def main():
with BiaflowsJob.from_cli(sys.argv[1:]) as nj:
nj.job.update(status=Job.RUNNING,
progress=0,
statusComment="Initialisation...")
# 1. Create working directories on the machine:
# 2. Download (or read) data
problem_cls = get_discipline(nj, default=CLASS_SPTCNT)
is_2d = False
in_images, gt_images, in_path, gt_path, out_path, tmp_path = prepare_data(
problem_cls, nj, is_2d=False, **nj.flags)
# 3. Execute workflow
scale = nj.parameters.icy_scale
sensitivity = nj.parameters.icy_sensitivity
nj.job.update(progress=25, statusComment="Launching workflow...")
# Modify the parameters in the job.xml file
replaceScaleParameterCommand = "sed -i '/scale/c\\t<parameter name=\"scale\">{}</parameter>' job.xml".format(
scale)
replaceSensitivityParameterCommand = "sed -i '/sensitivity/c\\t<parameter name=\"sensitivity\">{}</parameter>' job.xml".format(
sensitivity)
replaceInputFolderParameterCommand = "sed -i '/inputFolder/c\\t<parameter name=\"inputFolder\">{}</parameter>' job.xml".format(
'"' + in_path + '"')
replaceOutputFolderParameterCommand = "sed -i '/outputFolder/c\\t<parameter name=\"outputFolder\">{}</parameter>' job.xml".format(
'"' + out_path + '"')
call(replaceScaleParameterCommand, shell=True, cwd="/icy")
call(replaceSensitivityParameterCommand, shell=True, cwd="/icy")
call(replaceInputFolderParameterCommand, shell=True, cwd="/icy")
call(replaceOutputFolderParameterCommand, shell=True, cwd="/icy")
# Run script in ICY
command = "java -jar icy.jar --headless --execute plugins.volker.commandlinescriptrunner.CommandLineScriptRunner"
call(command, shell=True, cwd="/icy")
# 4. Upload the annotation and labels to Cytomine
upload_data(problem_cls,
nj,
in_images,
out_path,
**nj.flags,
is_2d=is_2d,
monitor_params={
"start": 60,
"end": 90,
"period": 0.1
})
# 5. Compute and upload the metrics
nj.job.update(progress=90,
statusComment="Computing and uploading metrics...")
upload_metrics(problem_cls, nj, in_images, gt_path, out_path, tmp_path,
**nj.flags)
nj.job.update(progress=100,
status=Job.TERMINATED,
status_comment="Finished.")
def main():
with BiaflowsJob.from_cli(sys.argv[1:]) as nj:
nj.job.update(status=Job.RUNNING,
progress=0,
statusComment="Initialization...")
# 1. Create working directories on the machine:
# 2. Download (or read) data
problem_cls = get_discipline(nj, default=CLASS_SPTCNT)
is_2d = True
nj.job.update(
progress=1,
statusComment="Execute workflow on problem class '{}'".format(
problem_cls))
in_images, gt_images, in_path, gt_path, out_path, tmp_path = prepare_data(
problem_cls, nj, is_2d=is_2d, **nj.flags)
# 3. Execute workflow
scale3sens = nj.parameters.icy_scale3sensitivity
nj.job.update(progress=25, statusComment="Launching workflow...")
call("java -cp /icy/lib/ -jar /icy/icy.jar -hl",
shell=True,
cwd="/icy")
call(
"java -cp /icy/lib/ -jar /icy/icy.jar -hl -x plugins.adufour.protocols.Protocols "
"protocol=\"/icy/protocols/protocol.protocol\" inputFolder=\"{}\" outputFolder=\"{}\" extension=tif "
"scale2enable=true scale2sensitivity={}".format(
in_path, out_path, scale3sens),
shell=True,
cwd="/icy")
# 3.5 Remove the xml-output files
for p in Path(out_path).glob("*.xml"):
p.unlink()
# 4. Upload the annotation and labels to Cytomine
upload_data(problem_cls,
nj,
in_images,
out_path,
**nj.flags,
is_2d=is_2d,
monitor_params={
"start": 60,
"end": 90,
"period": 0.1
})
# 5. Compute and upload the metrics
nj.job.update(progress=90,
statusComment="Computing and uploading metrics...")
