def calibrate():
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
Nx_cor = 9
Ny_cor = 6
objp = np.zeros((Nx_cor * Ny_cor, 3), np.float32)
objp[:, :2] = np.mgrid[0:Nx_cor, 0:Ny_cor].T.reshape(-1, 2)
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
count = 0 # count 用来标志成功检测到的棋盘格画面数量
while (True):
ret, frame = cap.read()
if cv2.waitKey(1) & 0xFF == ord(' '):
# Our operations on the frame come here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (Nx_cor, Ny_cor),
None) # Find the corners
# If found, add object points, image points
if ret == True:
corners = cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1),
criteria)
objpoints.append(objp)
imgpoints.append(corners)
cv2.drawChessboardCorners(frame, (Nx_cor, Ny_cor), corners,
ret)
count += 1
if count > 20:
break
# Display the resulting frame
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
global mtx, dist
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
gray.shape[::-1], None,
None)
print(mtx, dist)
mean_error = 0
for i in range(len(objpoints)):
imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i],
mtx, dist)
error = cv2.norm(imgpoints[i], imgpoints2,
cv2.NORM_L2) / len(imgpoints2)
mean_error += error
print("total error: ", mean_error / len(objpoints))
# # When everything done, release the capture
np.savez('calibrate.npz', mtx=mtx, dist=dist[0:4])
def calibrate_intrinsic(object_points, image_points, image_size):
"""Calibrate camera intrinsic pattern from input object points(real
coordinates of points on chessboard calibration rig) and input image
points(points on the chessboard as seen by camera).
"""
__, cam_mat, dist_coeff, __, __ = cv2.calibrateCamera(
object_points, image_points, image_size, 0, 0)
return cam_mat, dist_coeff
def calibrateOne(img_path, cameraParam):
criteria = (cv2.TERM_CRITERIA_MAX_ITER | cv2.TERM_CRITERIA_EPS, 30, 0.001)
#设置寻找亚像素角点的参数，采用的停止准则是最大循环次数30和最大误差容限0.001
#获取标定板角点的位置
objp = np.zeros((4 * 7, 3), np.float32)
objp[:, :2] = np.mgrid[0:7, 0:4].T.reshape(-1, 2) #将世界坐标系建在标定板上
obj_points = [] #3D points
img_points = [] #2D points
images = glob.glob(img_path)
i = 0
for fname in images:
img = cv2.imread(fname)
#print(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
size = gray.shape[::-1]
h, w = gray.shape[:2]
ret, corners = cv2.findChessboardCorners(gray, (7, 4), None)
if ret:
obj_points.append(objp)
corners2 = cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1),
criteria)
#在原角点的基础上寻找亚像素点
#print(corners2)
if [corners2]:
img_points.append(corners2)
else:
img_points.append(corners)
cv2.drawChessboardCorners(img, (7, 4), corners, ret)
i += 1
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points,
size, None, None,
criteria)
#dist——>dist coffients(就是k、p的值)
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(
mtx, dist, (w, h), 1, (w, h)) #显示更大范围的图片（正常重映射之后会删掉一部分图像）
#将fx，fy（世界坐标系的焦距）投射到fu，fv（图像坐标系的焦距）上，立体矫正时会用
#dst = cv2.undistort(img,mtx,dist,None,newcameramtx)
#x,y,w,h = roi
cameraParam['ret'] = ret
cameraParam['mtx'] = mtx
cameraParam['dist'] = dist
cameraParam['rvecs'] = rvecs
cameraParam['tvecs'] = tvecs
cameraParam['img_points'] = img_points
cameraParam['obj_points'] = obj_points
cameraParam['size'] = size
cameraParam['newmtx'] = newcameramtx
print("Calibrate One Camera Success")
def cal_undistort(img, obj_points, img_points):
"""
Parameter:
img:
obj_points:
img_points:
Return:
dst:
"""
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(
obj_points, img_points, img.shape[1::-1], None, None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
return dst
def calculate_camera_matrix_and_distortion_coefficients(camera_model_name):
# 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) ....,(7,5,0)
object_points = np.zeros((6 * 8, 3), np.float32)
object_points[:, :2] = np.mgrid[0:8, 0:6].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
real_world_points = [] # 3d point in real world space
image_points = [] # 2d points in image plane.
