Convert to uv and split corner finding
This commit is contained in:
Ruben van de Ven
parent
29060750b7
commit
5f836efcad
2 changed files with 199 additions and 0 deletions
102
00-find-corners.py
Normal file
102
00-find-corners.py
Normal file
|
|
@ -0,0 +1,102 @@
|
|||
'''
|
||||
Find camera intrinsicts:
|
||||
camera matrix and distortion coefficients
|
||||
Largely a copy from https://longervision.github.io/2017/03/16/ComputerVision/OpenCV/opencv-internal-calibration-chessboard/
|
||||
|
||||
|
||||
Usage:
|
||||
|
||||
1. Set dataset variable to point to a directory containing chessboard.mp4
|
||||
2. make sure CHECKERBOARD has the nr of corners in the printed board used. Use (6,9) for https://github.com/opencv/opencv/blob/4.x/doc/pattern.png
|
||||
3. Scripts creates a `calibration.json` in the dataset folder
|
||||
|
||||
'''
|
||||
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import cv2
|
||||
import json
|
||||
import tqdm
|
||||
import math
|
||||
|
||||
dataset = Path('hof3-cam-baumer')
|
||||
|
||||
# set needed detections. Use math.inf to scan the whole video
|
||||
needed_detections = math.inf # 20
|
||||
|
||||
# Defining the dimensions of checkerboard
|
||||
CHECKERBOARD = (6,9)
|
||||
|
||||
# termination criteria
|
||||
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
|
||||
|
||||
|
||||
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
|
||||
objp = np.zeros((CHECKERBOARD[0] * CHECKERBOARD[1],3), np.float32)
|
||||
objp[:,:2] = np.mgrid[0:CHECKERBOARD[0],0:CHECKERBOARD[1]].T.reshape(-1,2)
|
||||
|
||||
# Arrays to store object points and image points from all the images.
|
||||
objpoints = [] # 3d point in real world space
|
||||
imgpoints = [] # 2d points in image plane.
|
||||
|
||||
videofile = dataset / "chessboard7.mp4"
|
||||
cap = cv2.VideoCapture(videofile)
|
||||
|
||||
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
dim = {
|
||||
'width': frame_width,
|
||||
'height': frame_height,
|
||||
}
|
||||
found = 0
|
||||
|
||||
p = tqdm.tqdm()
|
||||
p2 = tqdm.tqdm(total=needed_detections)
|
||||
|
||||
first_found=False
|
||||
no_frames_for = 0
|
||||
|
||||
while ((found < needed_detections) if math.isfinite(needed_detections) else True):
|
||||
ret, img = cap.read() # Capture frame-by-frame
|
||||
if not ret:
|
||||
break
|
||||
|
||||
p.update()
|
||||
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
# Find the chess board corners
|
||||
ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD,None)
|
||||
|
||||
# If found, add object points, image points (after refining them)
|
||||
if ret == True:
|
||||
if not first_found:
|
||||
first_found = True
|
||||
print(f"first at {p.n}")
|
||||
no_frames_for = 0
|
||||
objpoints.append(objp) # Certainly, every loop objp is the same, in 3D.
