def main(argv):
# 0. Initialize Cytomine client and job
with CytomineJob.from_cli(argv) as cj:
cj.job.update(status=Job.RUNNING,
progress=0,
statusComment="Initialisation...")
# 1. Create working directories on the machine:
# - WORKING_PATH/in: input images
# - WORKING_PATH/out: output images
# - WORKING_PATH/ground_truth: ground truth images
base_path = "{}".format(os.getenv("HOME"))
gt_suffix = "_lbl"
working_path = os.path.join(base_path, str(cj.job.id))
in_path = os.path.join(working_path, "in")
out_path = os.path.join(working_path, "out")
gt_path = os.path.join(working_path, "ground_truth")
if not os.path.exists(working_path):
os.makedirs(working_path)
os.makedirs(in_path)
os.makedirs(out_path)
os.makedirs(gt_path)
# 2. Download the images (first input, then ground truth image)
cj.job.update(
progress=1,
statusComment="Downloading images (to {})...".format(in_path))
image_group = ImageGroupCollection().fetch_with_filter(
"project", cj.parameters.cytomine_id_project)
input_images = [i for i in image_group if gt_suffix not in i.name]
gt_images = [i for i in image_group if gt_suffix in i.name]
for input_image in input_images:
input_image.download(os.path.join(in_path, "{id}.tif"))
for gt_image in gt_images:
related_name = gt_image.name.replace(gt_suffix, '')
related_image = [i for i in input_images if related_name == i.name]
if len(related_image) == 1:
gt_image.download(
os.path.join(gt_path,
"{}.tif".format(related_image[0].id)))
# 3. Call the image analysis workflow using the run script
cj.job.update(progress=25, statusComment="Launching workflow...")
command = "/usr/bin/xvfb-run java -Xmx6000m -cp /fiji/jars/ij-1.52d.jar ij.ImageJ --headless --console " \
"-macro macro.ijm \"input={}, output={}\"".format(in_path, out_path)
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)
cj.job.update(progress=50, statusComment=err_desc)
raise ValueError(err_desc)
# 4. Upload the annotation and labels to Cytomine (annotations are extracted from the mask using
# the AnnotationExporter module)
# for image in cj.monitor(input_images, start=60, end=80, period=0.1, prefix="Extracting and uploading polygons from masks"):
# file = "{}.tif".format(image.id)
# path = os.path.join(out_path, file)
# data = io.imread(path)
# extract objects
# slices = mask_to_objects_2d(data)
# print("Found {} polygons in this image {}.".format(len(slices), image.id))
# upload
# collection = AnnotationCollection()
# for obj_slice in slices:
# collection.append(Annotation(
# location=affine_transform(obj_slice.polygon, [1, 0, 0, -1, 0, image.height]).wkt,
# id_image=image.id, id_project=cj.parameters.cytomine_id_project, property=[
# {"key": "index", "value": str(obj_slice.label)}
# ]
# ))
# collection.save()
# 5. Compute the metrics
cj.job.update(progress=80, statusComment="Computing metrics...")
for image in cj.monitor(input_images,
start=80,
end=98,
period=0.1,
prefix="computing metrics"):
afile = "{}.tif".format(image.id)
pathi = os.path.join(in_path, afile)
patho = os.path.join(out_path, afile)
# data = io.imread(path)
metrics, params = computemetrics(pathi, patho, "TreTrc", '/tmp')
print('metrics for ' + pathi)
print(metrics)
# TODO: compute metrics:
# in /out: output files {id}.tiff
# in /ground_truth: label files {id}.tiff
cj.job.update(progress=99, statusComment="Cleaning...")