upload_metrics(problem_cls, nj, in_images, gt_path, out_path, tmp_path,
**nj.flags)
nj.job.update(progress=100,
status=Job.TERMINATED,
status_comment="Finished.")
def main(argv):
# 0. Initialize Cytomine client and job if necessary and parse inputs
with BiaflowsJob.from_cli(argv) as nj:
problem_cls = get_discipline(nj, default=CLASS_OBJTRK)
is_2d = False
nj.job.update(
status=Job.RUNNING,
progress=0,
statusComment="Running workflow for problem class '{}'".format(
problem_cls))
# 1. Create working directories on the machine
# 2. Download the images
nj.job.update(progress=0, statusComment="Initialisation...")
in_images, gt_images, in_path, gt_path, out_path, tmp_path = prepare_data(
problem_cls, nj, **nj.flags)
# 3. Call the image analysis workflow using the run script
nj.job.update(progress=25, statusComment="Launching workflow...")
command = "/usr/bin/xvfb-run java -Xmx6000m -cp /fiji/jars/ij.jar ij.ImageJ --headless --console " \
"-batch macro.ijm \"input={}, output={}, medrad={}, thr={}, erodrad={}, dmapds={}, noisetol={}\""\
.format(in_path, out_path, nj.parameters.medrad, nj.parameters.threshold,
nj.parameters.erodrad, nj.parameters.dmapds, nj.parameters.noisetol)
return_code = call(command, shell=True,
cwd="/fiji") # waits for the subprocess to return
if return_code != 0:
err_desc = "Failed to execute the ImageJ macro (return code: {})".format(
return_code)
nj.job.update(progress=50, statusComment=err_desc)
raise ValueError(err_desc)
# 4. Upload the annotation and labels to Cytomine
upload_data(problem_cls,
nj,
in_images,
out_path,
**nj.flags,
is_2d=is_2d,
monitor_params={
"start": 60,
"end": 90,
"period": 0.1
})
# 5. Compute and upload the metrics
nj.job.update(progress=90,
statusComment="Computing and uploading metrics...")
upload_metrics(problem_cls, nj, in_images, gt_path, out_path, tmp_path,
**nj.flags)
# 6. End
nj.job.update(status=Job.TERMINATED,
progress=100,
statusComment="Finished.")
def main(argv):
base_path = "{}".format(os.getenv("HOME")) # Mandatory for Singularity
with BiaflowsJob.from_cli(argv) as bj:
# Change following to the actual problem class of the workflow
problem_cls = get_discipline(bj, default=CLASS_OBJSEG)
bj.job.update(status=Job.RUNNING,
progress=0,
statusComment="Initialisation...")
# 1. Prepare data for workflow
in_imgs, gt_imgs, in_path, gt_path, out_path, tmp_path = prepare_data(
problem_cls, bj, is_2d=True, **bj.flags)
# 2. Run image analysis workflow
bj.job.update(progress=25, statusComment="Launching workflow...")
# Add here the code for running the analysis script
# 3. Upload data to BIAFLOWS
upload_data(problem_cls,
bj,
in_imgs,
out_path,
**bj.flags,
monitor_params={
"start": 60,
"end": 90,
"period": 0.1,
"prefix":
"Extracting and uploading polygons from masks"
})
# 4. Compute and upload metrics
bj.job.update(progress=90,
statusComment="Computing and uploading metrics...")
upload_metrics(problem_cls, bj, in_imgs, gt_path, out_path, tmp_path,
**bj.flags)
# 5. Pipeline finished
bj.job.update(progress=100,
status=Job.TERMINATED,
status_comment="Finished.")
