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.
"""
[docs]
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_macrophages = 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_macrophages)
cancer_image= imread(rawimagepath_cancer)
#add images as layers
image_layer = self.viewer.add_image(input_image, gamma=vis_param['raw_gamma'], contrast_limits=vis_param['raw_contrast_limits'], colormap=vis_param['raw_colormap'])
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=vis_param['cancer_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['input_image'].scale = vis_param['raw_rescale_factor']
self.viewer.layers['cancer_image'].scale = vis_param['raw_rescale_factor']
#interpolation to cubic
self.viewer.layers['input_image'].interpolation3d ='cubic'
self.viewer.layers['cancer_image'].interpolation3d ='cubic'
#self.viewer.layers['label_image_rescaled'].scale = vis_param['label_rescale_factor']
self.viewer.layers.remove('cancer_image')
#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']
#self.viewer.layers.remove('cancer_image')
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('input_image')
#self.viewer.layers.remove('cancer_image')
if __name__ == '__main__':
visualization_param = dict(
camera_angle1=(-179.3128327479437, 22.754843487827394, 94.38287509770109),
camera_angle2=(-3.372776166910392, -1.3888899435527813, -105.76142446338508),
camera_zoom=0.27,
#raw_contrast_limits=(126,482),
raw_contrast_limits=(87, 657),
raw_contrast_limits_cancer=(102, 1529),
#raw_gamma=0.67,
raw_gamma=0.45,
raw_gamma_cancer=0.59,
raw_colormap ='gray_r',
cancer_colormap='gray',
opacity_cancer=0.58,
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 =[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_raw="1_CH488_000000.tif",
imagename_cancer="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_bright4')
imagevisu.region = 'high_stack_002'
imagevisu.establish_param = 0
imagevisu.load_images(visualization_param)
#adapt image
pathfile = os.path.join(experimentfolder_result, "visualized_bright4", "angle_1a.tif")
finalimage = imread(pathfile)
# make boundingbox and bound image
binarybox = np.zeros(finalimage.shape)
binarybox[:, 812:2151, 348:3076,:] = 1
pathfile_save = os.path.join(experimentfolder_result, "binarybox.tif")
imwrite(pathfile_save, np.uint8(binarybox))
finalimage[np.logical_and(finalimage==0, np.logical_not(binarybox))]=202
print("test")
selectedimage = finalimage + binarybox
anotherstack_indices = np.where(selectedimage == 0)
anotherstack_indices2 = np.where(finalimage == 0)
finalimage[anotherstack_indices] = 202
print("start file writing")
pathfile_save = os.path.join(experimentfolder_result, "angle_1aV2.tif")
imwrite(pathfile_save, np.uint8(finalimage))