From 563393f8540115860e083c545a5f5ffd838a21fa Mon Sep 17 00:00:00 2001 From: binliu Date: Tue, 23 Jul 2024 22:07:14 +0800 Subject: [PATCH 01/30] first version of cellprofiler plugin Signed-off-by: binliu --- plugins/cellprofiler/README.md | 0 plugins/cellprofiler/runvista2d.py | 441 +++++++++++++++++++++++++++++ 2 files changed, 441 insertions(+) create mode 100644 plugins/cellprofiler/README.md create mode 100644 plugins/cellprofiler/runvista2d.py diff --git a/plugins/cellprofiler/README.md b/plugins/cellprofiler/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/plugins/cellprofiler/runvista2d.py b/plugins/cellprofiler/runvista2d.py new file mode 100644 index 000000000..b5d59666e --- /dev/null +++ b/plugins/cellprofiler/runvista2d.py @@ -0,0 +1,441 @@ +################################# +# +# Imports from useful Python libraries +# +################################# + +import numpy +import os +import monai +import skimage +import importlib.metadata +import subprocess +import uuid +import shutil +import tempfile +import logging +import sys + +################################# +# +# Imports from CellProfiler +# +################################## + +from cellprofiler_core.image import Image +from cellprofiler_core.module.image_segmentation import ImageSegmentation +from cellprofiler_core.object import Objects +from cellprofiler_core.setting import Binary, ValidationError +from cellprofiler_core.setting.choice import Choice +from cellprofiler_core.setting.do_something import DoSomething +from cellprofiler_core.setting.subscriber import ImageSubscriber +from cellprofiler_core.preferences import get_default_output_directory +from cellprofiler_core.setting.text import ( + Integer, + ImageName, + Directory, + Filename, + Float, +) + +CUDA_LINK = "https://pytorch.org/get-started/locally/" +Cellpose_link = " https://doi.org/10.1038/s41592-020-01018-x" +Omnipose_link = "https://doi.org/10.1101/2021.11.03.467199" +LOGGER = logging.getLogger(__name__) + +__doc__ = f"""\ +RunCellpose +=========== + +**RunCellpose** uses a pre-trained machine learning model (Cellpose) to detect cells or nuclei in an image. + +This module is useful for automating simple segmentation tasks in CellProfiler. +The module accepts greyscale input images and produces an object set. Probabilities can also be captured as an image. + +Loading in a model will take slightly longer the first time you run it each session. When evaluating +performance you may want to consider the time taken to predict subsequent images. + +This module now also supports Ominpose. Omnipose builds on Cellpose, for the purpose of **RunCellpose** it adds 2 additional +features: additional models; bact-omni and cyto2-omni which were trained using the Omnipose architechture, and bact +and the mask reconstruction algorithm for Omnipose that was created to solve over-segemnation of large cells; useful for bacterial cells, +but can be used for other arbitrary and anisotropic shapes. You can mix and match Omnipose models with Cellpose style masking or vice versa. + +The module has been updated to be compatible with the latest release of Cellpose. From the old version of the module the 'cells' model corresponds to 'cyto2' model. + +Installation: + +It is necessary that you have installed Cellpose version >= 1.0.2 + +You'll want to run `pip install cellpose` on your CellProfiler Python environment to setup Cellpose. If you have an older version of Cellpose +run 'python -m pip install cellpose --upgrade'. + +To use Omnipose models, and mask reconstruction method you'll want to install Omnipose 'pip install omnipose' and Cellpose version 1.0.2 'pip install cellpose==1.0.2'. + +On the first time loading into CellProfiler, Cellpose will need to download some model files from the internet. This +may take some time. If you want to use a GPU to run the model, you'll need a compatible version of PyTorch and a +supported GPU. Instructions are avaiable at this link: {CUDA_LINK} + +Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). {Cellpose_link} +Kevin J. Cutler, Carsen Stringer, Paul A. Wiggins, Joseph D. Mougous. Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation. bioRxiv 2021.11.03.467199. {Omnipose_link} +============ ============ =============== +Supports 2D? Supports 3D? Respects masks? +============ ============ =============== +YES YES NO +============ ============ =============== + +""" + +"Select Cellpose Docker Image" +CELLPOSE_DOCKER_NO_PRETRAINED = "cellprofiler/runcellpose_no_pretrained:0.1" +CELLPOSE_DOCKER_IMAGE_WITH_PRETRAINED = "cellprofiler/runcellpose_with_pretrained:0.1" + +"Detection mode" +MODEL_NAMES = ['cyto','nuclei','tissuenet','livecell', 'cyto2', 'general', + 'CP', 'CPx', 'TN1', 'TN2', 'TN3', 'LC1', 'LC2', 'LC3', 'LC4', 'custom'] + +"Bundle save path" +BUNDLE_PATH = os.path.join(tempfile.gettempdir(), "bundles") + + +class RunVISTA2D(ImageSegmentation): + category = "Object Processing" + + module_name = "RunVISTA2D" + + variable_revision_number = 1 + + doi = { + "Please cite the following when using RunVISTA2D:": "https://doi.org/10.48550/arXiv.2406.05285", + } + + VISTA2D_BUNDLE_NAME = "cell_vista_segmentation" + VISTA2D_BUNDLE_VERSION = "0.2.1" + + def create_settings(self): + super(RunVISTA2D, self).create_settings() + + self.docker_or_python = Choice( + text="Run CellPose in docker or local python environment", + choices=["Docker", "Python"], + value="Docker", + doc="""\ +If Docker is selected, ensure that Docker Desktop is open and running on your +computer. On first run of the RunCellpose plugin, the Docker container will be +downloaded. However, this slow downloading process will only have to happen +once. + +If Python is selected, the Python environment in which CellProfiler and Cellpose +are installed will be used. +""", + ) + + self.use_gpu = Binary( + text="Use GPU", + value=False, + doc=f"""\ +If enabled, Cellpose will attempt to run detection on your system's graphics card (GPU). +Note that you will need a CUDA-compatible GPU and correctly configured PyTorch version, see this link for details: +{CUDA_LINK} + +If disabled or incorrectly configured, Cellpose will run on your CPU instead. This is much slower but more compatible +with different hardware setups. + +Note that, particularly when in 3D mode, lack of GPU memory can become a limitation. If a model crashes you may need to +re-start CellProfiler to release GPU memory. Resizing large images prior to running them through the model can free up +GPU memory. +""", + ) + + self.model_directory = Directory( + "Location of the pre-trained model file", + doc=f"""\ +*(Used only when using a custom pre-trained model)* +Select the location of the pre-trained CellPose model file that will be used for detection.""", + ) + + def get_directory_fn(): + """Get the directory for the rules file name""" + return self.model_directory.get_absolute_path() + + def set_directory_fn(path): + dir_choice, custom_path = self.model_directory.get_parts_from_path(path) + + self.model_directory.join_parts(dir_choice, custom_path) + + self.model_file_name = Filename( + "Pre-trained model file name", + "cyto_0", + get_directory_fn=get_directory_fn, + set_directory_fn=set_directory_fn, + doc=f"""\ +*(Used only when using a custom pre-trained model)* +This file can be generated by training a custom model withing the CellPose GUI or command line applications.""", + ) + + self.gpu_test = DoSomething( + "", + "Test GPU", + self.do_check_gpu, + doc=f"""\ +Press this button to check whether a GPU is correctly configured. + +If you have a dedicated GPU, a failed test usually means that either your GPU does not support deep learning or the +required dependencies are not installed. +If you have multiple GPUs on your system, this button will only test the first one. +""", + ) + + self.sliding_window_size_0 = Integer( + text="First dimension of the MONAI SlidingWindowInferer roi_size parameter", + value=256, + minval=128, + doc="""\ +First dimension size of the sliding window roi_size parameter, default to 256 +""", + ) + + self.sliding_window_size_1 = Integer( + text="Second dimension of the MONAI SlidingWindowInferer roi_size parameter", + value=256, + minval=128, + doc="""\ +Second dimension size of the sliding window roi_size parameter, default to 256 +""", + ) + + self.multigpu_infer = Binary( + text="Whether using multi-GPU to perform the inference", + value=False, + doc="""\ +Whether using multiple GPUs to perform the inference +""" + ) + + def settings(self): + return [ + self.x_name, + self.docker_or_python, + self.y_name, + self.use_gpu, + self.model_directory, + self.model_file_name, + self.sliding_window_size_0, + self.sliding_window_size_1, + self.multigpu_infer, + ] + + def visible_settings(self): + return [ + self.x_name, + self.docker_or_python, + self.y_name, + self.use_gpu, + self.model_directory, + self.model_file_name, + self.sliding_window_size_0, + self.sliding_window_size_1, + self.multigpu_infer, + ] + + def verify_bundle(self, pipeline): + """If using custom model, validate the model file opens and works""" + pass + + @classmethod + def download_vista2d(cls, save_path): + if os.path.exists(save_path): + return + + os.makedirs(save_path) + monai.bundle.download(name=cls.VISTA2D_BUNDLE_NAME, version=cls.VISTA2D_BUNDLE_VERSION, bundle_dir=save_path) + + + def run(self, workspace): + x_name = self.x_name.value + y_name = self.y_name.value + images = workspace.image_set + x = images.get_image(x_name) + dimensions = x.dimensions + x_data = x.pixel_data + self.download_vista2d(BUNDLE_PATH) + bundle_path = os.path.join(BUNDLE_PATH, self.VISTA2D_BUNDLE_NAME) + + + with tempfile.TemporaryDirectory() as temp_dir: + temp_img_dir = os.path.join(temp_dir, "img") + temp_img_path = os.path.join(temp_img_dir, x_name+".tiff") + temp_mask_dir = os.path.join(temp_dir, "mask") + temp_label_path = os.path.join(temp_mask_dir, y_name+".tif") + skimage.io.imsave(temp_img_path, x_data) + if self.docker_or_python.value == "Python": + if self.multigpu_infer: + pass + else: + cmd = f"""cd {bundle_path}; + python -m monai.bundle run_workflow "scripts.workflow.VistaCell"\ + --config_file configs/hyper_parameters.yaml\ + --mode infer --pretrained_ckpt_name vista2d_v1.pt + """ + + elif self.docker_or_python.value == "Docker": + # Define how to call docker + docker_path = "docker" if sys.platform.lower().startswith("win") else "/usr/local/bin/docker" + # Create a UUID for this run + unique_name = str(uuid.uuid4()) + # Directory that will be used to pass images to the docker container + temp_dir = os.path.join(get_default_output_directory(), ".cellprofiler_temp", unique_name) + temp_img_dir = os.path.join(temp_dir, "img") + + os.makedirs(temp_dir, exist_ok=True) + os.makedirs(temp_img_dir, exist_ok=True) + + + if self.mode.value == "custom": + model_file = self.model_file_name.value + model_directory = self.model_directory.get_absolute_path() + model_path = os.path.join(model_directory, model_file) + temp_model_dir = os.path.join(temp_dir, "model") + + os.makedirs(temp_model_dir, exist_ok=True) + # Copy the model + shutil.copy(model_path, os.path.join(temp_model_dir, model_file)) + + # Save the image to the Docker mounted directory + skimage.io.imsave(temp_img_path, x_data) + + cmd = f""" + {docker_path} run --rm -v {temp_dir}:/data + {self.docker_image.value} + {'--gpus all' if self.use_gpu.value else ''} + cellpose + --dir /data/img + {'--pretrained_model ' + self.mode.value if self.mode.value != 'custom' else '--pretrained_model /data/model/' + model_file} + --chan {channels[0]} + --chan2 {channels[1]} + --diameter {diam} + {'--net_avg' if self.use_averaging.value else ''} + {'--do_3D' if self.do_3D.value else ''} + --anisotropy {anisotropy} + --flow_threshold {self.flow_threshold.value} + --cellprob_threshold {self.cellprob_threshold.value} + --stitch_threshold {self.stitch_threshold.value} + --min_size {self.min_size.value} + {'--invert' if self.invert.value else ''} + {'--exclude_on_edges' if self.remove_edge_masks.value else ''} + --verbose + """ + + try: + subprocess.run(cmd.split(), text=True) + cellpose_output = numpy.load(os.path.join(temp_img_dir, unique_name + "_seg.npy"), allow_pickle=True).item() + + y_data = cellpose_output["masks"] + flows = cellpose_output["flows"] + finally: + # Delete the temporary files + try: + shutil.rmtree(temp_dir) + except: + LOGGER.error("Unable to delete temporary directory, files may be in use by another program.") + LOGGER.error("Temp folder is subfolder {tempdir} in your Default Output Folder.