def gui():
# setting up tkinter window
root = tkinter.Tk()
root.title("Generate Fluorescence Aggregates")
# root.geometry("300x125")
# root.configure(background='white')
root.resizable(False, False)
# configuring grid for resizable buttons
tkinter.Grid.columnconfigure(root, 0, weight=1)
for y in range(3):
tkinter.Grid.rowconfigure(root, y, weight=1)
# creating a button for each main function
bin_to_binary_btn = tkinter.ttk.Button(
root,
text="Binary To PNG",
command=lambda: bin_conversion.convert_dirs(fd.askdirectory(title="Please select a ps2 collection"))
)
bin_to_binary_btn.grid(column=0, row=0, sticky="nsew", padx=2, pady=2, ipadx=10, ipady=7, columnspan=2)
imagej_macro_btn = tkinter.ttk.Button(
root,
text="ImageJ Macro",
command=run_imagej_macro
)
imagej_macro_btn.grid(column=0, row=1, sticky="nsew", padx=2, pady=2, ipadx=10, ipady=7)
image_segmentation_btn = tkinter.ttk.Button(
root,
text="Python Macro",
command=lambda: image_segmentation.process_collection(fd.askdirectory(title="Please select a ps2 collection"))
)
image_segmentation_btn.grid(column=1, row=1, sticky="nsew", padx=2, pady=2, ipadx=10, ipady=7)
# getting offsets
with open(os.path.join(this_dir, "static", "ps2", "sensor_fixed_metadata.json"), 'r') as f:
ps2_dict = json.load(f)
ps2_x = float(ps2_dict[0]['location_in_camera_box_m']['x'])
ps2_y = float(ps2_dict[0]['location_in_camera_box_m']['y'])
# calling single process
# using a file dialog, ask the user for a collection path
# use the Plot boundaries.xlsx and multithresh json next to this file
# use offsets from sensor_fixed_metadata.json
generate_aggregate_btn = tkinter.ttk.Button(
root,
text="Generate Aggregate and Fluorescence",
command=lambda: fluorescence_aggregation.single_process(
filepath=fd.askdirectory(title="Please select a ps2 collection"),
plot_boundaries=os.path.join(this_dir, 'static', 'Plot boundaries.xlsx'),
multithresh_json=os.path.join(this_dir, 'static', 'multithresh.json'),
offset_x=ps2_x,
offset_y=ps2_y
),
)
generate_aggregate_btn.grid(column=0, row=2, sticky="nsew", padx=2, pady=2, ipadx=10, ipady=7, columnspan=2)
# starting gui
root.mainloop()
def headless(directory, output, processes=-1):
"""
run the full pipeline on a directory without a gui
parameters:
directory: input directory with ps2 data
output: output directory
processes: max number of multiprocesses. default: max available
1. convert bins to pngs
2. do image segmentation (this is where the imagej macro
would go, but it has been rewritten)
3. get ps2 offsets
4. generate aggregate files
5. generate fluorescence file
"""
print("Converting bins to pngs")
bin_conversion.convert_dirs(
directory,
processes=processes) # converting all images in a dir from bin to png
print("segmenting images")
# imagej_macro(args.macro, args.directory) # old fiji macro
image_segmentation.process_collection(directory) # new python macro
# getting offsets
with open(
os.path.join(this_dir, "static", "ps2",
"sensor_fixed_metadata.json"), 'r') as f:
ps2_dict = json.load(f)
ps2_x = float(ps2_dict[0]['location_in_camera_box_m']['x'])
ps2_y = float(ps2_dict[0]['location_in_camera_box_m']['y'])
print("generating aggregates")
fluorescence_aggregation.generate_aggregate(
directory, output,
os.path.join(this_dir, "static", 'S10_Plot_boundaries.csv'),
os.path.join(this_dir, "static", 'multithresh.json'), ps2_x,
ps2_y) # generating aggregates
fluorescence_aggregation.generate_fluorescence(
directory, output, True) # generate fluorescence files
print("Finished processing", directory)
def headless(directory):
"""run the full pipeline on a directory without a gui"""
bin_conversion.convert_dirs(directory) # converting all images in a dir from bin to png
