def main(argv):
with CytomineJob.from_cli(argv) as conn:
conn.job.update(status=Job.RUNNING,
progress=0,
statusComment="Initialization...")
# base_path = "{}".format(os.getenv("HOME")) # Mandatory for Singularity
base_path = "/home/mmu/Desktop"
working_path = os.path.join(base_path, str(conn.job.id))
#Loading pre-trained Stardist model
np.random.seed(17)
lbl_cmap = random_label_cmap()
#Stardist H&E model downloaded from https://github.com/mpicbg-csbd/stardist/issues/46
#Stardist H&E model downloaded from https://drive.switch.ch/index.php/s/LTYaIud7w6lCyuI
model = StarDist2D(
None, name='2D_versatile_HE', basedir='/models/'
) #use local model file in ~/models/2D_versatile_HE/
#Select images to process
images = ImageInstanceCollection().fetch_with_filter(
"project", conn.parameters.cytomine_id_project)
list_imgs = []
if conn.parameters.cytomine_id_images == 'all':
for image in images:
list_imgs.append(int(image.id))
else:
list_imgs = [
int(id_img)
for id_img in conn.parameters.cytomine_id_images.split(',')
]
#Go over images
for id_image in conn.monitor(list_imgs,
prefix="Running detection on image",
period=0.1):
#Dump ROI annotations in img from Cytomine server to local images
#conn.job.update(status=Job.RUNNING, progress=0, statusComment="Fetching ROI annotations...")
roi_annotations = AnnotationCollection()
roi_annotations.project = conn.parameters.cytomine_id_project
roi_annotations.term = conn.parameters.cytomine_id_roi_term
roi_annotations.image = id_image #conn.parameters.cytomine_id_image
roi_annotations.showWKT = True
roi_annotations.fetch()
print(roi_annotations)
#Go over ROI in this image
#for roi in conn.monitor(roi_annotations, prefix="Running detection on ROI", period=0.1):
for roi in roi_annotations:
#Get Cytomine ROI coordinates for remapping to whole-slide
#Cytomine cartesian coordinate system, (0,0) is bottom left corner
print(
"----------------------------ROI------------------------------"
)
roi_geometry = wkt.loads(roi.location)
print("ROI Geometry from Shapely: {}".format(roi_geometry))
print("ROI Bounds")
print(roi_geometry.bounds)
minx = roi_geometry.bounds[0]
miny = roi_geometry.bounds[3]
#Dump ROI image into local PNG file
roi_path = os.path.join(
working_path,
str(roi_annotations.project) + '/' +
str(roi_annotations.image) + '/' + str(roi.id))
roi_png_filename = os.path.join(roi_path + '/' + str(roi.id) +
'.png')
print("roi_png_filename: %s" % roi_png_filename)
roi.dump(dest_pattern=roi_png_filename, mask=True, alpha=True)
#roi.dump(dest_pattern=os.path.join(roi_path,"{id}.png"), mask=True, alpha=True)
#Stardist works with TIFF images without alpha channel, flattening PNG alpha mask to TIFF RGB
im = Image.open(roi_png_filename)
bg = Image.new("RGB", im.size, (255, 255, 255))
bg.paste(im, mask=im.split()[3])
roi_tif_filename = os.path.join(roi_path + '/' + str(roi.id) +
'.tif')
bg.save(roi_tif_filename, quality=100)
X_files = sorted(glob(roi_path + '/' + str(roi.id) + '*.tif'))
X = list(map(imread, X_files))
n_channel = 3 if X[0].ndim == 3 else X[0].shape[-1]
axis_norm = (
0, 1
) # normalize channels independently (0,1,2) normalize channels jointly
if n_channel > 1:
print("Normalizing image channels %s." %
('jointly' if axis_norm is None or 2 in axis_norm
else 'independently'))
#Going over ROI images in ROI directory (in our case: one ROI per directory)
for x in range(0, len(X)):
print("------------------- Processing ROI file %d: %s" %
(x, roi_tif_filename))
img = normalize(X[x],
conn.parameters.stardist_norm_perc_low,
conn.parameters.stardist_norm_perc_high,
axis=axis_norm)
#Stardist model prediction with thresholds
labels, details = model.predict_instances(
img,
prob_thresh=conn.parameters.stardist_prob_t,
nms_thresh=conn.parameters.stardist_nms_t)
print("Number of detected polygons: %d" %
len(details['coord']))
cytomine_annotations = AnnotationCollection()
#Go over detections in this ROI, convert and upload to Cytomine
for pos, polygroup in enumerate(details['coord'], start=1):
#Converting to Shapely annotation
points = list()
for i in range(len(polygroup[0])):
#Cytomine cartesian coordinate system, (0,0) is bottom left corner
#Mapping Stardist polygon detection coordinates to Cytomine ROI in whole slide image
p = Point(minx + polygroup[1][i],
miny - polygroup[0][i])
points.append(p)
annotation = Polygon(points)
#Append to Annotation collection
cytomine_annotations.append(
Annotation(
location=annotation.wkt,
id_image=
id_image, #conn.parameters.cytomine_id_image,
id_project=conn.parameters.cytomine_id_project,
id_terms=[
conn.parameters.cytomine_id_cell_term
]))
print(".", end='', flush=True)
#Send Annotation Collection (for this ROI) to Cytomine server in one http request
ca = cytomine_annotations.save()
conn.job.update(status=Job.TERMINATED,
progress=100,
statusComment="Finished.")
