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Charuco_Chess_combine.py
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Charuco_Chess_combine.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 glob
import numpy as np
import cv2
import cv2.aruco as aruco
import pandas as pd
from CameraCalibration.scripts.MonoCharuco import MonoCharuco_Calibrator
from CameraCalibration.scripts.MonoChess import MonoChess_Calibrator
from CameraCalibration.scripts.StereoCharuco import StereoCharuco_Calibrator
from CameraCalibration.scripts.StereoChess import StereoChess_Calibrator
from utils import *
class CombinedCalibration(object):
def __init__(self, name = '', flipVertically = True):
self.name = name
self.term_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 500, 0.0001)
self.flipVertically = flipVertically
self.fx = 0.3
self.fy = 0.35
self.image_width = 1936
self.image_height = 1216
def calibrateMono_(self, imgChess, imgCharuco,see = False, save = True, cam = 'left'):
Chess_calibrator = MonoChess_Calibrator(pattern_type="chessboard", pattern_rows=10,
pattern_columns=7, distance_in_world_units=10, # square is 10 cm
figsize=(14, 10))
Chess_calibrator.see = see
Chess_calibrator.read_images(images_path_list=imgChess)
Chess_objectPoints = np.array(Chess_calibrator.calibration_df.obj_points)
Chess_imagePoints = np.array(Chess_calibrator.calibration_df.img_points)
Charuco_calibrator = MonoCharuco_Calibrator()
Charuco_calibrator.see = see
Charuco_calibrator.createCalibrationBoard()
Charuco_calibrator.read_images(images=imgCharuco, threads=1)
Charuco_objectPoints = np.array(Charuco_calibrator.calibration_df.obj_points)
Charuco_imagePoints = np.array(Charuco_calibrator.calibration_df.img_points)
self.image_size = Charuco_calibrator.image_size
print('Chess_objectPoints:{}, Chess_imagePoints:{}'.format(np.shape(Chess_objectPoints), np.shape(Chess_imagePoints)))
print('Charuco_objectPoints:{}, Chess_imagePoints:{}'.format(np.shape(Charuco_objectPoints), np.shape(Charuco_imagePoints)))
print('self.image_size ',self.image_size)
_objectPoints = np.append(Chess_objectPoints,Charuco_objectPoints)
_imagePoints = np.append(Chess_imagePoints, Charuco_imagePoints)
print('_objectPoints:{}, _imagePoints:{}'.format(np.shape(_objectPoints), np.shape(_imagePoints)))
self.rms, self.K, self.D, self.rvecs, self.tvecs = cv2.calibrateCamera(
objectPoints=_objectPoints,
imagePoints=_imagePoints,
imageSize=self.image_size,
cameraMatrix=None, distCoeffs=None,
flags=0, criteria=self.term_criteria)
print("\nRMS:", self.rms)
print("camera matrix:\n", self.K)
print("distortion coefficients: ", self.D.ravel())
if save:
result_dictionary = {
"rms": self.rms,
"K": self.K,
"D": self.D,
}
save_obj(obj=result_dictionary, name='combined_{}_{}'.format(self.name, cam))
def calibrateStereo(self,imgChess, imgCharuco,see = False, save = True, flags=None,):
total = 0
Chess_calibrator = StereoChess_Calibrator(imgChess)
Chess_calibrator.see = see
Chess_calibrator.read_images(test=False)
total+=Chess_calibrator.total
Charuco_calibrator = StereoCharuco_Calibrator(imgCharuco)
Charuco_calibrator.see = see
Charuco_calibrator.createCalibrationBoard()
Charuco_calibrator.read_images(test=False)
total+=Charuco_calibrator.total
self.image_size = Charuco_calibrator.img_shape
print('self.image_size ',self.image_size)
if flags is None:
flags = 0
flags |= cv2.CALIB_USE_INTRINSIC_GUESS
flags |= cv2.CALIB_FIX_FOCAL_LENGTH
flags |= cv2.CALIB_FIX_ASPECT_RATIO
Chess_objectPoints = np.array(Chess_calibrator.objpoints)
Chess_imagePointsL = np.array(Chess_calibrator.imgpoints_l)
Chess_imagePointsR = np.array(Chess_calibrator.imgpoints_r)
Charuco_objectPoints = np.array(Charuco_calibrator.objpoints)
Charuco_imagePointsL = np.array(Charuco_calibrator.imgpoints_l)
