[calib] update calibration
This commit is contained in:
shuaiqing
parent
a0653e2d4f
commit
719c3d1f68
@ -2,17 +2,85 @@
|
||||
@ Date: 2021-03-02 16:12:59
|
||||
@ Author: Qing Shuai
|
||||
@ LastEditors: Qing Shuai
|
||||
@ LastEditTime: 2021-03-02 16:12:59
|
||||
@ FilePath: /EasyMocap/scripts/calibration/calib_intri.py
|
||||
@ LastEditTime: 2021-05-26 23:22:26
|
||||
@ FilePath: /EasyMocap/apps/calibration/calib_intri.py
|
||||
'''
|
||||
# This script calibrate each intrinsic parameters
|
||||
from easymocap.mytools import write_intri
|
||||
from easymocap.mytools.vis_base import plot_points2d
|
||||
from easymocap.mytools import write_intri, read_json, Timer
|
||||
import numpy as np
|
||||
import cv2
|
||||
import os
|
||||
from os.path import join
|
||||
from glob import glob
|
||||
from easymocap.mytools import read_json, Timer
|
||||
from easymocap.annotator.chessboard import get_lines_chessboard
|
||||
|
||||
def read_chess(chessname):
|
||||
data = read_json(chessname)
|
||||
k3d = np.array(data['keypoints3d'], dtype=np.float32)
|
||||
k2d = np.array(data['keypoints2d'], dtype=np.float32)
|
||||
if k2d[:, -1].sum() < 0.01:
|
||||
return False, k2d, k3d
|
||||
return True, k2d, k3d
|
||||
|
||||
def calib_intri_share(path, step):
|
||||
camnames = sorted(os.listdir(join(path, 'images')))
|
||||
imagenames = sorted(glob(join(path, 'images', '*', '*.jpg')))
|
||||
chessnames = sorted(glob(join(path, 'chessboard', '*', '*.json')))
|
||||
k3ds_, k2ds_, imgs = [], [], []
|
||||
valid_idx = []
|
||||
for i, chessname in enumerate(chessnames):
|
||||
flag, k2d, k3d = read_chess(chessname)
|
||||
k3ds_.append(k3d)
|
||||
k2ds_.append(k2d)
|
||||
if not flag:
|
||||
continue
|
||||
valid_idx.append(i)
|
||||
MAX_ERROR_PIXEL = 1.
|
||||
lines, line_cols = get_lines_chessboard()
|
||||
valid_idx = valid_idx[::step]
|
||||
len_valid = len(valid_idx)
|
||||
cameras = {}
|
||||
while True:
|
||||
# sample
|
||||
imgs = [imagenames[i] for i in valid_idx]
|
||||
k3ds = [k3ds_[i] for i in valid_idx]
|
||||
k2ds = [np.ascontiguousarray(k2ds_[i][:, :-1]) for i in valid_idx]
|
||||
gray = cv2.imread(imgs[0], 0)
|
||||
print('>> Detect {:3d} frames'.format(len(valid_idx)))
|
||||
with Timer('calibrate'):
|
||||
ret, K, dist, rvecs, tvecs = cv2.calibrateCamera(
|
||||
k3ds, k2ds, gray.shape[::-1], None, None)
|
||||
with Timer('check'):
|
||||
removed = []
|
||||
for i in range(len(imgs)):
|
||||
img = cv2.imread(imgs[i])
|
||||
points2d_repro, _ = cv2.projectPoints(
|
||||
k3ds[i], rvecs[i], tvecs[i], K, dist)
|
||||
points2d_repro = points2d_repro.squeeze()
|
||||
points2d = k2ds_[valid_idx[i]]
|
||||
err = np.linalg.norm(points2d_repro - points2d[:, :2], axis=1).mean()
|
||||
plot_points2d(img, points2d_repro, lines, col=(0, 0, 255), lw=1, putText=False)
|
||||
plot_points2d(img, points2d, lines, lw=1, putText=False)
|
||||
print(imgs[i], err)
|
||||
# cv2.imshow('vis', img)
|
||||
# cv2.waitKey(0)
|
||||
if err > MAX_ERROR_PIXEL:
|
||||
removed.append(i)
|
||||
for i in removed[::-1]:
|
||||
valid_idx.pop(i)
|
||||
if len_valid == len(valid_idx) or not args.remove:
|
||||
print(K)
|
||||
print(dist)
|
||||
for cam in camnames:
|
||||
cameras[cam] = {
|
||||
'K': K,
|
||||
'dist': dist # dist: (1, 5)
|
||||
}
|
||||
break
|
||||
