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MonoCharuco.py
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MonoCharuco.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.
'''
from __future__ import print_function
import matplotlib.pyplot as plt
import mpl_toolkits
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from numpy import linspace
import numpy as np
import cv2
import math
import os
import glob
import pickle
from multiprocessing.dummy import Pool as ThreadPool
import pandas as pd
import cv2.aruco as aruco
from utils import *
from scipy.spatial.distance import cdist
np.set_printoptions(suppress=True)
class MonoCharuco_Calibrator(object):
def __init__(self, name='', figsize=(12, 10)):
self.name=name
self.image_size = None # Determined at runtime
self.term_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30000, 0.0000001)
self.term_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.01)
#self.term_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.001)
self.figure_size=(12,10)
self.debug_dir = None
self.figsize = figsize
self.image_width= 1936
self.image_height= 1216
self.image_center = np.array([self.image_width/2, self.image_height/2])
self.optical_area = (11.345, 7.126) # mm
self.K = None
self.D = None
self.fx = 0.45
self.fy = 0.4
self.see = True
self.flipVertically = True
self.stdDeviationsIntrinsics, self.stdDeviationsExtrinsics, self.perViewErrors = None,None,None
def createCalibrationBoard(self, squaresY = 9, squaresX = 12, squareLength = .06, markerLength = 0.045, display=False):
'''
squaresX number of chessboard squares in X direction
squaresY number of chessboard squares in Y direction
squareLength chessboard square side length (normally in meters)
markerLength marker side length (same unit than squareLength)
'''
self.ARUCO_DICT = aruco.Dictionary_get(aruco.DICT_5X5_1000)
#self.ARUCO_DICT = aruco.Dictionary_get(aruco.DICT_4X4_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 reprojection_error_plot(self, errors_, N, figure_size=(16, 12)):
if len(errors_)>1:
errInside = np.array(errors_[0]).squeeze()
errOutside = np.array(errors_[1]).squeeze()
data = []
for i in range(N):
data.append(['img_{}'.format(i), errInside[i], errOutside[i]])
df = pd.DataFrame(data, columns=["Name", "Inside(px)", "Outside(px)"])
ax = df.plot(x="Name", y=["Inside(px)", "Outside(px)"], kind="bar", figsize=figure_size, #grid=True,
title='Reprojection_error plot - {}'.format(name))
avg_error_inside = np.sum(errInside) / len(errInside)
y_mean_inside = [avg_error_inside] * N
ax.plot(y_mean_inside, label='Mean Reprojection error inside:{}'.format(round(avg_error_inside,2)), linestyle='--')
avg_error_outside = np.sum(errOutside) / len(errOutside)
y_mean_outside = [avg_error_outside] * N
ax.plot(y_mean_outside, label='Mean Reprojection error outside:{}'.format(round(avg_error_outside,2)), linestyle='--')
ax.legend(loc='upper right')
ax.set_xlabel("Image_names")
ax.set_ylabel("Reprojection error in px")
plt.show()
def _calc_reprojection_error(self, figure_size=(16, 12), save_dir=None, limit=False):
if self.perViewErrors is not None:
print('Reproject train images')
self.perViewErrors = np.array(self.perViewErrors).squeeze()
avg_error = np.sum(np.array(self.perViewErrors)) / len(self.perViewErrors)
print("The Mean Reprojection Error in pixels is: {}".format(avg_error))
x = ['img_{}'.format(i) for i,p in enumerate(self.calibration_df.image_names)]
y_mean = [avg_error] * len(self.calibration_df.image_names)
fig, ax = plt.subplots()
fig.set_figwidth(figure_size[0])
fig.set_figheight(figure_size[1])