\nYou may need to remove it manually.") + + + y = Objects() + y.segmented = y_data + y.parent_image = x.parent_image + objects = workspace.object_set + objects.add_objects(y, y_name) + + if self.save_probabilities.value: + # Flows come out sized relative to CellPose's inbuilt model size. + # We need to slightly resize to match the original image. + size_corrected = skimage.transform.resize(flows[2], y_data.shape) + prob_image = Image( + size_corrected, + parent_image=x.parent_image, + convert=False, + dimensions=len(size_corrected.shape), + ) + + workspace.image_set.add(self.probabilities_name.value, prob_image) + + if self.show_window: + workspace.display_data.probabilities = size_corrected + + self.add_measurements(workspace) + + if self.show_window: + workspace.display_data.x_data = x_data + workspace.display_data.y_data = y_data + workspace.display_data.dimensions = dimensions + + def display(self, workspace, figure): + if self.save_probabilities.value: + layout = (2, 2) + else: + layout = (2, 1) + + figure.set_subplots( + dimensions=workspace.display_data.dimensions, subplots=layout + ) + + figure.subplot_imshow( + colormap="gray", + image=workspace.display_data.x_data, + title="Input Image", + x=0, + y=0, + ) + + figure.subplot_imshow_labels( + image=workspace.display_data.y_data, + sharexy=figure.subplot(0, 0), + title=self.y_name.value, + x=1, + y=0, + ) + if self.save_probabilities.value: + figure.subplot_imshow( + colormap="gray", + image=workspace.display_data.probabilities, + sharexy=figure.subplot(0, 0), + title=self.probabilities_name.value, + x=0, + y=1, + ) + + def do_check_gpu(self): + import importlib.util + torch_installed = importlib.util.find_spec('torch') is not None + self.cellpose_ver = importlib.metadata.version('cellpose') + #if the old version of cellpose <2.0, then use istorch kwarg + if float(self.cellpose_ver[0:3]) >= 0.7 and int(self.cellpose_ver[0])<2: + GPU_works = core.use_gpu(istorch=torch_installed) + else: # if new version of cellpose, use use_torch kwarg + GPU_works = core.use_gpu(use_torch=torch_installed) + if GPU_works: + message = "GPU appears to be working correctly!" + else: + message = ( + "GPU test failed. There may be something wrong with your configuration." + ) + import wx + + wx.MessageBox(message, caption="GPU Test") + + def upgrade_settings(self, setting_values, variable_revision_number, module_name): + if variable_revision_number == 1: + setting_values = setting_values + ["0.4", "0.0"] + variable_revision_number = 2 + if variable_revision_number == 2: + setting_values = setting_values + ["0.0", False, "15", "1.0", False, False] + variable_revision_number = 3 + if variable_revision_number == 3: + setting_values = [setting_values[0]] + ["Python",CELLPOSE_DOCKER_IMAGE_WITH_PRETRAINED] + setting_values[1:] + variable_revision_number = 4 + if variable_revision_number == 4: + setting_values = [setting_values[0]] + ['No'] + setting_values[1:] + variable_revision_number = 5 + return setting_values, variable_revision_number + + From 386307c75daba60a8dfc601925d9bc5fc81c9260 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 23 Jul 2024 14:09:40 +0000 Subject: [PATCH 02/30] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- plugins/cellprofiler/runvista2d.py | 114 +++++++++++++++-------------- 1 file changed, 61 insertions(+), 53 deletions(-) diff --git a/plugins/cellprofiler/runvista2d.py b/plugins/cellprofiler/runvista2d.py index b5d59666e..1aabd3323 100644 --- a/plugins/cellprofiler/runvista2d.py +++ b/plugins/cellprofiler/runvista2d.py @@ -4,39 +4,34 @@ # ################################# -import numpy -import os -import monai -import skimage import importlib.metadata -import subprocess -import uuid -import shutil -import tempfile import logging +import os +import shutil +import subprocess import sys +import tempfile +import uuid -################################# -# -# Imports from CellProfiler -# -################################## - +import monai +import numpy +import skimage from cellprofiler_core.image import Image from cellprofiler_core.module.image_segmentation import ImageSegmentation from cellprofiler_core.object import Objects +from cellprofiler_core.preferences import get_default_output_directory from cellprofiler_core.setting import Binary, ValidationError from cellprofiler_core.setting.choice import Choice from cellprofiler_core.setting.do_something import DoSomething from cellprofiler_core.setting.subscriber import ImageSubscriber -from cellprofiler_core.preferences import get_default_output_directory -from cellprofiler_core.setting.text import ( - Integer, - ImageName, - Directory, - Filename, - Float, -) +from cellprofiler_core.setting.text import Directory, Filename, Float, ImageName, Integer + +################################# +# +# Imports from CellProfiler +# +################################## + CUDA_LINK = "https://pytorch.org/get-started/locally/" Cellpose_link = " https://doi.org/10.1038/s41592-020-01018-x" @@ -90,8 +85,24 @@ CELLPOSE_DOCKER_IMAGE_WITH_PRETRAINED = "cellprofiler/runcellpose_with_pretrained:0.1" "Detection mode" -MODEL_NAMES = ['cyto','nuclei','tissuenet','livecell', 'cyto2', 'general', - 'CP', 'CPx', 'TN1', 'TN2', 'TN3', 'LC1', 'LC2', 'LC3', 'LC4', 'custom'] +MODEL_NAMES = [ + "cyto", + "nuclei", + "tissuenet", + "livecell", + "cyto2", + "general", + "CP", + "CPx", + "TN1", + "TN2", + "TN3", + "LC1", + "LC2", + "LC3", + "LC4", + "custom", +] "Bundle save path" BUNDLE_PATH = os.path.join(tempfile.gettempdir(), "bundles") @@ -112,7 +123,7 @@ class RunVISTA2D(ImageSegmentation): VISTA2D_BUNDLE_VERSION = "0.2.1" def create_settings(self): - super(RunVISTA2D, self).create_settings() + super().create_settings() self.docker_or_python = Choice( text="Run CellPose in docker or local python environment", @@ -208,7 +219,7 @@ def set_directory_fn(path): value=False, doc="""\ Whether using multiple GPUs to perform the inference -""" +""", ) def settings(self): @@ -240,16 +251,15 @@ def visible_settings(self): def verify_bundle(self, pipeline): """If using custom model, validate the model file opens and works""" pass - + @classmethod def download_vista2d(cls, save_path): if os.path.exists(save_path): return - + os.makedirs(save_path) monai.bundle.download(name=cls.VISTA2D_BUNDLE_NAME, version=cls.VISTA2D_BUNDLE_VERSION, bundle_dir=save_path) - def run(self, workspace): x_name = self.x_name.value y_name = self.y_name.value @@ -259,13 +269,12 @@ def run(self, workspace): x_data = x.pixel_data self.download_vista2d(BUNDLE_PATH) bundle_path = os.path.join(BUNDLE_PATH, self.VISTA2D_BUNDLE_NAME) - with tempfile.TemporaryDirectory() as temp_dir: temp_img_dir = os.path.join(temp_dir, "img") - temp_img_path = os.path.join(temp_img_dir, x_name+".tiff") + temp_img_path = os.path.join(temp_img_dir, x_name + ".tiff") temp_mask_dir = os.path.join(temp_dir, "mask") - temp_label_path = os.path.join(temp_mask_dir, y_name+".tif") + temp_label_path = os.path.join(temp_mask_dir, y_name + ".tif") skimage.io.imsave(temp_img_path, x_data) if self.docker_or_python.value == "Python": if self.multigpu_infer: @@ -276,7 +285,7 @@ def run(self, workspace): --config_file configs/hyper_parameters.yaml\ --mode infer --pretrained_ckpt_name vista2d_v1.pt """ - + elif self.docker_or_python.value == "Docker": # Define how to call docker docker_path = "docker" if sys.platform.lower().startswith("win") else "/usr/local/bin/docker" @@ -285,11 +294,10 @@ def run(self, workspace): # Directory that will be used to pass images to the docker container temp_dir = os.path.join(get_default_output_directory(), ".cellprofiler_temp", unique_name) temp_img_dir = os.path.join(temp_dir, "img") - + os.makedirs(temp_dir, exist_ok=True) os.makedirs(temp_img_dir, exist_ok=True) - if self.mode.value == "custom": model_file = self.model_file_name.value model_directory = self.model_directory.get_absolute_path() @@ -327,18 +335,21 @@ def run(self, workspace): try: subprocess.run(cmd.split(), text=True) - cellpose_output = numpy.load(os.path.join(temp_img_dir, unique_name + "_seg.npy"), allow_pickle=True).item() + cellpose_output = numpy.load( + os.path.join(temp_img_dir, unique_name + "_seg.npy"), allow_pickle=True + ).item() y_data = cellpose_output["masks"] flows = cellpose_output["flows"] - finally: + finally: # Delete the temporary files try: shutil.rmtree(temp_dir) except: LOGGER.error("Unable to delete temporary directory, files may be in use by another program.") - LOGGER.error("Temp folder is subfolder {tempdir} in your Default Output Folder.\nYou may need to remove it manually.") - + LOGGER.error( + "Temp folder is subfolder {tempdir} in your Default Output Folder.\nYou may need to remove it manually." + ) y = Objects() y.segmented = y_data @@ -375,9 +386,7 @@ def display(self, workspace, figure): else: layout = (2, 1) - figure.set_subplots( - dimensions=workspace.display_data.dimensions, subplots=layout - ) + figure.set_subplots(dimensions=workspace.display_data.dimensions, subplots=layout) figure.subplot_imshow( colormap="gray", @@ -406,19 +415,18 @@ def display(self, workspace, figure): def do_check_gpu(self): import importlib.util - torch_installed = importlib.util.find_spec('torch') is not None - self.cellpose_ver = importlib.metadata.version('cellpose') - #if the old version of cellpose <2.0, then use istorch kwarg - if float(self.cellpose_ver[0:3]) >= 0.7 and int(self.cellpose_ver[0])<2: + + torch_installed = importlib.util.find_spec("torch") is not None + self.cellpose_ver = importlib.metadata.version("cellpose") + # if the old version of cellpose <2.0, then use istorch kwarg + if float(self.cellpose_ver[0:3]) >= 0.7 and int(self.cellpose_ver[0]) < 2: GPU_works = core.use_gpu(istorch=torch_installed) else: # if new version of cellpose, use use_torch kwarg GPU_works = core.use_gpu(use_torch=torch_installed) if GPU_works: message = "GPU appears to be working correctly!" else: - message = ( - "GPU test failed. There may be something wrong with your configuration." - ) + message = "GPU test failed. There may be something wrong with your configuration." import wx wx.MessageBox(message, caption="GPU Test") @@ -431,11 +439,11 @@ def upgrade_settings(self, setting_values, variable_revision_number, module_name setting_values = setting_values + ["0.0", False, "15", "1.0", False, False] variable_revision_number = 3 if variable_revision_number == 3: - setting_values = [setting_values[0]] + ["Python",CELLPOSE_DOCKER_IMAGE_WITH_PRETRAINED] + setting_values[1:] + setting_values = ( + [setting_values[0]] + ["Python", CELLPOSE_DOCKER_IMAGE_WITH_PRETRAINED] + setting_values[1:] + ) variable_revision_number = 4 if variable_revision_number == 4: - setting_values = [setting_values[0]] + ['No'] + setting_values[1:] + setting_values = [setting_values[0]] + ["No"] + setting_values[1:] variable_revision_number = 5 return setting_values, variable_revision_number - - From 965732002362e6dfce6f6b1b79ae355856623aca Mon Sep 17 00:00:00 2001 From: binliu Date: Tue, 30 Jul 2024 15:58:44 +0000 Subject: [PATCH 03/30] add vista support Signed-off-by: binliu --- sample-apps/vista/README.md | 200 ++++++++++++++++ sample-apps/vista/__init__.py | 10 + sample-apps/vista/lib/__init__.py | 10 + .../vista/lib/activelearning/__init__.py | 10 + sample-apps/vista/lib/infers/__init__.py | 10 + sample-apps/vista/lib/trainers/__init__.py | 10 + sample-apps/vista/main.py | 213 ++++++++++++++++++ sample-apps/vista/requirements.txt | 10 + 8 files changed, 473 insertions(+) create mode 100644 sample-apps/vista/README.md create mode 100644 sample-apps/vista/__init__.py create mode 100644 sample-apps/vista/lib/__init__.py create mode 100644 sample-apps/vista/lib/activelearning/__init__.py create mode 100644 sample-apps/vista/lib/infers/__init__.py create mode 100644 sample-apps/vista/lib/trainers/__init__.py create mode 100644 sample-apps/vista/main.py create mode 100644 sample-apps/vista/requirements.txt diff --git a/sample-apps/vista/README.md b/sample-apps/vista/README.md new file mode 100644 index 000000000..9b9b67191 --- /dev/null +++ b/sample-apps/vista/README.md @@ -0,0 +1,200 @@ + + +# MONAI Bundle Application +The MONAIBundle App allows you to easily pull any MONAI Bundle from the [MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo/tree/dev/models) and import it into MONAI Label. However, it's important to note that any MONAI Bundle used with MONAI Label must meet the following constraints: + +- It must comply with the [MONAI Bundle Specification](https://docs.monai.io/en/latest/mb_specification.html). +- For inference, the bundle must define either an `inference.json` or `inference.yaml` file, and it must include the keys described in the bundle.py file located in the `monailabel/tasks/infer/` directory. +- For training, the bundle must define either a `train.json` or `train.yaml file`, and it must include the keys described in the bundle.py file located in the `monailabel/tasks/train/` directory. +- For multi-GPU training, the bundle must define either a `multi_gpu_train.json` or `multi_gpu_train.yaml` file. + +These constraints ensure that any MONAI Bundle used with MONAI Label is compatible with the platform and can be seamlessly integrated into your workflow. + +### Table of Contents +- [Supported Models](#supported-models) +- [How To Use the App](#how-to-use-the-app) +- [Epistemic Scoring using MONAI Bundles](#epistemic-Scoring-for-monaibundle-app)\ +- [Detection Model](#Detection-Model) + +### Supported Models + + +The MONAIBundle App currently supports most labeling models in the Model-Zoo. You can find a table of supported labeling tasks below. Please note that the list of supported tasks is updated based on the latest release from the [Model-Zoo](https://github.com/Project-MONAI/model-zoo/tree/dev/models). + +| Bundle | Model | Objects | Modality | Note | +|:----:|:-----:|:-------:|:--------:|:----:| +| [spleen_ct_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation) | UNet | Spleen | CT | A model for (3D) segmentation of the spleen | +| [swin_unetr_btcv_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/swin_unetr_btcv_segmentation) | SwinUNETR | Multi-Organ | CT | A model for (3D) multi-organ segmentation | +| [prostate_mri_anatomy](https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy) | UNet | Prostate | MRI | A model for (3D) prostate segmentation from MRI image | +| [pancreas_ct_dints_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/pancreas_ct_dints_segmentation) | DiNTS | Pancreas/Tumor | CT | An automl method for (3D) pancreas/tumor segmentation | +| [renalStructures_UNEST_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/renalStructures_UNEST_segmentation) | UNesT | Kidney Substructure | CT | A pre-trained for inference (3D) kidney cortex/medulla/pelvis segmentation | +| [wholeBrainSeg_UNEST_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/wholeBrainSeg_Large_UNEST_segmentation) | UNesT | Whole Brain | MRI T1 | A pre-trained for inference (3D) 133 whole brain structures segmentation | +| [spleen_deepedit_annotation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_deepedit_annotation) | DeepEdit | Spleen| CT | An interactive method for 3D spleen Segmentation | +| [lung_nodule_ct_detection](https://github.com/Project-MONAI/model-zoo/tree/dev/models/lung_nodule_ct_detection) | RetinaNet | Lung Nodule| CT | The detection model for 3D CT images | +| [wholeBody_ct_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/wholeBody_ct_segmentation) | SegResNet | 104 body structures| CT | The segmentation model for 104 tissue from 3D CT images (TotalSegmentator Dataset) | + + +### How To Use the App + +```bash +# skip this if you have already downloaded the app or using github repository (dev mode) +monailabel apps --download --name monaibundle --output workspace + +# List all available models from zoo +monailabel start_server --app workspace/monaibundle --studies workspace/images + +# Pick spleen_ct_segmentation_v0.1.0 model +monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.1.0 + +# Pick spleen_ct_segmentation_v0.1.0 model and preload +monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.1.0 --conf preload true + +# Pick DeepEdit And Segmentation model (multiple models) +monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models "spleen_ct_segmentation_v0.1.0,spleen_deepedit_annotation_v0.1.0" + +# Skip Training Tasks or Infer only mode +monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.1.0 --conf skip_trainers true +``` + +**Specify bundle version** (Optional) +Above command will download the latest bundles from Model-Zoo by default. If a specific or older bundle version is used, users can add version `_v` followed by the bundle name. Example: + +```bash +monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.3.7 +``` +Note: bundles in Model-Zoo are continuously updated, old bundles may contain deprecated API, we recommend using the latest realeased MONAI Label and bundle for the best + experience. + +### Epistemic Scoring for monaibundle app +The MONAIBundle App supports epistemic scoring using bundle models. To use epistemic scoring, you can specify a valid scoring bundle model as the `epistemic_model` configuration parameter when running the app, E.g., `--conf epistemic_model `. If a valid scoring bundle model is provided, scoring inference will be triggered. The loaded scoring bundle model can be either a model from the Model Zoo or a local bundle, but it must support the `dropout` argument. + +With epistemic scoring, MONAIBundle can provide measures of uncertainty or confidence in the model's predictions, which can be useful in a variety of applications. + +```bash +# Use the UNet in spleen_ct_segmentation_v0.2.0 bundle as epistemic scoring model. +# Manual define epistemic scoring parameters +monailabel start_server \ + --app workspace/monaibundle \ + --studies workspace/images \ + --conf models spleen_ct_segmentation_v0.2.0,swin_unetr_btcv_segmentation_v0.2.0 \ + --conf epistemic_model spleen_ct_segmentation_v0.2.0 + --conf epistemic_max_samples 0 \ + --conf epistemic_simulation_size 5 + --conf epistemic_dropout 0.2 + +``` + +### Detection Model + +MONAI Label now supports detection models. We demonstrate an example of lung nodule detection model from model-zoo. + +3D Slicer plugin needs to be updated or use the latest developer mode. + +#### Step 1: **Prerequisites** + +**Get latest MONAI Label Slicer plugin** (Optional) + +If you are using older release of 3D Slicer MONAI Label plugin, you can update to the latest plugin which supports ***drawing ROI nodes from MONAI Label server prediction***. + +Or to use the developer mode of MONAI Label plugin by: + + - Pull latest MONAI Label repo: + ```bash + git clone https://github.com/Project-MONAI/MONAILabel.git + ``` + - Go to `MONAILabel/plugins/slicer/` + - Follow the Readme page to use developer mode of Slicer plugin. + https://github.com/Project-MONAI/MONAILabel/tree/main/plugins/slicer#install-plugin-in-developer-mode + - Install Plugin in Developer Mode + - Open 3D Slicer: Go to Edit -> Application Settings -> Modules -> Additional Module Paths + - Add New Module Path: /plugins/slicer/MONAILabel + - Restart 3D Slicer + +#### Step 2: Download `monaibundle` App + +Sample-app with built-in versions are in the downloads, fetch the `monaibundle` app using monailabel API + +```bash + monailabel apps --download --name monaibundle --output apps +``` + +#### Step 3: Prepare Sample Data + +The lung nodule detection model are trained and evaluated using [LUNA16](https://luna16.grand-challenge.org/Home/) dataset. + +LUNA16 is a public dataset of CT lung nodule detection. Using raw CT scans, the goal is to identify locations of possible nodules, and to assign a probability for being a nodule to each location. + +Disclaimer: We are not the host of the data. Please make sure to read the requirements and usage policies of the data and give credit to the authors of the dataset! We acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study. + + - If you are going to use full datset of LUNA16, please refer to the dataset link, download the data, create and preprocess the images following [this page]( https://github.com/Project-MONAI/model-zoo/tree/dev/models/lung_nodule_ct_detection#data +). + + - In this tutorial, we prepared a sample subset, resampled and ready to use. The subset is only for demonstration. Download [here](https://github.com/Project-MONAI/MONAILabel/releases/download/data/detection_dataset.zip). + + Or use these commands to download and extract directly. + +```bash +mkdir datasets +wget "https://github.com/Project-MONAI/MONAILabel/releases/download/data/detection_dataset.zip" -O datasets/detection_dataset.zip +unzip datasets/detection_dataset.zip -d datasets/detection_dataset +``` + + - The detection model also supports MSD Task06_Lung dataset for inference. Note this dataset is created for nodule segmentation task. We do not use the segmentation mask, only the images for inference. Use the following monailabel API to automatically download the dataset. + +```bash +monailabel datasets --download --name Task06_Lung --output datasets +``` + +#### Step 4: Start MONAI Label server and 3D Slicer to Annotate + +All models, datasets, and plugins are ready, start the MONAI Label server and open 3D Slicer to annotate! + +```bash +# 1: Use LUNA16 sample data +monailabel start_server --app apps/monaibundle --studies datasets/Task06_Lung/imagesTr --conf models lung_nodule_ct_detection +# 2: Use Task06_Lung data for inference demonstration +monailabel start_server --app apps/monaibundle --studies datasets/detection_dataset --conf models lung_nodule_ct_detection +``` + +#### Step 5: Modify Detection Parameters in the Bundle (Optional) + +The lung_nodule_ct_detection bundle can set the box prediction probability threshold. In `apps/monaibundle/model/lung_nodule_ct_detection/configs/inference.json`, it's define here `score_thresh=0.02`: + +```json + "detector_ops": [ + "$@detector.set_target_keys(box_key='box', label_key='label')", + "$@detector.set_box_selector_parameters(score_thresh=0.02,topk_candidates_per_level=1000,nms_thresh=0.22,detections_per_img=300)", + "$@detector.set_sliding_window_inferer(roi_size=@infer_patch_size,overlap=0.25,sw_batch_size=1,mode='constant',device='cpu')" + ], + +``` + +This parameter will impact the number of boxes in the final prediction output. Then show all boxes ROIs in 3D Slicer. + +If users want to limit the number of predicted boxes showing on 3D Slicer, users can set higher `score_thresh`, e.g., `score_thresh=0.5`, `score_thresh=0.6`, or even higher. + + +#### Additional Configs + +To set configuration parameters for MONAI Label Server, use the `--conf ` flag followed by the parameter name and value while starting the MONAI Label Server. + +| Name | Values | Description | +|---------------------------|-----------------|---------------------------------------------------------------------------------------------| +| zoo_source | string | _Default value:_ github | +| zoo_repo | string | _Default value:_ Project-MONAI/model-zoo/hosting_storage_v1 | +| preload | true, **false** | Preload model into GPU | +| skip_trainers | true, **false** | Skip adding training tasks (Run in Infer mode only) | +| epistemic_max_samples | int | _Default value:_ 0 ; Epistemic scoring parameters | +| epistemic_simulation_size | int | _Default value:_ 5 ; Epistemic simulation size parameters | +| epistemic_dropout | float | _Default value:_ 0.2 ; Epistemic scoring parameters: Dropout rate for scoring models | | diff --git a/sample-apps/vista/__init__.py b/sample-apps/vista/__init__.py new file mode 100644 index 000000000..1e97f8940 --- /dev/null +++ b/sample-apps/vista/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/sample-apps/vista/lib/__init__.py b/sample-apps/vista/lib/__init__.py new file mode 100644 index 000000000..1e97f8940 --- /dev/null +++ b/sample-apps/vista/lib/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/sample-apps/vista/lib/activelearning/__init__.py b/sample-apps/vista/lib/activelearning/__init__.py new file mode 100644 index 000000000..1e97f8940 --- /dev/null +++ b/sample-apps/vista/lib/activelearning/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/sample-apps/vista/lib/infers/__init__.py b/sample-apps/vista/lib/infers/__init__.py new file mode 100644 index 000000000..1e97f8940 --- /dev/null +++ b/sample-apps/vista/lib/infers/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/sample-apps/vista/lib/trainers/__init__.py b/sample-apps/vista/lib/trainers/__init__.py new file mode 100644 index 000000000..1e97f8940 --- /dev/null +++ b/sample-apps/vista/lib/trainers/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/sample-apps/vista/main.py b/sample-apps/vista/main.py new file mode 100644 index 000000000..ffc784be7 --- /dev/null +++ b/sample-apps/vista/main.py @@ -0,0 +1,213 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +from typing import Dict + +from monai.transforms import Invertd, SaveImaged + +import monailabel +from monailabel.interfaces.app import MONAILabelApp +from monailabel.interfaces.tasks.infer_v2 import InferTask +from monailabel.interfaces.tasks.scoring import ScoringMethod +from monailabel.interfaces.tasks.strategy import Strategy +from monailabel.interfaces.tasks.train import TrainTask +from monailabel.tasks.activelearning.first import First +from monailabel.tasks.activelearning.random import Random +from monailabel.tasks.infer.bundle import BundleInferTask +from monailabel.tasks.scoring.epistemic_v2 import EpistemicScoring +from monailabel.tasks.train.bundle import BundleTrainTask +from monailabel.utils.others.generic import get_bundle_models, strtobool + +logger = logging.getLogger(__name__) + + +class VISTAApp(MONAILabelApp): + def __init__(self, app_dir, studies, conf): + self.models = get_bundle_models(app_dir, conf) + # Add Epistemic model for scoring + self.epistemic_models = ( + get_bundle_models(app_dir, conf, conf_key="epistemic_model") if conf.get("epistemic_model") else None + ) + if self.epistemic_models: + # Get epistemic parameters + self.epistemic_max_samples = int(conf.get("epistemic_max_samples", "0")) + self.epistemic_simulation_size = int(conf.get("epistemic_simulation_size", "5")) + self.epistemic_dropout = float(conf.get("epistemic_dropout", "0.2")) + + super().__init__( + app_dir=app_dir, + studies=studies, + conf=conf, + name=f"MONAILabel - Zoo/Bundle ({monailabel.__version__})", + description="DeepLearning models provided via MONAI Zoo/Bundle", + version=monailabel.