image_paths = glob.glob("calibration_images/*.png")
for path_to_image in image_paths:
img = cv2.imread(path_to_image)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
chess_board_successfully_detected, corners = cv2.findChessboardCorners(
gray, (8, 6), None)
print("Found chess board in: " + path_to_image + " : " +
str(chess_board_successfully_detected))
# If found, add object points, image points (after refining them)
if chess_board_successfully_detected:
real_world_points.append(object_points)
refined_corners = cv2.cornerSubPix(gray, corners, (11, 11),
(-1, -1), criteria)
image_points.append(refined_corners)
# Draw and display the corners
# img = cv2.drawChessboardCorners(img, (7, 6), refined_corners, chess_board_successfully_detected)
# cv2.imshow('img', resize_image(img, (1920, 1080)))
# cv2.waitKey()
cv2.destroyAllWindows()
ret, camera_matrix, distortion_coeffs, rvecs, tvecs = cv2.calibrateCamera(
real_world_points, image_points, gray.shape[::-1], None, None)
h, w = gray.shape[:2]
camera_matrix_with_crop, roi = cv2.getOptimalNewCameraMatrix(
camera_matrix, distortion_coeffs, (w, h), 1, (w, h))
file_name = f"camera_calibration_{camera_model_name}.npz"
np.savez(file_name,
camera_matrix=camera_matrix,
distortion_coeffs=distortion_coeffs,
camera_matrix_with_crop=camera_matrix_with_crop)
def find_calibration_matrix(images):
# Prepare real world points
rwp = np.zeros((7 * 9, 3), np.float32)
rwp[:, :2] = np.mgrid[0:7, 0:9].T.reshape(-1, 2)
# Arrays to store 3D points in the real world and 2D points in image plane
rw_points = []
image_points = []
for image in images:
img = cv2.imread(image)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Take in a 8-bit grayscale image and the number of internal corners of chessboard and flags.
# CLAIB_CV_FAST_CHECK = check quickly for corners and end if none to save time
found, corners = cv2.findChessboardCorners(
img_gray, (7, 9), flags=cv2.CALIB_CB_FAST_CHECK
)
if found:
rw_points.append(rwp)
# Needs accuracy below pixel level
corners = cv2.cornerSubPix(
img_gray,
corners,
(11, 11),
(-1, -1),
(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001),
)
image_points.append(corners)
# Draws the corners on picture
# cv2.drawChessboardCorners(img, (7, 9), cornersnew, retval)
# cv2.imshow('img', img)
# cv2.waitKey()
else:
print("no corners found in " + image, file=sys.stderr)
cv2.destroyAllWindows()
_, cameramatrix, distortioncoefficients, _, _ = cv2.calibrateCamera(
rw_points, image_points, img_gray.shape[::-1], None, None
)
return cameramatrix, distortioncoefficients
def generate_distortion_config(self, image_shape: Tuple[int, int]):
"""This method should only be called if the distortion matrix and
coefficients have not been calculated or the calibration images
have changed
"""
dist_pickle = pickle.load(
open(self.uuid + '_' + _OBJ_IMG_PTS_PICKLE_FILE_NAME, "rb"))
objpoints = dist_pickle["objpoints"]
imgpoints = dist_pickle["imgpoints"]
_, mtx, dist, _, _ = cv2.calibrateCamera(objpoints, imgpoints,
image_shape, None, None)
self._distortion_matrix = mtx
self._distortion_coefficients = dist
self._store_config(
self.uuid + '_' + _DIST_MTX_COE_PICKLE_FILE_NAME, {
'mtx': self._distortion_matrix,
'coefficients': self._distortion_coefficients,
'pattern_size': self.pattern_size
})
def get_callibration_stuff():
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
cap = cv2.VideoCapture('pictures/ChessboardPattern/11.mp4')
nx, ny = 8, 6
objp = np.zeros((8 * 6, 3), np.float32)
objp[:, :2] = np.mgrid[0:8, 0:6].T.reshape(-1, 2)
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
fps = get_fps(cap)
num_frames = get_frames(cap)
frames_with_corners = 0
def skip_frame(video):
ret2, frame2 = video.read()
if ret2:
curr_frame = video.get(cv2.CAP_PROP_POS_FRAMES)
if curr_frame <= num_frames:
cap.set(cv2.CAP_PROP_POS_FRAMES, curr_frame + 0.5 * fps)
return frame2
while (cap.isOpened()):
ret, frame = cap.read()
if ret == True:
frame = skip_frame(cap)
r_frame_bw = get_resized_and_bw(frame, 40)
found_corners, corners = cv2.findChessboardCorners(
r_frame_bw, (nx, ny), None)
if found_corners:
frames_with_corners += 1
objpoints.append(objp)
corners2 = cv2.cornerSubPix(r_frame_bw, corners, (11, 11),
(-1, -1), criteria)
imgpoints.append(corners2)
if frames_with_corners == 16:
break
cap.release()
cv2.destroyAllWindows()
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
r_frame_bw.shape[::-1],
None, None)
return ret, mtx, dist, rvecs, tvecs
def findcorners(self):
plt.close('all')
# termination criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.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((11 * 8, 3), np.float32)
objp[:, :2] = np.mgrid[0:11, 0:8].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.