# for image in input_images:
# os.remove(os.path.join(in_path, "{}.tif".format(image.id)))
cj.job.update(status=Job.TERMINATED,
progress=100,
statusComment="Finished.")
def main(argv):
with CytomineJob.from_cli(argv) as cj:
# prepare paths
working_path = str(Path.home())
data_path = os.path.join(working_path, "pred_data")
if not os.path.exists(data_path):
os.makedirs(data_path)
model_filename = "model.pkl"
cj.job.update(progress=5, statusComment="Download model ...")
model_job = Job().fetch(cj.parameters.cytomine_model_job_id)
attached_files = AttachedFileCollection(model_job).fetch_with_filter(
"project", cj.project.id)
if not (0 < len(attached_files) < 2):
raise ValueError(
"More or less than 1 file attached to the Job (found {} file(s))."
.format(len(attached_files)))
attached_file = attached_files[0]
if attached_file.filename != model_filename:
raise ValueError(
"Expected model file name is '{}' (found: '{}').".format(
model_filename, attached_file.filename))
model_path = os.path.join(working_path, model_filename)
attached_file.download(model_path)
# load model
with open(model_path, "rb") as file:
data = pickle.load(file)
model = data["model"]
classifier = data["classifier"]
network = data["network"]
reduction = data["reduction"]
# load and dump annotations
cj.job.update(progress=10, statusComment="Download annotations.")
annotations = get_annotations(
project_id=cj.parameters.cytomine_project_id,
images=parse_list_or_none(cj.parameters.cytomine_images_ids),
users=parse_list_or_none(cj.parameters.cytomine_users_ids),
showWKT=True)
cj.job.update(statusComment="Fetch crops.", progress=15)
n_samples = len(annotations)
x = np.zeros([n_samples], dtype=np.object)
for i, annotation in cj.monitor(enumerate(annotations),
start=15,
end=40,
prefix="Fetch crops",
period=0.1):
file_format = os.path.join(data_path, "{id}.png")
if not annotation.dump(dest_pattern=file_format):
raise ValueError("Download error for annotation '{}'.".format(
annotation.id))
x[i] = file_format.format(id=annotation.id)
available_nets = {
MODEL_RESNET50, MODEL_VGG19, MODEL_VGG16, MODEL_INCEPTION_V3,
MODEL_INCEPTION_RESNET_V2, MODEL_MOBILE, MODEL_DENSE_NET_201,
MODEL_NASNET_LARGE, MODEL_NASNET_MOBILE
}
if network not in available_nets:
raise ValueError(
"Invalid value (='{}'} for parameter 'network'.".format(
network))
if reduction not in {"average_pooling"}:
raise ValueError(
"Invalid value (='{}') for parameter 'reduction'.".format(
reduction))
if classifier not in {"svm"}:
raise ValueError(
"Invalid value (='{}') for parameter 'classifier'.".format(
classifier))
# prepare network
cj.job.update(statusComment="Load neural network '{}'".format(network),
progress=40)
features = PretrainedModelFeatures(model=network,
layer="last",
reduction=reduction,
weights="imagenet")
height, width, _ = features._get_input_shape(network)
loader = ImageLoader(load_size_range=(height, height),
crop_size=height,
random_crop=False)
cj.job.update(statusComment="Transform features.", progress=50)
x_feat = batch_transform(loader,
features,
x,
logger=cj.logger(start=50, end=70,
period=0.1),
batch_size=128)
cj.job.update(statusComment="Prediction with '{}'.".format(classifier),
progress=70)
if hasattr(model, "n_jobs"):
model.n_jobs = cj.parameters.n_jobs
probas = None
if hasattr(model, "predict_proba"):
probas = model.predict_proba(x_feat)
y_pred = model.classes_.take(np.argmax(probas, axis=1), axis=0)
else:
y_pred = model.predict(x_feat)
cj.job.update(statusComment="Upload annotations.", progress=80)
annotation_collection = AnnotationCollection()
for i, annotation in cj.monitor(enumerate(annotations),
start=80,
end=100,
period=0.1,
prefix="Upload annotations"):
annotation_collection.append(
Annotation(location=annotation.location,
id_image=annotation.image,
id_project=annotation.project,
term=[int(y_pred[i])],
rate=float(probas[i])
if probas is not None else 1.0).save())
annotation_collection.save()
cj.job.update(statusComment="Finished.", progress=100)