|
||||
corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
|
||||
imgpoints.append(corners2)
|
||||
p2.update()
|
||||
p2.n
|
||||
|
||||
# Draw and display the corners
|
||||
img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret)
|
||||
found += 1
|
||||
else:
|
||||
no_frames_for += 1
|
||||
|
||||
if first_found and no_frames_for > 10:
|
||||
break
|
||||
|
||||
cv2.imshow('img', img)
|
||||
cv2.waitKey(1)
|
||||
|
||||
# When everything done, release the capture
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
print(f"Found {found} detections")
|
||||
|
||||
|
||||
np.savez(dataset / "chessboard7-points.npz", objpoints=objpoints, imgpoints=imgpoints)
|
||||
|
||||
97
uv.lock
Normal file
97
uv.lock
Normal file
|
|
@ -0,0 +1,97 @@
|
|||
version = 1
|
||||
revision = 1
|
||||
requires-python = ">=3.12, <4"
|
||||
resolution-markers = [
|
||||
"sys_platform == 'darwin'",
|
||||
"platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "colorama"
|
||||
version = "0.4.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "2.1.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/4b/d1/8a730ea07f4a37d94f9172f4ce1d81064b7a64766b460378be278952de75/numpy-2.1.2.tar.gz", hash = "sha256:13532a088217fa624c99b843eeb54640de23b3414b14aa66d023805eb731066c", size = 18878063 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a0/7d/554a6838f37f3ada5a55f25173c619d556ae98092a6e01afb6e710501d70/numpy-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d7bf0a4f9f15b32b5ba53147369e94296f5fffb783db5aacc1be15b4bf72f43b", size = 20848077 },
|
||||
{ url = "https://files.pythonhosted.org/packages/b0/29/cb48a402ea879e645b16218718f3f7d9588a77d674a9dcf22e4c43487636/numpy-2.1.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b1d0fcae4f0949f215d4632be684a539859b295e2d0cb14f78ec231915d644db", size = 13493242 },
|
||||
{ url = "https://files.pythonhosted.org/packages/56/44/f899b0581766c230da42f751b7b8896d096640b19b312164c267e48d36cb/numpy-2.1.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:f751ed0a2f250541e19dfca9f1eafa31a392c71c832b6bb9e113b10d050cb0f1", size = 5089219 },
|
||||
{ url = "https://files.pythonhosted.org/packages/79/8f/b987070d45161a7a4504afc67ed38544ed2c0ed5576263599a0402204a9c/numpy-2.1.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:bd33f82e95ba7ad632bc57837ee99dba3d7e006536200c4e9124089e1bf42426", size = 6620167 },
|
||||
{ url = "https://files.pythonhosted.org/packages/c4/a7/af3329fda3c3ec31d9b650e42bbcd3422fc62a765cbb1405fde4177a0996/numpy-2.1.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1b8cde4f11f0a975d1fd59373b32e2f5a562ade7cde4f85b7137f3de8fbb29a0", size = 13604905 },
|
||||
{ url = "https://files.pythonhosted.org/packages/9b/b4/e3c7e6fab0f77fff6194afa173d1f2342073d91b1d3b4b30b17c3fb4407a/numpy-2.1.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d95f286b8244b3649b477ac066c6906fbb2905f8ac19b170e2175d3d799f4df", size = 16041825 },
|
||||
{ url = "https://files.pythonhosted.org/packages/e9/50/6828e66a78aa03147c111f84d55f33ce2dde547cb578d6744a3b06a0124b/numpy-2.1.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:ab4754d432e3ac42d33a269c8567413bdb541689b02d93788af4131018cbf366", size = 16409541 },
|
||||
{ url = "https://files.pythonhosted.org/packages/bf/72/66af7916d9c3c6dbfbc8acdd4930c65461e1953374a2bc43d00f948f004a/numpy-2.1.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e585c8ae871fd38ac50598f4763d73ec5497b0de9a0ab4ef5b69f01c6a046142", size = 14081134 },
|
||||
{ url = "https://files.pythonhosted.org/packages/dc/5a/59a67d84f33fe00ae74f0b5b69dd4f93a586a4aba7f7e19b54b2133db038/numpy-2.1.2-cp312-cp312-win32.whl", hash = "sha256:9c6c754df29ce6a89ed23afb25550d1c2d5fdb9901d9c67a16e0b16eaf7e2550", size = 6237784 },
|
||||
{ url = "https://files.pythonhosted.org/packages/4c/79/73735a6a5dad6059c085f240a4e74c9270feccd2bc66e4d31b5ca01d329c/numpy-2.1.2-cp312-cp312-win_amd64.whl", hash = "sha256:456e3b11cb79ac9946c822a56346ec80275eaf2950314b249b512896c0d2505e", size = 12568254 },