max_intensity = np.max(self.perViewErrors)
cmap = cm.get_cmap('jet')
colors = cmap(self.perViewErrors / max_intensity) # * 255
print('self.perViewErrors:{}, colors:{}, max_intensity:{}'.format(np.shape(self.perViewErrors), np.shape(colors), max_intensity))
ax.scatter(x, self.perViewErrors, label='Reprojection error', c=colors, marker='o') # plot before
ax.bar(x, self.perViewErrors, color=colors, alpha=0.3)
ax.plot(x, y_mean, label='Mean Reprojection error', linestyle='--')
ax.legend(loc='upper right')
for tick in ax.get_xticklabels():
tick.set_rotation(90)
ax.set_title("{} - Reprojection_error plot, Mean:{}".format(self.name, round(avg_error, 2)))
ax.set_xlabel("Image_names")
ax.set_ylabel("Reprojection error in pixels")
if limit:
ax.set_ylim(0, self.rms)
if save_dir:
plt.savefig(os.path.join(save_dir, "reprojection_error.png"))
plt.show()
else:
print('Reproject test images')
limit = True
reprojection_error = []
for i in range(len(self.calibration_df)):
imgpoints2, _ = cv2.projectPoints(self.calibration_df.obj_points[i], self.calibration_df.rvecs[i],
self.calibration_df.tvecs[i], self.K, self.D)
temp_error = cv2.norm(self.calibration_df.img_points[i], imgpoints2, cv2.NORM_L2) / len(imgpoints2)
reprojection_error.append(temp_error)
self.calibration_df['reprojection_error'] = pd.Series(reprojection_error)
avg_error = np.sum(np.array(reprojection_error)) / len(self.calibration_df.obj_points)
x = [os.path.basename(p) for p in self.calibration_df.image_names]
y_mean = [avg_error] * len(self.calibration_df.image_names)
fig, ax = plt.subplots()
fig.set_figwidth(figure_size[0])
fig.set_figheight(figure_size[1])
max_intensity = np.max(reprojection_error)
cmap = cm.get_cmap('jet')
colors = cmap(reprojection_error / max_intensity) # * 255
ax.scatter(x, reprojection_error, label='Reprojection error', c=colors, marker='o') # plot before
ax.bar(x, reprojection_error, color=colors, alpha=0.3)
ax.plot(x, y_mean, label='Mean Reprojection error', linestyle='--')
ax.legend(loc='upper right')
for tick in ax.get_xticklabels():
tick.set_rotation(90)
ax.set_title("{} - Reprojection_error plot, Mean:{}".format(self.name, round(avg_error, 2)))
ax.set_xlabel("Image_names")
ax.set_ylabel("Reprojection error in pixels")
if limit:
ax.set_ylim(0, self.rms)
if save_dir:
plt.savefig(os.path.join(save_dir, "reprojection_error.png"))
plt.show()
print("The Mean Reprojection Error in pixels is: {}".format(avg_error))
def read_images(self, images, threads=5, K=None,D=None,limit = 9):
self.total = 0
self.images=images
print('There are {} images'.format(np.shape(images)))
self.h, self.w = cv2.imread(images[0], 0).shape[:2]
working_images, img_points, obj_points = [],[],[]
images.sort()
corners_all = [] # Corners discovered in all images processed - obj_points
ids_all = [] # Aruco ids corresponding to corners discovered - img_points
if K is not None and D is not None: # undistort images before calibration
self.calibration_df = None
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(K, D, (self.w, self.h), 1, (self.w, self.h))
def undistort_image(img):
img2 = img
mapx, mapy = cv2.initUndistortRectifyMap(K, D, None, newcameramtx, (self.w, self.h), 5)
dst = cv2.remap(img2, mapx, mapy, cv2.INTER_LINEAR)
x, y, w, h = roi
dst = dst[y:y + h, x:x + w]
#print('dst:{}'.format(np.shape(dst)))
self.h, self.w = dst.shape[:2]
return dst
h, w = self.CHARUCO_BOARD.chessboardCorners.shape
def process_single_image(img_path):
img = cv2.imread(img_path) # gray scale
if img is None:
print("Failed to load {}".format(img_path))
