155 lines
3.9 KiB
Plaintext
155 lines
3.9 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 50,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import cv2\n",
|
|
"from cv2 import aruco\n",
|
|
"from datetime import datetime\n",
|
|
"from loguru import logger\n",
|
|
"from pathlib import Path\n",
|
|
"from typing import cast, Final, TypeAlias\n",
|
|
"from cv2.typing import MatLike\n",
|
|
"from matplotlib import pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"NDArray: TypeAlias = np.ndarray"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"INPUT_IMAGE = Path(\"merged_uv_layout.png\")\n",
|
|
"# 7x7\n",
|
|
"DICTIONARY: Final[int] = aruco.DICT_7X7_1000\n",
|
|
"# 400mm\n",
|
|
"MARKER_LENGTH: Final[float] = 0.4"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 52,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"aruco_dict = aruco.getPredefinedDictionary(DICTIONARY)\n",
|
|
"detector = aruco.ArucoDetector(\n",
|
|
" dictionary=aruco_dict, detectorParams=aruco.DetectorParameters()\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 53,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"frame = cv2.imread(str(INPUT_IMAGE))\n",
|
|
"grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
|
|
"# pylint: disable-next=unpacking-non-sequence\n",
|
|
"markers, ids, rejected = detector.detectMarkers(grey)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 54,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Note: BGR\n",
|
|
"RED = (0, 0, 255)\n",
|
|
"GREEN = (0, 255, 0)\n",
|
|
"BLUE = (255, 0, 0)\n",
|
|
"YELLOW = (0, 255, 255)\n",
|
|
"GREY = (128, 128, 128)\n",
|
|
"CYAN = (255, 255, 0)\n",
|
|
"MAGENTA = (255, 0, 255)\n",
|
|
"ORANGE = (0, 165, 255)\n",
|
|
"PINK = (147, 20, 255)\n",
|
|
"\n",
|
|
"UI_SCALE = 10\n",
|
|
"UI_SCALE_FONT = 8\n",
|
|
"UI_SCALE_FONT_WEIGHT = 20"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 55,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"out = frame.copy()\n",
|
|
"# `markers` is [N, 1, 4, 2]\n",
|
|
"# `ids` is [N, 1]\n",
|
|
"if ids is not None:\n",
|
|
" markers = np.reshape(markers, (-1, 4, 2))\n",
|
|
" ids = np.reshape(ids, (-1, 1))\n",
|
|
" # logger.info(\"markers={}, ids={}\", np.array(markers).shape, np.array(ids).shape)\n",
|
|
" for m, i in zip(markers, ids):\n",
|
|
" # logger.info(\"id={}, center={}\", i, center)\n",
|
|
" center = np.mean(m, axis=0).astype(int) # type: ignore\n",
|
|
" # BGR\n",
|
|
" color_map = [RED, GREEN, BLUE, YELLOW]\n",
|
|
" for color, corners in zip(color_map, m):\n",
|
|
" corners = corners.astype(int)\n",
|
|
" out = cv2.circle(out, corners, 5*UI_SCALE, color, -1)\n",
|
|
" cv2.circle(out, tuple(center), 5*UI_SCALE, CYAN, -1)\n",
|
|
" cv2.putText(\n",
|
|
" out,\n",
|
|
" str(i),\n",
|
|
" tuple(center),\n",
|
|
" cv2.FONT_HERSHEY_SIMPLEX,\n",
|
|
" 1*UI_SCALE_FONT,\n",
|
|
" MAGENTA,\n",
|
|
" UI_SCALE_FONT_WEIGHT,\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.imshow(cv2.cvtColor(out, cv2.COLOR_BGR2RGB))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cv2.imwrite(\"merged_uv_layout_with_markers.png\", out)"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.12.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|