len_valid = len(valid_idx)
|
||||
write_intri(join(path, 'output', 'intri.yml'), cameras)
|
||||
|
||||
|
||||
def calib_intri(path, step):
|
||||
camnames = sorted(os.listdir(join(path, 'images')))
|
||||
@ -22,11 +90,8 @@ def calib_intri(path, step):
|
||||
chessnames = sorted(glob(join(path, 'chessboard', cam, '*.json')))
|
||||
k3ds, k2ds = [], []
|
||||
for chessname in chessnames[::step]:
|
||||
data = read_json(chessname)
|
||||
k3d = np.array(data['keypoints3d'], dtype=np.float32)
|
||||
k2d = np.array(data['keypoints2d'], dtype=np.float32)
|
||||
if k2d[:, -1].sum() < 0.01:
|
||||
continue
|
||||
flag, k2d, k3d = read_chess(chessname)
|
||||
if not flag:continue
|
||||
k3ds.append(k3d)
|
||||
k2ds.append(np.ascontiguousarray(k2d[:, :-1]))
|
||||
gray = cv2.imread(imagenames[0], 0)
|
||||
@ -40,11 +105,17 @@ def calib_intri(path, step):
|
||||
}
|
||||
write_intri(join(path, 'output', 'intri.yml'), cameras)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('path', type=str, default='/home/')
|
||||
parser.add_argument('--step', type=int, default=1)
|
||||
parser.add_argument('--share_intri', action='store_true')
|
||||
parser.add_argument('--debug', action='store_true')
|
||||
parser.add_argument('--remove', action='store_true')
|
||||
args = parser.parse_args()
|
||||
if args.share_intri:
|
||||
calib_intri_share(args.path, step=args.step)
|
||||
else:
|
||||
calib_intri(args.path, step=args.step)
|
||||
@ -2,8 +2,8 @@
|
||||
@ Date: 2021-03-27 19:13:50
|
||||
@ Author: Qing Shuai
|
||||
@ LastEditors: Qing Shuai
|
||||
@ LastEditTime: 2021-04-02 22:01:10
|
||||
@ FilePath: /EasyMocap/scripts/calibration/check_calib.py
|
||||
@ LastEditTime: 2021-04-15 22:53:23
|
||||
@ FilePath: /EasyMocap/apps/calibration/check_calib.py
|
||||
'''
|
||||
import cv2
|
||||
import numpy as np
|
||||
@ -14,7 +14,21 @@ from easymocap.mytools import read_camera, plot_points2d
|
||||
from easymocap.mytools import batch_triangulate, projectN3, Undistort
|
||||
from tqdm import tqdm
|
||||
|
||||
def load_grids():
|
||||
POINTS_SQUARE = np.array([
|
||||
[0., 0., 0.],
|
||||
[1., 0., 0.],
|
||||
[1., 1., 0.],
|
||||
[0., 1., 0.]
|
||||
])
|
||||
|
||||
LINES_SQUARE = np.array([
|
||||
[0, 1],
|
||||
[1, 2],
|
||||
[2, 3],
|
||||
[3, 0]
|
||||
])
|
||||
|
||||
def load_cube():
|
||||
points3d = np.array([
|
||||
[0., 0., 0.],
|
||||
[1., 0., 0.],
|
||||
@ -42,6 +56,39 @@ def load_grids():
|
||||
points3d = np.hstack((points3d, np.ones((points3d.shape[0], 1))))
|
||||
return points3d, lines
|
||||
|
||||
def merge_points_lines(points3d, lines):
|
||||
dist = np.linalg.norm(points3d[:, None, :] - points3d[None, :, :], axis=-1)
|
||||
mapid = np.arange(points3d.shape[0])
|
||||
for i in range(dist.shape[0]):
|
||||
if mapid[i] != i:
|
||||
continue
|
||||
equal = np.where(dist[i] < 1e-3)[0]
|
||||
for j in equal:
|
||||
if j == i:
|
||||
continue
|
||||
mapid[j] = i
|
||||
newid = sorted(list(set(mapid)))
|
||||
newpoints = points3d[newid]
|
||||
for i, newi in enumerate(newid):
|
||||
mapid[mapid==newi] = i
|
||||
return newpoints, mapid[lines]
|
||||
|
||||
def load_grid(xrange=10, yrange=10):
|
||||
start = np.array([0., 0., 0.])
|
||||
xdir = np.array([1., 0., 0.])
|
||||
ydir = np.array([0., 1., 0.])
|
||||
stepx = 1.
|
||||
stepy = 1.