return None
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
resized1 = img
if K is not None and D is not None: # undistort images before calibration
img = undistort_image(img)
if threads<=0:
resized2 = img
if self.flipVertically:
resized1 = cv2.flip(resized1, -1)
resized2 = cv2.flip(resized2, -1)
cv2.imshow('original ', cv2.resize(resized1, (0, 0), fx=self.fx, fy=self.fy))
cv2.imshow('undistorted', cv2.resize(resized2, (0, 0), fx=self.fx, fy=self.fy))
cv2.waitKey(0)
assert self.w == img.shape[1] and self.h == img.shape[0], "All the images must have same shape"
corners, ids, rejected = aruco.detectMarkers(image=gray, dictionary=self.ARUCO_DICT)
corners, ids, rejectedImgPoints, recoveredIds = aruco.refineDetectedMarkers(image=gray,board=self.CHARUCO_BOARD,
detectedCorners=corners,detectedIds=ids,
rejectedCorners=rejected,cameraMatrix=K,
distCoeffs=D)
if len(corners) >= limit:
response, charuco_corners, charuco_ids = aruco.interpolateCornersCharuco(
markerCorners=corners, markerIds=ids,
image=gray, board=self.CHARUCO_BOARD)
if response >= limit:
#objPts, imgPts = aruco.getBoardObjectAndImagePoints(self.CHARUCO_BOARD, charuco_corners, charuco_ids)
imgPts = np.array(charuco_corners)
objPts = self.CHARUCO_BOARD.chessboardCorners.reshape((h, 1, 3))[np.asarray(charuco_ids).squeeze()] * self.distance_in_world_units
self.total += len(imgPts)
if self.see:
#img1 = aruco.drawDetectedMarkers(image=img, corners=corners)
#img1 = cv2.drawChessboardCorners(img, (11, 8), charuco_corners, True)
img1 = img.copy()
for i in imgPts:
(x,y) = i.ravel()
cv2.circle(img1, (x,y),3,(0,0,255),3)
img2 = aruco.drawDetectedCornersCharuco(image=img.copy(), charucoCorners=charuco_corners,charucoIds=charuco_ids)
if self.flipVertically:
img1 = cv2.flip(img1, -1)
img2 = cv2.flip(img2, -1)
img1 = cv2.resize(img1, (0, 0), fx=self.fx, fy=self.fy)
img2 = cv2.resize(img2, (0, 0), fx=self.fx, fy=self.fy)
_horizontal = np.vstack((img1, img2))
cv2.imshow('Images', _horizontal)
k = cv2.waitKey(0)
if k % 256 == 32: # pressed space
self.see = False
cv2.destroyAllWindows()
if not self.image_size:
self.image_size = gray.shape[::-1]
return (img_path, charuco_ids, charuco_corners, objPts, imgPts)
else:
# print("Calibration board NOT FOUND")
return (None)
# print("Calibration board NOT FOUND")
return (None)
threads_num = int(threads)
if threads_num <= 1:
calibrationBoards = [process_single_image(img_path) for img_path in images]
else:
print("Running with %d threads..." % threads_num)
self.see = False
pool = ThreadPool(threads_num)
calibrationBoards = pool.map(process_single_image, images)
calibrationBoards = [x for x in calibrationBoards if x is not None]
for (img_path, corners, pattern_points, objPts, imgPts) in calibrationBoards:
working_images.append(img_path)
ids_all.append(corners)
corners_all.append(pattern_points)
img_points.append(imgPts)
obj_points.append(objPts)
self.calibration_df = pd.DataFrame({"image_names": working_images,
"ids_all": ids_all,
"corners_all": corners_all,
"img_points": img_points,
"obj_points": obj_points,
})
self.calibration_df.sort_values("image_names")
self.calibration_df = self.calibration_df.reset_index(drop=True)
cv2.destroyAllWindows()
print('Total datapoints:{}'.format(self.total))
print('start calibration corners_all:{}, ids_all:{}'.format(np.shape(np.array(self.calibration_df.corners_all).squeeze()),
np.shape(np.array(self.calibration_df.ids_all).ravel())))
def calibrate(self, flags=0, project=True, K=None, D=None, save=False, extended = False, old_style = False):
self.K = K
self.D = D
if self.K is not None:
print('Use fixed K - estimate only distortion')
flags |= cv2.CALIB_FIX_INTRINSIC
flags |= cv2.CALIB_FIX_PRINCIPAL_POINT
flags |= cv2.CALIB_USE_INTRINSIC_GUESS
flags |= cv2.CALIB_FIX_FOCAL_LENGTH
charucoCorners = np.array(self.calibration_df.corners_all)
charucoIds = np.array(self.calibration_df.ids_all)
if extended:
print('Extended version - calibration')
self.rms, self.K, self.D, self.rvecs, self.tvecs, \
self.stdDeviationsIntrinsics, self.stdDeviationsExtrinsics, self.perViewErrors = aruco.calibrateCameraCharucoExtended(
charucoCorners=charucoCorners, charucoIds=charucoIds,
board=self.CHARUCO_BOARD, imageSize=self.image_size,
cameraMatrix=K, distCoeffs=None,
flags=flags, criteria=self.term_criteria)
else:
if old_style:
objectPoints = np.array(self.calibration_df.obj_points)
imagePoints = np.array(self.calibration_df.img_points)
self.rms, self.K, self.D, self.rvecs, self.tvecs = cv2.calibrateCamera(
objectPoints=objectPoints,
imagePoints=imagePoints,
imageSize=self.image_size,
cameraMatrix=K, distCoeffs=None,
flags=flags, criteria=self.term_criteria)
else:
self.rms, self.K, self.D, self.rvecs, self.tvecs = aruco.calibrateCameraCharuco(
charucoCorners=charucoCorners, charucoIds=charucoIds,
board=self.CHARUCO_BOARD, imageSize=self.image_size,
cameraMatrix=K, distCoeffs=None,
flags=flags, criteria = self.term_criteria)
self.calibration_df['rvecs'] = pd.Series(self.rvecs)
self.calibration_df['tvecs'] = pd.Series(self.tvecs)
print("\nRMS:", self.rms)
print("camera matrix:\n", self.K)
print("distortion coefficients: ", self.D.ravel())
if project:
self._calc_reprojection_error(figure_size=self.figsize)
result_dictionary = {
"rms": self.rms,
"K": self.K,
"D": self.D,
}
if save:
save_obj(obj=result_dictionary, name=self.name)
return result_dictionary
def calibrationReport(self, K=None, old_style = False):
if K is None:
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']}
else:
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']
}
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']
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.calibrate(flags=flags, project=False, K=K, old_style = old_style)
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[str(key)] = calibration_results[str(key)].map(
lambda x: round(x, 4) if isinstance(x, (int, float)) else x).astype(str)
#if self.rms < min_error:
# min_error=self.rms
# 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+"_givenK" if K is not None else self.name)
return flag
def visualize_calibration_boards(self,cam_width=2,cam_height=1,scale_focal=4,scale = .05):
# Plot the camera centric view
self.visualize_views(
board_width=self.pattern_columns,
board_height=self.pattern_rows,
square_size=self.distance_in_world_units,
cam_width=cam_width*scale,
cam_height=cam_height*scale,
scale_focal=scale_focal*scale,
patternCentric=False,
)
# Plot the pattern centric view
self.visualize_views(
board_width=self.pattern_columns,
board_height=self.pattern_rows,
square_size=self.distance_in_world_units,
cam_width=cam_width*scale,
cam_height=cam_height*scale,
scale_focal=scale_focal*scale,
patternCentric=True,
)
def visualize_views(self,board_width,board_height,square_size,cam_width=32,cam_height=16,scale_focal=25,patternCentric=False,Animate=False):
def _inverse_homogeneoux_matrix(M):
# util_function
R = M[0:3, 0:3]
T = M[0:3, 3]
M_inv = np.identity(4)
M_inv[0:3, 0:3] = R.T
M_inv[0:3, 3] = -(R.T).dot(T)
return M_inv
def _transform_to_matplotlib_frame(cMo, X, inverse=False):
# util function
M = np.identity(4)
M[1, 1] = 0
M[1, 2] = 1
M[2, 1] = -1