for i in range(1, 16):
img_name = str(i) + '.bmp'
fname = os.path.join('Datasets/Q2_Image', img_name)
img = cv.imread(fname)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv.findChessboardCorners(gray, (11, 8), None)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
corners2 = cv.cornerSubPix(gray, corners, (11, 11), (-1, -1),
criteria)
imgpoints.append(corners)
# Draw and display the corners
cv.drawChessboardCorners(img, (11, 8), corners2, ret)
plt_img = img[:, :, ::-1]
plt.figure(fname)
plt.imshow(plt_img)
ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(
objpoints, imgpoints, gray.shape[::-1], None, None)
np.savez('output.npz', mtx, dist, rvecs, tvecs)
np.savez('img_obj_points.npz', imgpoints, objpoints)
plt.show()
def calib(imgpath,camroot,nx,ny):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny * nx, 3), np.float32)
objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2)*290
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob(imgpath+'/*.jpg')
if len(images) > 0:
print("images num for calibration : ", len(images))
else:
print("No image for calibration.")
return
ret_count = 0
for _, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = (img.shape[1], img.shape[0])
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
#cv2.drawChessboardCorners(img,(nx,ny),corners,ret)
# If found, add object points, image points
if ret == True:
ret_count += 1
objpoints.append(objp)
imgpoints.append(corners)
print("read image:",ret_count)
if len(objpoints)==0:
print("failed to find corners")
return
ret, cameraMatrix, distCoeffs, _, _ = cv2.calibrateCamera(objpoints, imgpoints,img_size, None, None)
print('Do calibration successfully')
np.savetxt(camroot+"cameraMatrix.txt",cameraMatrix,fmt="%.18f",delimiter=',')
np.savetxt(camroot+"distCoeffs.txt",distCoeffs,fmt="%.18f",delimiter=',')
return ret,cameraMatrix, distCoeffs
img_name = str(i) + '.bmp'
fname = os.path.join('Datasets/Q2_Image', img_name)
img = cv.imread(fname)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv.findChessboardCorners(gray, (11, 8), None)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
corners2 = cv.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
imgpoints.append(corners)
# Draw and display the corners
cv.drawChessboardCorners(img, (11, 8), corners2, ret)
ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(
objpoints, imgpoints, gray.shape[::-1], None, None)
h, w = img.shape[:2]
newcameramtx, roi = cv.getOptimalNewCameraMatrix(
mtx, dist, (w, h), 1, (w, h))
# print(roi)
mapx, mapy = cv.initUndistortRectifyMap(mtx, dist, None, newcameramtx,
(w, h), 5)
dst = cv.remap(img, mapx, mapy, cv.INTER_LINEAR)
x, y, w, h = roi
dst = dst[y:y + h, x:x + w]
npz = np.load('./output.npz')
print(npz.files)
# print(corners2)
if [corners2]:
img_points.append(corners2)
else:
img_points.append(corners)
# 把角点画出来
cv2.drawChessboardCorners(img, (8, 6), corners,
ret) # 记住，OpenCV的绘制函数一般无返回值
# cv2.imwrite('c-'+str(0).zfill(4)+'.png', img) # 文件命名补齐函数zfill
# 保存绘制角度后的图像
# cv2.imwrite(image_name +".png", img)
# print(len(img_points))
# cv2.destroyAllWindows()
# 标定
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points,
size, None, None)