|
||||
points3d, lines = [], []
|
||||
for i in range(xrange):
|
||||
for j in range(yrange):
|
||||
base = start + xdir*i*stepx + ydir*j*stepy
|
||||
points3d.append(POINTS_SQUARE+base)
|
||||
lines.append(LINES_SQUARE+4*(i*yrange+j))
|
||||
points3d = np.vstack(points3d)
|
||||
lines = np.vstack(lines)
|
||||
return merge_points_lines(points3d, lines)
|
||||
|
||||
def check_calib(path, out, vis=False, show=False, debug=False):
|
||||
if vis:
|
||||
out_dir = join(out, 'check')
|
||||
@ -91,10 +138,9 @@ def check_calib(path, out, vis=False, show=False, debug=False):
|
||||
cv2.imwrite(outname, imgout)
|
||||
print('{:.2f}/{} = {:.2f} pixel'.format(total_sum, int(cnt), total_sum/cnt))
|
||||
|
||||
def check_scene(path, out):
|
||||
def check_scene(path, out, points3d, lines):
|
||||
cameras = read_camera(join(out, 'intri.yml'), join(out, 'extri.yml'))
|
||||
cameras.pop('basenames')
|
||||
points3d, lines = load_grids()
|
||||
nf = 0
|
||||
for cam, camera in cameras.items():
|
||||
imgname = join(path, 'images', cam, '{:06d}.jpg'.format(nf))
|
||||
@ -106,6 +152,37 @@ def check_scene(path, out):
|
||||
cv2.imshow('vis', img)
|
||||
cv2.waitKey(0)
|
||||
|
||||
def check_match(path, out):
|
||||
os.makedirs(out, exist_ok=True)
|
||||
cameras = read_camera(join(path, 'intri.yml'), join(path, 'extri.yml'))
|
||||
cams = cameras.pop('basenames')
|
||||
annots = read_json(join(path, 'calib.json'))
|
||||
points_global = annots['points_global']
|
||||
points3d = np.ones((len(points_global), 4))
|
||||
# first triangulate
|
||||
points2d = np.zeros((len(cams), len(points_global), 3))
|
||||
for i, record in enumerate(points_global):
|
||||
for cam, (x, y) in record.items():
|
||||
points2d[cams.index(cam), i] = (x, y, 1)
|
||||
# 2. undistort
|
||||
for nv in range(points2d.shape[0]):
|
||||
camera = cameras[cams[nv]]
|
||||
points2d[nv] = Undistort.points(points2d[nv], camera['K'], camera['dist'])
|
||||
Pall = np.stack([cameras[cam]['P'] for cam in cams])
|
||||
points3d = batch_triangulate(points2d, Pall)
|
||||
lines = []
|
||||
nf = 0
|
||||
for cam, camera in cameras.items():
|
||||
imgname = join(path, 'images', cam, '{:06d}.jpg'.format(nf))
|
||||
assert os.path.exists(imgname), imgname
|
||||
img = cv2.imread(imgname)
|
||||
img = Undistort.image(img, camera['K'], camera['dist'])
|
||||
kpts_repro = projectN3(points3d, camera['P'][None, :, :])[0]
|
||||
plot_points2d(img, kpts_repro, lines, col=(0, 0, 255), lw=1, putText=True)
|
||||
plot_points2d(img, points2d[cams.index(cam)], lines, col=(0, 255, 0), lw=1, putText=True)
|
||||
outname = join(out, cam+'.jpg')
|
||||
cv2.imwrite(outname, img)
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser()
|
||||
@ -117,9 +194,17 @@ if __name__ == "__main__":
|
||||
parser.add_argument('--show', action='store_true')
|
||||
parser.add_argument('--debug', action='store_true')
|
||||
parser.add_argument('--cube', action='store_true')
|
||||
parser.add_argument('--grid', action='store_true')
|
||||
parser.add_argument('--calib', action='store_true')
|
||||
|
||||
args = parser.parse_args()
|
||||
if args.cube:
|
||||
check_scene(args.path, args.out)
|
||||
points, lines = load_cube()
|
||||
check_scene(args.path, args.out, points, lines)
|
||||
elif args.grid:
|
||||
points, lines = load_grid(xrange=15, yrange=14)
|
||||
check_scene(args.path, args.out, points, lines)
|
||||
elif args.calib:
|
||||
check_match(args.path, args.out)
|
||||
else:
|
||||
check_calib(args.path, args.out, args.vis, args.show, args.debug)
|
||||
@ -1,32 +1,25 @@
|
||||
# detect the corner of chessboard
|
||||
from easymocap.annotator.file_utils import getFileList, read_json, save_json
|
||||
from tqdm import tqdm
|
||||
from easymocap.annotator import ImageFolder, findChessboardCorners
|
||||