M[2, 2] = 0
if inverse:
return M.dot(_inverse_homogeneoux_matrix(cMo).dot(X))
else:
return M.dot(cMo.dot(X))
def _create_camera_model(camera_matrix, width, height, scale_focal, draw_frame_axis=False):
# util function
fx = camera_matrix[0, 0]
fy = camera_matrix[1, 1]
focal = 2 / (fx + fy)
f_scale = scale_focal * focal
# draw image plane
X_img_plane = np.ones((4, 5))
X_img_plane[0:3, 0] = [-width, height, f_scale]
X_img_plane[0:3, 1] = [width, height, f_scale]
X_img_plane[0:3, 2] = [width, -height, f_scale]
X_img_plane[0:3, 3] = [-width, -height, f_scale]
X_img_plane[0:3, 4] = [-width, height, f_scale]
# draw triangle above the image plane
X_triangle = np.ones((4, 3))
X_triangle[0:3, 0] = [-width, -height, f_scale]
X_triangle[0:3, 1] = [0, -2 * height, f_scale]
X_triangle[0:3, 2] = [width, -height, f_scale]
# draw camera
X_center1 = np.ones((4, 2))
X_center1[0:3, 0] = [0, 0, 0]
X_center1[0:3, 1] = [-width, height, f_scale]
X_center2 = np.ones((4, 2))
X_center2[0:3, 0] = [0, 0, 0]
X_center2[0:3, 1] = [width, height, f_scale]
X_center3 = np.ones((4, 2))
X_center3[0:3, 0] = [0, 0, 0]
X_center3[0:3, 1] = [width, -height, f_scale]
X_center4 = np.ones((4, 2))
X_center4[0:3, 0] = [0, 0, 0]
X_center4[0:3, 1] = [-width, -height, f_scale]
# draw camera frame axis
X_frame1 = np.ones((4, 2))
X_frame1[0:3, 0] = [0, 0, 0]
X_frame1[0:3, 1] = [f_scale / 2, 0, 0]
X_frame2 = np.ones((4, 2))
X_frame2[0:3, 0] = [0, 0, 0]
X_frame2[0:3, 1] = [0, f_scale / 2, 0]
X_frame3 = np.ones((4, 2))
X_frame3[0:3, 0] = [0, 0, 0]
X_frame3[0:3, 1] = [0, 0, f_scale / 2]
if draw_frame_axis:
return [X_img_plane, X_triangle, X_center1, X_center2, X_center3, X_center4, X_frame1, X_frame2,
X_frame3]
else:
return [X_img_plane, X_triangle, X_center1, X_center2, X_center3, X_center4]
def _create_board_model(extrinsics, board_width, board_height, square_size, draw_frame_axis=False):
# util function
width = board_width * square_size
height = board_height * square_size
# draw calibration board
X_board = np.ones((4, 5))
# X_board_cam = np.ones((extrinsics.shape[0],4,5))
X_board[0:3, 0] = [0, 0, 0]
X_board[0:3, 1] = [width, 0, 0]
X_board[0:3, 2] = [width, height, 0]
X_board[0:3, 3] = [0, height, 0]
X_board[0:3, 4] = [0, 0, 0]
# draw board frame axis
X_frame1 = np.ones((4, 2))
X_frame1[0:3, 0] = [0, 0, 0]
X_frame1[0:3, 1] = [height / 2, 0, 0]
X_frame2 = np.ones((4, 2))
X_frame2[0:3, 0] = [0, 0, 0]
X_frame2[0:3, 1] = [0, height / 2, 0]
X_frame3 = np.ones((4, 2))
X_frame3[0:3, 0] = [0, 0, 0]
X_frame3[0:3, 1] = [0, 0, height / 2]
if draw_frame_axis:
return [X_board, X_frame1, X_frame2, X_frame3]
else:
return [X_board]
def _draw_camera_boards(ax, camera_matrix, cam_width, cam_height, scale_focal,
extrinsics, board_width, board_height, square_size,
patternCentric):
min_values = np.zeros((3, 1))
min_values = np.inf
max_values = np.zeros((3, 1))
max_values = -np.inf
if patternCentric:
X_moving = _create_camera_model(camera_matrix, cam_width, cam_height, scale_focal)
X_static = _create_board_model(extrinsics, board_width, board_height, square_size)
X_static = []
# Make data.
X = np.arange(0, 11, 1) * square_size
xlen = len(X)
Y = np.arange(-8, 0, 1) * square_size
ylen = len(Y)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X ** 2 + Y ** 2)
Z = np.zeros_like(R)
# Create an empty array of strings with the same shape as the meshgrid, and
# populate it with two colors in a checkerboard pattern.
colortuple = ('w', 'k')
colors = np.empty(X.shape, dtype=str)
for y in range(ylen):
for x in range(xlen):
colors[y, x] = colortuple[(x + y) % len(colortuple)]