help="The Cytomine private key")
parser.add_argument('--cytomine_id_image_instance',
dest='id_image_instance',
help="The image in which we work")
parser.add_argument('--cytomine_id_roi_term',
dest='id_roi_term',
help="The term that represents regions of interest")
parser.add_argument('--cytomine_id_object_term',
dest='id_object_term',
help="The term that represents objects")
params, other = parser.parse_known_args(sys.argv[1:])
with Cytomine(host=params.host,
public_key=params.public_key,
private_key=params.private_key) as cytomine:
roi_annotations = AnnotationCollection()
roi_annotations.image = params.id_image_instance
roi_annotations.term = params.id_roi_term
roi_annotations.fetch()
print(roi_annotations)
for roi_annotation in roi_annotations:
included_annotations = AnnotationCollection()
included_annotations.image = params.id_image_instance
included_annotations.term = params.id_object_term
included_annotations.annotation = roi_annotation.id
included_annotations.fetch()
print("Number of annotations of term {} included in ROI {}: {}".
format(params.id_object_term, roi_annotation.id,
len(included_annotations)))
def main(argv):
with CytomineJob.from_cli(argv) as conn:
conn.job.update(progress=0, statusComment="Initialization..")
base_path = "{}".format(os.getenv("HOME")) # Mandatory for Singularity
working_path = os.path.join(base_path, str(conn.job.id))
# Load pretrained model (assume the best of all)
conn.job.update(progress=0,
statusComment="Loading segmentation model..")
with open("/models/resnet50b_fpn256/config.json") as f:
config = json.load(f)
model = FPN.build_resnet_fpn(
name=config['name'],
input_size=conn.parameters.dataset_patch_size, # must be / by 16
input_channels=1 if config['input']['mode'] == 'grayscale' else 3,
output_channels=config['fpn']['out_channels'],
num_classes=2, # legacy
in_features=config['fpn']['in_features'],
out_features=config['fpn']['out_features'])
model.to(_DEVICE)
model_dict = torch.load(config['weights'],
map_location=torch.device(_DEVICE))
model.load_state_dict(model_dict['model'])
# Select images to process
images = ImageInstanceCollection().fetch_with_filter(
"project", conn.parameters.cytomine_id_project)
if conn.parameters.cytomine_id_images != 'all':
images = [
_ for _ in images
if _.id in map(lambda x: int(x.strip()),
conn.parameters.cytomine_id_images.split(','))
]
images_id = [image.id for image in images]
# Download selected images into "working_directory"
img_path = os.path.join(working_path, "images")
os.makedirs(img_path)
for image in conn.monitor(
images,
start=2,
end=50,
period=0.1,
prefix="Downloading images into working directory.."):
fname, fext = os.path.splitext(image.filename)
if image.download(dest_pattern=os.path.join(
img_path, "{}{}".format(image.id, fext))) is not True:
print("Failed to download image {}".format(image.filename))
# create a file that lists all images (used by PatchBasedDataset
conn.job.update(progress=50,
statusComment="Preparing data for execution..")