def main(argv):
with BiaflowsJob.from_cli(argv) as nj:
problem_cls = get_discipline(nj, default=CLASS_PIXCLA)
is_2d = True
print(nj.parameters)
nj.job.update(status=Job.RUNNING, progress=0, statusComment="Initialisation...")
in_images, gt_images, in_path, gt_path, out_path, tmp_path = prepare_data(problem_cls, nj, **nj.flags)
# 2. Call the image analysis workflow
nj.job.update(progress=10, statusComment="Load model...")
net = load_model("/app/CP58_dice_0.9373_loss_0.0265.pth")
for in_image in nj.monitor(in_images, start=20, end=75, period=0.05, prefix="Apply UNet to input images"):
img = imread(in_image.filepath, is_2d=is_2d)
mask = predict_img(
net=net, full_img=img,
scale_factor=0.5, # value used at training
out_threshold=nj.parameters.threshold
)
imwrite(
path=os.path.join(out_path, in_image.filename),
image=mask.astype(np.uint8),
is_2d=is_2d
)
# 4. Create and upload annotations
nj.job.update(progress=70, statusComment="Uploading extracted annotation...")
upload_data(problem_cls, nj, in_images, out_path, **nj.flags, is_2d=is_2d, monitor_params={
"start": 70, "end": 90, "period": 0.1
})
# 5. Compute and upload the metrics
nj.job.update(progress=90, statusComment="Computing and uploading metrics (if necessary)...")
upload_metrics(problem_cls, nj, in_images, gt_path, out_path, tmp_path, **nj.flags)
# 6. End the job
nj.job.update(status=Job.TERMINATED, progress=100, statusComment="Finished.")
def main(argv):
# 0. Initialize Cytomine client and job if necessary and parse inputs
with BiaflowsJob.from_cli(argv) as nj:
problem_cls = get_discipline(nj, default=CLASS_OBJTRK)
is_2d = False
nj.job.update(
status=Job.RUNNING,
progress=0,
statusComment="Running workflow for problem class '{}'".format(
problem_cls))
# 1. Prepare data for workflow
in_imgs, gt_imgs, in_path, gt_path, out_path, tmp_path = prepare_data(
problem_cls, nj, **nj.flags)
# 2. Run image analysis workflow
nj.job.update(progress=25, statusComment="Launching workflow...")
for in_img in in_imgs:
# convert the image data to the Cell Tracking Challenge format
img = io.imread(in_img.filepath)
T = img.shape[0]
Y = img.shape[1]
X = img.shape[2]
img_data = img.ravel()
index = 0
offset = Y * X
for t in range(T):
io.imsave(os.path.join(tmp_path, 't{0:03d}.tif'.format(t)),
img_data[index:index + offset].reshape((Y, X)))
index += offset
# do segmentation and tracking
process_dataset(tmp_path, tmp_path, '/app/model.h5')
# convert the tracking results to the required format
index = 0
res_img = np.zeros((T, Y, X), np.uint16)
res_data = res_img.ravel()
for t in range(T):
res = io.imread(
os.path.join(tmp_path, 'mask{0:03d}.tif'.format(t)))
res_data[index:index + offset] = res.ravel()
index += offset
io.imsave(os.path.join(out_path, in_img.filename), res_img)
os.rename(
os.path.join(tmp_path, 'res_track.txt'),
os.path.join(out_path, in_img.filename_no_extension + '.txt'))
# 4. Upload the annotation and labels to Cytomine
upload_data(problem_cls,
nj,
in_imgs,
out_path,
**nj.flags,
is_2d=is_2d,
monitor_params={
"start": 60,
"end": 90,
"period": 0.1
})
# 5. Compute and upload the metrics
nj.job.update(progress=90,
statusComment="Computing and uploading metrics...")
upload_metrics(problem_cls, nj, in_imgs, gt_path, out_path, tmp_path,
**nj.flags)
# 6. End
nj.job.update(status=Job.TERMINATED,
progress=100,
statusComment="Finished.")
def main(argv):
base_path = "{}".format(os.getenv("HOME")) # Mandatory for Singularity
with BiaflowsJob.from_cli(argv) as bj:
# Change following to the actual problem class of the workflow
problem_cls = get_discipline(bj, default=CLASS_OBJSEG)
bj.job.update(status=Job.RUNNING, progress=0, statusComment="Initialisation...")
