I got it working.
You must create a new venv and install pip install opencv-contrib-python. Don’t install pip install opencv-python! Since this removes essential functionalities. It seems that the new objdetect in the non-contrib-opencv is quite buggy.
I am not he only one telling this, see cv2.aruco.calibrateCameraCharuco does not exist · Issue #23493 · opencv/opencv · GitHub
So here a fully standalone working version of test code you can try:
Warning: It fails from time to time. Thats because the homograhies are randomly choosen. In some instances they distort the image so much that no detection may happen. Just restart and try again. (Or cover that “bug” by checking for None.)
from itertools import cycle
from collections import namedtuple
import math
import matplotlib.pyplot as plt
import cv2 as cv
import numpy as np
ArucoResults = namedtuple(
"ArucoResults",
[
"marker_corners",
"marker_ids",
]
)
CharucoCResults = namedtuple(
"CharucoResults",
["number_of_corners",
"corner_coordinates",
"corner_id",
]
)
CameraCalibrationResults = namedtuple(
"CameraCalibrationResults",
[
"repError",
"camMatrix",
"distcoeff",
"rvecs",
"tvecs",
]
)
SQUARE_LENGTH = 500
MARKER_LENGHT = 300
NUMBER_OF_SQUARES_VERTICALLY = 11
NUMBER_OF_SQUARES_HORIZONTALLY = 8
charuco_marker_dictionary = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_6X6_250)
charuco_board = cv.aruco.CharucoBoard(
size=(NUMBER_OF_SQUARES_HORIZONTALLY, NUMBER_OF_SQUARES_VERTICALLY),
squareLength=SQUARE_LENGTH,
markerLength=MARKER_LENGHT,
dictionary=charuco_marker_dictionary
)
image_name = f'ChArUco_Marker_{NUMBER_OF_SQUARES_HORIZONTALLY}x{NUMBER_OF_SQUARES_VERTICALLY}.png'
cv.imwrite(
image_name,
charuco_board.generateImage(
[i*SQUARE_LENGTH
for i in (NUMBER_OF_SQUARES_HORIZONTALLY, NUMBER_OF_SQUARES_VERTICALLY)])
)
def generate_test_images(image):
"""Use random homograpy.
-> Just to test detection. This doesn't simulate a perspective
projection of one single camera! (Intrinsics change randomly.)
For a "camera simulation" one would need to define fixed intrinsics
and random extrinsics. Then cobine them into a projective matrix.
And apply this to the Image. -> Also you need to add fixed z
coordinate to the image (may be randomly choosen in the first place), since a projection is from 3d space into 2d
space.
"""
h, w = image.shape[:2]
src_points = np.float32([[0, 0], [w, 0], [w, h], [0, h]])
dst_points = np.float32([
[np.random.uniform(w * -0.2, w * 0.2), np.random.uniform(0, h * 0.2)],
[np.random.uniform(w * 0.8, w*1.2), np.random.uniform(0, h * 0.6)],
[np.random.uniform(w * 0.8, w), np.random.uniform(h * 0.8, h)],
[np.random.uniform(0, w * 0.2), np.random.uniform(h * 0.8, h*1.5)]
])
homography_matrix, _ = cv.findHomography(src_points, dst_points)
image_projected = cv.warpPerspective(image, homography_matrix, (w, h))
return image_projected
def detect_charuco(image):
"""Detect the charuco corners in given image.
"""
detector_charuco = cv.aruco.CharucoDetector(charuco_board)
result = detector_charuco.detectBoard(image)
return ArucoResults(*result[2:])
def show_charuco_corners_and_id(image, markers, marker_ids=None):
"""Plot the detected corners for debugging.
"""
colors = [
(0, 0, 255), # red
(0, 255, 0), # green
(255, 0, 0), # blue
(255, 0, 255), # something
]
if marker_ids is not None:
ids = iter(marker_ids)
color_cycler = cycle(colors)
for i, marker in enumerate(markers, start=1):
image = cv.circle(
image,
(int(marker[0]), int(marker[1])),
radius=100,
color=next(color_cycler),
thickness=10
)
if i%4 == 0 and marker_ids is not None:
try:
id = str(next(ids))
except StopIteration:
break
image = cv.putText(
image,
id,
(int(marker[0]), int(marker[1])),
cv.FONT_HERSHEY_SIMPLEX,
3,
(255, 0, 255),
10,
cv.LINE_AA
)
cv.namedWindow("Markers - detected", cv.WINDOW_NORMAL)
cv.imshow("Markers - detected", image)
cv.waitKey(-1)
def display_images(images):
N = len(images)
cols = math.ceil(math.sqrt(N))
rows = math.ceil(N / cols)
for i, img in enumerate(images):
plt.subplot(rows, cols, i + 1)
plt.imshow(img, cmap='gray')
plt.axis('off')
plt.tight_layout()
plt.show()