|
||||
{ url = "https://files.pythonhosted.org/packages/16/72/716fa1dbe92395a9a623d5049203ff8ddb0cfce65b9df9117c3696ccc011/numpy-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a84498e0d0a1174f2b3ed769b67b656aa5460c92c9554039e11f20a05650f00d", size = 20834690 },
|
||||
{ url = "https://files.pythonhosted.org/packages/1e/fb/3e85a39511586053b5c6a59a643879e376fae22230ebfef9cfabb0e032e2/numpy-2.1.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4d6ec0d4222e8ffdab1744da2560f07856421b367928026fb540e1945f2eeeaf", size = 13507474 },
|
||||
{ url = "https://files.pythonhosted.org/packages/35/eb/5677556d9ba13436dab51e129f98d4829d95cd1b6bd0e199c14485a4bdb9/numpy-2.1.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:259ec80d54999cc34cd1eb8ded513cb053c3bf4829152a2e00de2371bd406f5e", size = 5074742 },
|
||||
{ url = "https://files.pythonhosted.org/packages/3e/c5/6c5ef5ba41b65a7e51bed50dbf3e1483eb578055633dd013e811a28e96a1/numpy-2.1.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:675c741d4739af2dc20cd6c6a5c4b7355c728167845e3c6b0e824e4e5d36a6c3", size = 6606787 },
|
||||
{ url = "https://files.pythonhosted.org/packages/08/ac/f2f29dd4fd325b379c7dc932a0ebab22f0e031dbe80b2f6019b291a3a544/numpy-2.1.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:05b2d4e667895cc55e3ff2b56077e4c8a5604361fc21a042845ea3ad67465aa8", size = 13601333 },
|
||||
{ url = "https://files.pythonhosted.org/packages/44/26/63f5f4e5089654dfb858f4892215ed968cd1a68e6f4a83f9961f84f855cb/numpy-2.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:43cca367bf94a14aca50b89e9bc2061683116cfe864e56740e083392f533ce7a", size = 16038090 },
|
||||
{ url = "https://files.pythonhosted.org/packages/1d/21/015e0594de9c3a8d5edd24943d2bd23f102ec71aec026083f822f86497e2/numpy-2.1.2-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:76322dcdb16fccf2ac56f99048af32259dcc488d9b7e25b51e5eca5147a3fb98", size = 16410865 },
|
||||
{ url = "https://files.pythonhosted.org/packages/df/01/c1bcf9e6025d79077fbf3f3ee503b50aa7bfabfcd8f4b54f5829f4c00f3f/numpy-2.1.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:32e16a03138cabe0cb28e1007ee82264296ac0983714094380b408097a418cfe", size = 14078077 },
|
||||
{ url = "https://files.pythonhosted.org/packages/ba/06/db9d127d63bd11591770ba9f3d960f8041e0f895184b9351d4b1b5b56983/numpy-2.1.2-cp313-cp313-win32.whl", hash = "sha256:242b39d00e4944431a3cd2db2f5377e15b5785920421993770cddb89992c3f3a", size = 6234904 },
|
||||
{ url = "https://files.pythonhosted.org/packages/a9/96/9f61f8f95b6e0ea0aa08633b704c75d1882bdcb331bdf8bfd63263b25b00/numpy-2.1.2-cp313-cp313-win_amd64.whl", hash = "sha256:f2ded8d9b6f68cc26f8425eda5d3877b47343e68ca23d0d0846f4d312ecaa445", size = 12561910 },
|
||||
{ url = "https://files.pythonhosted.org/packages/36/b8/033f627821784a48e8f75c218033471eebbaacdd933f8979c79637a1b44b/numpy-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:2ffef621c14ebb0188a8633348504a35c13680d6da93ab5cb86f4e54b7e922b5", size = 20857719 },
|
||||
{ url = "https://files.pythonhosted.org/packages/96/46/af5726fde5b74ed83f2f17a73386d399319b7ed4d51279fb23b721d0816d/numpy-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:ad369ed238b1959dfbade9018a740fb9392c5ac4f9b5173f420bd4f37ba1f7a0", size = 13518826 },
|
||||
{ url = "https://files.pythonhosted.org/packages/db/6e/8ce677edf36da1c4dae80afe5529f47690697eb55b4864673af260ccea7b/numpy-2.1.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:d82075752f40c0ddf57e6e02673a17f6cb0f8eb3f587f63ca1eaab5594da5b17", size = 5115036 },
|
||||
{ url = "https://files.pythonhosted.org/packages/6a/ba/3cce44fb1b8438042c11847048812a776f75ee0e7070179c22e4cfbf420c/numpy-2.1.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:1600068c262af1ca9580a527d43dc9d959b0b1d8e56f8a05d830eea39b7c8af6", size = 6628641 },
|
||||