# print("ret:", ret)
# print("mtx:\n", mtx) # 内参数矩阵
# print("dist:\n", dist) # 畸变系数 distortion cofficients = (k_1,k_2,p_1,p_2,k_3)
# print("rvecs:\n", rvecs) # 旋转向量 # 外参数
# print("tvecs:\n", tvecs ) # 平移向量 # 外参数
# print("-----------------------------------------------------")
# img = cv2.imread(images[2])
# h, w = img.shape[:2]
# newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),1,(w,h))#显示更大范围的图片（正常重映射之后会删掉一部分图像）
# print (newcameramtx)
# print("------------------使用undistort函数-------------------")
# dst = cv2.undistort(img,mtx,dist,None,newcameramtx)
# x,y,w,h = roi
# dst1 = dst[y:y+h,x:x+w]
def calibrate():
#棋盘角点数col*row
col = 13
row = 6
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
objp = np.zeros((row * col, 3), np.float32)
# 用于标定的棋盘每个方格边长为22mm
objp[:, :2] = 22*np.mgrid[0:col, 0:row].T.reshape(-1, 2)
objpoints = [] # 世界坐标系下的点坐标
imgpoints = [] # 像素平面坐标系下的点坐标
print("请选择标定用到的照片所在的文件夹", "\n")
root = tkinter.Tk()
root.withdraw()
global path # 用于标定的照片所在目录
path = tkinter.filedialog.askdirectory(
title="选择标定用到的照片所在的文件夹") # 选择标定用到的照片所在的文件夹
images = glob.glob(path+"/*.jpg")
found = 0 # 记录用于标定的图像数目
for k, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (col, row), None)
# 角点检测
if ret is True:
print("读取", fname)
objpoints.append(objp)
# 角点检测精度会影响标定的精度
corners2 = cv2.cornerSubPix(
gray, corners, (11, 11), (-1, -1), criteria)#亚像素角点位置
# corners2=corners
#_,corners2=cv2.find4QuadCornerSubpix(gray, corners, (11, 11))
imgpoints.append(corners2)
img = cv2.drawChessboardCorners(img, (col, row), corners2, ret)#标记角点
found += 1
if len(images) < 16: # 图片过多时，不在UI中展示，避免弹窗过多
cv2.namedWindow('press any key to continue', cv2.WINDOW_NORMAL)
cv2.imshow('press any key to continue', img)
cv2.waitKey(0)
#image_name = path2 + "//corner"+str(found) + '.png'
#cv2.imwrite(image_name, img)
#存储已标出角点的照片
global path2 # 存放结果的目录（含记录相机参数的文件，和畸变矫正后的照片，3-D box照片）
path2 = tkinter.filedialog.askdirectory(
title="选择结果存放的文件夹(应与用于标定的照片所在的文件夹不同)") # 选择结果存放的文件夹
print("Number of images used for calibration: ", found)
# 相机标定
ret2, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
gray.shape[::-1], None, None)
print("reprojection error:", ret2)
print("内参矩阵:", mtx)
print("畸变系数：", dist)
print("旋转向量：", rvecs)
print("平移向量：", tvecs)
images = glob.glob(path+"//*.jpg")
for i, fname in enumerate(images):
img = cv2.imread(fname)
if img is None:
continue
h, w = img.shape[:2]
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1,
(w, h))
dst = cv2.undistort(img, mtx, dist, None, newcameramtx) # 矫正畸变
x, y, w, h = roi
dst = dst[y:y + h, x:x + w]#裁剪
outpath = path2+"//tianyi_gao_undistorted" + str(i + 1) + ".jpg"
cv2.imwrite(outpath, dst)
print("新内参矩阵：", newcameramtx)
data = {
'camera_matrix': np.asarray(mtx).tolist(),
'dist_coeff': np.asarray(dist).tolist(),
'new_camera_matrix': np.asarray(newcameramtx).tolist(),
'rvecs': np.asarray(rvecs).tolist(),
'tvecs': np.asarray(tvecs).tolist(),
'reprojection_error': np.asarray(ret2).tolist()
}
# 存储相机参数(yaml)
with open(path2+"//calibration_parameters.yaml", "w") as f:
yaml.dump(data, f)
# 存储相机参数(txt)
with open(path2+"//tianyi_gao_cam.txt", "w") as f2:
name = list(data.keys())
value = list(data.values())
for i in range(len(name)):
f2.write(name[i] + ":" + "\n" + str(value[i]) + "\n")
print('Calibrate Done')
cv2.destroyAllWindows()
return mtx, dist, rvecs, tvecs, ret2, path2
print("Image loaded, Analyzing...")
