def registration(
self,
laser="640",
grid_points_x=2,
grid_points_y=3,
registration_pattern="circles",
):
x_coords = np.linspace(0, 768, grid_points_x + 2)[1:-1]
y_coords = np.linspace(0, 1024, grid_points_y + 2)[1:-1]
x_mesh, y_mesh = np.meshgrid(x_coords, y_coords)
x_coords = np.ravel(x_mesh)
y_coords = np.ravel(y_mesh)
dmd_coordinates = np.stack((x_coords, y_coords), axis=1)
camera_coordinates = np.zeros(dmd_coordinates.shape)
for i in range(dmd_coordinates.shape[0]):
x = int(dmd_coordinates[i, 0])
y = int(dmd_coordinates[i, 1])
if registration_pattern == "squares":
mask = DMDRegistator.create_registration_image_touching_squares(
x, y)
else:
mask = DMDRegistator.create_registration_image_circle(x, y)
self.DMD.send_data_to_DMD(mask)
self.DMD.start_projection()
image = self.cam.SnapImage(0.01)
plt.imsave(
os.getcwd() +
"/CoordinatesManager/Registration_Images/TouchingSquares/image_"
+ str(i) + ".png",
image,
)
camera_coordinates[
i, :] = readRegistrationImages.touchingCoordinateFinder(
image, method="curvefit")
self.DMD.stop_projection()
print("DMD coordinates:")
print(dmd_coordinates)
print("Found camera coordinates:")
print(camera_coordinates)
self.DMD.free_memory()
self.cam.Exit()
transformation = CoordinateTransformations.polynomial2DFit(
camera_coordinates, dmd_coordinates, order=1)
print("Transformation found for x:")
print(transformation[:, :, 0])
print("Transformation found for y:")
print(transformation[:, :, 1])
return transformation
def get_transformation(self):
order = self.transformation_order_spinbox.value()
self.transformation = CoordinateTransformations.polynomial2DFit(
np.asarray(self.img_coords), np.asarray(self.abs_coords), order)
string_x = self.create_string_to_print(self.transformation[:, :, 0])
string_y = self.create_string_to_print(self.transformation[:, :, 1])
self.found_transformation_text.setText("for x:\n" + string_x +
"\n for y:\n" + string_y)
def transform_coordinates(self, list_of_rois):
new_list_of_rois = []
for roi in list_of_rois:
new_list_of_rois.append(
np.flip(
CoordinateTransformations.transform(
np.flip(roi), self.transform)))
return new_list_of_rois
def transform_coordinates(self, list_of_rois):
laser = self.transform_for_laser_menu.selectedItems()[0].text()
new_list_of_rois = []
for roi in list_of_rois:
new_list_of_rois.append(
CoordinateTransformations.transform(roi,
self.transform[laser]))
return new_list_of_rois
def transform_coordinates(self, list_of_rois, for_which_laser):
"""
Receive a list of rois and targeted laser on which the DMD will be shined on, calculate the transformation.
"""
new_list_of_rois = []
for roi in list_of_rois:
new_list_of_rois.append(
CoordinateTransformations.transform(
roi, self.transform[for_which_laser]))
return new_list_of_rois
def registration(self, grid_points_x=3, grid_points_y=3):
galvothread = DAQmission()
readinchan = []
x_coords = np.linspace(-10, 10, grid_points_x + 2)[1:-1]
y_coords = np.linspace(-10, 10, grid_points_y + 2)[1:-1]
xy_mesh = np.reshape(np.meshgrid(x_coords, y_coords), (2, -1),
order='F').transpose()
galvo_coordinates = xy_mesh
camera_coordinates = np.zeros((galvo_coordinates.shape))
for i in range(galvo_coordinates.shape[0]):
galvothread.sendSingleAnalog('galvosx', galvo_coordinates[i, 0])
galvothread.sendSingleAnalog('galvosy', galvo_coordinates[i, 1])
time.sleep(1)
image = self.cam.SnapImage(0.06)
plt.imsave(
os.getcwd() +
'/CoordinatesManager/Registration_Images/2P/image_' + str(i) +
'.png', image)
camera_coordinates[i, :] = readRegistrationImages.gaussian_fitting(
image)
print('Galvo Coordinate')
print(galvo_coordinates)
print('Camera coordinates')
print(camera_coordinates)
del galvothread
self.cam.Exit()
transformation = CoordinateTransformations.polynomial2DFit(
camera_coordinates, galvo_coordinates, order=1)
print('Transformation found for x:')
print(transformation[:, :, 0])
print('Transformation found for y:')
print(transformation[:, :, 1])
return transformation
def transform_coordinates(self, list_of_rois):
"""
Given list of roi positions in camera image, transform into corrseponding
voltage positions.
Parameters
----------
list_of_rois : list
DESCRIPTION.
Returns
-------
new_list_of_rois : TYPE
DESCRIPTION.
"""
new_list_of_rois = []
for roi in list_of_rois:
new_list_of_rois.append(np.flip(CoordinateTransformations.transform(np.flip(roi), self.transform)))
return new_list_of_rois
def registration(self, grid_points_x=3, grid_points_y=3):
"""
By default, generate 9 galvo voltage coordinates from (-5,-5) to (5,5),
take the camera images of these points, return a function matrix that
transforms camera_coordinates into galvo_coordinates using polynomial transform.
Parameters
----------
grid_points_x : TYPE, optional
DESCRIPTION. The default is 3.
grid_points_y : TYPE, optional
DESCRIPTION. The default is 3.
Returns
-------
transformation : TYPE
DESCRIPTION.
"""
galvothread = DAQmission()
readinchan = []
x_coords = np.linspace(-10, 10, grid_points_x + 2)[1:-1]
y_coords = np.linspace(-10, 10, grid_points_y + 2)[1:-1]
xy_mesh = np.reshape(np.meshgrid(x_coords, y_coords), (2, -1),
order='F').transpose()
galvo_coordinates = xy_mesh
camera_coordinates = np.zeros((galvo_coordinates.shape))
for i in range(galvo_coordinates.shape[0]):
galvothread.sendSingleAnalog('galvosx', galvo_coordinates[i, 0])
galvothread.sendSingleAnalog('galvosy', galvo_coordinates[i, 1])
time.sleep(1)
image = self.cam.SnapImage(0.06)
plt.imsave(
os.getcwd() +
'/CoordinatesManager/Registration_Images/2P/image_' + str(i) +
'.png', image)
camera_coordinates[i, :] = readRegistrationImages.gaussian_fitting(
image)
print('Galvo Coordinate')
print(galvo_coordinates)
print('Camera coordinates')
print(camera_coordinates)
del galvothread
self.cam.Exit()
transformation_cam2galvo = CoordinateTransformations.polynomial2DFit(
camera_coordinates, galvo_coordinates, order=1)
transformation_galvo2cam = CoordinateTransformations.polynomial2DFit(
galvo_coordinates, camera_coordinates, order=1)
print('Transformation found for x:')
print(transformation_cam2galvo[:, :, 0])
print('Transformation found for y:')
print(transformation_cam2galvo[:, :, 1])
print('galvo2cam found for x:')
print(transformation_galvo2cam[:, :, 0])
print('galvo2cam found for y:')
print(transformation_galvo2cam[:, :, 1])
return transformation_cam2galvo