__version__, + ) + + def init_infers(self) -> Dict[str, InferTask]: + infers: Dict[str, InferTask] = {} + ################################################# + # Models + ################################################# + + for n, b in self.models.items(): + i = BundleInferTask(b, self.conf) + logger.info(f"+++ Adding Inferer:: {n} => {i}") + infers[n] = i + + return infers + + def init_trainers(self) -> Dict[str, TrainTask]: + trainers: Dict[str, TrainTask] = {} + if strtobool(self.conf.get("skip_trainers", "false")): + return trainers + + for n, b in self.models.items(): + t = BundleTrainTask(b, self.conf) + if not t or not t.is_valid(): + continue + + logger.info(f"+++ Adding Trainer:: {n} => {t}") + trainers[n] = t + return trainers + + def init_strategies(self) -> Dict[str, Strategy]: + strategies: Dict[str, Strategy] = { + "random": Random(), + "first": First(), + } + + logger.info(f"Active Learning Strategies:: {list(strategies.keys())}") + return strategies + + def init_scoring_methods(self) -> Dict[str, ScoringMethod]: + methods: Dict[str, ScoringMethod] = {} + if not self.conf.get("epistemic_model"): + return methods + + for n, b in self.epistemic_models.items(): + # Create BundleInferTask task with dropout instantiation for scoring inference + i = BundleInferTask( + b, + self.conf, + train_mode=True, + skip_writer=True, + dropout=self.epistemic_dropout, + post_filter=[SaveImaged, Invertd], + ) + methods[n] = EpistemicScoring( + i, max_samples=self.epistemic_max_samples, simulation_size=self.epistemic_simulation_size + ) + if not methods: + continue + methods = methods if isinstance(methods, dict) else {n: methods[n]} + logger.info(f"+++ Adding Scoring Method:: {n} => {b}") + + logger.info(f"Active Learning Scoring Methods:: {list(methods.keys())}") + return methods + + +""" +Example to run train/infer/scoring task(s) locally without actually running MONAI Label Server +""" + + +def main(): + import argparse + import shutil + from pathlib import Path + + from monailabel.utils.others.generic import device_list, file_ext + + os.putenv("MASTER_ADDR", "127.0.0.1") + os.putenv("MASTER_PORT", "1234") + + logging.basicConfig( + level=logging.INFO, + format="[%(asctime)s] [%(process)s] [%(threadName)s] [%(levelname)s] (%(name)s:%(lineno)d) - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + force=True, + ) + + home = str(Path.home()) + studies = f"{home}/Datasets/cellpose" + + parser = argparse.ArgumentParser() + parser.add_argument("-s", "--studies", default=studies) + parser.add_argument("-m", "--model", default="cell_vista_segmentation") + parser.add_argument("-t", "--test", default="infer", choices=("train", "infer", "batch_infer")) + args = parser.parse_args() + + app_dir = os.path.dirname(__file__) + studies = args.studies + conf = { + "models": args.model, + "preload": "false", + } + + app = VISTAApp(app_dir, studies, conf) + + # Infer + if args.test == "infer": + sample = app.next_sample(request={"strategy": "first"}) + image_id = sample["id"] + image_path = sample["path"] + + # Run on all devices + for device in device_list(): + res = app.infer(request={"model": args.model, "image": image_id, "device": device}) + label = res["file"] + label_json = res["params"] + test_dir = os.path.join(args.studies, "test_labels") + os.makedirs(test_dir, exist_ok=True) + + label_file = os.path.join(test_dir, image_id + file_ext(image_path)) + shutil.move(label, label_file) + + print(label_json) + print(f"++++ Image File: {image_path}") + print(f"++++ Label File: {label_file}") + break + return + + # Batch Infer + if args.test == "batch_infer": + app.batch_infer( + request={ + "model": args.model, + "multi_gpu": False, + "save_label": True, + "label_tag": "original", + "max_workers": 1, + "max_batch_size": 0, + } + ) + return + + # Train + app.train( + request={ + "model": args.model, + "max_epochs": 10, + "dataset": "Dataset", # PersistentDataset, CacheDataset + "train_batch_size": 1, + "val_batch_size": 1, + "multi_gpu": False, + "val_split": 0.1, + }, + ) + + +if __name__ == "__main__": + # export PYTHONPATH=~/Projects/MONAILabel:`pwd` + # python main.py + main() diff --git a/sample-apps/vista/requirements.txt b/sample-apps/vista/requirements.txt new file mode 100644 index 000000000..1e97f8940 --- /dev/null +++ b/sample-apps/vista/requirements.txt @@ -0,0 +1,10 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. From 02745d595cc662a6c1547c5c7ba9572ef95c1272 Mon Sep 17 00:00:00 2001 From: binliu Date: Thu, 1 Aug 2024 14:55:13 +0000 Subject: [PATCH 04/30] fix some issues Signed-off-by: binliu --- monailabel/tasks/infer/basic_infer.py | 5 ++-- monailabel/tasks/infer/vista.py | 16 ++++++++++++ sample-apps/vista/lib/infers/__init__.py | 2 ++ sample-apps/vista/lib/infers/vista.py | 31 ++++++++++++++++++++++++ sample-apps/vista/main.py | 3 ++- 5 files changed, 53 insertions(+), 4 deletions(-) create mode 100644 monailabel/tasks/infer/vista.py create mode 100644 sample-apps/vista/lib/infers/vista.py diff --git a/monailabel/tasks/infer/basic_infer.py b/monailabel/tasks/infer/basic_infer.py index e0b12eddc..930bf6b85 100644 --- a/monailabel/tasks/infer/basic_infer.py +++ b/monailabel/tasks/infer/basic_infer.py @@ -467,11 +467,10 @@ def _get_network(self, device, data): if path: checkpoint = torch.load(path, map_location=torch.device(device)) model_state_dict = checkpoint.get(self.model_state_dict, checkpoint) - - if set(self.network.state_dict().keys()) != set(checkpoint.keys()): + if set(self.network.state_dict().keys()) != set(model_state_dict.keys()): logger.warning( f"Checkpoint keys don't match network.state_dict()! Items that exist in only one dict" - f" but not in the other: {set(self.network.state_dict().keys()) ^ set(checkpoint.keys())}" + f" but not in the other: {set(self.network.state_dict().keys()) ^ set(model_state_dict.keys())}" ) logger.warning( "The run will now continue unless load_strict is set to True. " diff --git a/monailabel/tasks/infer/vista.py b/monailabel/tasks/infer/vista.py new file mode 100644 index 000000000..40c825d07 --- /dev/null +++ b/monailabel/tasks/infer/vista.py @@ -0,0 +1,16 @@ + def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, Any]: + d = dict(data) + output_dir = d.get("output_dir", "") + output_ext = d.get("output_ext", "") + + os.makedirs(self.output_dir, exist_ok=True) + + img = d.get(self.image_key, None) + filename = img.meta.get(ImageMetaKey.FILENAME_OR_OBJ) if img is not None else None + basename = os.path.splitext(os.path.basename(filename))[0] if filename else "mask" + logger.info(f"File: {filename}; Base: {basename}") + + for key in self.key_iterator(d): + label = d[key] + output_filename = f"{basename}{'_' + self.output_postfix if self.output_postfix else ''}{output_ext}" + output_filepath = os.path.join(output_dir, output_filename) \ No newline at end of file diff --git a/sample-apps/vista/lib/infers/__init__.py b/sample-apps/vista/lib/infers/__init__.py index 1e97f8940..db78968da 100644 --- a/sample-apps/vista/lib/infers/__init__.py +++ b/sample-apps/vista/lib/infers/__init__.py @@ -8,3 +8,5 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + +from .vista import VISTAInfer diff --git a/sample-apps/vista/lib/infers/vista.py b/sample-apps/vista/lib/infers/vista.py new file mode 100644 index 000000000..4a327e083 --- /dev/null +++ b/sample-apps/vista/lib/infers/vista.py @@ -0,0 +1,31 @@ +import os +import logging +from typing import Any, Dict, Tuple + +from monai.utils import ImageMetaKey +from monailabel.tasks.infer.bundle import BundleInferTask + +logger = logging.getLogger(__name__) + + +class VISTAInfer(BundleInferTask): + """ + This provides Inference Engine for pre-trained VISTA segmentation model. + """ + def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, Any]: + d = dict(data) + output_dir = self.bundle_config.get_parsed_content("output_dir", instantiate=True) + output_ext = self.bundle_config.get_parsed_content("output_ext", instantiate=True) + image_key = self.bundle_config.get_parsed_content("image_key", instantiate=True) + output_postfix = self.bundle_config.get_parsed_content("output_postfix", instantiate=True) + + img = d.get(image_key, None) + filename = img.meta.get(ImageMetaKey.FILENAME_OR_OBJ) if img is not None else None + basename = os.path.splitext(os.path.basename(filename))[0] if filename else "mask" + output_filename = f"{basename}{'_' + output_postfix if output_postfix else ''}{output_ext}" + output_filepath = os.path.join(output_dir, output_filename) + if os.path.exists(output_filepath): + logger.info(f"Reusing the bundle output {output_filepath}.") + return output_filepath, {} + else: + super().writer(data=data, extension=extension, dtype=dtype) \ No newline at end of file diff --git a/sample-apps/vista/main.py b/sample-apps/vista/main.py index ffc784be7..e2e57558b 100644 --- a/sample-apps/vista/main.py +++ b/sample-apps/vista/main.py @@ -15,6 +15,7 @@ from monai.transforms import Invertd, SaveImaged +from lib.infers import VISTAInfer import monailabel from monailabel.interfaces.app import MONAILabelApp from monailabel.interfaces.tasks.infer_v2 import InferTask @@ -60,7 +61,7 @@ def init_infers(self) -> Dict[str, InferTask]: ################################################# for n, b in self.models.items(): - i = BundleInferTask(b, self.conf) + i = VISTAInfer(b, self.conf, model_state_dict="state_dict") logger.info(f"+++ Adding Inferer:: {n} => {i}") infers[n] = i From ab24f928f79b92f78c20132254b0672e1134b30b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 1 Aug 2024 14:55:41 +0000 Subject: [PATCH 05/30] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- monailabel/tasks/infer/vista.py | 4 ++-- sample-apps/vista/lib/infers/vista.py | 6 ++++-- sample-apps/vista/main.py | 2 +- 3 files changed, 7 insertions(+), 5 deletions(-) diff --git a/monailabel/tasks/infer/vista.py b/monailabel/tasks/infer/vista.py index 40c825d07..2b39ac63e 100644 --- a/monailabel/tasks/infer/vista.py +++ b/monailabel/tasks/infer/vista.py @@ -1,4 +1,4 @@ - def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, Any]: +def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, Any]: d = dict(data) output_dir = d.get("output_dir", "") output_ext = d.get("output_ext", "") @@ -13,4 +13,4 @@ def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, for key in self.key_iterator(d): label = d[key] output_filename = f"{basename}{'_' + self.output_postfix if self.output_postfix else ''}{output_ext}" - output_filepath = os.path.join(output_dir, output_filename) \ No newline at end of file + output_filepath = os.path.join(output_dir, output_filename) diff --git a/sample-apps/vista/lib/infers/vista.py b/sample-apps/vista/lib/infers/vista.py index 4a327e083..b206f7550 100644 --- a/sample-apps/vista/lib/infers/vista.py +++ b/sample-apps/vista/lib/infers/vista.py @@ -1,8 +1,9 @@ -import os import logging +import os from typing import Any, Dict, Tuple from monai.utils import ImageMetaKey + from monailabel.tasks.infer.bundle import BundleInferTask logger = logging.getLogger(__name__) @@ -12,6 +13,7 @@ class VISTAInfer(BundleInferTask): """ This provides Inference Engine for pre-trained VISTA segmentation model. """ + def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, Any]: d = dict(data) output_dir = self.bundle_config.get_parsed_content("output_dir", instantiate=True) @@ -28,4 +30,4 @@ def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, logger.info(f"Reusing the bundle output {output_filepath}.") return output_filepath, {} else: - super().writer(data=data, extension=extension, dtype=dtype) \ No newline at end of file + super().writer(data=data, extension=extension, dtype=dtype) diff --git a/sample-apps/vista/main.py b/sample-apps/vista/main.py index e2e57558b..05148ba5a 100644 --- a/sample-apps/vista/main.py +++ b/sample-apps/vista/main.py @@ -13,9 +13,9 @@ import os from typing import Dict +from lib.infers import VISTAInfer from monai.transforms import Invertd, SaveImaged -from lib.infers import VISTAInfer import monailabel from monailabel.interfaces.app import MONAILabelApp from monailabel.interfaces.tasks.infer_v2 import InferTask From dcfe2b1b652652fcf9b9155ea2acc6d3e8065746 Mon Sep 17 00:00:00 2001 From: binliu Date: Fri, 2 Aug 2024 15:56:50 +0000 Subject: [PATCH 06/30] update the plugin part Signed-off-by: binliu --- plugins/cellprofiler/runvista2d.py | 985 ++++++++++++++++++++--------- 1 file changed, 690 insertions(+), 295 deletions(-) diff --git a/plugins/cellprofiler/runvista2d.py b/plugins/cellprofiler/runvista2d.py index 1aabd3323..4c4cace17 100644 --- a/plugins/cellprofiler/runvista2d.py +++ b/plugins/cellprofiler/runvista2d.py @@ -4,27 +4,28 @@ # ################################# -import importlib.metadata -import logging +import numpy import os -import shutil +import monai +import skimage +import importlib.metadata import subprocess -import sys -import tempfile import uuid +import shutil +import tempfile +import logging +import sys -import monai -import numpy -import skimage -from cellprofiler_core.image import Image -from cellprofiler_core.module.image_segmentation import ImageSegmentation -from cellprofiler_core.object import Objects -from cellprofiler_core.preferences import get_default_output_directory -from cellprofiler_core.setting import Binary, ValidationError -from cellprofiler_core.setting.choice import Choice -from cellprofiler_core.setting.do_something import DoSomething -from cellprofiler_core.setting.subscriber import ImageSubscriber -from cellprofiler_core.setting.text import Directory, Filename, Float, ImageName, Integer +import cgi +import http.client +import json +import mimetypes +import re +import ssl +from pathlib import Path +from urllib.parse import quote_plus, unquote, urlencode, urlparse + +import requests ################################# # @@ -32,6 +33,15 @@ # ################################## +from cellprofiler_core.image import Image +from cellprofiler_core.module.image_segmentation import ImageSegmentation +from cellprofiler_core.object import Objects +from cellprofiler_core.setting import Binary, ValidationError +from cellprofiler_core.setting.choice import Choice +from cellprofiler_core.setting.do_something import DoSomething +from cellprofiler_core.setting.subscriber import ImageSubscriber +from cellprofiler_core.preferences import get_default_output_directory +from cellprofiler_core.setting.text import Text, URL CUDA_LINK = "https://pytorch.org/get-started/locally/" Cellpose_link = " https://doi.org/10.1038/s41592-020-01018-x" @@ -80,186 +90,697 @@ """ -"Select Cellpose Docker Image" -CELLPOSE_DOCKER_NO_PRETRAINED = "cellprofiler/runcellpose_no_pretrained:0.1" -CELLPOSE_DOCKER_IMAGE_WITH_PRETRAINED = "cellprofiler/runcellpose_with_pretrained:0.1" - -"Detection mode" -MODEL_NAMES = [ - "cyto", - "nuclei", - "tissuenet", - "livecell", - "cyto2", - "general", - "CP", - "CPx", - "TN1", - "TN2", - "TN3", - "LC1", - "LC2", - "LC3", - "LC4", - "custom", -] - -"Bundle save path" -BUNDLE_PATH = os.path.join(tempfile.gettempdir(), "bundles") +def bytes_to_str(b): + return b.decode("utf-8") if isinstance(b, bytes) else b -class RunVISTA2D(ImageSegmentation): - category = "Object Processing" - module_name = "RunVISTA2D" +class MONAILabelClient: + """ + Basic MONAILabel Client to invoke infer/train APIs over http/https + """ - variable_revision_number = 1 + def __init__(self, server_url, tmpdir=None, client_id=None): + """ + :param server_url: Server URL for MONAILabel (e.g. http://127.0.0.1:8000) + :param tmpdir: Temp directory to save temporary