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 high-resolution 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.segmentationfolder)
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
segmentedimagepath = os.path.join(self.segmentationfolder, i_time, vis_param['imagename_label'])
rawimagepath = os.path.join(self.rawdatafolder, i_time, self.region, vis_param['imagename_raw'])
rawimagepath_cancer = os.path.join(self.rawdatafolder, i_time, self.region, vis_param['imagename_cancer'])
visualization_folder1 = os.path.join(self.visualizedfolder, self.region, "angle_1a")
visualization_folder2 = os.path.join(self.visualizedfolder, self.region, "angle_2a")
try:
os.makedirs(visualization_folder1)
except OSError as error:
pass
try:
os.makedirs(visualization_folder2)
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")
#open images
input_image = imread(rawimagepath)
cancer_image= imread(rawimagepath_cancer)
label_image = imread(segmentedimagepath)
print(input_image.shape)
label_image_rescaled = skimage.transform.resize(label_image, input_image.shape, order=0)
print("image rescaled")
#add images as layers
image_layer = self.viewer.add_image(input_image, gamma=vis_param['raw_gamma'], contrast_limits=vis_param['raw_contrast_limits'])
cancer_layer = self.viewer.add_image(cancer_image, gamma=vis_param['raw_gamma_cancer'], opacity=vis_param['opacity_cancer'], contrast_limits=vis_param['raw_contrast_limits_cancer'], colormap='red')
layer_image_rescaled = self.viewer.add_labels(label_image_rescaled, opacity=vis_param['opacity_label'], blending=vis_param['label_blending'])
#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['input_image'].scale = vis_param['raw_rescale_factor']
self.viewer.layers['cancer_image'].scale = vis_param['raw_rescale_factor']
self.viewer.layers['label_image_rescaled'].scale = vis_param['label_rescale_factor']
#save a first camera position
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 without vasculature
#get angle from napari by entering: viewer.camera.angles in console
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)
#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('label_image_rescaled')
self.viewer.layers.remove('input_image')
self.viewer.layers.remove('cancer_image')
if __name__ == '__main__':
visualization_param = dict(
camera_angle1=(172, -32, 115),
camera_angle2=(6, -49, -95),
camera_zoom=0.32,
raw_contrast_limits=(77,730),
raw_contrast_limits_cancer=(104, 201),
raw_gamma=0.7,
raw_gamma_cancer=1,
opacity_cancer=0.32,
opacity_label=1,
rendering_dimension=3,
label_blending='additive',
# raw_rescale_factor =[9.210526, 1, 1],
# label_rescale_factor = [9.210526, 1, 1],
raw_rescale_factor =[3.418, 1, 1],
label_rescale_factor =[3.418, 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_label="labels_xy-merged.tif",
imagename_raw="1_CH594_000000.tif",
imagename_cancer="1_CH552_000000.tif"
)
imagevisu = image_visualizer()
imagevisu.rawdatafolder = "/archive/bioinformatics/Danuser_lab/Fiolka/LabMembers/Stephan/multiscale_data/xenograft_experiments/U2OS_WT/20220729_Daetwyler_U2OS/Experiment0001"
experimentfolder_result = imagevisu.rawdatafolder + "_highres_manuallyCompiled2"
imagevisu.segmentationfolder = os.path.join(experimentfolder_result, 'high_stack_002')
imagevisu.visualizedfolder = os.path.join(experimentfolder_result, 'visualized_bright2')
imagevisu.region = 'high_stack_002'
imagevisu.establish_param = 0
imagevisu.load_images(visualization_param)