def acquireImage(channelGroup, channelName, hook):
x_array = []
y_array = []
z_array = []
for idx in range(pos_list.get_number_of_positions()):
pos = pos_list.get_position(idx)
#pos.go_to_position(pos, mmc)
x = pos_list.get_position(idx).get(0).x
y = pos_list.get_position(idx).get(0).y
z = pos_list.get_position(idx).get(1).x
x_array.append(x)
y_array.append(y)
z_array.append(z)
x_array = np.array(x_array)
y_array = np.array(y_array)
z_array = np.array(z_array)
with Acquisition(directory=directoryPATH,
name=nameofSAVEDFILE,
post_hardware_hook_fn=hook,
post_camera_hook_fn=hook_fn) as acq:
x = np.hstack([x_array[:, None]])
y = np.hstack([y_array[:, None]])
z = np.hstack([z_array[:, None]])
#Generate the events for a single z-stack
xyz = np.hstack([x_array[:, None], y_array[:, None], z_array[:, None]])
events = multi_d_acquisition_events(xyz_positions=xyz,
channel_group=channelGroup,
channels=[channelName])
acq.acquire(events)
#acquire a 2 x 1 grid
#acq.acquire({'row': 0, 'col': 0})
#acq.acquire({'row': 1, 'col': 0})
stackfolder = "**/*"
folder = Path(directoryPATH)
foldernames = []
for name in folder.glob('saving_name_*'):
print(name.stem)
foldernames.append(name.stem)
maximum = 1
for file in foldernames:
number = int(
re.search(nameofSAVEDFILE + "_" + '(\d*)',
file).group(1)) # assuming filename is "filexxx.txt"
# compare num to previous max, e.g.
maximum = number if number > maximum else maximum # set max = 0 before for-loop
print(number)
highest = nameofSAVEDFILE + "_" + str(maximum)
data_path = os.path.join(folder, highest)
dataset = Dataset(data_path)
#dataset = acq.get_dataset()
#dataset = acq.get_dataset()
#print(dataset)
#data_path=str(directoryPATH/saving_name)
dataset = Dataset(data_path)
print(dataset.axes)
print("data_path", data_path)
length = (len(xyz))
dataset_metadata = dataset.read_metadata(channel=0, position=1)
print(dataset_metadata)
pos = dataset_metadata["Axes"]["position"]
print(pos)
if (dataset):
sizeimg = dataset.read_image(channel=0, position=0)
sizeimg = cv2.cvtColor(sizeimg, cv2.COLOR_GRAY2RGB)
h, w, c = sizeimg.shape
length = int(
(sqrt(length)
)) #size of the grid (row or column should be same technically)
blank_image = np.zeros((h * (math.ceil(math.sqrt(length)) + 2), w *
(math.ceil(math.sqrt(length)) + 2), 3), np.uint16)
print("image size ", blank_image.shape)
pixelsizeinum = dataset_metadata["PixelSizeUm"] #get size of pixel in um
print(pixelsizeinum)
"""
for datarow in range(10):
for datacolumn in range(10):
metadata = dataset.read_metadata(row=datarow, col=datacolumn)
if(metadata["Axes"]["position"]>=0):
pos=metadata["Axes"]["position"]
#print(pos)
img = dataset.read_image(position=pos)
img = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
cv2.imshow("test",img)
"""
xtotaloffset = 0
ytotaloffset = 0
for dataposition in range(
len(xyz)): #do range for all positions in micromanager
print(dataposition)
metadata = dataset.read_metadata(channel=0, position=dataposition)
img = dataset.read_image(channel=0, position=dataposition)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img = cv2.flip(img, 1)
xoffset_um = metadata["XPosition_um_Intended"]
yoffset_um = metadata["YPosition_um_Intended"]
print("Intended location is : ", xoffset_um, yoffset_um)
# cv2.imshow("test",img)
# cv2.waitKey(0)
xoffset_px = (xoffset_um - dataset.read_metadata(
channel=0, position=0)['XPosition_um_Intended']) / pixelsizeinum
yoffset_px = (yoffset_um - dataset.read_metadata(
channel=0, position=0)['YPosition_um_Intended']) / pixelsizeinum
xoffset_px = int(xoffset_px)
print("Xoffset ", xoffset_px)
#print("img max X ",blank_image.shape[0])
yoffset_px = int(yoffset_px)
print("Yoffset ", yoffset_px)
