return multichannel image to use in cellpose worklfow

[pr4deepr]

Currently, napari-lattice Workflow module iterates through each channel and applies the custom processing defined in the workflow.
For cellpose, if a multichannel image is given, this can be used as input for improved prediction using cyto models.
For example if channel 1 is nuclei and channel 2 is cytoplasm, segmentation maybe more accurate on the combined multichannel image than on channel 1 or two individually.

Either new function for this or alter the code here:

napari_lattice/src/napari_lattice/io.py

Lines 358 to 395 in e77e4b5

	for time_point in tqdm(time_range, desc="Time", position=0):
	output_array = []
	data_table = []
	for ch in tqdm(channel_range, desc="Channels", position=1,leave=False):
	if len(vol.shape) == 3:
	raw_vol = vol
	else:
	raw_vol = vol[time_point, ch, :, :, :]
	#TODO: disable if support for resourc backed dask array is added
	if type(raw_vol) in [resource_backed_dask_array]:
	raw_vol = raw_vol.compute() #convert to numpy array as resource backed dask array not su
	#to access current time and channel, create a file config.py in same dir as workflow or in home directory
	#add "channel = 0" and "time=0" in the file and save
	#https://docs.python.org/3/faq/programming.html?highlight=global#how-do-i-share-global-variables-across-modules
	config.channel = ch
	config.time = time_point
	#if deconvolution, need to define psf and choose the channel appropriate one
	if deconvolution:
	workflow.set(psf_arg,psf[ch])
	#if decon_processing == "cuda_gpu":
	#workflow.set("psf",psf[ch])
	#else:
	#workflow.set("psf",psf[ch])
	#Set input to the workflow to be volume from each time point and channel
	workflow.set(input_arg,raw_vol)
	#execute workflow
	processed_vol = workflow.get(last_task)
	output_array.append(processed_vol)
	output_array = np.array(output_array)