import numpy as np
import napari
import os
import skimage.transform
from tifffile import imread, imwrite
[docs]
class image_visualizer():
"""
This class generates visualizations of low-resolution rawdata of spheroids.
"""
[docs]
def __init__(self):
"""
Initialize data folders and start Napari
"""
# initialize data folders
rawdatafolder_parent = "folderpath"
self.rawdatafolder = "rawdatafolder"
self.segmentationfolder = "segmenteddatafolder"
self.visualizedfolder = "outputfolder"
# start napari viewer
self.viewer = napari.Viewer()
[docs]
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:
print(i_time)
#generate filepaths and folders
rawimagepath_spheroid_magenta = os.path.join(self.rawdatafolder, i_time, vis_param['imagename_magenta'])
rawimagepath_spheroid_cyan = os.path.join(self.rawdatafolder, i_time, vis_param['imagename_cyan'])
visualization_folder1 = os.path.join(self.visualizedfolder, "angle_1a")
visualization_folder2 = os.path.join(self.visualizedfolder, "angle_2a")
visualization_folder3 = os.path.join(self.visualizedfolder, "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_magentabig = imread(rawimagepath_spheroid_magenta)
image_cyanbig= imread(rawimagepath_spheroid_cyan)
print(image_magentabig.shape)
# downsample huge low-resolution raw images to same size as label images (which are isotropic in voxel size)
image_magenta = skimage.transform.resize(image_magentabig, output_shape=(200, 666, 1000), order=0)
image_cyan = skimage.transform.resize(image_cyanbig, output_shape=(200, 666, 1000), order=0)
#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'], blending='additive')
#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=(-90.12192998901911, -11.908473792258878, 59.457226017303086),
camera_angle2=(7.6471045194606635, -20.182789486876842, 72.3066107960275),
camera_zoom=0.42,
raw_contrast_limits_magenta=(160, 2742),
raw_contrast_limits_cyan=(335, 2944),
raw_gamma_magenta=0.78,
raw_gamma_cyan=0.76,
magenta_colormap ='magenta',
cyan_colormap='cyan',
opacity_cyan=0.64,
rendering_dimension=3,
raw_rescale_factor =[3.58, 1, 1],
establish_param=0,
scale_to_save=5,
imagename_cyan="1_CH488_000000.tif",
imagename_magenta="1_CH594_000000.tif"
)
imagevisu = image_visualizer()
imagevisu.rawdatafolder = "/archive/bioinformatics/Danuser_lab/Fiolka/Manuscripts/2023-multiscale/rawdata/Spheroid/Low_resolution/"
experimentfolder_result = imagevisu.rawdatafolder + "_lowres_visualized_rotated"
imagevisu.visualizedfolder = os.path.join(experimentfolder_result, 'visualized_frame')
imagevisu.establish_param = 0
imagevisu.load_images(visualization_param)