# imagej_macro(args.macro, args.directory) # old fiji macro
image_segmentation.process_collection(directory) # new python macro
# getting offsets
with open(os.path.join(this_dir, "static", "ps2", "sensor_fixed_metadata.json"), 'r') as f:
ps2_dict = json.load(f)
ps2_x = float(ps2_dict[0]['location_in_camera_box_m']['x'])
ps2_y = float(ps2_dict[0]['location_in_camera_box_m']['y'])
fluorescence_aggregation.generate_aggregate(
directory,
os.path.join(this_dir, "static", 'Plot boundaries.xlsx'),
os.path.join(this_dir, "static", 'multithresh.json'),
ps2_x,
ps2_y
) # generating aggregates
fluorescence_aggregation.generate_fluorescence(directory, True) # generate fluorescence files
print("Finished processing", directory)
def main():
args = parse_args()
# converting all images in a dir from bin to png
print("Converting bins to pngs")
bin_conversion.convert_dirs(args.directory, processes=args.processes)
def gui():
import tkinter
import tkinter.filedialog as fd
import tkinter.ttk
def run_imagej_macro():
"""
runs a command that will call imagej with a specified macro
"""
# trying to find a local imagej installation, but if one is not found, ask the user where it is
imagej_path = os.path.join(this_dir, 'Fiji.app/ImageJ-win64.exe')
if not os.path.exists(imagej_path):
imagej_path = fd.askopenfilename(
title="Could not find imagej, please select your imagej.exe")
# asking the user where their imagej macro is.
macro_path = fd.askopenfilename(title="Please select an imagej macro")
# if the user cancels either of the selections, return
if not macro_path or not imagej_path:
return
subprocess.call([imagej_path, "-macro", macro_path])
# setting up tkinter window
root = tkinter.Tk()
root.title("Generate Fluorescence Aggregates")
# root.geometry("300x125")
# root.configure(background='white')
root.resizable(False, False)
# creating a button for each main function
bin_to_binary_btn = tkinter.ttk.Button(
root,
text="Binary To PNG",
command=lambda: bin_conversion.convert_dirs(
fd.askdirectory(title="Please select a ps2 collection")))
bin_to_binary_btn.grid(column=0,
row=0,
sticky="nsew",
padx=2,
pady=2,
ipadx=10,
ipady=7,
columnspan=2)
imagej_macro_btn = tkinter.ttk.Button(root,
text="ImageJ Macro",
command=run_imagej_macro)
imagej_macro_btn.grid(column=0,
row=1,
sticky="nsew",
padx=2,
pady=2,
ipadx=10,
ipady=7)
image_segmentation_btn = tkinter.ttk.Button(
root,
text="Python Macro",
command=lambda: image_segmentation.process_collection(
fd.askdirectory(title="Please select a ps2 collection")))
image_segmentation_btn.grid(column=1,
row=1,
sticky="nsew",
padx=2,
pady=2,
ipadx=10,
ipady=7)
# getting offsets
with open(
os.path.join(this_dir, "static", "ps2",
"sensor_fixed_metadata.json"), 'r') as f:
ps2_dict = json.load(f)
ps2_x = float(ps2_dict[0]['location_in_camera_box_m']['x'])
ps2_y = float(ps2_dict[0]['location_in_camera_box_m']['y'])
# calling single process
# using a file dialog, ask the user for a collection path
# use the Plot boundaries.csv and multithresh json next to this file
# use offsets from sensor_fixed_metadata.json
generate_aggregate_btn = tkinter.ttk.Button(
root,
text="Generate Aggregate and Fluorescence",
command=lambda: fluorescence_aggregation.single_process(
filepath=fd.askdirectory(title="Please select a ps2 collection"),
plot_boundaries=os.path.join(this_dir, 'static',
'S10_Plot_boundaries.csv'),
multithresh_json=os.path.join(this_dir, 'static',
'multithresh.json'),
offset_x=ps2_x,
offset_y=ps2_y),
)
generate_aggregate_btn.grid(column=0,
row=2,
sticky="nsew",
padx=2,
pady=2,
ipadx=10,
ipady=7,
columnspan=2)
# starting gui
root.mainloop()