{ url = "https://files.pythonhosted.org/packages/59/c8/e722998720ccbd35ffbcf1d1b8ed0aa2304af88d3f1c38e06ebf983599b3/numpy-2.1.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a26ae94658d3ba3781d5e103ac07a876b3e9b29db53f68ed7df432fd033358a8", size = 13574803 },
|
||||
{ url = "https://files.pythonhosted.org/packages/7c/8e/fc1fdd83a55476765329ac2913321c4aed5b082a7915095628c4ca30ea72/numpy-2.1.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13311c2db4c5f7609b462bc0f43d3c465424d25c626d95040f073e30f7570e35", size = 16021174 },
|
||||
{ url = "https://files.pythonhosted.org/packages/2a/b6/a790742aa88067adb4bd6c89a946778c1417d4deaeafce3ca928f26d4c52/numpy-2.1.2-cp313-cp313t-musllinux_1_1_x86_64.whl", hash = "sha256:2abbf905a0b568706391ec6fa15161fad0fb5d8b68d73c461b3c1bab6064dd62", size = 16400117 },
|
||||
{ url = "https://files.pythonhosted.org/packages/48/6f/129e3c17e3befe7fefdeaa6890f4c4df3f3cf0831aa053802c3862da67aa/numpy-2.1.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:ef444c57d664d35cac4e18c298c47d7b504c66b17c2ea91312e979fcfbdfb08a", size = 14066202 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "opencv-python"
|
||||
version = "4.10.0.84"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/b70a2d9ab205110d715906fc8ec83fbb00404aeb3a37a0654fdb68eb0c8c/opencv-python-4.10.0.84.tar.gz", hash = "sha256:72d234e4582e9658ffea8e9cae5b63d488ad06994ef12d81dc303b17472f3526", size = 95103981 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/66/82/564168a349148298aca281e342551404ef5521f33fba17b388ead0a84dc5/opencv_python-4.10.0.84-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:fc182f8f4cda51b45f01c64e4cbedfc2f00aff799debebc305d8d0210c43f251", size = 54835524 },
|
||||
{ url = "https://files.pythonhosted.org/packages/64/4a/016cda9ad7cf18c58ba074628a4eaae8aa55f3fd06a266398cef8831a5b9/opencv_python-4.10.0.84-cp37-abi3-macosx_12_0_x86_64.whl", hash = "sha256:71e575744f1d23f79741450254660442785f45a0797212852ee5199ef12eed98", size = 56475426 },
|
||||
{ url = "https://files.pythonhosted.org/packages/81/e4/7a987ebecfe5ceaf32db413b67ff18eb3092c598408862fff4d7cc3fd19b/opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:09a332b50488e2dda866a6c5573ee192fe3583239fb26ff2f7f9ceb0bc119ea6", size = 41746971 },
|
||||
{ url = "https://files.pythonhosted.org/packages/3f/a4/d2537f47fd7fcfba966bd806e3ec18e7ee1681056d4b0a9c8d983983e4d5/opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ace140fc6d647fbe1c692bcb2abce768973491222c067c131d80957c595b71f", size = 62548253 },
|
||||
{ url = "https://files.pythonhosted.org/packages/1e/39/bbf57e7b9dab623e8773f6ff36385456b7ae7fa9357a5e53db732c347eac/opencv_python-4.10.0.84-cp37-abi3-win32.whl", hash = "sha256:2db02bb7e50b703f0a2d50c50ced72e95c574e1e5a0bb35a8a86d0b35c98c236", size = 28737688 },
|
||||
{ url = "https://files.pythonhosted.org/packages/ec/6c/fab8113424af5049f85717e8e527ca3773299a3c6b02506e66436e19874f/opencv_python-4.10.0.84-cp37-abi3-win_amd64.whl", hash = "sha256:32dbbd94c26f611dc5cc6979e6b7aa1f55a64d6b463cc1dcd3c95505a63e48fe", size = 38842521 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
version = "4.66.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/e9/34/bef135b27fe1864993a5284ad001157ee9b5538e859ac90f5b0e8cc8c9ec/tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090", size = 169533 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/41/73/02342de9c2d20922115f787e101527b831c0cffd2105c946c4a4826bcfd4/tqdm-4.66.6-py3-none-any.whl", hash = "sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63", size = 78326 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "traptools"
|
||||
version = "0.1.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "opencv-python" },
|
||||
{ name = "tqdm" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "opencv-python", specifier = ">=4.10.0.84,<5" },
|
||||
{ name = "tqdm", specifier = ">=4.66.6,<5" },
|
||||
]
|
||||
Loading…
Reference in a new issue