from easymocap.annotator import ImageFolder
|
||||
from easymocap.annotator.chessboard import getChessboard3d, findChessboardCorners
|
||||
import numpy as np
|
||||
from os.path import join
|
||||
import cv2
|
||||
import os
|
||||
|
||||
def get_object(pattern, gridSize):
|
||||
object_points = np.zeros((pattern[1]*pattern[0], 3), np.float32)
|
||||
# 注意：这里为了让标定板z轴朝上，设定了短边是x，长边是y
|
||||
object_points[:,:2] = np.mgrid[0:pattern[0], 0:pattern[1]].T.reshape(-1,2)
|
||||
object_points[:, [0, 1]] = object_points[:, [1, 0]]
|
||||
object_points = object_points * gridSize
|
||||
return object_points
|
||||
|
||||
def create_chessboard(path, pattern, gridSize):
|
||||
def create_chessboard(path, pattern, gridSize, ext):
|
||||
print('Create chessboard {}'.format(pattern))
|
||||
keypoints3d = get_object(pattern, gridSize=gridSize)
|
||||
keypoints3d = getChessboard3d(pattern, gridSize=gridSize)
|
||||
keypoints2d = np.zeros((keypoints3d.shape[0], 3))
|
||||
imgnames = getFileList(path, ext='.jpg')
|
||||
imgnames = getFileList(path, ext=ext)
|
||||
template = {
|
||||
'keypoints3d': keypoints3d.tolist(),
|
||||
'keypoints2d': keypoints2d.tolist(),
|
||||
'visited': False
|
||||
}
|
||||
for imgname in tqdm(imgnames, desc='create template chessboard'):
|
||||
annname = imgname.replace('images', 'chessboard').replace('.jpg', '.json')
|
||||
annname = imgname.replace('images', 'chessboard').replace(ext, '.json')
|
||||
annname = join(path, annname)
|
||||
if os.path.exists(annname):
|
||||
# 覆盖keypoints3d
|
||||
@ -37,8 +30,8 @@ def create_chessboard(path, pattern, gridSize):
|
||||
save_json(annname, template)
|
||||
|
||||
def detect_chessboard(path, out, pattern, gridSize):
|
||||
create_chessboard(path, pattern, gridSize)
|
||||
dataset = ImageFolder(path, annot='chessboard')
|
||||
create_chessboard(path, pattern, gridSize, ext=args.ext)
|
||||
dataset = ImageFolder(path, annot='chessboard', ext=args.ext)
|
||||
dataset.isTmp = False
|
||||
for i in tqdm(range(len(dataset))):
|
||||
imgname, annotname = dataset[i]
|
||||
@ -58,6 +51,7 @@ if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('path', type=str)
|
||||
parser.add_argument('--out', type=str)
|
||||
parser.add_argument('--ext', type=str, default='.jpg', choices=['.jpg', '.png'])
|
||||
parser.add_argument('--pattern', type=lambda x: (int(x.split(',')[0]), int(x.split(',')[1])),
|
||||
help='The pattern of the chessboard', default=(9, 6))
|
||||
parser.add_argument('--grid', type=float, default=0.1,
|
||||
|
||||
@ -1,6 +1,40 @@
|
||||
'''
|
||||
@ Date: 2021-04-13 16:14:36
|
||||
@ Author: Qing Shuai
|
||||
@ LastEditors: Qing Shuai
|
||||
@ LastEditTime: 2021-05-26 15:42:02
|
||||
@ FilePath: /EasyMocap/easymocap/annotator/chessboard.py
|
||||
'''
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
def getChessboard3d(pattern, gridSize):
|
||||
object_points = np.zeros((pattern[1]*pattern[0], 3), np.float32)
|
||||
# 注意：这里为了让标定板z轴朝上，设定了短边是x，长边是y
|
||||
object_points[:,:2] = np.mgrid[0:pattern[0], 0:pattern[1]].T.reshape(-1,2)
|
||||
object_points[:, [0, 1]] = object_points[:, [1, 0]]
|
||||
object_points = object_points * gridSize
|
||||
return object_points
|
||||
|
||||
colors_chessboard_bar = [
|
||||
[0, 0, 255],
|
||||
[0, 128, 255],
|
||||
[0, 200, 200],
|
||||
[0, 255, 0],
|
||||
[200, 200, 0],
|
||||
[255, 0, 0],
|
||||
[255, 0, 250]
|
||||
]
|
||||
|
||||
def get_lines_chessboard(pattern=(9, 6)):
|
||||
w, h = pattern[0], pattern[1]
|
||||
lines = []
|
||||
lines_cols = []
|
||||
for i in range(w*h-1):
|
||||
lines.append([i, i+1])
|
||||
lines_cols.append(colors_chessboard_bar[i//w])
|
||||
return lines, lines_cols
|
||||
|
||||
def _findChessboardCorners(img, pattern):
|
||||
"basic function"
|
||||
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user