files. If None then it uses tempfile.tempdir + :param client_id: Client ID that will be added for all basic requests + """ - doi = { - "Please cite the following when using RunVISTA2D:": "https://doi.org/10.48550/arXiv.2406.05285", - } + self._server_url = server_url.rstrip("/").strip() + self._tmpdir = tmpdir if tmpdir else tempfile.tempdir if tempfile.tempdir else "/tmp" + self._client_id = client_id + self._headers = {} - VISTA2D_BUNDLE_NAME = "cell_vista_segmentation" - VISTA2D_BUNDLE_VERSION = "0.2.1" + def _update_client_id(self, params): + if params: + params["client_id"] = self._client_id + else: + params = {"client_id": self._client_id} + return params + + def update_auth(self, token): + if token: + self._headers["Authorization"] = f"{token['token_type']} {token['access_token']}" + + def get_server_url(self): + """ + Return server url + + :return: the url for monailabel server + """ + return self._server_url + + def set_server_url(self, server_url): + """ + Set url for monailabel server + + :param server_url: server url for monailabel + """ + self._server_url = server_url.rstrip("/").strip() + + def auth_enabled(self) -> bool: + """ + Check if Auth is enabled + + """ + selector = "/auth/" + status, response, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector) + if status != 200: + return False + + response = bytes_to_str(response) + LOGGER.debug(f"Response: {response}") + enabled = json.loads(response).get("enabled", False) + return True if enabled else False + + def auth_token(self, username, password): + """ + Fetch Auth Token. Currently only basic authentication is supported. + + :param username: UserName for basic authentication + :param password: Password for basic authentication + """ + selector = "/auth/token" + data = urlencode({"username": username, "password": password, "grant_type": "password"}) + status, response, _, _ = MONAILabelUtils.http_method( + "POST", self._server_url, selector, data, None, "application/x-www-form-urlencoded" + ) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response + ) - def create_settings(self): - super().create_settings() + response = bytes_to_str(response) + LOGGER.debug(f"Response: {response}") + return json.loads(response) + + def auth_valid_token(self) -> bool: + selector = "/auth/token/valid" + status, _, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector, headers=self._headers) + return True if status == 200 else False + + def info(self): + """ + Invoke /info/ request over MONAILabel Server + + :return: json response + """ + selector = "/info/" + status, response, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector, headers=self._headers) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response + ) - self.docker_or_python = Choice( - text="Run CellPose in docker or local python environment", - choices=["Docker", "Python"], - value="Docker", - doc="""\ -If Docker is selected, ensure that Docker Desktop is open and running on your -computer. On first run of the RunCellpose plugin, the Docker container will be -downloaded. However, this slow downloading process will only have to happen -once. + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) + + def next_sample(self, strategy, params): + """ + Get Next sample + + :param strategy: Name of strategy to be used for fetching next sample + :param params: Additional JSON params as part of strategy request + :return: json response which contains information about next image selected for annotation + """ + params = self._update_client_id(params) + selector = f"/activelearning/{MONAILabelUtils.urllib_quote_plus(strategy)}" + status, response, _, _ = MONAILabelUtils.http_method( + "POST", self._server_url, selector, params, headers=self._headers + ) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response + ) -If Python is selected, the Python environment in which CellProfiler and Cellpose -are installed will be used. -""", + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) + + def create_session(self, image_in, params=None): + """ + Create New Session + + :param image_in: filepath for image to be sent to server as part of session creation + :param params: additional JSON params as part of session reqeust + :return: json response which contains session id and other details + """ + selector = "/session/" + params = self._update_client_id(params) + + status, response, _ = MONAILabelUtils.http_upload( + "PUT", self._server_url, selector, params, [image_in], headers=self._headers ) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response + ) - self.use_gpu = Binary( - text="Use GPU", - value=False, - doc=f"""\ -If enabled, Cellpose will attempt to run detection on your system's graphics card (GPU). -Note that you will need a CUDA-compatible GPU and correctly configured PyTorch version, see this link for details: -{CUDA_LINK} + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) + + def get_session(self, session_id): + """ + Get Session + + :param session_id: Session Id + :return: json response which contains more details about the session + """ + selector = f"/session/{MONAILabelUtils.urllib_quote_plus(session_id)}" + status, response, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector, headers=self._headers) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response + ) -If disabled or incorrectly configured, Cellpose will run on your CPU instead. This is much slower but more compatible -with different hardware setups. + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) -Note that, particularly when in 3D mode, lack of GPU memory can become a limitation. If a model crashes you may need to -re-start CellProfiler to release GPU memory. Resizing large images prior to running them through the model can free up -GPU memory. -""", + def remove_session(self, session_id): + """ + Remove any existing Session + + :param session_id: Session Id + :return: json response + """ + selector = f"/session/{MONAILabelUtils.urllib_quote_plus(session_id)}" + status, response, _, _ = MONAILabelUtils.http_method( + "DELETE", self._server_url, selector, headers=self._headers + ) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response + ) + + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) + + def upload_image(self, image_in, image_id=None, params=None): + """ + Upload New Image to MONAILabel Datastore + + :param image_in: Image File Path + :param image_id: Force Image ID; If not provided then Server it auto generate new Image ID + :param params: Additional JSON params + :return: json response which contains image id and other details + """ + selector = f"/datastore/?image={MONAILabelUtils.urllib_quote_plus(image_id)}" + + files = {"file": image_in} + params = self._update_client_id(params) + fields = {"params": json.dumps(params) if params else "{}"} + + status, response, _, _ = MONAILabelUtils.http_multipart( + "PUT", self._server_url, selector, fields, files, headers=self._headers + ) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, + f"Status: {status}; Response: {bytes_to_str(response)}", + ) + + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) + + def save_label(self, image_id, label_in, tag="", params=None): + """ + Save/Submit Label + + :param image_id: Image Id for which label needs to saved/submitted + :param label_in: Label File path which shall be saved/submitted + :param tag: Save label against tag in datastore + :param params: Additional JSON params for the request + :return: json response + """ + selector = f"/datastore/label?image={MONAILabelUtils.urllib_quote_plus(image_id)}" + if tag: + selector += f"&tag={MONAILabelUtils.urllib_quote_plus(tag)}" + + params = self._update_client_id(params) + fields = { + "params": json.dumps(params), + } + files = {"label": label_in} + + status, response, _, _ = MONAILabelUtils.http_multipart( + "PUT", self._server_url, selector, fields, files, headers=self._headers + ) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, + f"Status: {status}; Response: {bytes_to_str(response)}", + ) + + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) + + def datastore(self): + selector = "/datastore/?output=all" + status, response, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector, headers=self._headers) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response + ) + + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) + + def download_label(self, label_id, tag): + selector = "/datastore/label?label={}&tag={}".format( + MONAILabelUtils.urllib_quote_plus(label_id), MONAILabelUtils.urllib_quote_plus(tag) + ) + status, response, _, headers = MONAILabelUtils.http_method( + "GET", self._server_url, selector, headers=self._headers + ) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, f"Status: {status}; Response: {bytes_to_str(response)}", status, response + ) + + content_disposition = headers.get("content-disposition") + + if not content_disposition: + logging.warning("Filename not found. Fall back to no loaded labels") + file_name = MONAILabelUtils.get_filename(content_disposition) + + file_ext = "".join(Path(file_name).suffixes) + local_filename = tempfile.NamedTemporaryFile(dir=self._tmpdir, suffix=file_ext).name + with open(local_filename, "wb") as f: + f.write(response) + + return local_filename + + def infer(self, model, image_id, params, label_in=None, file=None, session_id=None): + """ + Run Infer + + :param model: Name of Model + :param image_id: Image Id + :param params: Additional configs/json params as part of Infer request + :param label_in: File path for label mask which is needed to run Inference (e.g. In case of Scribbles) + :param file: File path for Image (use raw image instead of image_id) + :param session_id: Session ID (use existing session id instead of image_id) + :return: response_file (label mask), response_body (json result/output params) + """ + selector = "/infer/{}?image={}".format( + MONAILabelUtils.urllib_quote_plus(model), + MONAILabelUtils.urllib_quote_plus(image_id), + ) + if session_id: + selector += f"&session_id={MONAILabelUtils.urllib_quote_plus(session_id)}" + + params = self._update_client_id(params) + fields = {"params": json.dumps(params) if params else "{}"} + files = {"label": label_in} if label_in else {} + files.update({"file": file} if file and not session_id else {}) + + status, form, files, _ = MONAILabelUtils.http_multipart( + "POST", self._server_url, selector, fields, files, headers=self._headers ) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, + f"Status: {status}; Response: {bytes_to_str(form)}", + ) - self.model_directory = Directory( - "Location of the pre-trained model file", - doc=f"""\ -*(Used only when using a custom pre-trained model)* -Select the location of the pre-trained CellPose model file that will be used for detection.""", + form = json.loads(form) if isinstance(form, str) else form + params = form.get("params") if files else form + params = json.loads(params) if isinstance(params, str) else params + + image_out = MONAILabelUtils.save_result(files, self._tmpdir) + return image_out, params + + def wsi_infer(self, model, image_id, body=None, output="dsa", session_id=None): + """ + Run WSI Infer in case of Pathology App + + :param model: Name of Model + :param image_id: Image Id + :param body: Additional configs/json params as part of Infer request + :param output: Output File format (dsa|asap|json) + :param session_id: Session ID (use existing session id instead of image_id) + :return: response_file (None), response_body + """ + selector = "/infer/wsi/{}?image={}".format( + MONAILabelUtils.urllib_quote_plus(model), + MONAILabelUtils.urllib_quote_plus(image_id), ) + if session_id: + selector += f"&session_id={MONAILabelUtils.urllib_quote_plus(session_id)}" + if output: + selector += f"&output={MONAILabelUtils.urllib_quote_plus(output)}" + + body = self._update_client_id(body if body else {}) + status, form, _, _ = MONAILabelUtils.http_method("POST", self._server_url, selector, body) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, + f"Status: {status}; Response: {bytes_to_str(form)}", + ) + + return None, form - def get_directory_fn(): - """Get the directory for the rules file name""" - return self.model_directory.get_absolute_path() + def train_start(self, model, params): + """ + Run Train Task - def set_directory_fn(path): - dir_choice, custom_path = self.model_directory.get_parts_from_path(path) + :param model: Name of Model + :param params: Additional configs/json params as part of Train request + :return: json response + """ + params = self._update_client_id(params) - self.model_directory.join_parts(dir_choice, custom_path) + selector = "/train/" + if model: + selector += MONAILabelUtils.urllib_quote_plus(model) - self.model_file_name = Filename( - "Pre-trained model file name", - "cyto_0", - get_directory_fn=get_directory_fn, - set_directory_fn=set_directory_fn, - doc=f"""\ -*(Used only when using a custom pre-trained model)* -This file can be generated by training a custom model withing the CellPose GUI or command line applications.""", + status, response, _, _ = MONAILabelUtils.http_method( + "POST", self._server_url, selector, params, headers=self._headers ) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, + f"Status: {status}; Response: {bytes_to_str(response)}", + ) - self.gpu_test = DoSomething( - "", - "Test GPU", - self.do_check_gpu, - doc=f"""\ -Press this button to check whether a GPU is correctly configured. + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) -If you have a dedicated GPU, a failed test usually means that either your GPU does not support deep learning or the -required dependencies are not