# Plot the surface with face colors taken from the array we made.
surf = ax.plot_surface(X, Z, Y, facecolors=colors, linewidth=0)
else:
X_static = _create_camera_model(camera_matrix, cam_width, cam_height, scale_focal, True)
X_moving = _create_board_model(extrinsics, board_width, board_height, square_size)
cm_subsection = np.linspace(0.0, 1.0, extrinsics.shape[0])
colors = [cm.jet(x) for x in cm_subsection]
for i in range(len(X_static)):
X = np.zeros(X_static[i].shape)
for j in range(X_static[i].shape[1]):
X[:, j] = _transform_to_matplotlib_frame(np.eye(4), X_static[i][:, j])
ax.plot3D(X[0, :], X[1, :], X[2, :], color='r')
min_values = np.minimum(min_values, X[0:3, :].min(1))
max_values = np.maximum(max_values, X[0:3, :].max(1))
for idx in range(extrinsics.shape[0]):
R, _ = cv2.Rodrigues(extrinsics[idx, 0:3])
cMo = np.eye(4, 4)
cMo[0:3, 0:3] = R
cMo[0:3, 3] = extrinsics[idx, 3:6]
for i in range(len(X_moving)):
X = np.zeros(X_moving[i].shape)
for j in range(X_moving[i].shape[1]):
X[0:4, j] = _transform_to_matplotlib_frame(cMo, X_moving[i][0:4, j], patternCentric)
ax.plot3D(X[0, :], X[1, :], X[2, :], color=colors[idx])
min_values = np.minimum(min_values, X[0:3, :].min(1))
max_values = np.maximum(max_values, X[0:3, :].max(1))
return min_values, max_values
i = 0
extrinsics = np.zeros((len(self.rvecs), 6))
for rot, trans in zip(self.rvecs, self.tvecs):
extrinsics[i] = np.append(rot.flatten(), trans.flatten())
i += 1
# The extrinsics matrix is of shape (N,6) (No default)
# Where N is the number of board patterns
# the first 3 columns are rotational vectors
# the last 3 columns are translational vectors
fig = plt.figure(figsize=self.figure_size)
ax = fig.gca(projection='3d')
ax.set_aspect("auto")
#ax.set_aspect("equal")
min_values, max_values = _draw_camera_boards(ax, self.K, cam_width, cam_height,
scale_focal, extrinsics, board_width,
board_height, square_size, patternCentric)
X_min = min_values[0]
X_max = max_values[0]
Y_min = min_values[1]
Y_max = max_values[1]
Z_min = min_values[2]
Z_max = max_values[2]
max_range = np.array([X_max - X_min, Y_max - Y_min, Z_max - Z_min]).max() / 2.0
mid_x = (X_max + X_min) * 0.5
mid_y = (Y_max + Y_min) * 0.5
mid_z = (Z_max + Z_min) * 0.5
ax.set_xlim(mid_x - max_range, mid_x + max_range)
ax.set_ylim(mid_y - max_range, mid_y + max_range)
ax.set_zlim(mid_z - max_range, mid_z + max_range)
ax.set_xlabel('x')
ax.set_ylabel('z')
ax.set_zlabel('-y')
if patternCentric:
ax.set_title('Pattern Centric View')
if self.debug_dir:
plt.savefig(os.path.join(self.debug_dir, "pattern_centric_view.png"))
else:
ax.set_title('Camera Centric View')
if self.debug_dir:
plt.savefig(os.path.join(self.debug_dir, "camera_centric_view.png"))
plt.show()
def doStuff(self, images, project=False, single_flag=True, K=None, extended = False):
self.createCalibrationBoard()
self.read_images(images = images, threads=1)
if single_flag:
flags = 0
else:
flags = self.calibrationReport() # report with original data
self.calibrate(flags=flags, project=project, K=K, save=False, extended = extended)
#self.visualize_calibration_boards()
def adjustCalibration(self, K, D):
print('adjustCalibration -> estimate Windshield distortion -> undistort images -> calibrate again')
K_outside = K #assume the outside K matrix is ideal
D_outside = D #take the outside camera distortion
self.doStuff(images=self.images, project=False, single_flag=True, K=K_outside) #calibration with given K -> estimate only distortion params
D_inside_full = self.D #Estimated distortion = lens D + Windshield D
Windshield_distortion = np.abs(D_inside_full - D_outside) * np.sign(D_outside)
print('Windshield_distortion')
print(Windshield_distortion)
#read images and undistort them
self.read_images(images=self.images, K=K_outside, D=Windshield_distortion)
self.name = 'adjusted_'+self.name
flags = self.calibrationReport() # report with original data
#for the final calibration fix aspect ration
flags |= cv2.CALIB_FIX_ASPECT_RATIO
self.calibrate(flags=flags, project=True, save=True)
if __name__ == '__main__':
images = glob.glob(
'/home/eugeniu/Desktop/my_data/CameraCalibration/data/car_cam_data/charuco/outside/Left/*.png')
name = 'charuco_outside_left'
calibrator = MonoCharuco_Calibrator(name=name,figsize=(16, 14))
calibrator.createCalibrationBoard()
calibrator.read_images(images=images, threads=1)
calibrator.calibrate(extended=False, project=True, old_style=True)