How to use aicsimageprocessing - 6 common examples
To help you get started, we’ve selected a few aicsimageprocessing examples, based on popular ways it is used in public projects.
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AllenInstitute / aics-ml-segmentation / aicsmlsegment / bin / exp_scheduler.py View on Github 
if len(args.ResizeRatio)>0:
img = resize(img, (1, args.ResizeRatio[0], args.ResizeRatio[1], args.ResizeRatio[2]), method='cubic')
for ch_idx in range(img.shape[0]):
struct_img = img[ch_idx,:,:,:] # note that struct_img is only a view of img, so changes made on struct_img also affects img
struct_img = (struct_img - struct_img.min())/(struct_img.max() - struct_img.min())
img[ch_idx,:,:,:] = struct_img
# apply the model
output_img = model_inference(model, img, softmax, args)
for ch_idx in range(len(args.OutputCh)//2):
writer = omeTifWriter.OmeTifWriter(args.OutputDir + pathlib.PurePosixPath(fn).stem + '_T_'+ f'{tt:03}' +'_seg_'+ str(args.OutputCh[2*ch_idx])+'.ome.tif')
if args.Threshold<0:
out = output_img[ch_idx].astype(float)
out = resize(out, (1.0, 1/args.ResizeRatio[0], 1/args.ResizeRatio[1], 1/args.ResizeRatio[2]), method='cubic')
writer.save(out)
else:
out = output_img[ch_idx] > args.Threshold
out = resize(out, (1.0, 1/args.ResizeRatio[0], 1/args.ResizeRatio[1], 1/args.ResizeRatio[2]), method='nearest')
out = out.astype(np.uint8)
out[out>0]=255
writer.save(out)
else:
img = img0[0,:,:,:].astype(float)
if img.shape[1] < img.shape[0]:
img = np.transpose(img,(1,0,2,3))
img = img[args.InputCh,:,:,:]
img = input_normalization(img, args)
if len(args.ResizeRatio)>0:
img = resize(img, (1, args.ResizeRatio[0], args.ResizeRatio[1], args.ResizeRatio[2]), method='cubic')writer.save(out)
else:
out = output_img[ch_idx] > args.Threshold
out = resize(out, (1.0, 1/args.ResizeRatio[0], 1/args.ResizeRatio[1], 1/args.ResizeRatio[2]), method='nearest')
out = out.astype(np.uint8)
out[out>0]=255
writer.save(out)
else:
img = img0[0,:,:,:].astype(float)
if img.shape[1] < img.shape[0]:
img = np.transpose(img,(1,0,2,3))
img = img[args.InputCh,:,:,:]
img = input_normalization(img, args)
if len(args.ResizeRatio)>0:
img = resize(img, (1, args.ResizeRatio[0], args.ResizeRatio[1], args.ResizeRatio[2]), method='cubic')
for ch_idx in range(img.shape[0]):
struct_img = img[ch_idx,:,:,:] # note that struct_img is only a view of img, so changes made on struct_img also affects img
struct_img = (struct_img - struct_img.min())/(struct_img.max() - struct_img.min())
img[ch_idx,:,:,:] = struct_img
# apply the model
output_img = model_inference(model, img, softmax, args)
for ch_idx in range(len(args.OutputCh)//2):
writer = omeTifWriter.OmeTifWriter(args.OutputDir + pathlib.PurePosixPath(fn).stem +'_seg_'+ str(args.OutputCh[2*ch_idx])+'.ome.tif')
if args.Threshold<0:
writer.save(output_img[ch_idx].astype(float))
else:
out = output_img[ch_idx] > args.Threshold
out = out.astype(np.uint8)
out[out>0]=255img = img.astype(float)
if img.shape[1] < img.shape[0]:
img = np.transpose(img,(1,0,2,3))
img = img[args.InputCh,:,:,:] # fancy indexing atually creates a copy, not a view
# normalization
if args.mode == 'train':
for ch_idx in range(args.nchannel):
struct_img = img[ch_idx,:,:,:] # note that struct_img is only a view of img, so changes made on struct_img also affects img
struct_img = (struct_img - struct_img.min() )/(struct_img.max() - struct_img.min())
elif not args.Normalization == 0:
img = input_normalization(img, args)
# rescale
if len(args.ResizeRatio)>0:
img = resize(img, (1, args.ResizeRatio[0], args.ResizeRatio[1], args.ResizeRatio[2]), method='cubic')
for ch_idx in range(img.shape[0]):
struct_img = img[ch_idx,:,:,:] # note that struct_img is only a view of img, so changes made on struct_img also affects img
struct_img = (struct_img - struct_img.min())/(struct_img.max() - struct_img.min())
img[ch_idx,:,:,:] = struct_img
return imgelif args.mode == 'eval_file':
fn = args.InputFile
print(fn)
data_reader = AICSImage(fn)
img0 = data_reader.data
if args.timelapse:
