [ 13.237 4.2617]\n",
" [ 13.241 4.165]\n",
" [ 13.242 4.1164]\n",
" [ 13.233 4.1089]\n",
" [ 13.238 4.0344]\n",
" [ 13.24 3.967]\n",
" [ 13.318 3.5567]\n",
" [ 13.329 3.4015]\n",
" [ 13.344 3.3385]\n",
" [ 13.357 3.3064]\n",
" [ 13.331 3.3068]\n",
" [ 13.298 3.0786]\n",
" [ 13.35 2.8114]\n",
" [ 13.364 2.6867]\n",
" [ 13.346 2.6791]\n",
" [ 13.326 2.6335]] [[1224 682]\n",
" [1241 659]\n",
" [1252 650]\n",
" [1259 647]\n",
" [1263 647]\n",
" [1266 642]\n",
" [1270 636]\n",
" [1278 620]\n",
" [1284 600]\n",
" [1291 576]\n",
" [1295 567]\n",
" [1297 564]\n",
" [1298 561]\n",
" [1302 545]\n",
" [1307 523]\n",
" [1312 499]\n",
" [1315 489]\n",
" [1316 483]\n",
" [1316 481]\n",
" [1317 475]\n",
" [1319 455]\n",
" [1323 426]\n",
" [1324 416]\n",
" [1324 411]\n",
" [1323 410]\n",
" [1323 403]\n",
" [1324 396]\n",
" [1331 355]\n",
" [1332 340]\n",
" [1334 333]\n",
" [1335 330]\n",
" [1333 330]\n",
" [1329 307]\n",
" [1334 281]\n",
" [1336 268]\n",
" [1334 267]\n",
" [1332 263]]\n"
]
}
],
"source": [
"# track = tracks[0]\n",
"for track in tracks:\n",
" history = track.get_projected_history(None, camera)\n",
" points = imgmap.to_map_points(history)\n",
" print(history, points)\n",
" if not ((points[:,0] > 0 ) & (points[:,0] < 2440) & (points[:,1] > 0) & (points[:,1] < 1440)).all():\n",
" print(\"not all points between limits\")\n",
" print(points)\n",
" break\n",
"\n",
"# track.to_trajectron_node(camera, env)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in a new issue View git blame Copy permalink