Charuco_imagePointsR = np.array(Charuco_calibrator.imgpoints_r)
print('Chess_objectPoints:{}, Chess_imagePointsL:{}, Chess_imagePointsR:{}'.format(np.shape(Chess_objectPoints),
np.shape(Chess_imagePointsL), np.shape(Chess_imagePointsR)))
print('Charuco_objectPoints:{}, Charuco_imagePointsL:{}, Charuco_imagePointsR:{}'.format(np.shape(Charuco_objectPoints),
np.shape(Charuco_imagePointsL),
np.shape(
Charuco_imagePointsR)))
print('Total of {} points'.format(total))
_objectPoints = []# np.append(Chess_objectPoints, Charuco_objectPoints)
_imagePointsL = []# np.append(Chess_imagePointsL, Charuco_imagePointsL)
_imagePointsR = []# np.append(Chess_imagePointsR, Charuco_imagePointsR)
for i in range(len(Chess_objectPoints)):
_objectPoints.append(Chess_objectPoints[i])
_imagePointsL.append(Chess_imagePointsL[i])
_imagePointsR.append(Chess_imagePointsR[i])
for i in range(len(Charuco_objectPoints)):
_objectPoints.append(Charuco_objectPoints[i])
_imagePointsL.append(Charuco_imagePointsL[i])
_imagePointsR.append(Charuco_imagePointsR[i])
print('_objectPoints:{}, _imagePointsL:{}, _imagePointsR:{}'.format(np.shape(_objectPoints), np.shape(_imagePointsL), np.shape(_imagePointsR)))
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(
_objectPoints, _imagePointsL,
_imagePointsR, self.K_left, self.D_left, self.K_right, self.D_right, self.image_size,
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.image_size, 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.image_size, cv2.CV_32FC1)
self.rightMapX, self.rightMapY = cv2.initUndistortRectifyMap(
self.K_right, self.D_right, R2,
P2, self.image_size, 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 + '_combined_camera_model')
save_obj(camera_model_rectify, self.name + '_combined_camera_model_rectify')
def calibrationReport(self,imgChess, imgCharuco):
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.calibrateStereo(imgChess, imgCharuco, 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 + '_combined_StereoCalibration')
print('Best distortion model for stereo calib is {}'.format(flag))
#self.stereoCalibrate(flags=flag, save=True)
return flag
def readMonoData(self):
left_ = load_obj(name='combined_{}_left'.format(self.name))
self.K_left = left_['K']
self.D_left = left_['D']
right_ = load_obj(name='combined_{}_right'.format(self.name))
self.K_right = right_['K']
self.D_right = right_['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('{}_combined_camera_model'.format(self.name ))
camera_model_rectify = load_obj('{}_combined_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.P1 = camera_model_rectify['P1']
self.P2 = camera_model_rectify['P2']
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...')
self.image_size = (self.image_width, self.image_height)
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.image_size, self.R,
self.T,
flags=cv2.CALIB_ZERO_DISPARITY,
alpha=0)
#flags=cv2.CALIB_ZERO_DISPARITY,
#alpha=-1)
'''self.leftMapX, self.leftMapY = cv2.initUndistortRectifyMap(
self.K_left, self.D_left, R1,
P1, self.image_size, cv2.CV_32FC1)
self.rightMapX, self.rightMapY = cv2.initUndistortRectifyMap(
self.K_right, self.D_right, R2,
P2, self.image_size, cv2.CV_32FC1)'''
def read_images(self, path):
images_right = glob.glob(path + '/Right/*.png')
images_left = glob.glob(path + '/Left/*.png')
images_left.sort()
images_right.sort()
self.LeftImg, self.RightImg = [], []
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)
def depth_map_SGBM(self, imgL, imgR, numDisparities = 128, window_size = 5):
if self.left_matcher is None:
self.left_matcher = cv2.StereoSGBM_create(minDisparity=5,
numDisparities=numDisparities,
blockSize=window_size,
)
self.stereo = cv2.StereoBM_create(numDisparities=numDisparities, blockSize=window_size)
