def calibrate(imagedir, cbrow, cbcol):
nimages = 0
datapoints = []
im_dims = (0,0)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = numpy.zeros((cbrow * cbcol, 3), numpy.float32)
objp[:, :2] = numpy.mgrid[0:cbcol, 0:cbrow].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.
files = file_list(imagedir, ['jpg', 'jpeg', 'png'])
for f in files:
colour = cv2.imread(f)
grey = cv2.cvtColor(colour, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(grey, (cbcol, cbrow), flags=cv2.CALIB_CB_ADAPTIVE_THRESH)
if (ret):
print('using ' + f)
cv2.cornerSubPix(grey,corners,(11,11),(-1,-1),(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.01))
objpoints.append(objp)
imgpoints.append(corners)
im_dims = grey.shape[:2]
if len(imgpoints) == 0:
print("Not enough good quality images. Aborting")
return
ret, K, D, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, grey.shape[::-1], None, None)
# storing results using CameraParams
C = CameraParams(lens=lens, sensorwidth=sensorwidth, xresolution=im_dims[1], yresolution=im_dims[0])
C.setParams(K, D)
C.save(os.path.join(imagedir, "paramsout.json"))
print("Saved params in " + os.path.join(imagedir, "paramsout.json"))
def test_cam_params_dict():
C = CameraParams(lens=4.0, sensorwidth=5.0, xresolution=3000, yresolution=4000)
C.setParams([[1, 2, 3], [4, 5, 6]], [0, 6, 7, 8])
Cdict = C.todict()
C2dict = CameraParams.fromdict(Cdict)
assert C.todict() == C2dict.todict()
assert numpy.array_equal(C.K, C2dict.K)
assert numpy.array_equal(C.D, C2dict.D)
def test_cam_params_txt():
C = CameraParams(lens=4.0, sensorwidth=5.0, xresolution=1280, yresolution=960)
C.setParams([[1, 2, 3], [4, 5, 6]], [0, 6, 7, 8])
C.save('foo.txt')
C2 = CameraParams.fromfile('foo.txt')
assert str(C) == str(C2)
assert numpy.array_equal(C.K, C2.K)
assert numpy.array_equal(C.D, C2.D)
os.remove('foo.txt')
def test_cam_params_dict():
C = CameraParams(lens=4.0,
sensorwidth=5.0,
xresolution=3000,
yresolution=4000)
C.setParams([[1, 2, 3], [4, 5, 6]], [0, 6, 7, 8])
Cdict = C.todict()
C2dict = CameraParams.fromdict(Cdict)
assert C.todict() == C2dict.todict()
assert numpy.array_equal(C.K, C2dict.K)
assert numpy.array_equal(C.D, C2dict.D)
def test_cam_params_txt():
C = CameraParams(lens=4.0,
sensorwidth=5.0,
xresolution=1280,
yresolution=960)
C.setParams([[1, 2, 3], [4, 5, 6]], [0, 6, 7, 8])
C.save('foo.txt')
C2 = CameraParams.fromfile('foo.txt')
assert str(C) == str(C2)
assert numpy.array_equal(C.K, C2.K)
assert numpy.array_equal(C.D, C2.D)
os.remove('foo.txt')
def calibrate(imagedir, cbrow, cbcol):
nimages = 0
datapoints = []
im_dims = (0, 0)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = numpy.zeros((cbrow * cbcol, 3), numpy.float32)
objp[:, :2] = numpy.mgrid[0:cbcol, 0:cbrow].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.
files = file_list(imagedir, ['jpg', 'jpeg', 'png'])
for f in files:
colour = cv2.imread(f)
grey = cv2.cvtColor(colour, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(
grey, (cbcol, cbrow), flags=cv2.CALIB_CB_ADAPTIVE_THRESH)
if (ret):
print('using ' + f)
cv2.cornerSubPix(
grey, corners, (11, 11), (-1, -1),
(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.01))
objpoints.append(objp)
imgpoints.append(corners)
im_dims = grey.shape[:2]
if len(imgpoints) == 0:
print("Not enough good quality images. Aborting")
return
ret, K, D, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
grey.shape[::-1], None, None)
# storing results using CameraParams
C = CameraParams(lens=lens,
sensorwidth=sensorwidth,
xresolution=im_dims[1],
yresolution=im_dims[0])
C.setParams(K, D)
C.save(os.path.join(imagedir, "paramsout.json"))
print("Saved params in " + os.path.join(imagedir, "paramsout.json"))