installed. -If you have multiple GPUs on your system, this button will only test the first one. -""", + def train_stop(self): + """ + Stop any running Train Task(s) + + :return: json response + """ + selector = "/train/" + status, response, _, _ = MONAILabelUtils.http_method( + "DELETE", self._server_url, selector, headers=self._headers ) + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, + f"Status: {status}; Response: {bytes_to_str(response)}", + ) - self.sliding_window_size_0 = Integer( - text="First dimension of the MONAI SlidingWindowInferer roi_size parameter", - value=256, - minval=128, - doc="""\ -First dimension size of the sliding window roi_size parameter, default to 256 -""", + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) + + def train_status(self, check_if_running=False): + """ + Check Train Task Status + + :param check_if_running: Fast mode. Only check if training is Running + :return: boolean if check_if_running is enabled; else json response that contains of full details + """ + selector = "/train/" + if check_if_running: + selector += "?check_if_running=true" + status, response, _, _ = MONAILabelUtils.http_method("GET", self._server_url, selector, headers=self._headers) + if check_if_running: + return status == 200 + + if status != 200: + raise MONAILabelClientException( + MONAILabelError.SERVER_ERROR, + f"Status: {status}; Response: {bytes_to_str(response)}", + ) + + response = bytes_to_str(response) + logging.debug(f"Response: {response}") + return json.loads(response) + + +class MONAILabelError: + """ + Type of Inference Model + + Attributes: + SERVER_ERROR - Server Error + SESSION_EXPIRED - Session Expired + UNKNOWN - Unknown Error + """ + + SERVER_ERROR = 1 + SESSION_EXPIRED = 2 + UNKNOWN = 3 + + +class MONAILabelClientException(Exception): + """ + MONAILabel Client Exception + """ + + __slots__ = ["error", "msg"] + + def __init__(self, error, msg, status_code=None, response=None): + """ + :param error: Error code represented by MONAILabelError + :param msg: Error message + :param status_code: HTTP Response code + :param response: HTTP Response + """ + self.error = error + self.msg = msg + self.status_code = status_code + self.response = response + + +class MONAILabelUtils: + @staticmethod + def http_method(method, server_url, selector, body=None, headers=None, content_type=None): + logging.debug(f"{method} {server_url}{selector}") + + parsed = urlparse(server_url) + path = parsed.path.rstrip("/") + selector = path + "/" + selector.lstrip("/") + logging.debug(f"URI Path: {selector}") + + parsed = urlparse(server_url) + if parsed.scheme == "https": + LOGGER.debug("Using HTTPS mode") + # noinspection PyProtectedMember + conn = http.client.HTTPSConnection(parsed.hostname, parsed.port, context=ssl._create_unverified_context()) + else: + conn = http.client.HTTPConnection(parsed.hostname, parsed.port) + + headers = headers if headers else {} + if body: + if not content_type: + if isinstance(body, dict): + body = json.dumps(body) + content_type = "application/json" + else: + content_type = "text/plain" + headers.update({"content-type": content_type, "content-length": str(len(body))}) + + conn.request(method, selector, body=body, headers=headers) + return MONAILabelUtils.send_response(conn) + + @staticmethod + def http_upload(method, server_url, selector, fields, files, headers=None): + logging.debug(f"{method} {server_url}{selector}") + + url = server_url.rstrip("/") + "/" + selector.lstrip("/") + logging.debug(f"URL: {url}") + + files = [("files", (os.path.basename(f), open(f, "rb"))) for f in files] + headers = headers if headers else {} + response = ( + requests.post(url, files=files, headers=headers) + if method == "POST" + else requests.put(url, files=files, data=fields, headers=headers) ) + return response.status_code, response.text, None - self.sliding_window_size_1 = Integer( - text="Second dimension of the MONAI SlidingWindowInferer roi_size parameter", - value=256, - minval=128, - doc="""\ -Second dimension size of the sliding window roi_size parameter, default to 256 -""", + @staticmethod + def http_multipart(method, server_url, selector, fields, files, headers={}): + logging.debug(f"{method} {server_url}{selector}") + + content_type, body = MONAILabelUtils.encode_multipart_formdata(fields, files) + headers = headers if headers else {} + headers.update({"content-type": content_type, "content-length": str(len(body))}) + + parsed = urlparse(server_url) + path = parsed.path.rstrip("/") + selector = path + "/" + selector.lstrip("/") + logging.debug(f"URI Path: {selector}") + + if parsed.scheme == "https": + LOGGER.debug("Using HTTPS mode") + # noinspection PyProtectedMember + conn = http.client.HTTPSConnection(parsed.hostname, parsed.port, context=ssl._create_unverified_context()) + else: + conn = http.client.HTTPConnection(parsed.hostname, parsed.port) + + conn.request(method, selector, body, headers) + return MONAILabelUtils.send_response(conn, content_type) + + @staticmethod + def send_response(conn, content_type="application/json"): + response = conn.getresponse() + logging.debug(f"HTTP Response Code: {response.status}") + logging.debug(f"HTTP Response Message: {response.reason}") + logging.debug(f"HTTP Response Headers: {response.getheaders()}") + + response_content_type = response.getheader("content-type", content_type) + logging.debug(f"HTTP Response Content-Type: {response_content_type}") + + if "multipart" in response_content_type: + if response.status == 200: + form, files = MONAILabelUtils.parse_multipart(response.fp if response.fp else response, response.msg) + logging.debug(f"Response FORM: {form}") + logging.debug(f"Response FILES: {files.keys()}") + return response.status, form, files, response.headers + else: + return response.status, response.read(), None, response.headers + + logging.debug("Reading status/content from simple response!") + return response.status, response.read(), None, response.headers + + @staticmethod + def save_result(files, tmpdir): + for name in files: + data = files[name] + result_file = os.path.join(tmpdir, name) + + logging.debug(f"Saving {name} to {result_file}; Size: {len(data)}") + dir_path = os.path.dirname(os.path.realpath(result_file)) + if not os.path.exists(dir_path): + os.makedirs(dir_path) + + with open(result_file, "wb") as f: + if isinstance(data, bytes): + f.write(data) + else: + f.write(data.encode("utf-8")) + + # Currently only one file per response supported + return result_file + + @staticmethod + def encode_multipart_formdata(fields, files): + limit = "----------lImIt_of_THE_fIle_eW_$" + lines = [] + for key, value in fields.items(): + lines.append("--" + limit) + lines.append('Content-Disposition: form-data; name="%s"' % key) + lines.append("") + lines.append(value) + for key, filename in files.items(): + lines.append("--" + limit) + lines.append(f'Content-Disposition: form-data; name="{key}"; filename="{filename}"') + lines.append("Content-Type: %s" % MONAILabelUtils.get_content_type(filename)) + lines.append("") + with open(filename, mode="rb") as f: + data = f.read() + lines.append(data) + lines.append("--" + limit + "--") + lines.append("") + + body = bytearray() + for line in lines: + body.extend(line if isinstance(line, bytes) else line.encode("utf-8")) + body.extend(b"\r\n") + + content_type = "multipart/form-data; boundary=%s" % limit + return content_type, body + + @staticmethod + def get_content_type(filename): + return mimetypes.guess_type(filename)[0] or "application/octet-stream" + + @staticmethod + def parse_multipart(fp, headers): + fs = cgi.FieldStorage( + fp=fp, + environ={"REQUEST_METHOD": "POST"}, + headers=headers, + keep_blank_values=True, ) + form = {} + files = {} + if hasattr(fs, "list") and isinstance(fs.list, list): + for f in fs.list: + LOGGER.debug(f"FILE-NAME: {f.filename}; NAME: {f.name}; SIZE: {len(f.value)}") + if f.filename: + files[f.filename] = f.value + else: + form[f.name] = f.value + return form, files + + @staticmethod + def urllib_quote_plus(s): + return quote_plus(s) + + @staticmethod + def get_filename(content_disposition): + file_name = re.findall(r"filename\*=([^;]+)", content_disposition, flags=re.IGNORECASE) + if not file_name: + file_name = re.findall('filename="(.+)"', content_disposition, flags=re.IGNORECASE) + if "utf-8''" in file_name[0].lower(): + file_name = re.sub("utf-8''", "", file_name[0], flags=re.IGNORECASE) + file_name = unquote(file_name) + else: + file_name = file_name[0] + return file_name + - self.multigpu_infer = Binary( - text="Whether using multi-GPU to perform the inference", - value=False, +class RunVISTA2D(ImageSegmentation): + category = "Object Processing" + + module_name = "RunVISTA2D" + + variable_revision_number = 1 + + doi = { + "Please cite the following when using RunVISTA2D:": "https://doi.org/10.48550/arXiv.2406.05285", + } + + def create_settings(self): + super(RunVISTA2D, self).create_settings() + + self.server_address = URL( + text="MONAI label server address", doc="""\ -Whether using multiple GPUs to perform the inference +Please set up the MONAI label server in local/cloud environment and fill the server address here. """, ) + self.model_name = Choice( + text="The model for running the inference", + choices=["cell_vista_segmentation"], + value="cell_vista_segmentation", + doc=""" +Pick the model for running infernce. Now only VISTA2D is available. +""" + ) + def settings(self): return [ self.x_name, - self.docker_or_python, self.y_name, - self.use_gpu, - self.model_directory, - self.model_file_name, - self.sliding_window_size_0, - self.sliding_window_size_1, - self.multigpu_infer, + self.server_address, + self.model_name, ] - + def visible_settings(self): return [ self.x_name, - self.docker_or_python, self.y_name, - self.use_gpu, - self.model_directory, - self.model_file_name, - self.sliding_window_size_0, - self.sliding_window_size_1, - self.multigpu_infer, + self.server_address, + self.model_name, ] - def verify_bundle(self, pipeline): - """If using custom model, validate the model file opens and works""" - pass - - @classmethod - def download_vista2d(cls, save_path): - if os.path.exists(save_path): - return - - os.makedirs(save_path) - monai.bundle.download(name=cls.VISTA2D_BUNDLE_NAME, version=cls.VISTA2D_BUNDLE_VERSION, bundle_dir=save_path) - def run(self, workspace): x_name = self.x_name.value y_name = self.y_name.value @@ -267,89 +788,21 @@ def run(self, workspace): x = images.get_image(x_name) dimensions = x.dimensions x_data = x.pixel_data - self.download_vista2d(BUNDLE_PATH) - bundle_path = os.path.join(BUNDLE_PATH, self.VISTA2D_BUNDLE_NAME) with tempfile.TemporaryDirectory() as temp_dir: temp_img_dir = os.path.join(temp_dir, "img") - temp_img_path = os.path.join(temp_img_dir, x_name + ".tiff") + os.makedirs(temp_img_dir, exist_ok=True) + temp_img_path = os.path.join(temp_img_dir, x_name+".tiff") temp_mask_dir = os.path.join(temp_dir, "mask") - temp_label_path = os.path.join(temp_mask_dir, y_name + ".tif") + os.makedirs(temp_mask_dir, exist_ok=True) skimage.io.imsave(temp_img_path, x_data) - if self.docker_or_python.value == "Python": - if self.multigpu_infer: - pass - else: - cmd = f"""cd {bundle_path}; - python -m monai.bundle run_workflow "scripts.workflow.VistaCell"\ - --config_file configs/hyper_parameters.yaml\ - --mode infer --pretrained_ckpt_name vista2d_v1.pt - """ - - elif self.docker_or_python.value == "Docker": - # Define how to call docker - docker_path = "docker" if sys.platform.lower().startswith("win") else "/usr/local/bin/docker" - # Create a UUID for this run - unique_name = str(uuid.uuid4()) - # Directory that will be used to pass images to the docker container - temp_dir = os.path.join(get_default_output_directory(), ".cellprofiler_temp", unique_name) - temp_img_dir = os.path.join(temp_dir, "img") - - os.makedirs(temp_dir, exist_ok=True) - os.makedirs(temp_img_dir, exist_ok=True) - - if self.mode.value == "custom": - model_file = self.model_file_name.value - model_directory = self.model_directory.get_absolute_path() - model_path = os.path.join(model_directory, model_file) - temp_model_dir = os.path.join(temp_dir, "model") - - os.makedirs(temp_model_dir, exist_ok=True) - # Copy the model - shutil.copy(model_path, os.path.join(temp_model_dir, model_file)) - - # Save the image to the Docker mounted directory - skimage.io.imsave(temp_img_path, x_data) - - cmd = f""" - {docker_path} run --rm -v {temp_dir}:/data - {self.docker_image.value} - {'--gpus all' if self.use_gpu.value else ''} - cellpose - --dir /data/img - {'--pretrained_model ' + self.mode.value if self.mode.value != 'custom' else '--pretrained_model /data/model/' + model_file} - --chan {channels[0]} - --chan2 {channels[1]} - --diameter {diam} - {'--net_avg' if self.use_averaging.value else ''} - {'--do_3D' if self.do_3D.value else ''} - --anisotropy {anisotropy} - --flow_threshold {self.flow_threshold.value} - --cellprob_threshold {self.cellprob_threshold.value} - --stitch_threshold {self.stitch_threshold.value} - --min_size {self.min_size.value} - {'--invert' if self.invert.value else ''} - {'--exclude_on_edges' if self.remove_edge_masks.value else ''} - --verbose - """ - - try: - subprocess.run(cmd.split(), text=True) - cellpose_output = numpy.load( - os.path.join(temp_img_dir, unique_name + "_seg.npy"), allow_pickle=True - ).item() - - y_data = cellpose_output["masks"] - flows = cellpose_output["flows"] - finally: - # Delete the temporary files - try: - shutil.rmtree(temp_dir) - except: - LOGGER.error("Unable to delete temporary directory, files may be in use by another program.") - LOGGER.error( - "Temp folder is subfolder {tempdir} in your Default Output Folder.