# Create N test images based on the originaly created pattern.
original_image = cv.imread(image_name)
N = 10
random_images = []
for _ in range(N):
random_images.append(generate_test_images(original_image))
display_images(random_images)
all_charuco_corners = []
all_charuco_ids = []
for img_bgr in random_images:
img_gray = cv.cvtColor(img_bgr, cv.COLOR_BGR2GRAY)
detection_results = detect_charuco(img_gray)
show_charuco_corners_and_id(
img_bgr.copy(),
np.array(detection_results.marker_corners).copy().reshape(-1, 2),
detection_results.marker_ids
)
detected_charuco_corners = CharucoCResults(
*cv.aruco.interpolateCornersCharuco(
markerCorners=detection_results.marker_corners,
markerIds=detection_results.marker_ids,
image=img_gray,
board=charuco_board
)
)
show_charuco_corners_and_id(
img_bgr.copy(),
detected_charuco_corners.corner_coordinates.copy().reshape(-1, 2),
)
all_charuco_corners.append(detected_charuco_corners.corner_coordinates)
all_charuco_ids.append(detected_charuco_corners.corner_id)
calibration_results = CameraCalibrationResults(
*cv.aruco.calibrateCameraCharuco(
all_charuco_corners,
all_charuco_ids,
charuco_board,
img_gray.shape[0:2],
None,
None
)
)
Desired output something like:
Detected Aruco markers:
Detected Charuco corners:
My requirements.txt
asttokens==2.4.1
backcall==0.2.0
colorama==0.4.6
comm==0.2.2
contourpy==1.1.1
cycler==0.12.1
debugpy==1.8.7
decorator==5.1.1
executing==2.1.0
fonttools==4.54.1
importlib_metadata==8.5.0
importlib_resources==6.4.5
ipykernel==6.29.5
ipython==8.12.3
jedi==0.19.1
jupyter_client==8.6.3
jupyter_core==5.7.2
kiwisolver==1.4.7
matplotlib==3.7.5
matplotlib-inline==0.1.7
nest-asyncio==1.6.0
numpy==1.24.4
opencv-contrib-python==4.10.0.84
packaging==24.1
parso==0.8.4
pickleshare==0.7.5
pillow==10.4.0
platformdirs==4.3.6
prompt_toolkit==3.0.48
psutil==6.0.0
pure_eval==0.2.3
Pygments==2.18.0
pyparsing==3.1.4
python-dateutil==2.9.0.post0
pywin32==308
pyzmq==26.2.0
six==1.16.0
stack-data==0.6.3
tornado==6.4.1
traitlets==5.14.3
typing_extensions==4.12.2
wcwidth==0.2.13
zipp==3.20.2
opencv build info:
General configuration for OpenCV 4.10.0 =====================================
Version control: 4.10.0
Extra modules:
Location (extra): D:/a/opencv-python/opencv-python/opencv_contrib/modules
Version control (extra): 4.10.0
Platform:
Timestamp: 2024-06-17T18:00:01Z
Host: Windows 10.0.17763 AMD64
CMake: 3.24.2
CMake generator: Visual Studio 14 2015
CMake build tool: MSBuild.exe
MSVC: 1900
Configuration: Debug Release
CPU/HW features:
Baseline: SSE SSE2 SSE3
requested: SSE3
Dispatched code generation: SSE4_1 SSE4_2 FP16 AVX AVX2
requested: SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX
SSE4_1 (16 files): + SSSE3 SSE4_1
SSE4_2 (1 files): + SSSE3 SSE4_1 POPCNT SSE4_2
FP16 (0 files): + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX
AVX (8 files): + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
AVX2 (36 files): + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2
C/C++:
Built as dynamic libs?: NO
C++ standard: 11
C++ Compiler: C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe (ver 19.0.24247.2)
C++ flags (Release): /DWIN32 /D_WINDOWS /W4 /GR /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /fp:precise /EHa /wd4127 /wd4251 /wd4324 /wd4275 /wd4512 /wd4589 /wd4819 /MP /O2 /Ob2 /DNDEBUG
C++ flags (Debug): /DWIN32 /D_WINDOWS /W4 /GR /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /fp:precise /EHa /wd4127 /wd4251 /wd4324 /wd4275 /wd4512 /wd4589 /wd4819 /MP /Zi /Ob0 /Od /RTC1
C Compiler: C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe
C flags (Release): /DWIN32 /D_WINDOWS /W3 /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /fp:precise /MP /O2 /Ob2 /DNDEBUG
C flags (Debug): /DWIN32 /D_WINDOWS /W3 /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /fp:precise /MP /Zi /Ob0 /Od /RTC1
Linker flags (Release): /machine:x64 /NODEFAULTLIB:atlthunk.lib /INCREMENTAL:NO /NODEFAULTLIB:libcmtd.lib /NODEFAULTLIB:libcpmtd.lib /NODEFAULTLIB:msvcrtd.lib
Linker flags (Debug): /machine:x64 /NODEFAULTLIB:atlthunk.lib /debug /INCREMENTAL /NODEFAULTLIB:libcmt.lib /NODEFAULTLIB:libcpmt.lib /NODEFAULTLIB:msvcrt.lib
ccache: NO
Precompiled headers: YES
Extra dependencies: wsock32 comctl32 gdi32 ole32 setupapi ws2_32
3rdparty dependencies: libprotobuf ade ittnotify libjpeg-turbo libwebp libpng libtiff libopenjp2 IlmImf zlib ippiw ippicv
OpenCV modules:
To be built: aruco bgsegm bioinspired calib3d ccalib core datasets dnn dnn_objdetect dnn_superres dpm face features2d flann fuzzy gapi hfs highgui img_hash imgcodecs imgproc intensity_transform line_descriptor mcc ml objdetect optflow phase_unwrapping photo plot python3 quality rapid reg rgbd saliency shape signal stereo stitching structured_light superres surface_matching text tracking video videoio videostab wechat_qrcode xfeatures2d ximgproc xobjdetect xphoto
Disabled: java world
Disabled by dependency: -
Unavailable: alphamat cannops cudaarithm cudabgsegm cudacodec cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev cvv freetype hdf julia matlab ovis python2 sfm ts viz
Applications: -
Documentation: NO
Non-free algorithms: NO
Windows RT support: NO
GUI: WIN32UI
Win32 UI: YES
VTK support: NO
Media I/O:
ZLib: build (ver 1.3.1)
JPEG: build-libjpeg-turbo (ver 3.0.3-70)
SIMD Support Request: YES
SIMD Support: NO
WEBP: build (ver encoder: 0x020f)
PNG: build (ver 1.6.43)
SIMD Support Request: YES
SIMD Support: YES (Intel SSE)
TIFF: build (ver 42 - 4.6.0)
JPEG 2000: build (ver 2.5.0)
OpenEXR: build (ver 2.3.0)
HDR: YES
SUNRASTER: YES
PXM: YES
PFM: YES
Video I/O:
DC1394: NO
FFMPEG: YES (prebuilt binaries)
avcodec: YES (58.134.100)
avformat: YES (58.76.100)
avutil: YES (56.70.100)
swscale: YES (5.9.100)
avresample: YES (4.0.0)
GStreamer: NO
DirectShow: YES
Media Foundation: YES
DXVA: YES
Parallel framework: Concurrency
Trace: YES (with Intel ITT)
Other third-party libraries:
Intel IPP: 2021.11.0 [2021.11.0]
at: D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.9/cmake-build/3rdparty/ippicv/ippicv_win/icv
Intel IPP IW: sources (2021.11.0)
at: D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.9/cmake-build/3rdparty/ippicv/ippicv_win/iw
Lapack: NO
Eigen: NO
Custom HAL: NO
Protobuf: build (3.19.1)
Flatbuffers: builtin/3rdparty (23.5.9)
OpenCL: YES (NVD3D11)
Include path: D:/a/opencv-python/opencv-python/opencv/3rdparty/include/opencl/1.2
Link libraries: Dynamic load
Python 3:
Interpreter: C:/hostedtoolcache/windows/Python/3.9.13/x64/python.exe (ver 3.9.13)
Libraries: C:/hostedtoolcache/windows/Python/3.9.13/x64/libs/python39.lib (ver 3.9.13)
Limited API: YES (ver 0x03060000)
numpy: C:/hostedtoolcache/windows/Python/3.9.13/x64/lib/site-packages/numpy/_core/include (ver 2.0.0)
install path: python/cv2/python-3
Python (for build): C:\hostedtoolcache\windows\Python\3.9.13\x64\python.exe
Java:
ant: NO
Java: YES (ver 1.8.0.412)
JNI: C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.412-8/x64/include C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.412-8/x64/include/win32 C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.412-8/x64/include
Java wrappers: NO
Java tests: NO
Install to: D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.9/cmake-install
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