#find chessboard corners
ret,corners = cv2.findChessboardCorners(gray_image, chessboard_size, None)
if ret == True:
print("Chessboard detected!")
print(image_path)
#define criteria for subpixel accuracy
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
#refine corner location (to subpixel accuracy) based on criteria.
cv2.cornerSubPix(gray_image, corners, (5,5), (-1,-1), criteria)
obj_points.append(objp)
img_points.append(corners)
#Calibrate camera
ret, K, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points,gray_image.shape[::-1], None, None)
#Save parameters into numpy file
np.save("./camera_params/ret", ret)
np.save("./camera_params/K", K)
np.save("./camera_params/dist", dist)
np.save("./camera_params/rvecs", rvecs)
np.save("./camera_params/tvecs", tvecs)
#Get exif data in order to get focal length.
exif_img = PIL.Image.open(calibration_paths[0])
stuff = exif_img.getexif().items()
exif_data = {
PIL.ExifTags.TAGS[k]:v
for k, v in exif_img.getexif().items()
cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
#追加进入世界三维点和平面二维点中
objpoints.append(objp)
imgpoints.append(corners)
# 将角点在图像上显示
cv2.drawChessboardCorners(img, (w,h), corners, ret)
cv2.namedWindow('findCorners', cv2.WINDOW_NORMAL)
cv2.resizeWindow('findCorners', 640, 480)
cv2.imshow('findCorners',img)
cv2.waitKey(200)
cv2.destroyAllWindows()
#%% 标定
print('正在计算')
#标定
ret, mtx, dist, rvecs, tvecs = \
cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
print("ret:",ret )
print("mtx:\n",mtx) # 内参数矩阵
print("dist畸变值:\n",dist ) # 畸变系数 distortion cofficients = (k_1,k_2,p_1,p_2,k_3)
print("rvecs旋转（向量）外参:\n",rvecs) # 旋转向量 # 外参数
print("tvecs平移（向量）外参:\n",tvecs ) # 平移向量 # 外参数
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (u, v), 0, (u, v))
print('newcameramtx外参',newcameramtx)
#打开摄像机
camera=cv2.VideoCapture(0)
while True:
(grabbed,frame)=camera.read()
h1, w1 = frame.shape[:2]
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (u, v), 0, (u, v))
print("Couple with " + t + " has been added")
if (True == retR) & (True == retL):
objpoints.append(objp)
cv2.cornerSubPix(ChessImaR, cornersR, (11, 11), (-1, -1),
criteria)
cv2.cornerSubPix(ChessImaL, cornersL, (11, 11), (-1, -1),
criteria)
imgpointsR.append(cornersR)
imgpointsL.append(cornersL)
break
elif (cv2.waitKey(2) & 0xFF == ord('q')):
break
cv2.destroyAllWindows()
# Determine the new values for different parameters
# Right Side
retR, mtxR, distR, rvecsR, tvecsR = cv2.calibrateCamera(
objpoints, imgpointsR, ChessImaR.shape[::-1], None, None)
hR, wR = ChessImaR.shape[:2]
OmtxR, roiR = cv2.getOptimalNewCameraMatrix(mtxR, distR, (wR, hR), 1,
(wR, hR))
# Left Side
retL, mtxL, distL, rvecsL, tvecsL = cv2.calibrateCamera(
objpoints, imgpointsL, ChessImaL.shape[::-1], None, None)
hL, wL = ChessImaL.shape[:2]
OmtxL, roiL = cv2.getOptimalNewCameraMatrix(mtxL, distL, (wL, hL), 1,
(wL, hL))
print('Cameras Ready to use')
print("Camera calibration value: " + str(retL) + " and " + str(retR))
# ********************************************
r_frame = cv2_helpers.get_resized(frame, 40)
r_frame_bw = cv2_helpers.get_resized_and_bw(frame, 40)
cv2.imshow("something", r_frame)
found_corners, corners = cv2.findChessboardCorners(
r_frame_bw, (nx, ny), None)
if found_corners:
frames_with_corners += 1
objpoints.append(objp)
corners2 = cv2.cornerSubPix(r_frame_bw, corners, (11, 11),
(-1, -1), criteria)
imgpoints.append(corners2)
new_img = cv2.drawChessboardCorners(r_frame, (nx, ny), corners,
ret)
cv2.imshow("test", new_img)
if frames_with_corners == 16:
break
cap.release()
cv2.destroyAllWindows()
print("callibrating the given video...")
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
r_frame_bw.shape[::-1],
None, None)
print("ret: ", ret)
print("mtx: ", mtx)
print("dist: ", dist)
print("rvecs: ", rvecs)
print("tvecs: ", tvecs)