self.right_matcher = cv2.ximgproc.createRightMatcher(self.left_matcher)
# FILTER Parameters
lmbda = 80000
sigma = 1.3
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)
#SGBM_disp = SGBM_disp.astype(np.float32) / 16.0
#SGBM_disp = cv2.normalize(SGBM_disp, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
#BM_disp = self.stereo.compute(imgL, imgR)
#BM_disp = cv2.normalize(src=BM_disp, dst=BM_disp, beta=0, alpha=255, norm_type=cv2.NORM_MINMAX)
#BM_disp = np.uint8(BM_disp)
#BM_disp = BM_disp.astype(np.float32) / 16.0
window_size = 9
min_disp = 16
num_disp = 112 - min_disp
stereo = cv2.StereoSGBM_create(minDisparity=min_disp,
numDisparities=num_disp,
blockSize=window_size,
)
#BM_disp = stereo.compute(imgL, imgR).astype(np.float32) / 16.0
BM_disp = stereo.compute(imgR, imgL).astype(np.float32) / 16.0
return SGBM_disp, BM_disp
def depth_and_color(self, img, img2=None, left_rectified=None, name = 'out.ply', gray_left=None,gray_right=None):
disparity_map = img2
#plt.imshow(disparity_map, cmap='gray', vmin=225, vmax=255)
plt.imshow(disparity_map, cmap='gray')
plt.show()
focalLength, centerX, centerY = self.K_left[0,0], self.K_left[0,2], self.K_left[1,2]
fx,fy = self.K_left[0,0],self.K_left[1,1]
Baseline = self.T[0]
h,w = np.shape(disparity_map)
print('min {}, max:{}'.format(np.min(disparity_map), np.max(disparity_map)))
print('np.shape(depth) {}'.format(np.shape(disparity_map)))
scalingFactor = 150
points, colors = [],[]
D = np.zeros_like(disparity_map)
for v in range(250,w):
for u in range(h):
#Z = (Baseline * focalLength) / (depth[u, v] * p)
if disparity_map[u, v] != 0:
color = left_rectified[u, v]
Z = disparity_map[u, v] / scalingFactor
X = (u - centerX) * Z / fx
Y = (v - centerY) * Z / fy
Z = (fy*Baseline)/disparity_map[u,v]
'''Z = disparity_map[u, v] / scalingFactor
if Z == 0: continue
X = (u - centerX) * Z / focalLength
Y = (v - centerY) * Z / focalLength
Z = Baseline * focalLength / disparity_map[u, v]'''
points.append([X,Y,Z])
colors.append(color)
print('points:{}, colors:{}'.format(np.shape(points), np.shape(colors)))
points,colors = np.array(points), np.array(colors)
write_ply('out1.ply', points, colors)
print('out1.ply saved')
#-------------------------------------- ----------------------------------------------------------------------------
points = cv2.reprojectImageTo3D(disparity_map, self.Q)
print('points:{}---------------------------------------------------------------'.format(np.shape(points)))
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]) < 2250 # 10.5 # filter by dimension
out_points = out_points[idx]
out_colors = out_colors.reshape(-1, 3)
out_colors = out_colors[idx]
write_ply(name, out_points, out_colors)
print('%s saved' % name)
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 d2(self, imgL, imgR):
import csv
import random
camera_focal_length_px = 1364.3372
stereo_camera_baseline_m = 0.2090607502 # camera baseline in metres
stereo_camera_baseline_m = 0.9663
image_centre_h = 968.2298
image_centre_w = 605.0811
## project_disparity_to_3d : project a given disparity image
def project_disparity_to_3d(disparity, max_disparity, rgb=np.array([])):
points = []
f = camera_focal_length_px
B = stereo_camera_baseline_m
height, width = disparity.shape[:2]
# Zmax = ((f * B) / 2)
for y in range(height): # 0 - height is the y axis index
for x in range(width): # 0 - width is the x axis index
if (disparity[y, x] > 0):
# calculate corresponding 3D point [X, Y, Z]
Z = (f * B) / disparity[y, x]
X = ((x - image_centre_w) * Z) / f
Y = ((y - image_centre_h) * Z) / f
if (rgb.size > 0):
points.append([X, Y, Z, rgb[y, x, 2], rgb[y, x, 1], rgb[y, x, 0]])
else:
points.append([X, Y, Z])
return points
# project a set of 3D points back the 2D image domain
def project_3D_points_to_2D_image_points(points):