#print("img max Y ",blank_image.shape[1])
alpha = 0
blank_image[xoffset_px:xoffset_px + (img.shape[1]),
yoffset_px:yoffset_px + (img.shape[0])] = cv2.addWeighted(
blank_image[xoffset_px:xoffset_px + (img.shape[1]),
yoffset_px:yoffset_px + (img.shape[0])],
alpha, img, 1 - alpha, 0)
#blank_image[:yoffset_px+img.shape[0], :xoffset_px+img.shape[1]] = img
#blank_image = cv2.addWeighted(blank_image[yoffset_px:yoffset_px+img.shape[0], xoffset_px:xoffset_px+img.shape[1]],img)
####################
#printout only ignore
####################
scale_percent = 5
width = int(blank_image.shape[1] * scale_percent / 100)
height = int(blank_image.shape[0] * scale_percent / 100)
dim = (width, height)
resized = cv2.resize(blank_image, dim, interpolation=cv2.INTER_AREA)
'''
#show image
winname = "test"
cv2.namedWindow(winname) # Create a named window
cv2.moveWindow(winname, 1000,1000) # Move it to (40,30)
cv2.imshow(winname, resized)
cv2.waitKey(0)
'''
blank_image = cv2.cvtColor(blank_image, cv2.COLOR_BGR2GRAY)
return blank_image, pixelsizeinum
def direct_stitch(data_path):
dataset = Dataset(data_path)
print(dataset.axes)
dataset_metadata = dataset.read_metadata(channel=0, position=0)
print(dataset_metadata)
pos = dataset_metadata["Axes"]["position"]
print(pos)
if (dataset):
sizeimg = dataset.read_image(channel=0, position=0)
sizeimg = cv2.cvtColor(sizeimg, cv2.COLOR_GRAY2RGB)
h, w, c = sizeimg.shape
length = 10 #size of the grid (row or column should be same technically)
blank_image = np.ones((h * (length + 1), w * (length + 1), 3), np.uint16)
print("image size ", blank_image.shape)
print(dataset_metadata)
pixelsizeinum = dataset_metadata["PixelSizeUm"] #get size of pixel in um
print(pixelsizeinum)
"""
for datarow in range(10):
for datacolumn in range(10):
metadata = dataset.read_metadata(row=datarow, col=datacolumn)
if(metadata["Axes"]["position"]>=0):
pos=metadata["Axes"]["position"]
#print(pos)
img = dataset.read_image(position=pos)
img = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
cv2.imshow("test",img)
"""
xtotaloffset = 0
ytotaloffset = 0
for dataposition in range(70):
print(dataposition)
metadata = dataset.read_metadata(channel=0, position=dataposition)
img = dataset.read_image(channel=0, position=dataposition)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img = cv2.flip(img, 1)
xoffset_um = metadata["XPosition_um_Intended"]
yoffset_um = metadata["YPosition_um_Intended"]
print("Intended location is : ", xoffset_um, yoffset_um)
# cv2.imshow("test",img)
# cv2.waitKey(0)
xoffset_px = (xoffset_um - dataset.read_metadata(
channel=0, position=0)['XPosition_um_Intended']) / pixelsizeinum
yoffset_px = (yoffset_um - dataset.read_metadata(
channel=0, position=0)['YPosition_um_Intended']) / pixelsizeinum
xoffset_px = int(xoffset_px)
print("Xoffset ", xoffset_px)
#print("img max X ",blank_image.shape[0])
yoffset_px = int(yoffset_px)
print("Yoffset ", yoffset_px)
#print("img max Y ",blank_image.shape[1])
blank_image[xoffset_px:xoffset_px + (img.shape[1]),
yoffset_px:yoffset_px + (img.shape[0])] += img
#blank_image[:yoffset_px+img.shape[0], :xoffset_px+img.shape[1]] = img
#blank_image = cv2.addWeighted(blank_image[yoffset_px:yoffset_px+img.shape[0], xoffset_px:xoffset_px+img.shape[1]],img)
####################
#printout only ignore
####################
scale_percent = 5
width = int(blank_image.shape[1] * scale_percent / 100)
height = int(blank_image.shape[0] * scale_percent / 100)
dim = (width, height)
resized = cv2.resize(blank_image, dim, interpolation=cv2.INTER_AREA)
winname = "test"
cv2.namedWindow(winname) # Create a named window
cv2.moveWindow(winname, 1000, 1000) # Move it to (40,30)
cv2.imshow(winname, resized)
cv2.waitKey(0)
return blank_image