def get_interpolator(self, image, threshold):
im = imread(image)
if threshold is not None:
im[im < threshold] = 0.0
im_sparse = sparse.coo_matrix(im)
# Interpolation function
return interpolate.Rbf(
im_sparse.col, im_sparse.row, im_sparse.data, function="gaussian"
)
def get_interpolator(self, image, threshold):
im = imread(image)
if threshold is not None:
im[im < threshold] = 0.0
im_sparse = sparse.coo_matrix(im)
# Interpolation function
return interpolate.NearestNDInterpolator(
(im_sparse.col, im_sparse.row), im_sparse.data
)
def construct_image_points(path, origin):
image = imread(str(path))
# image = image.astype(np.uint8)
# Rescale
scale = image.max()
image = image * (256 / scale)
params = SimpleCircleGrid.create_default_params()
circle_grid = SimpleCircleGrid(params)
centers = circle_grid.detect_grid(image, origin, nx=7, ny=7, ds=50)
return centers
def view(self, path, title=None, hide_crosshair=True):
"""
ImageView, a high-level widget for displaying and analyzing 2D and 3D
data. ImageView provides:
1. A zoomable region (ViewBox) for displaying the image
2. A combination histogram and gradient editor (HistogramLUTItem)
for controlling the visual appearance of the image
3. A timeline for selecting the currently displayed frame (for 3D
data only).
4. Tools for very basic analysis of image data (see ROI and Norm
buttons)
"""
imv = pg.ImageView()
win = self._win(title)
self._add_gfx_item(win, imv)
if not isinstance(path, str):
data = []
for p in path:
logger.info(f"Viewing {p}")
data.append(imread(p).transpose())
data = np.array(data)
imv.setImage(data, xvals=np.linspace(0, len(path), data.shape[0]))
elif Path(path).is_dir():
raise ValueError("Expected files not directory.")
else:
logger.info(f"Viewing {path}")
try:
data = imread(path).transpose()
except AttributeError:
raise ValueError(f"Is {path} an image?")
imv.setImage(data)
vb = imv.imageItem.getViewBox()
self._add_crosshair(win, imv, vb, hide_lines=hide_crosshair)
def compute_kx(self, serie):
if len(serie) == 0:
logger.warning("0 ref image. Use of default k_x = 70.75.")
return 70.75
names = serie.get_name_arrays()
ref = np.zeros((self.ymax - self.ymin, self.xmax - self.xmin))
ii = 0
for name in names:
array = imread(str(Path(self.path_ref) / name))
frame = array[self.ymin:self.ymax,
self.xmin:self.xmax].astype(float)
frame = self.frame_normalize(frame)
ref = ref + frame
ii += 1
ref = ref / ii
return self.wave_vector(ref, self.ymin, self.ymax, self.xmin,
self.xmax, self.sur)
def test_calibrate(self):
"""Tests construct_object_points and CalibCV methods."""
path_input = pathbase / "cam0" / "0mm_cam0.tif"
with stdout_redirected():
calib = CalibCV("fluidimage_test_calib_cv.h5")
result_cache = self.calib_cache.params
origin = (250, 250) # Hardcoded origin
imgpoints = [construct_image_points(path_input, origin)]
objpoints = [construct_object_points(7, 7, 0.0, 3.0)]
zs = [0]
im_shape = imread(str(path_input)).shape[::-1]
ret, mtx, dist, rvecs, tvecs = calib.calibrate(
imgpoints, objpoints, zs, im_shape, origin, debug=True
)
assert_array_equal(mtx, result_cache.cam_mtx)
assert_almost_equal(rvecs[0], result_cache.rotation[2])
assert_almost_equal(tvecs[0], result_cache.translate[2])
def imread(self, path):
array = imread(path)
return (array, path)
def imread(self, path):
"""Read an image"""
array = imread(path)
return (array, path)
def _imread(path):
image = imread(path)
return image, Path(path).name
def __init__(self, params, logging_level="info", nb_max_workers=None):
self.params = params
self.serie = SerieOfArraysFromFiles(
params.images.path, params.images.str_slice
)
path_dir = Path(self.serie.path_dir)
path_dir_result, self.how_saving = prepare_path_dir_result(
path_dir, params.saving.path, params.saving.postfix, params.saving.how
)
self.path_dir_result = path_dir_result
self.path_dir_src = Path(path_dir)
if not isinstance(params.reference, int):
reference = Path(params.reference).expanduser()
else:
reference = params.reference
if isinstance(reference, int):
names = self.serie.get_name_arrays()
names = sorted(names)
path_reference = path_dir / names[reference]
else:
reference = Path(reference)
if reference.is_file():
path_reference = reference
else:
path_reference = path_dir_result / reference
if not path_reference.is_file():
raise ValueError(
"Bad value of params.reference:" + path_reference
)
self.name_reference = path_reference.name
self.path_reference = path_reference
self.image_reference = imread(path_reference)
super().__init__(
path_dir_result=path_dir_result,
logging_level=logging_level,
nb_max_workers=nb_max_workers,
)
queue_paths = self.add_queue("paths")
queue_arrays = queue_arrays1 = self.add_queue("arrays")
queue_bos = self.add_queue("bos")
if params.preproc.im2im is not None:
queue_arrays1 = self.add_queue("arrays1")
self.add_work(
"fill paths",
func_or_cls=self.fill_queue_paths,
output_queue=queue_paths,
kind=("global", "one shot"),
)
def _imread(path):
image = imread(path)
return image, Path(path).name
self.add_work(
"read array",
func_or_cls=_imread,
input_queue=queue_paths,
output_queue=queue_arrays,
kind="io",
)
if params.preproc.im2im is not None:
im2im_func = image2image.TopologyImage2Image.init_im2im(
self, params.preproc
)
self.add_work(
"image2image",
func_or_cls=im2im_func,
input_queue=queue_arrays,
output_queue=queue_arrays1,
)
self.add_work(
"compute bos",
func_or_cls=self.calcul,
params_cls=params,
input_queue=queue_arrays,
output_queue=queue_bos,
)
self.add_work(
"save bos",
func_or_cls=self.save_bos_object,
input_queue=queue_bos,
kind="io",
)