assert data_reader.shape[0]>1
for tt in range(data_reader.shape[0]):
# Assume: TCZYX
img = img0[tt, args.InputCh,:,:,:].astype(float)
img = input_normalization(img, args)
if len(args.ResizeRatio)>0:
img = resize(img, (1, args.ResizeRatio[0], args.ResizeRatio[1], args.ResizeRatio[2]), method='cubic')
for ch_idx in range(img.shape[0]):
struct_img = img[ch_idx,:,:,:] # note that struct_img is only a view of img, so changes made on struct_img also affects img
struct_img = (struct_img - struct_img.min())/(struct_img.max() - struct_img.min())
img[ch_idx,:,:,:] = struct_img
# apply the model
output_img = model_inference(model, img, softmax, args)
for ch_idx in range(len(args.OutputCh)//2):
writer = omeTifWriter.OmeTifWriter(args.OutputDir + pathlib.PurePosixPath(fn).stem + '_T_'+ f'{tt:03}' +'_seg_'+ str(args.OutputCh[2*ch_idx])+'.ome.tif')
if args.Threshold<0:
out = output_img[ch_idx].astype(float)
out = resize(out, (1.0, 1/args.ResizeRatio[0], 1/args.ResizeRatio[1], 1/args.ResizeRatio[2]), method='cubic')
writer.save(out)
else:
out = output_img[ch_idx] > args.Threshold# for ch_idx in range(img.shape[0]):
# struct_img = img[ch_idx,:,:,:] # note that struct_img is only a view of img, so changes made on struct_img also affects img
# struct_img = (struct_img - struct_img.min())/(struct_img.max() - struct_img.min())
# img[ch_idx,:,:,:] = struct_img
# apply the model
# import pdb; pdb.set_trace()
output_img = apply_on_image(model, img, model.final_activation, args_inference)
# extract the result and write the output
if len(config['OutputCh'])==2:
if config['Threshold']<0:
out = output_img[0]
out = (out - out.min()) / (out.max()-out.min())
if len(config['ResizeRatio'])>0:
out = resize(out, (1.0, 1/config['ResizeRatio'][0], 1/config['ResizeRatio'][1], 1/config['ResizeRatio'][2]), method='cubic')
out = out.astype(np.float32)
out = (out - out.min()) / (out.max()-out.min())
else:
out = remove_small_objects(output_img[0] > config['Threshold'], min_size=2, connectivity=1)
out = out.astype(np.uint8)
out[out>0]=255
imsave(config['OutputDir'] + os.sep + pathlib.PurePosixPath(fn).stem + '_struct_segmentation.tiff', out)
else:
for ch_idx in range(len(config['OutputCh'])//2):
if config['Threshold']<0:
out = output_img[ch_idx]
out = (out - out.min()) / (out.max()-out.min())
out = out.astype(np.float32)
else:
out = output_img[ch_idx] > config['Threshold']
out = out.astype(np.uint8)struct_img = img[ch_idx,:,:,:] # note that struct_img is only a view of img, so changes made on struct_img also affects img
struct_img = (struct_img - struct_img.min())/(struct_img.max() - struct_img.min())
img[ch_idx,:,:,:] = struct_img
# apply the model
output_img = model_inference(model, img, softmax, args)
for ch_idx in range(len(args.OutputCh)//2):
writer = omeTifWriter.OmeTifWriter(args.OutputDir + pathlib.PurePosixPath(fn).stem + '_T_'+ f'{tt:03}' +'_seg_'+ str(args.OutputCh[2*ch_idx])+'.ome.tif')
if args.Threshold<0:
out = output_img[ch_idx].astype(float)
out = resize(out, (1.0, 1/args.ResizeRatio[0], 1/args.ResizeRatio[1], 1/args.ResizeRatio[2]), method='cubic')
writer.save(out)
else:
out = output_img[ch_idx] > args.Threshold
out = resize(out, (1.0, 1/args.ResizeRatio[0], 1/args.ResizeRatio[1], 1/args.ResizeRatio[2]), method='nearest')
out = out.astype(np.uint8)
out[out>0]=255
writer.save(out)
else:
img = img0[0,:,:,:].astype(float)
if img.shape[1] < img.shape[0]:
img = np.transpose(img,(1,0,2,3))
img = img[args.InputCh,:,:,:]
img = input_normalization(img, args)
if len(args.ResizeRatio)>0:
img = resize(img, (1, args.ResizeRatio[0], args.ResizeRatio[1], args.ResizeRatio[2]), method='cubic')
for ch_idx in range(img.shape[0]):
struct_img = img[ch_idx,:,:,:] # note that struct_img is only a view of img, so changes made on struct_img also affects img
struct_img = (struct_img - struct_img.min())/(struct_img.max() - struct_img.min())
img[ch_idx,:,:,:] = struct_imgaicsimageprocessing
A generalized scientific image processing module from the Allen Institute for Cell Science.
