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StereoCharuco.py
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''' CONFIDENTIAL
Copyright (c) 2021 Eugeniu Vezeteu,
Department of Remote Sensing and Photogrammetry,
Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS)
PERMISSION IS HEREBY LIMITED TO FGI'S INTERNAL USE ONLY. THE CODE
MAY BE RE-LICENSED, SHARED, OR TAKEN INTO OTHER USE ONLY WITH
A WRITTEN CONSENT FROM THE HEAD OF THE DEPARTMENT.
The software is provided "as is", without warranty of any kind, express or
implied, including but not limited to the warranties of merchantability,
fitness for a particular purpose and noninfringement. In no event shall the
authors or copyright holders be liable for any claim, damages or other
liability, whether in an action of contract, tort or otherwise, arising from,
out of or in connection with the software or the use or other dealings in the
software.
'''
import numpy as np
import cv2
import glob
import pickle
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
import cv2.aruco as aruco
from utils import *
np.set_printoptions(suppress=True)
class StereoCharuco_Calibrator(object):
def __init__(self, filepath, name = ''):
self.term_criteria=(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 1000, 0.0001)
self.objpoints = []
self.imgpoints_l = []
self.imgpoints_r = []
self.cal_path = filepath
self.image_width = 1936
self.image_height = 1216
self.image_size = None
self.fx = 0.25
self.fy = 0.3
self.position = (20, 30)
self.see = True
self.flipVertically = True
self.name = name
self.K_left,self.K_right,self.D_left,self.D_right = None,None,None,None
self.total = 0
def createCalibrationBoard(self, squaresY = 9, squaresX = 12, squareLength = .06, markerLength = 0.045, display=False):
self.ARUCO_DICT = aruco.Dictionary_get(aruco.DICT_5X5_1000)
self.CHARUCO_BOARD = aruco.CharucoBoard_create(
squaresX=squaresX, squaresY=squaresY,
squareLength=squareLength,
markerLength=markerLength,
dictionary=self.ARUCO_DICT)
self.pattern_columns = squaresX
self.pattern_rows = squaresY
self.distance_in_world_units = squareLength
if display:
imboard = self.CHARUCO_BOARD.draw((900, 700))
cv2.imshow('CharucoBoard target', imboard)
cv2.waitKey(0)
cv2.destroyAllWindows()
def readMonoData(self):
left_outside = load_obj(name='charuco_{}_left'.format(self.name))
self.K_left = left_outside['K']
self.D_left = left_outside['D']
right_outside = load_obj(name='charuco_{}_right'.format(self.name))
self.K_right = right_outside['K']
self.D_right = right_outside['D']
print("Left K:\n", self.K_left)
print("Left distortion : ", self.D_left.ravel())
print("Right K:\n", self.K_right)
print("Right distortion : ", self.D_right.ravel())
print('Loaded mono internal calibration data')
def readStereoData(self):
camera_model = load_obj('{}_charuco_camera_model'.format(self.name ))
camera_model_rectify = load_obj('{}_charuco_camera_model_rectify'.format(self.name))
self.K_left = camera_model['K_left']
self.K_right = camera_model['K_right']
self.D_left = camera_model['D_left']
self.D_right = camera_model['D_right']
self.R = camera_model['R']
self.T = camera_model['T']
self.Q = camera_model_rectify['Q']
self.roi_left, self.roi_right = camera_model_rectify['roi_left'],camera_model_rectify['roi_right']
self.leftMapX, self.leftMapY = camera_model_rectify['leftMapX'], camera_model_rectify['leftMapY']
self.rightMapX, self.rightMapY = camera_model_rectify['rightMapX'], camera_model_rectify['rightMapY']
print('Rotation R')
print(self.R)
print('Translation T')
print(self.T)
print('Stereo data has been loaded...')