# 1. Prepare data for workflow
in_imgs, gt_imgs, in_path, gt_path, out_path, tmp_path = prepare_data(problem_cls, bj, is_2d=True, **bj.flags)
# Make sure all images have at least 224x224 dimensions
# and that minshape / maxshape * minshape >= 224
# 0 = Grayscale (if input RGB, convert to grayscale)
# 1,2,3 = rgb channel
nuc_channel = bj.parameters.nuc_channel
resized = {}
for bfimg in in_imgs:
fn = os.path.join(in_path, bfimg.filename)
img = imageio.imread(fn)
if len(img.shape) > 2 and nuc_channel == 0:
gray_rgb = False
if np.array_equal(img[:,:,0],img[:,:,1]) and np.array_equal(img[:,:,0],img[:,:,2]):
gray_rgb = True
img = skimage.color.rgb2gray(img) * 255
img = img.astype(np.uint8)
# Invert intensity if not grayscale img ie. expect the image
# to be H&E stained image with dark nuclei
if not gray_rgb:
img = np.invert(img)
minshape = min(img.shape[:2])
maxshape = max(img.shape[:2])
if minshape != maxshape or minshape < 224:
resized[bfimg.filename] = img.shape
padshape = []
for i in range(2):
if img.shape[i] < max(224,maxshape):
padshape.append((0,max(224,maxshape)-img.shape[i]))
else:
padshape.append((0,0))
if len(img.shape) == 3:
padshape.append((0,0))
img = np.pad(img, padshape, 'constant', constant_values=0)
imageio.imwrite(os.path.join(tmp_path, bfimg.filename), img)
# 2. Run image analysis workflow
bj.job.update(progress=25, statusComment="Launching workflow...")
# Add here the code for running the analysis script
#"--chan", "{:d}".format(nuc_channel)
cmd = ["python", "-m", "cellpose", "--dir", tmp_path, "--pretrained_model", "nuclei", "--save_tif", "--no_npy", "--chan", "{:d}".format(nuc_channel), "--diameter", "{:f}".format(bj.parameters.diameter), "--cellprob_threshold", "{:f}".format(bj.parameters.prob_threshold)]
status = subprocess.run(cmd)
if status.returncode != 0:
print("Running Cellpose failed, terminate")
sys.exit(1)
# Crop to original shape
for bimg in in_imgs:
shape = resized.get(bimg.filename, None)
if shape:
img = imageio.imread(os.path.join(tmp_path,bimg.filename_no_extension+"_cp_masks.tif"))
img = img[0:shape[0], 0:shape[1]]
imageio.imwrite(os.path.join(out_path,bimg.filename), img)
else:
shutil.copy(os.path.join(tmp_path,bimg.filename_no_extension+"_cp_masks.tif"), os.path.join(out_path,bimg.filename))
# 3. Upload data to BIAFLOWS
upload_data(problem_cls, bj, in_imgs, out_path, **bj.flags, monitor_params={
"start": 60, "end": 90, "period": 0.1,
"prefix": "Extracting and uploading polygons from masks"})
# 4. Compute and upload metrics
bj.job.update(progress=90, statusComment="Computing and uploading metrics...")
upload_metrics(problem_cls, bj, in_imgs, gt_path, out_path, tmp_path, **bj.flags)
# 5. Pipeline finished
bj.job.update(progress=100, status=Job.TERMINATED, status_comment="Finished.")