points2 = []
# Zmax = (camera_focal_length_px * stereo_camera_baseline_m) / 2
for i1 in range(len(points)):
# reverse earlier projection for X and Y to get x and y again
x = ((points[i1][0] * camera_focal_length_px) / points[i1][2]) + image_centre_w
y = ((points[i1][1] * camera_focal_length_px) / points[i1][2]) + image_centre_h
points2.append([x, y])
return points2
max_disparity = 256
stereoProcessor = cv2.StereoSGBM_create(0, max_disparity, 5)
grayL = cv2.cvtColor(imgL, cv2.COLOR_BGR2GRAY)
grayR = cv2.cvtColor(imgR, cv2.COLOR_BGR2GRAY)
disparity_scaled = self.SGBM_disp
points = project_disparity_to_3d(disparity_scaled, max_disparity)
#points = project_disparity_to_3d(disparity_scaled, max_disparity, imgL)
point_cloud_file = open('3d_points.txt', 'w')
csv_writer = csv.writer(point_cloud_file, delimiter=' ')
csv_writer.writerows(points)
point_cloud_file.close()
def depth(self, testImages, second = False):
self.read_images(path=testImages)
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)
left_rectified = cv2.remap(leftFrame, self.rightMapX, self.rightMapY, cv2.INTER_LINEAR, cv2.BORDER_CONSTANT)
right_rectified = cv2.remap(rightFrame, self.leftMapX, self.leftMapY, 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)
#disparity_image2 = cv2.applyColorMap(disparity_image2, cv2.COLORMAP_JET)
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)])
#cv2.imwrite('left_rectified1.png',left_rectified)
#cv2.imwrite('right_rectified1.png', right_rectified)
SGBM_disp, BM_disp = self.depth_map_SGBM(gray_left, gray_right, ) # Get the disparity map
self.SGBM_disp = SGBM_disp
#if second:
# self.d2(left_rectified, right_rectified) # Get the disparity map
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(),img2=BM_disp.copy(), left_rectified=left_rectified, gray_left=gray_left, gray_right=gray_right)
cv2.imshow('img_bot ', img_bot)
#cv2.imshow('out ', cv2.resize(out, None, fx=self.fx, fy=self.fy))
cv2.imshow('Result', img_top)
cv2.imshow('pointCloudColor', cv2.resize(pointCloudColor, None, fx=self.fx, fy=self.fy))
k = cv2.waitKey(wait)
if k & 0xFF == ord('q'):
wait = 1
#break
cv2.waitKey(0)
cv2.destroyAllWindows()
def gridSearchdepth(self, testImages):
self.read_images(path=testImages)
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)
gray_left = cv2.cvtColor(left_rectified, cv2.COLOR_BGR2GRAY)
gray_right = cv2.cvtColor(right_rectified, cv2.COLOR_BGR2GRAY)
numDisparities = [128, 256,256*2,256*3,256*4,256*5,256*6,256*7,256*8,256*9]
winsize = [5,15,35,75,125,235]
for d in numDisparities:
for w in winsize:
print('numDisparities:{}, winsize:{}'.format(d,w))
name = 'out_d{}_w{}.ply'.format(d,w)
self.left_matcher = None
SGBM_disp, BM_disp = self.depth_map_SGBM(gray_left, gray_right, numDisparities = d,window_size = w) # Get the disparity map
pointCloudColor = self.depth_and_color(img=SGBM_disp.copy(), left_rectified=left_rectified, name=name)
cv2.imshow('pointCloudColor', cv2.resize(pointCloudColor, None, fx=self.fx, fy=self.fy))
cv2.waitKey(100)
cv2.waitKey(0)
cv2.destroyAllWindows()
def calibrateMono(self, imgChess, imgCharuco,see = False, save = True, cam = 'left'):
Chess_calibrator = MonoChess_Calibrator(pattern_type="chessboard", pattern_rows=10,
pattern_columns=7, distance_in_world_units=10, # square is 10 cm
figsize=(14, 10))
Chess_calibrator.see = see
Chess_calibrator.read_images(images_path_list=imgChess)
Chess_objectPoints = np.array(Chess_calibrator.calibration_df.obj_points)
Chess_imagePoints = np.array(Chess_calibrator.calibration_df.img_points)
Charuco_calibrator = MonoCharuco_Calibrator()
Charuco_calibrator.see = see
Charuco_calibrator.createCalibrationBoard()
Charuco_calibrator.read_images(images=imgCharuco, threads=1)