def read_images(self, test=False, limit = 9 ):
images_right = glob.glob(self.cal_path + '/Right/*.png')
images_left = glob.glob(self.cal_path + '/Left/*.png')
images_left.sort()
images_right.sort()
self.LeftImg, self.RightImg = [], []
h, w = self.CHARUCO_BOARD.chessboardCorners.shape
wait = 0
for i, fname in enumerate(images_right):
img_l = cv2.imread(images_left[i])
img_r = cv2.imread(images_right[i])
if self.flipVertically:
img_l = cv2.flip(img_l, -1)
img_r = cv2.flip(img_r, -1)
self.LeftImg.append(img_l)
self.RightImg.append(img_r)
gray_l = cv2.cvtColor(img_l, cv2.COLOR_BGR2GRAY)
gray_r = cv2.cvtColor(img_r, cv2.COLOR_BGR2GRAY)
if test:
ret_l = True
ret_r = True
else:
cornersL, idsL, rejectedL = aruco.detectMarkers(image=gray_l, dictionary=self.ARUCO_DICT)
cornersL, idsL, rejectedImgPointsL, recoveredIdsL = aruco.refineDetectedMarkers(image=gray_l,
board=self.CHARUCO_BOARD,
detectedCorners=cornersL,
detectedIds=idsL,
rejectedCorners=rejectedL,
cameraMatrix=self.K_left,
distCoeffs=self.D_left)
ret_l = len(cornersL) >= limit
cornersR, idsR, rejectedR = aruco.detectMarkers(image=gray_r, dictionary=self.ARUCO_DICT)
cornersR, idsR, rejectedImgPointsR, recoveredIdsR = aruco.refineDetectedMarkers(image=gray_r,
board=self.CHARUCO_BOARD,
detectedCorners=cornersR,
detectedIds=idsR,
rejectedCorners=rejectedR,
cameraMatrix=self.K_right,
distCoeffs=self.D_right)
ret_r = len(cornersR) >= limit
if ret_l and ret_r:
if test==False:
img_l = aruco.drawDetectedMarkers(image=img_l, corners=cornersL)
img_r = aruco.drawDetectedMarkers(image=img_r, corners=cornersR)
responseL, charuco_cornersL, charuco_idsL = aruco.interpolateCornersCharuco(markerCorners=cornersL, markerIds=idsL,
image=gray_l, board=self.CHARUCO_BOARD)
responseR, charuco_cornersR, charuco_idsR = aruco.interpolateCornersCharuco(markerCorners=cornersR,
markerIds=idsR,
image=gray_r,
board=self.CHARUCO_BOARD)
if responseL >= limit and responseR >= limit:
imgPtsL = np.array(charuco_cornersL)
imgPtsR = np.array(charuco_cornersR)
objPtsL = self.CHARUCO_BOARD.chessboardCorners.reshape((h, 1, 3))[
np.asarray(charuco_idsL).squeeze()]
objPtsR = self.CHARUCO_BOARD.chessboardCorners.reshape((h, 1, 3))[
np.asarray(charuco_idsR).squeeze()]
if objPtsL is not None and objPtsR is not None:
if len(objPtsL) > 1 and len(objPtsR) >= limit:
#print('objPtsL:{},imgPtsL:{}'.format(np.shape(objPtsL), np.shape(imgPtsL)))
#print('objPtsR:{},imgPtsR:{}'.format(np.shape(objPtsR), np.shape(imgPtsR)))
ptsL = {tuple(a): tuple(b) for a, b in zip(objPtsL[:, 0], imgPtsL[:, 0])}
ptsR = {tuple(a): tuple(b) for a, b in zip(objPtsR[:, 0], imgPtsR[:, 0])}
common = set(ptsL.keys()) & set(ptsR.keys()) # intersection between obj points
#print('common {}'.format(len(common)))
#print('')
if len(common) >= limit:
obj3D, imgL_2D, imgR_2D = [], [], []
for objP in common:
_3Dpoint = np.reshape(objP, (1, 3))
_2DpointsL = np.reshape(ptsL[objP], (1, 2))
_2DpointsR = np.reshape(ptsR[objP], (1, 2))
obj3D.append(_3Dpoint)
