import numpy as np
import napari
import os
import skimage.transform
from tifffile import imread, imwrite
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class image_visualizer():
"""
This class generates visualizations of highres segmentation results.
"""
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def __init__(self):
"""
Initialize data folders and start Napari
"""
self.rawdatafolder = "rawdatafolder"
self.segmentationfolder = "segmenteddatafolder"
self.visualizedfolder = "outputfolder"
self.region = "high_stack_001"
self.establish_param = 0
self.viewer = napari.Viewer()
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def load_images(self, vis_param):
""""
Load images from folder and render them according Visualization parameters specified.
:param vis_param: Python dictionary of visulization paramters, e.g. vis_param['camera_angle1']
"""
#get all timepoints from folder
dir_list = os.listdir(self.rawdatafolder)
timepointlist = []
for path in dir_list:
if path.startswith('t'):
timepointlist.append(path)
timepointlist.sort()
print(timepointlist)
#if you establish parameters, only open first timepoint
if self.establish_param==1:
timepointlist = ["t00000"]
i_time="t00000"
for i_time in timepointlist:
#generate filepaths and folders
rawimagepath_spheroid_magenta = os.path.join(self.rawdatafolder, i_time, self.region, vis_param['imagename_magenta'])
rawimagepath_spheroid_cyan = os.path.join(self.rawdatafolder, i_time, self.region, vis_param['imagename_cyan'])
visualization_folder1 = os.path.join(self.visualizedfolder, self.region, "angle_1a")
visualization_folder2 = os.path.join(self.visualizedfolder, self.region, "angle_2a")
visualization_folder3 = os.path.join(self.visualizedfolder, self.region, "angle_2a_frame")
try:
os.makedirs(visualization_folder1)
except OSError as error:
pass
try:
os.makedirs(visualization_folder2)
except OSError as error:
pass
try:
os.makedirs(visualization_folder3)
except OSError as error:
pass
visualized_file = os.path.join(visualization_folder1, i_time + ".tif")
visualized_file2 = os.path.join(visualization_folder2, i_time + ".tif")
visualized_file3 = os.path.join(visualization_folder3, i_time + ".tif")
#open images
image_magenta = imread(rawimagepath_spheroid_magenta)
image_cyan= imread(rawimagepath_spheroid_cyan)
#add images as layers
image_layer = self.viewer.add_image(image_magenta, gamma=vis_param['raw_gamma_magenta'], contrast_limits=vis_param['raw_contrast_limits_magenta'], colormap=vis_param['magenta_colormap'])
cancer_layer = self.viewer.add_image(image_cyan, gamma=vis_param['raw_gamma_cyan'], opacity=vis_param['opacity_cyan'], contrast_limits=vis_param['raw_contrast_limits_cyan'], colormap=vis_param['cyan_colormap'])
#set rendering to 3D and set camera zoom parameters
self.viewer.dims.ndisplay = vis_param['rendering_dimension']
self.viewer.camera.zoom = vis_param['camera_zoom']
#rescale 3D data to be correct dimensions
self.viewer.layers['image_magenta'].scale = vis_param['raw_rescale_factor']
self.viewer.layers['image_cyan'].scale = vis_param['raw_rescale_factor']
#interpolation to cubic
self.viewer.layers['image_magenta'].interpolation3d ='cubic'
self.viewer.layers['image_cyan'].interpolation3d ='cubic'
#save a first camera position
#get angle from napari by entering: viewer.camera.angles in console
self.viewer.layers['image_cyan'].bounding_box.visible=False
self.viewer.camera.angles = vis_param['camera_angle1']
imagereturn = self.viewer.screenshot(canvas_only=True, scale=vis_param['scale_to_save'])
imwrite(visualized_file, imagereturn)
#save a second camera position
self.viewer.camera.angles = vis_param['camera_angle2']
imagereturn2 = self.viewer.screenshot(canvas_only=True, scale=vis_param['scale_to_save'])
imwrite(visualized_file2, imagereturn2)
self.viewer.layers['image_cyan'].bounding_box.visible=True
imagereturn3 = self.viewer.screenshot(canvas_only=True, scale=vis_param['scale_to_save'])
imwrite(visualized_file3, imagereturn3)
#if you establish the parameters, run napari, otherwise delete the layers for next timepoint
if self.establish_param==1:
napari.run()
else:
self.viewer.layers.remove('image_magenta')
self.viewer.layers.remove('image_cyan')
if __name__ == '__main__':
visualization_param = dict(
camera_angle1=(9.00833898096139, -20.46583878838201, 63.542392104418575),
camera_angle2=(-6.2050461716664795, 25.240752035065167, -103.13040370948575),
camera_zoom=0.28,
#raw_contrast_limits=(126,482),
raw_contrast_limits_magenta=(78, 480),
raw_contrast_limits_cyan=(73, 1800),
#raw_gamma=0.67,
raw_gamma_magenta=0.60,
raw_gamma_cyan=0.33,
magenta_colormap ='magenta',
cyan_colormap='gray_r',
opacity_cyan=1,
opacity_label=1,
rendering_dimension=3,
label_blending='translucent_no_depth',
# raw_rescale_factor =[9.210526, 1, 1],
# label_rescale_factor = [9.210526, 1, 1],
raw_rescale_factor =[2.564, 1, 1],
label_rescale_factor =[2.564, 1, 1],
#raw_rescale_factor=[1, 1, 1],
#label_rescale_factor=[1, 1, 1],
establish_param=0,
set_label_colormap='default',
scale_to_save=5,
display_rawcancersignal=0,
imagename_cyan="1_CH488_000000.tif",
imagename_magenta="1_CH594_000000.tif"
)
imagevisu = image_visualizer()
imagevisu.rawdatafolder = "/archive/bioinformatics/Danuser_lab/Fiolka/LabMembers/Stephan/multiscale_data/xenograft_experiments/MDA-MB-231/20230720_control/Experiment0010"
experimentfolder_result = imagevisu.rawdatafolder + "_highres_visualized"
imagevisu.segmentationfolder = os.path.join(experimentfolder_result, 'high_stack_001')
imagevisu.visualizedfolder = os.path.join(experimentfolder_result, 'visualized_frame')
imagevisu.region = 'high_stack_002'
imagevisu.establish_param = 0
imagevisu.load_images(visualization_param)