Charuco_objectPoints = np.array(Charuco_calibrator.calibration_df.obj_points)
Charuco_imagePoints = np.array(Charuco_calibrator.calibration_df.img_points)
self.image_size = Charuco_calibrator.image_size
print('Chess_objectPoints:{}, Chess_imagePoints:{}'.format(np.shape(Chess_objectPoints), np.shape(Chess_imagePoints)))
print('Charuco_objectPoints:{}, Chess_imagePoints:{}'.format(np.shape(Charuco_objectPoints), np.shape(Charuco_imagePoints)))
print('self.image_size ',self.image_size)
_objectPoints = np.append(Chess_objectPoints,Charuco_objectPoints)
_imagePoints = np.append(Chess_imagePoints, Charuco_imagePoints)
print('Total charuco datapoints:{}'.format(Charuco_calibrator.total))
total = Charuco_calibrator.total + (len(Chess_objectPoints)*70)
print('Total datapoints:{}'.format(total))
#for each distortion model perform the fucking calibration
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']}
calibration_results = pd.DataFrame(
{"params": ['fx', 'fy', 'px', 'py', 'sk', 'k1', 'k2', 'p1', 'p2', 'k3', 'k4', 'k5', 'k6',
's1', 's2', 's3', 's4', 'tx', 'ty', 'Error']})
rms_all = ['Error']
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.calibrate(flags=flags, project=False, K=K)
print('_objectPoints:{}, _imagePoints:{}'.format(np.shape(_objectPoints), np.shape(_imagePoints)))
self.rms, self.K, self.D, self.rvecs, self.tvecs = cv2.calibrateCamera(
objectPoints=_objectPoints,
imagePoints=_imagePoints,
imageSize=self.image_size,
cameraMatrix=None, distCoeffs=None,
flags=flags, criteria=self.term_criteria)
print("\nRMS:", self.rms)
print("camera matrix:\n", self.K)
print("distortion coefficients: ", self.D.ravel())
s = np.array([self.K[0, 0], self.K[1, 1], self.K[0, 2], self.K[1, 2], self.K[0, 1]])
s = np.append(s, self.D)
calibration_results[str(key)] = pd.Series(s)
calibration_results.fillna('---', inplace=True)
rms_all.append(self.rms)
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='FinalMonoCalibration_{}_{}_points'.format(self.name, total))
def MonoCombinedCalibration():
calibrator = CombinedCalibration(name='right_outside')
# calibrate right camera outside --------------------------------------------------------
#imgCharuco = glob.glob('/home/eugeniu/Desktop/my_data/CameraCalibration/data/car_cam_data/charuco/outside/Right/*.png')
#imgChess = glob.glob('/home/eugeniu/Desktop/my_data/CameraCalibration/data/car_cam_data/chess/outside/Right/*.png')
#calibrator.calibrateMono(imgChess, imgCharuco, save=True, cam='right')
# calibrate left camera outside-----------------------------------------------------------
#calibrator.name = 'left_outside'
#imgCharuco = glob.glob('/home/eugeniu/Desktop/my_data/CameraCalibration/data/car_cam_data/charuco/outside/Left/*.png')
#imgChess = glob.glob('/home/eugeniu/Desktop/my_data/CameraCalibration/data/car_cam_data/chess/outside/Left/*.png')
#calibrator.calibrateMono(imgChess, imgCharuco, save=True, cam='left')
# calibrate right camera inside ----------------------------------------------------------
calibrator.name = 'right_inside'
imgCharuco = glob.glob('/home/eugeniu/Desktop/my_data/CameraCalibration/data/car_cam_data/charuco/inside/Right/*.png')
imgChess = glob.glob('/home/eugeniu/Desktop/my_data/CameraCalibration/data/car_cam_data/chess/inside/Right/*.png')
calibrator.calibrateMono(imgChess, imgCharuco, save=True, cam='right')
# calibrate left camera inside-----------------------------------------------------------
#calibrator.name = 'left_inside'
#imgCharuco = glob.glob('/home/eugeniu/Desktop/my_data/CameraCalibration/data/car_cam_data/charuco/inside/Left/*.png')
#imgChess = glob.glob('/home/eugeniu/Desktop/my_data/CameraCalibration/data/car_cam_data/chess/inside/Left/*.png')
#calibrator.calibrateMono(imgChess, imgCharuco, save=True, cam='left')
return calibrator