imgL_2D.append(_2DpointsL)
imgR_2D.append(_2DpointsR)
self.objpoints.append(np.array(obj3D))
self.imgpoints_l.append(np.array(imgL_2D))
self.imgpoints_r.append(np.array(imgR_2D))
self.total+=len(obj3D)
for i in imgL_2D:
(x, y) = i.ravel()
cv2.circle(img_l, (x, y), 3, (0, 0, 255), 3)
for i in imgR_2D:
(x, y) = i.ravel()
cv2.circle(img_r, (x, y), 3, (0, 0, 255), 3)
if self.see:
cam_right_resized = cv2.resize(img_r, None, fx=self.fx, fy=self.fy)
cam_left_resized = cv2.resize(img_l, None, fx=self.fx, fy=self.fy)
cv2.putText(cam_left_resized, "Left", self.position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255, 0))
cv2.putText(cam_right_resized, "Right", self.position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255, 0))
im_h = cv2.hconcat([cam_left_resized, cam_right_resized])
cv2.imshow('Stereo camera', im_h)
k = cv2.waitKey(wait)
if k % 256 == 32: # pressed space
self.see = False
cv2.destroyAllWindows()
elif k & 0xFF == ord('q'):
wait = 50
self.img_shape = gray_l.shape[::-1]
cv2.destroyAllWindows()
def stereoCalibrate(self, flags=None, save=False):
if flags is None:
flags = 0
flags |= cv2.CALIB_USE_INTRINSIC_GUESS
#flags |= cv2.CALIB_FIX_FOCAL_LENGTH
#flags |= cv2.CALIB_FIX_ASPECT_RATIO
print('Stereo calib with objpoints:{}, imgpoints_l:{}, imgpoints_r:{}'.format(np.shape(self.objpoints), np.shape(self.imgpoints_l), np.shape(self.imgpoints_r,)))
self.rms_stereo, self.K_left, self.D_left, self.K_right, self.D_right, self.R, self.T, self.E, self.F = cv2.stereoCalibrate(
self.objpoints, self.imgpoints_l,
self.imgpoints_r, self.K_left, self.D_left, self.K_right, self.D_right, self.img_shape,
criteria=self.term_criteria, flags=flags)
print('Stereo calibraion done')
print('rms_stereo:{}'.format(self.rms_stereo))
print('Rotation R')
print(self.R)
print('Translation T')
print(self.T)
R1, R2, P1, P2, self.Q, self.roi_left, self.roi_right = cv2.stereoRectify(self.K_left, self.D_left, self.K_right,
self.D_right, self.img_shape, self.R, self.T,
flags=cv2.CALIB_ZERO_DISPARITY, alpha=-1)
self.leftMapX, self.leftMapY = cv2.initUndistortRectifyMap(
self.K_left, self.D_left, R1,
P1, self.img_shape, cv2.CV_32FC1)
self.rightMapX, self.rightMapY = cv2.initUndistortRectifyMap(
self.K_right, self.D_right, R2,
P2, self.img_shape, cv2.CV_32FC1)
if save:
camera_model_rectify = dict([('R1', R1), ('R2', R2), ('P1', P1),
('P2', P2), ('Q', self.Q),
('roi_left', self.roi_left), ('roi_right', self.roi_right),
('leftMapX', self.leftMapX), ('leftMapY', self.leftMapY),
('rightMapX', self.rightMapX), ('rightMapY', self.rightMapY)])
camera_model = dict([('K_left', self.K_left), ('K_right', self.K_right), ('D_left', self.D_left),
('D_right', self.D_right), ('R', self.R), ('T', self.T),
('E', self.E), ('F', self.F)])
save_obj(camera_model,self.name+'_charuco_camera_model')
save_obj(camera_model_rectify, self.name+'_charuco_camera_model_rectify')
def depth_map_SGBM(self, imgL, imgR):
window_size = 5
if self.left_matcher is None:
numDisparities = 256