images = os.listdir(img_path)
images = list(map(lambda x: x + '\n', images))
with open(os.path.join(working_path, 'images.txt'), 'w') as f:
f.writelines(images)
# Prepare dataset and dataloader objects
ImgTypeBits = {'.dcm': 16}
channel_bits = ImgTypeBits.get(fext.lower(), 8)
mean, std = compute_mean_and_std(img_path, bits=channel_bits)
dataset = InferencePatchBasedDataset(
path=working_path,
subset='images',
patch_size=conn.parameters.dataset_patch_size,
mode=config['input']['mode'],
bits=channel_bits,
mean=mean,
std=std)
dataloader = DataLoader(
dataset=dataset,
batch_size=conn.parameters.model_batch_size,
drop_last=False,
shuffle=False,
num_workers=0,
collate_fn=InferencePatchBasedDataset.collate_fn)
# Go over images
conn.job.update(status=Job.RUNNING,
progress=55,
statusComment="Running inference on images..")
results = inference_on_segmentation(
model, dataloader, conn.parameters.postprocess_p_threshold)
for id_image in conn.monitor(
images_id,
start=90,
end=95,
prefix="Deleting old annotations on images..",
period=0.1):
# Delete old annotations
del_annotations = AnnotationCollection()
del_annotations.image = id_image
del_annotations.user = conn.job.id
del_annotations.project = conn.parameters.cytomine_id_project
del_annotations.term = conn.parameters.cytomine_id_predict_term,
del_annotations.fetch()
for annotation in del_annotations:
annotation.delete()
conn.job.update(
status=Job.RUNNING,
progress=95,
statusComment="Uploading new annotations to Cytomine server..")
annotations = AnnotationCollection()
for instance in results:
idx, _ = os.path.splitext(instance['filename'])
width, height = instance['size']
for box in instance['bbox']:
points = [
Point(box[0], height - 1 - box[1]),
Point(box[0], height - 1 - box[3]),
Point(box[2], height - 1 - box[3]),
Point(box[2], height - 1 - box[1])
]
annotation = Polygon(points)
annotations.append(
Annotation(
location=annotation.wkt,
id_image=int(idx),
id_terms=[conn.parameters.cytomine_id_predict_term],
id_project=conn.parameters.cytomine_id_project))
annotations.save()
conn.job.update(status=Job.TERMINATED,
status_comment="Finish",
progress=100)
def main(argv):
with CytomineJob.from_cli(argv) as conn:
# with Cytomine(argv) as conn:
print(conn.parameters)
conn.job.update(status=Job.RUNNING,
progress=0,
statusComment="Initialization...")
base_path = "{}".format(os.getenv("HOME")) # Mandatory for Singularity
working_path = os.path.join(base_path, str(conn.job.id))
# with Cytomine(host=params.host, public_key=params.public_key, private_key=params.private_key,
# verbose=logging.INFO) as cytomine:
# ontology = Ontology("classPNcells"+str(conn.parameters.cytomine_id_project)).save()
# ontology_collection=OntologyCollection().fetch()
# print(ontology_collection)
# ontology = Ontology("CLASSPNCELLS").save()
# terms = TermCollection().fetch_with_filter("ontology", ontology.id)
terms = TermCollection().fetch_with_filter(
"project", conn.parameters.cytomine_id_project)
conn.job.update(status=Job.RUNNING,
progress=1,
statusComment="Terms collected...")
print(terms)
# term_P = Term("PositiveCell", ontology.id, "#FF0000").save()
# term_N = Term("NegativeCell", ontology.id, "#00FF00").save()
# term_P = Term("PositiveCell", ontology, "#FF0000").save()
# term_N = Term("NegativeCell", ontology, "#00FF00").save()
# Get all the terms of our ontology
# terms = TermCollection().fetch_with_filter("ontology", ontology.id)
# terms = TermCollection().fetch_with_filter("ontology", ontology)
# print(terms)
# #Loading pre-trained Stardist model
# np.random.seed(17)
# lbl_cmap = random_label_cmap()
# #Stardist H&E model downloaded from https://github.com/mpicbg-csbd/stardist/issues/46
# #Stardist H&E model downloaded from https://drive.switch.ch/index.php/s/LTYaIud7w6lCyuI
# model = StarDist2D(None, name='2D_versatile_HE', basedir='/models/') #use local model file in ~/models/2D_versatile_HE/
#Select images to process
images = ImageInstanceCollection().fetch_with_filter(
"project", conn.parameters.cytomine_id_project)
conn.job.update(status=Job.RUNNING,
progress=2,
statusComment="Images gathered...")
list_imgs = []
if conn.parameters.cytomine_id_images == 'all':
for image in images:
list_imgs.append(int(image.id))
else:
list_imgs = [
int(id_img)
for id_img in conn.parameters.cytomine_id_images.split(',')
]
print(list_imgs)
#Go over images
conn.job.update(status=Job.RUNNING,
progress=10,
statusComment="Running PN classification on image...")