self.left_matcher = cv2.StereoSGBM_create(minDisparity=5,
numDisparities=numDisparities,
blockSize=window_size,
)
self.stereo = cv2.StereoBM_create(numDisparities=256, blockSize=5)
self.right_matcher = cv2.ximgproc.createRightMatcher(self.left_matcher)
lmbda,sigma = 80000 , 1.3
lmbda,sigma = 80000, .6
self.wls_filter = cv2.ximgproc.createDisparityWLSFilter(matcher_left=self.left_matcher)
self.wls_filter.setLambda(lmbda)
self.wls_filter.setSigmaColor(sigma)
displ = self.left_matcher.compute(imgL, imgR) # .astype(np.float32)/16
dispr = self.right_matcher.compute(imgR, imgL) # .astype(np.float32)/16
displ = np.int16(displ)
dispr = np.int16(dispr)
SGBM_disp = self.wls_filter.filter(displ, imgL, imgR, dispr)
SGBM_disp = cv2.normalize(src=SGBM_disp, dst=SGBM_disp, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX)
SGBM_disp = np.uint8(SGBM_disp)
disparity = self.stereo.compute(imgL, imgR)
disparity = cv2.normalize(src=disparity, dst=disparity, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX)
BM_disp = np.uint8(disparity)
return SGBM_disp, BM_disp
def depth_and_color(self, img, left_rectified):
points = cv2.reprojectImageTo3D(img, self.Q)
reflect_matrix = np.identity(3) # reflect on x axis
reflect_matrix[0] *= -1
points = np.matmul(points, reflect_matrix)
colors = cv2.cvtColor(left_rectified, cv2.COLOR_BGR2RGB)
# filter by min disparity
mask = img > img.min()
out_points = points[mask]
out_colors = colors[mask]
idx = np.fabs(out_points[:, 0]) < 250 # 10.5 # filter by dimension
out_points = out_points[idx]
out_colors = out_colors.reshape(-1, 3)
out_colors = out_colors[idx]
write_ply('BM_2.ply', out_points, out_colors)
print('%s saved' % 'out.ply')
reflected_pts = np.matmul(out_points, reflect_matrix)
projected_img, _ = cv2.projectPoints(reflected_pts, np.identity(3), np.array([0., 0., 0.]), self.K_left,
self.D_left)
projected_img = projected_img.reshape(-1, 2)
blank_img = np.zeros(left_rectified.shape, 'uint8')
img_colors = left_rectified[mask][idx].reshape(-1, 3)
for i, pt in enumerate(projected_img):
pt_x = int(pt[0])
pt_y = int(pt[1])
if pt_x > 0 and pt_y > 0:
# use the BGR format to match the original image type
col = (int(img_colors[i, 2]), int(img_colors[i, 1]), int(img_colors[i, 0]))
cv2.circle(blank_img, (pt_x, pt_y), 1, col)
return blank_img
def depth(self):
self.left_matcher = None
wait = 0
for i, fname in enumerate(self.LeftImg):
leftFrame = self.LeftImg[i]
rightFrame = self.RightImg[i]
left_rectified = cv2.remap(leftFrame, self.leftMapX, self.leftMapY, cv2.INTER_LINEAR, cv2.BORDER_CONSTANT)
right_rectified = cv2.remap(rightFrame, self.rightMapX, self.rightMapY, cv2.INTER_LINEAR,
cv2.BORDER_CONSTANT)
#out = right_rectified.copy()
#out[:, :, 0] = left_rectified[:, :, 0]
#out[:, :, 1] = left_rectified[:, :, 1]
#out[:, :, 2] = right_rectified[:, :, 2]
gray_left = cv2.cvtColor(left_rectified, cv2.COLOR_BGR2GRAY)
gray_right = cv2.cvtColor(right_rectified, cv2.COLOR_BGR2GRAY)