#for id_image in conn.monitor(list_imgs, prefix="Running PN classification on image", period=0.1):
for id_image in list_imgs:
roi_annotations = AnnotationCollection()
roi_annotations.project = conn.parameters.cytomine_id_project
roi_annotations.term = conn.parameters.cytomine_id_cell_term
roi_annotations.image = id_image #conn.parameters.cytomine_id_image
roi_annotations.job = conn.parameters.cytomine_id_annotation_job
roi_annotations.user = conn.parameters.cytomine_id_user_job
roi_annotations.showWKT = True
roi_annotations.fetch()
print(roi_annotations)
#Go over ROI in this image
#for roi in conn.monitor(roi_annotations, prefix="Running detection on ROI", period=0.1):
for roi in roi_annotations:
#Get Cytomine ROI coordinates for remapping to whole-slide
#Cytomine cartesian coordinate system, (0,0) is bottom left corner
print(
"----------------------------Cells------------------------------"
)
roi_geometry = wkt.loads(roi.location)
# print("ROI Geometry from Shapely: {}".format(roi_geometry))
# print("ROI Bounds")
# print(roi_geometry.bounds)
minx = roi_geometry.bounds[0]
miny = roi_geometry.bounds[3]
#Dump ROI image into local PNG file
# roi_path=os.path.join(working_path,str(roi_annotations.project)+'/'+str(roi_annotations.image)+'/'+str(roi.id))
roi_path = os.path.join(
working_path,
str(roi_annotations.project) + '/' +
str(roi_annotations.image) + '/')
# print(roi_path)
roi_png_filename = os.path.join(roi_path + str(roi.id) +
'.png')
conn.job.update(status=Job.RUNNING,
progress=20,
statusComment=roi_png_filename)
# print("roi_png_filename: %s" %roi_png_filename)
roi.dump(dest_pattern=roi_png_filename, alpha=True)
#roi.dump(dest_pattern=os.path.join(roi_path,"{id}.png"), mask=True, alpha=True)
# im=Image.open(roi_png_filename)
J = cv2.imread(roi_png_filename, cv2.IMREAD_UNCHANGED)
J = cv2.cvtColor(J, cv2.COLOR_BGRA2RGBA)
[r, c, h] = J.shape
# print("J: ",J)
if r < c:
blocksize = r
else:
blocksize = c
# print("blocksize:",blocksize)
rr = np.zeros((blocksize, blocksize))
cc = np.zeros((blocksize, blocksize))
zz = [*range(1, blocksize + 1)]
# print("zz:", zz)
for i in zz:
rr[i - 1, :] = zz
# print("rr shape:",rr.shape)
zz = [*range(1, blocksize + 1)]
for i in zz:
cc[:, i - 1] = zz
# print("cc shape:",cc.shape)
cc1 = np.asarray(cc) - 16.5
rr1 = np.asarray(rr) - 16.5
cc2 = np.asarray(cc1)**2
rr2 = np.asarray(rr1)**2
rrcc = np.asarray(cc2) + np.asarray(rr2)
weight = np.sqrt(rrcc)
# print("weight: ",weight)
weight2 = 1. / weight
# print("weight2: ",weight2)
# print("weight2 shape:",weight2.shape)
coord = [c / 2, r / 2]
halfblocksize = blocksize / 2
y = round(coord[1])
x = round(coord[0])
# Convert the RGB image to HSV
Jalpha = J[:, :, 3]
Jalphaloc = Jalpha / 255
Jrgb = cv2.cvtColor(J, cv2.COLOR_RGBA2RGB)
Jhsv = cv2.cvtColor(Jrgb, cv2.COLOR_RGB2HSV_FULL)
Jhsv = Jhsv / 255
Jhsv[:, :, 0] = Jhsv[:, :, 0] * Jalphaloc
Jhsv[:, :, 1] = Jhsv[:, :, 1] * Jalphaloc
Jhsv[:, :, 2] = Jhsv[:, :, 2] * Jalphaloc
# print("Jhsv: ",Jhsv)
# print("Jhsv size:",Jhsv.shape)
# print("Jhsv class:",Jhsv.dtype)
currentblock = Jhsv[0:blocksize, 0:blocksize, :]
# print("currentblock: ",currentblock)
# print(currentblock.dtype)
currentblockH = currentblock[:, :, 0]
currentblockV = 1 - currentblock[:, :, 2]
hue = sum(sum(currentblockH * weight2))
val = sum(sum(currentblockV * weight2))
# print("hue:", hue)
# print("val:", val)
if hue < 2:
cellclass = 1
elif val < 15:
cellclass = 2
else:
if hue < 30 or val > 40:
cellclass = 1
else:
cellclass = 2
# tags = TagCollection().fetch()
# tags = TagCollection()
# print(tags)
if cellclass == 1:
# print("Positive (H: ", str(hue), ", V: ", str(val), ")")
id_terms = conn.parameters.cytomine_id_positive_term
# tag = Tag("Positive (H: ", str(hue), ", V: ", str(val), ")").save()
# print(tag)
# id_terms=Term("PositiveCell", ontology.id, "#FF0000").save()
elif cellclass == 2:
# print("Negative (H: ", str(hue), ", V: ", str(val), ")")
id_terms = conn.parameters.cytomine_id_negative_term
# for t in tags:
# tag = Tag("Negative (H: ", str(hue), ", V: ", str(val), ")").save()
# print(tag)
# id_terms=Term("NegativeCell", ontology.id, "#00FF00").save()
# First we create the required resources
cytomine_annotations = AnnotationCollection()
# property_collection = PropertyCollection(uri()).fetch("annotation",id_image)
# property_collection = PropertyCollection().uri()
# print(property_collection)
# print(cytomine_annotations)
# property_collection.append(Property(Annotation().fetch(id_image), key="Hue", value=str(hue)))
# property_collection.append(Property(Annotation().fetch(id_image), key="Val", value=str(val)))
# property_collection.save()
# prop1 = Property(Annotation().fetch(id_image), key="Hue", value=str(hue)).save()
# prop2 = Property(Annotation().fetch(id_image), key="Val", value=str(val)).save()
# prop1.Property(Annotation().fetch(id_image), key="Hue", value=str(hue)).save()
# prop2.Property(Annotation().fetch(id_image), key="Val", value=str(val)).save()
# for pos, polygroup in enumerate(roi_geometry,start=1):
# points=list()
# for i in range(len(polygroup[0])):
# p=Point(minx+polygroup[1][i],miny-polygroup[0][i])
# points.append(p)
annotation = roi_geometry
# tags.append(TagDomainAssociation(Annotation().fetch(id_image, tag.id))).save()
# association = append(TagDomainAssociation(Annotation().fetch(id_image, tag.id))).save()
# print(association)
cytomine_annotations.append(
Annotation(
location=annotation.wkt, #location=roi_geometry,
id_image=id_image, #conn.parameters.cytomine_id_image,
id_project=conn.parameters.cytomine_id_project,
id_terms=[id_terms]))
print(".", end='', flush=True)
#Send Annotation Collection (for this ROI) to Cytomine server in one http request
ca = cytomine_annotations.save()
conn.job.update(status=Job.TERMINATED,
progress=100,
statusComment="Finished.")
parser.add_argument('--cytomine_host', dest='host',
default='demo.cytomine.be', help="The Cytomine host")
parser.add_argument('--cytomine_public_key', dest='public_key',
help="The Cytomine public key")
parser.add_argument('--cytomine_private_key', dest='private_key',
help="The Cytomine private key")
parser.add_argument('--cytomine_id_image_instance', dest='id_image_instance',
help="The image in which we work")
parser.add_argument('--cytomine_id_roi_term', dest='id_roi_term',
help="The term that represents regions of interest")
parser.add_argument('--cytomine_id_object_term', dest='id_object_term',
help="The term that represents objects")
params, other = parser.parse_known_args(sys.argv[1:])
with Cytomine(host=params.host, public_key=params.public_key, private_key=params.private_key,
verbose=logging.INFO) as cytomine:
roi_annotations = AnnotationCollection()
roi_annotations.image = params.id_image_instance
roi_annotations.term = params.id_roi_term
roi_annotations.fetch()
print(roi_annotations)
for roi_annotation in roi_annotations:
included_annotations = AnnotationCollection()
included_annotations.image = params.id_image_instance
included_annotations.term = params.id_object_term
included_annotations.annotation = roi_annotation.id
included_annotations.fetch()
print("Number of annotations of term {} included in ROI {}: {}".format(
params.id_object_term, roi_annotation.id, len(included_annotations)))