SGBM_disp, BM_disp = self.depth_map_SGBM(gray_left, gray_right, ) # Get the disparity map
img_top = cv2.hconcat(
[cv2.resize(left_rectified, None, fx=self.fx, fy=self.fy), cv2.resize(right_rectified, None, fx=self.fx, fy=self.fy)])
img_bot = np.hstack((cv2.resize(SGBM_disp, None, fx=self.fx, fy=self.fy), cv2.resize(BM_disp, None, fx=self.fx, fy=self.fy)))
#pointCloudColor = self.depth_and_color(img=BM_disp.copy(),left_rectified=left_rectified)
pointCloudColor = self.depth_and_color(img=SGBM_disp.copy(),left_rectified=left_rectified)
cv2.imshow('img_bot ', img_bot)
cv2.imshow('Result', img_top)
cv2.imshow('pointCloudColor', cv2.resize(pointCloudColor, None, fx=.4, fy=.4))
k = cv2.waitKey(wait)
if k & 0xFF == ord('q'):
wait = 1
cv2.waitKey(0)
cv2.destroyAllWindows()
def calibrationReport(self):
Distorsion_models = {'ST': ['Standard', 0, 'Standard'],
'RAT': ['Rational', cv2.CALIB_RATIONAL_MODEL, 'CALIB_RATIONAL_MODEL'],
'THP': ['Thin Prism', cv2.CALIB_THIN_PRISM_MODEL, 'CALIB_THIN_PRISM_MODEL'],
'TIL': ['Tilded', cv2.CALIB_TILTED_MODEL, 'CALIB_TILTED_MODEL'], # }
'RAT+THP': ['Rational+Thin Prism',
cv2.CALIB_RATIONAL_MODEL + cv2.CALIB_THIN_PRISM_MODEL,
'CALIB_RATIONAL_MODEL + CALIB_THIN_PRISM_MODEL'],
'THP+TIL': ['Thin Prism+Tilded', cv2.CALIB_THIN_PRISM_MODEL + cv2.CALIB_TILTED_MODEL,
'CALIB_THIN_PRISM_MODEL + CALIB_TILTED_MODEL'],
'RAT+TIL': ['Rational+Tilded', cv2.CALIB_RATIONAL_MODEL + cv2.CALIB_TILTED_MODEL,
'CALIB_RATIONAL_MODEL + CALIB_TILTED_MODEL'],
'CMP': ['Complete',
cv2.CALIB_RATIONAL_MODEL + cv2.CALIB_THIN_PRISM_MODEL + cv2.CALIB_TILTED_MODEL,
'Complete']}
def rot2eul(R):
beta = -np.arcsin(R[2, 0])
alpha = np.arctan2(R[2, 1] / np.cos(beta), R[2, 2] / np.cos(beta))
gamma = np.arctan2(R[1, 0] / np.cos(beta), R[0, 0] / np.cos(beta))
alpha, beta, gamma = math.degrees(alpha), math.degrees(beta), math.degrees(gamma)
return np.array((alpha, beta, gamma))
calibration_results = pd.DataFrame(
{"params": [ 'Tx','Ty','Tz','Rx','Ry','Rz', 'Error']})
rms_all = ['Error']
min_error, flag = 10000000, 0
for key in Distorsion_models:
print()
print(key, '->', Distorsion_models[key][0], ' , ', Distorsion_models[key][1], ' , ',
Distorsion_models[key][2])
flags = Distorsion_models[key][1]
self.stereoCalibrate(flags=flags, save=False)
s = np.append(self.T, rot2eul(self.R))
calibration_results[str(key)] = pd.Series(s)
calibration_results.fillna('---', inplace=True)
rms_all.append(self.rms_stereo)
calibration_results[str(key)] = calibration_results[str(key)].map(
lambda x: round(x, 4) if isinstance(x, (int, float)) else x).astype(str)
if self.rms_stereo < min_error:
min_error=self.rms_stereo
flag = flags
calibration_results.iloc[-1, :] = rms_all
calibration_results.iloc[-1, :] = calibration_results.iloc[-1, :].map(
lambda x: round(x, 4) if isinstance(x, (int, float)) else x)
save_csv(obj=calibration_results, name=self.name+'_charuco_StereoCalibration')
print('Best distortion model for stereo calib is {}'.format(flag))
self.stereoCalibrate(flags=flag,save=True)
return flag