\nYou may need to remove it manually." - ) + monailabel_client = MONAILabelClient(server_url=self.server_address.value, tmpdir=temp_mask_dir) + image_out, params = monailabel_client.infer(model=self.model_name.value, image_id="", params={}, file=temp_img_path) + print(f"Image out:\n{image_out}") + print(f"Params:\n{params}") + y_data = skimage.io.imread(image_out) + + y = Objects() y.segmented = y_data @@ -357,21 +810,6 @@ def run(self, workspace): objects = workspace.object_set objects.add_objects(y, y_name) - if self.save_probabilities.value: - # Flows come out sized relative to CellPose's inbuilt model size. - # We need to slightly resize to match the original image. - size_corrected = skimage.transform.resize(flows[2], y_data.shape) - prob_image = Image( - size_corrected, - parent_image=x.parent_image, - convert=False, - dimensions=len(size_corrected.shape), - ) - - workspace.image_set.add(self.probabilities_name.value, prob_image) - - if self.show_window: - workspace.display_data.probabilities = size_corrected self.add_measurements(workspace) @@ -381,12 +819,10 @@ def run(self, workspace): workspace.display_data.dimensions = dimensions def display(self, workspace, figure): - if self.save_probabilities.value: - layout = (2, 2) - else: - layout = (2, 1) - - figure.set_subplots(dimensions=workspace.display_data.dimensions, subplots=layout) + layout = (2, 1) + figure.set_subplots( + dimensions=workspace.display_data.dimensions, subplots=layout + ) figure.subplot_imshow( colormap="gray", @@ -403,47 +839,6 @@ def display(self, workspace, figure): x=1, y=0, ) - if self.save_probabilities.value: - figure.subplot_imshow( - colormap="gray", - image=workspace.display_data.probabilities, - sharexy=figure.subplot(0, 0), - title=self.probabilities_name.value, - x=0, - y=1, - ) - def do_check_gpu(self): - import importlib.util - - torch_installed = importlib.util.find_spec("torch") is not None - self.cellpose_ver = importlib.metadata.version("cellpose") - # if the old version of cellpose <2.0, then use istorch kwarg - if float(self.cellpose_ver[0:3]) >= 0.7 and int(self.cellpose_ver[0]) < 2: - GPU_works = core.use_gpu(istorch=torch_installed) - else: # if new version of cellpose, use use_torch kwarg - GPU_works = core.use_gpu(use_torch=torch_installed) - if GPU_works: - message = "GPU appears to be working correctly!" - else: - message = "GPU test failed. There may be something wrong with your configuration." - import wx - - wx.MessageBox(message, caption="GPU Test") - - def upgrade_settings(self, setting_values, variable_revision_number, module_name): - if variable_revision_number == 1: - setting_values = setting_values + ["0.4", "0.0"] - variable_revision_number = 2 - if variable_revision_number == 2: - setting_values = setting_values + ["0.0", False, "15", "1.0", False, False] - variable_revision_number = 3 - if variable_revision_number == 3: - setting_values = ( - [setting_values[0]] + ["Python", CELLPOSE_DOCKER_IMAGE_WITH_PRETRAINED] + setting_values[1:] - ) - variable_revision_number = 4 - if variable_revision_number == 4: - setting_values = [setting_values[0]] + ["No"] + setting_values[1:] - variable_revision_number = 5 - return setting_values, variable_revision_number + # def upgrade_settings(self, setting_values, variable_revision_number, module_name): + # ... From 25a3f331d06e7ecc5c27ddb005f1baa8d7bb08dd Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 2 Aug 2024 16:01:21 +0000 Subject: [PATCH 07/30] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- monailabel/tasks/infer/vista.py | 24 ++++++------ plugins/cellprofiler/runvista2d.py | 62 ++++++++++++++---------------- 2 files changed, 41 insertions(+), 45 deletions(-) diff --git a/monailabel/tasks/infer/vista.py b/monailabel/tasks/infer/vista.py index 2b39ac63e..e328bbedf 100644 --- a/monailabel/tasks/infer/vista.py +++ b/monailabel/tasks/infer/vista.py @@ -1,16 +1,16 @@ def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, Any]: - d = dict(data) - output_dir = d.get("output_dir", "") - output_ext = d.get("output_ext", "") + d = dict(data) + output_dir = d.get("output_dir", "") + output_ext = d.get("output_ext", "") - os.makedirs(self.output_dir, exist_ok=True) + os.makedirs(self.output_dir, exist_ok=True) - img = d.get(self.image_key, None) - filename = img.meta.get(ImageMetaKey.FILENAME_OR_OBJ) if img is not None else None - basename = os.path.splitext(os.path.basename(filename))[0] if filename else "mask" - logger.info(f"File: {filename}; Base: {basename}") + img = d.get(self.image_key, None) + filename = img.meta.get(ImageMetaKey.FILENAME_OR_OBJ) if img is not None else None + basename = os.path.splitext(os.path.basename(filename))[0] if filename else "mask" + logger.info(f"File: {filename}; Base: {basename}") - for key in self.key_iterator(d): - label = d[key] - output_filename = f"{basename}{'_' + self.output_postfix if self.output_postfix else ''}{output_ext}" - output_filepath = os.path.join(output_dir, output_filename) + for key in self.key_iterator(d): + label = d[key] + output_filename = f"{basename}{'_' + self.output_postfix if self.output_postfix else ''}{output_ext}" + output_filepath = os.path.join(output_dir, output_filename) diff --git a/plugins/cellprofiler/runvista2d.py b/plugins/cellprofiler/runvista2d.py index 4c4cace17..4a4335f69 100644 --- a/plugins/cellprofiler/runvista2d.py +++ b/plugins/cellprofiler/runvista2d.py @@ -4,44 +4,43 @@ # ################################# -import numpy -import os -import monai -import skimage -import importlib.metadata -import subprocess -import uuid -import shutil -import tempfile -import logging -import sys - import cgi import http.client +import importlib.metadata import json +import logging import mimetypes +import os import re +import shutil import ssl +import subprocess +import sys +import tempfile +import uuid from pathlib import Path from urllib.parse import quote_plus, unquote, urlencode, urlparse +import monai +import numpy import requests - -################################# -# -# Imports from CellProfiler -# -################################## - +import skimage from cellprofiler_core.image import Image from cellprofiler_core.module.image_segmentation import ImageSegmentation from cellprofiler_core.object import Objects +from cellprofiler_core.preferences import get_default_output_directory from cellprofiler_core.setting import Binary, ValidationError from cellprofiler_core.setting.choice import Choice from cellprofiler_core.setting.do_something import DoSomething from cellprofiler_core.setting.subscriber import ImageSubscriber -from cellprofiler_core.preferences import get_default_output_directory -from cellprofiler_core.setting.text import Text, URL +from cellprofiler_core.setting.text import URL, Text + +################################# +# +# Imports from CellProfiler +# +################################## + CUDA_LINK = "https://pytorch.org/get-started/locally/" Cellpose_link = " https://doi.org/10.1038/s41592-020-01018-x" @@ -747,7 +746,7 @@ class RunVISTA2D(ImageSegmentation): } def create_settings(self): - super(RunVISTA2D, self).create_settings() + super().create_settings() self.server_address = URL( text="MONAI label server address", @@ -762,8 +761,8 @@ def create_settings(self): value="cell_vista_segmentation", doc=""" Pick the model for running infernce. Now only VISTA2D is available. -""" - ) +""", + ) def settings(self): return [ @@ -772,7 +771,7 @@ def settings(self): self.server_address, self.model_name, ] - + def visible_settings(self): return [ self.x_name, @@ -792,17 +791,17 @@ def run(self, workspace): with tempfile.TemporaryDirectory() as temp_dir: temp_img_dir = os.path.join(temp_dir, "img") os.makedirs(temp_img_dir, exist_ok=True) - temp_img_path = os.path.join(temp_img_dir, x_name+".tiff") + temp_img_path = os.path.join(temp_img_dir, x_name + ".tiff") temp_mask_dir = os.path.join(temp_dir, "mask") os.makedirs(temp_mask_dir, exist_ok=True) skimage.io.imsave(temp_img_path, x_data) monailabel_client = MONAILabelClient(server_url=self.server_address.value, tmpdir=temp_mask_dir) - image_out, params = monailabel_client.infer(model=self.model_name.value, image_id="", params={}, file=temp_img_path) + image_out, params = monailabel_client.infer( + model=self.model_name.value, image_id="", params={}, file=temp_img_path + ) print(f"Image out:\n{image_out}") print(f"Params:\n{params}") y_data = skimage.io.imread(image_out) - - y = Objects() y.segmented = y_data @@ -810,7 +809,6 @@ def run(self, workspace): objects = workspace.object_set objects.add_objects(y, y_name) - self.add_measurements(workspace) if self.show_window: @@ -820,9 +818,7 @@ def run(self, workspace): def display(self, workspace, figure): layout = (2, 1) - figure.set_subplots( - dimensions=workspace.display_data.dimensions, subplots=layout - ) + figure.set_subplots(dimensions=workspace.display_data.dimensions, subplots=layout) figure.subplot_imshow( colormap="gray", From 6d972517b66812df8c4cf655453b743879b708df Mon Sep 17 00:00:00 2001 From: binliu Date: Tue, 6 Aug 2024 14:21:37 +0000 Subject: [PATCH 08/30] fix format Signed-off-by: binliu --- monailabel/tasks/infer/vista.py | 24 ++++++------ plugins/cellprofiler/runvista2d.py | 60 ++++++++++++++---------------- 2 files changed, 40 insertions(+), 44 deletions(-) diff --git a/monailabel/tasks/infer/vista.py b/monailabel/tasks/infer/vista.py index 2b39ac63e..e328bbedf 100644 --- a/monailabel/tasks/infer/vista.py +++ b/monailabel/tasks/infer/vista.py @@ -1,16 +1,16 @@ def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, Any]: - d = dict(data) - output_dir = d.get("output_dir", "") - output_ext = d.get("output_ext", "") + d = dict(data) + output_dir = d.get("output_dir", "") + output_ext = d.get("output_ext", "") - os.makedirs(self.output_dir, exist_ok=True) + os.makedirs(self.output_dir, exist_ok=True) - img = d.get(self.image_key, None) - filename = img.meta.get(ImageMetaKey.FILENAME_OR_OBJ) if img is not None else None - basename = os.path.splitext(os.path.basename(filename))[0] if filename else "mask" - logger.info(f"File: {filename}; Base: {basename}") + img = d.get(self.image_key, None) + filename = img.meta.get(ImageMetaKey.FILENAME_OR_OBJ) if img is not None else None + basename = os.path.splitext(os.path.basename(filename))[0] if filename else "mask" + logger.info(f"File: {filename}; Base: {basename}") - for key in self.key_iterator(d): - label = d[key] - output_filename = f"{basename}{'_' + self.output_postfix if self.output_postfix else ''}{output_ext}" - output_filepath = os.path.join(output_dir, output_filename) + for key in self.key_iterator(d): + label = d[key] + output_filename = f"{basename}{'_' + self.output_postfix if self.output_postfix else ''}{output_ext}" + output_filepath = os.path.join(output_dir, output_filename) diff --git a/plugins/cellprofiler/runvista2d.py b/plugins/cellprofiler/runvista2d.py index 4c4cace17..1796ea89d 100644 --- a/plugins/cellprofiler/runvista2d.py +++ b/plugins/cellprofiler/runvista2d.py @@ -4,44 +4,43 @@ # ################################# -import numpy -import os -import monai -import skimage -import importlib.metadata -import subprocess -import uuid -import shutil -import tempfile -import logging -import sys - import cgi import http.client +import importlib.metadata import json +import logging import mimetypes +import os import re +import shutil import ssl +import subprocess +import sys +import tempfile +import uuid from pathlib import Path from urllib.parse import quote_plus, unquote, urlencode, urlparse +import monai +import numpy import requests - -################################# -# -# Imports from CellProfiler -# -################################## - +import skimage from cellprofiler_core.image import Image from cellprofiler_core.module.image_segmentation import ImageSegmentation from cellprofiler_core.object import Objects +from cellprofiler_core.preferences import get_default_output_directory from cellprofiler_core.setting import Binary, ValidationError from cellprofiler_core.setting.choice import Choice from cellprofiler_core.setting.do_something import DoSomething from cellprofiler_core.setting.subscriber import ImageSubscriber -from cellprofiler_core.preferences import get_default_output_directory -from cellprofiler_core.setting.text import Text, URL +from cellprofiler_core.setting.text import URL, Text + +################################# +# +# Imports from CellProfiler +# +################################## + CUDA_LINK = "https://pytorch.org/get-started/locally/" Cellpose_link = " https://doi.org/10.1038/s41592-020-01018-x" @@ -762,8 +761,8 @@ def create_settings(self): value="cell_vista_segmentation", doc=""" Pick the model for running infernce. Now only VISTA2D is available. -""" - ) +""", + ) def settings(self): return [ @@ -772,7 +771,7 @@ def settings(self): self.server_address, self.model_name, ] - + def visible_settings(self): return [ self.x_name, @@ -792,17 +791,17 @@ def run(self, workspace): with tempfile.TemporaryDirectory() as temp_dir: temp_img_dir = os.path.join(temp_dir, "img") os.makedirs(temp_img_dir, exist_ok=True) - temp_img_path = os.path.join(temp_img_dir, x_name+".tiff") + temp_img_path = os.path.join(temp_img_dir, x_name + ".tiff") temp_mask_dir = os.path.join(temp_dir, "mask") os.makedirs(temp_mask_dir, exist_ok=True) skimage.io.imsave(temp_img_path, x_data) monailabel_client = MONAILabelClient(server_url=self.server_address.value, tmpdir=temp_mask_dir) - image_out, params = monailabel_client.infer(model=self.model_name.value, image_id="", params={}, file=temp_img_path) + image_out, params = monailabel_client.infer( + model=self.model_name.value, image_id="", params={}, file=temp_img_path + ) print(f"Image out:\n{image_out}") print(f"Params:\n{params}") y_data = skimage.io.imread(image_out) - - y = Objects() y.segmented = y_data @@ -810,7 +809,6 @@ def run(self, workspace): objects = workspace.object_set objects.add_objects(y, y_name) - self.add_measurements(workspace) if self.show_window: @@ -820,9 +818,7 @@ def run(self, workspace): def display(self, workspace, figure): layout = (2, 1) - figure.set_subplots( - dimensions=workspace.display_data.dimensions, subplots=layout - ) + figure.set_subplots(dimensions=workspace.display_data.dimensions, subplots=layout) figure.subplot_imshow( colormap="gray", From 23e6d7e0bf8b5198a07edd9b643f06e1048eb739 Mon Sep 17 00:00:00 2001 From: binliu Date: Wed, 7 Aug 2024 07:36:45 +0000 Subject: [PATCH 09/30] fix plugin doc Signed-off-by: binliu --- plugins/cellprofiler/runvista2d.py | 38 +++++++++--------------------- 1 file changed, 11 insertions(+), 27 deletions(-) diff --git a/plugins/cellprofiler/runvista2d.py b/plugins/cellprofiler/runvista2d.py index 4a4335f69..426706853 100644 --- a/plugins/cellprofiler/runvista2d.py +++ b/plugins/cellprofiler/runvista2d.py @@ -42,49 +42,33 @@ ################################## -CUDA_LINK = "https://pytorch.org/get-started/locally/" -Cellpose_link = " https://doi.org/10.1038/s41592-020-01018-x" -Omnipose_link = "https://doi.org/10.1101/2021.11.03.467199" +VISTA_link = "https://doi.org/10.48550/arXiv.2406.05285" LOGGER = logging.getLogger(__name__) __doc__ = f"""\ -RunCellpose +RunVISTA2D =========== -**RunCellpose** uses a pre-trained machine learning model (Cellpose) to detect cells or nuclei in an image. +**RunVISTA2D** uses a pre-trained VISTA2D model to detect cells in an image. This module is useful for automating simple segmentation tasks in CellProfiler. -The module accepts greyscale input images and produces an object set. Probabilities can also be captured as an image. +The module accepts tiff input images and produces an object set. -Loading in a model will take slightly longer the first time you run it each session. When evaluating -performance you may want to consider the time taken to predict subsequent images. - -This module now also supports Ominpose. Omnipose builds on Cellpose, for the purpose of **RunCellpose** it adds 2 additional -features: additional models; bact-omni and cyto2-omni which were trained using the Omnipose architechture, and bact -and the mask reconstruction algorithm for Omnipose that was created to solve over-segemnation of large cells; useful for bacterial cells, -but can be used for other arbitrary and anisotropic shapes. You can mix and match Omnipose models with Cellpose style masking or vice versa. - -The module has been updated to be compatible with the latest release of Cellpose. From the old version of the module the 'cells' model corresponds to 'cyto2' model. +This module is a client/frontend of a MONAI Label server. A VISTA2D based MONAI Label server needs to be set up and the address of the server needs to be passed to +this module, before running. Installation: -It is necessary that you have installed Cellpose version >= 1.0.2 - -You'll want to run `pip install cellpose` on your CellProfiler Python environment to setup Cellpose. If you have an older version of Cellpose -run 'python -m pip install cellpose --upgrade'. - -To use Omnipose models, and mask reconstruction method you'll want to install Omnipose 'pip install omnipose' and Cellpose version 1.0.2 'pip install cellpose==1.0.2'. +This module has no external dependencies other than the python(>3.8) build-in dependencies. -On the first time loading into CellProfiler, Cellpose will need to download some model files from the internet. This -may take some time. If you want to use a GPU to run the model, you'll need a compatible version of PyTorch and a -supported GPU. Instructions are avaiable at this link: {CUDA_LINK} +You'll need to set up the VISTA2D based MONAI Label server based on the tutorial https://github.com/Project-MONAI/MONAILabel/tree/main/plugins/cellprofiler. After setting up the server, please +provide the server address to this plugin.s -Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). {Cellpose_link} -Kevin J. Cutler, Carsen Stringer, Paul A. Wiggins, Joseph D. Mougous. Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation. bioRxiv 2021.11.03.467199. {Omnipose_link} +Yufan He, Pengfei Guo, Yucheng Tang, Andriy Myronenko, Vishwesh Nath, Ziyue Xu, Dong Yang, Can Zhao, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, Daguang Xu, & Wenqi Li. (2024). VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography.{VISTA_link} ============ ============ =============== Supports 2D? Supports 3D? Respects masks? ============ ============ =============== -YES YES NO +YES No NO ============ ============ =============== """ From 5b438bd547736df4939352557a1104a3bd25d7ca Mon Sep 17 00:00:00 2001 From: binliu Date: Fri, 9 Aug 2024 07:55:39 +0000 Subject: [PATCH 10/30] remove redundant imports Signed-off-by: binliu --- plugins/cellprofiler/runvista2d.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/plugins/cellprofiler/runvista2d.py b/plugins/cellprofiler/runvista2d.py index 426706853..1500406ab 100644 --- a/plugins/cellprofiler/runvista2d.py +++ b/plugins/cellprofiler/runvista2d.py @@ -6,33 +6,21 @@ import cgi import http.client -import importlib.metadata import json import logging import mimetypes import os import re -import shutil import ssl -import subprocess -import sys import tempfile -import uuid from pathlib import Path from urllib.parse import quote_plus, unquote, urlencode, urlparse -import monai -import numpy import requests import skimage -from cellprofiler_core.image import Image from cellprofiler_core.module.image_segmentation import ImageSegmentation from cellprofiler_core.object import Objects -from cellprofiler_core.preferences import get_default_output_directory -from cellprofiler_core.setting import Binary, ValidationError from cellprofiler_core.setting.choice import Choice -from cellprofiler_core.setting.do_something import DoSomething -from cellprofiler_core.setting.subscriber import ImageSubscriber from cellprofiler_core.setting.text import URL, Text ################################# From 5852e0a7af1b159d53b06431d6b0d792ba1ea26f Mon Sep 17 00:00:00 2001 From: "Bin Liu (SW-GPU)" Date: Mon, 12 Aug 2024 16:43:30 +0800 Subject: [PATCH 11/30] add the test case Signed-off-by: Bin Liu (SW-GPU) --- plugins/cellprofiler/test_runvista2d.py | 61 +++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 plugins/cellprofiler/test_runvista2d.py diff --git a/plugins/cellprofiler/test_runvista2d.py b/plugins/cellprofiler/test_runvista2d.py new file mode 100644 index 000000000..e53f1f7e5 --- /dev/null +++ b/plugins/cellprofiler/test_runvista2d.py @@ -0,0 +1,61 @@ +import numpy +import cellprofiler_core.image +import cellprofiler_core.measurement +import cellprofiler_core.object +import cellprofiler_core.pipeline +import cellprofiler_core.setting +import cellprofiler_core.workspace + +from runvista2d import RunVISTA2D + +IMAGE_NAME = "my_image" +OBJECTS_NAME = "my_objects" +MODEL_NAME = "cell_vista_segmentation" +SERVER_ADDRESS = "http://127.0.0.1:8000" + + +def test_mock_successful(): + x = RunVISTA2D() + x.y_name.value = OBJECTS_NAME + x.x_name.value = IMAGE_NAME + x.server_address.value = SERVER_ADDRESS + x.model_name.value = MODEL_NAME + + img = numpy.zeros((128, 128, 3)) + image = cellprofiler_core.image.Image(img) + image_set_list = cellprofiler_core.image.ImageSetList() + image_set = image_set_list.get_image_set(0) + image_set.providers.append( + cellprofiler_core.image.VanillaImage(IMAGE_NAME, image) + ) + object_set = cellprofiler_core.object.ObjectSet() + measurements = cellprofiler_core.measurement.Measurements() + pipeline = cellprofiler_core.pipeline.Pipeline() + x.run( + cellprofiler_core.workspace.Workspace( + pipeline, x, image_set, object_set, measurements, None + ) + ) + assert len(object_set.object_names) == 1 + assert "my_object" in object_set.object_names + objects = object_set.get_objects("my_object") + segmented = objects.segmented + assert numpy.all(segmented == 0) + assert "Image" in measurements.get_object_names() + assert "my_object" in measurements.get_object_names() + + assert "Count_my_object" in measurements.get_feature_names("Image") + count = measurements.get_current_measurement("Image", "Count_my_object") + assert count == 0 + assert "Location_Center_X" in measurements.get_feature_names("my_object") + location_center_x = measurements.get_current_measurement( + "my_object", "Location_Center_X" + ) + assert isinstance(location_center_x, numpy.ndarray) + assert numpy.product(location_center_x.shape) == 0 + assert "Location_Center_Y" in measurements.get_feature_names("my_object") + location_center_y = measurements.get_current_measurement( + "my_object", "Location_Center_Y" + ) + assert isinstance(location_center_y, numpy.ndarray) + assert numpy.product(location_center_y.shape) == 0 From f37c05d7dd4d14bf318f0fddeef1f06098087b41 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 12 Aug 2024 08:47:54 +0000 Subject: [PATCH 12/30] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- plugins/cellprofiler/test_runvista2d.py | 21 +++++---------------- 1 file changed, 5 insertions(+), 16 deletions(-) diff --git a/plugins/cellprofiler/test_runvista2d.py b/plugins/cellprofiler/test_runvista2d.py index e53f1f7e5..d3031cdfc 100644 --- a/plugins/cellprofiler/test_runvista2d.py +++ b/plugins/cellprofiler/test_runvista2d.py @@ -1,11 +1,10 @@ -import numpy import cellprofiler_core.image import cellprofiler_core.measurement import cellprofiler_core.object import cellprofiler_core.pipeline import cellprofiler_core.setting import cellprofiler_core.workspace - +import numpy from runvista2d import RunVISTA2D IMAGE_NAME = "my_image" @@ -25,17 +24,11 @@ def test_mock_successful(): image = cellprofiler_core.image.Image(img) image_set_list = cellprofiler_core.image.ImageSetList() image_set = image_set_list.get_image_set(0) - image_set.providers.append( - cellprofiler_core.image.VanillaImage(IMAGE_NAME, image) - ) + image_set.providers.append(cellprofiler_core.image.VanillaImage(IMAGE_NAME, image)) object_set = cellprofiler_core.object.ObjectSet() measurements = cellprofiler_core.measurement.Measurements() pipeline = cellprofiler_core.pipeline.Pipeline() - x.run( - cellprofiler_core.workspace.Workspace( - pipeline, x, image_set, object_set, measurements, None - ) - ) + x.run(cellprofiler_core.workspace.Workspace(pipeline, x, image_set, object_set, measurements, None)) assert len(object_set.object_names) == 1 assert "my_object" in object_set.object_names objects = object_set.get_objects("my_object") @@ -48,14 +41,10 @@ def test_mock_successful(): count = measurements.get_current_measurement("Image", "Count_my_object") assert count == 0 assert "Location_Center_X" in measurements.get_feature_names("my_object") - location_center_x = measurements.get_current_measurement( - "my_object", "Location_Center_X" - ) + location_center_x = measurements.get_current_measurement("my_object", "Location_Center_X") assert isinstance(location_center_x, numpy.ndarray) assert numpy.product(location_center_x.shape) == 0 assert "Location_Center_Y" in measurements.get_feature_names("my_object") - location_center_y = measurements.get_current_measurement( - "my_object", "Location_Center_Y" - ) + location_center_y = measurements.get_current_measurement("my_object", "Location_Center_Y") assert isinstance(location_center_y, numpy.ndarray) assert numpy.product(location_center_y.shape) == 0 From 71a13a9ac15897708570f1cf48ceeb805b7efac5 Mon Sep 17 00:00:00 2001 From: binliu Date: Tue, 13 Aug 2024 16:27:50 +0000 Subject: [PATCH 13/30] add mock test Signed-off-by: binliu --- plugins/cellprofiler/test_runvista2d.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/plugins/cellprofiler/test_runvista2d.py b/plugins/cellprofiler/test_runvista2d.py index d3031cdfc..6232994c6 100644 --- a/plugins/cellprofiler/test_runvista2d.py +++ b/plugins/cellprofiler/test_runvista2d.py @@ -5,7 +5,9 @@ import cellprofiler_core.setting import cellprofiler_core.workspace import numpy -from runvista2d import RunVISTA2D +import os +import pytest +from active_plugins.runvista2d import RunVISTA2D, MONAILabelClient IMAGE_NAME = "my_image" OBJECTS_NAME = "my_objects" @@ -13,6 +15,15 @@ SERVER_ADDRESS = "http://127.0.0.1:8000" +class MockResponse: + @staticmethod + def infer(*args, **kwargs): + filepath = os.path.abspath(__file__) + dir = os.path.dirname(filepath) + image = os.path.join(dir, "resources", "vista2d_test.tiff") + return image, {} + + def test_mock_successful(): x = RunVISTA2D() x.y_name.value = OBJECTS_NAME @@ -28,6 +39,8 @@ def test_mock_successful(): object_set = cellprofiler_core.object.ObjectSet() measurements = cellprofiler_core.measurement.Measurements() pipeline = cellprofiler_core.pipeline.Pipeline() + + pytest.MonkeyPatch.setattr(MONAILabelClient, "infer", MockResponse.infer) x.run(cellprofiler_core.workspace.Workspace(pipeline, x, image_set, object_set, measurements, None)) assert len(object_set.object_names) == 1 assert "my_object" in object_set.object_names From ca99c8996530a358f36b93f29fac2d7e0f510325 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 13 Aug 2024 16:28:21 +0000 Subject: [PATCH 14/30] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- plugins/cellprofiler/test_runvista2d.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/plugins/cellprofiler/test_runvista2d.py b/plugins/cellprofiler/test_runvista2d.py index 6232994c6..80214fed1 100644 --- a/plugins/cellprofiler/test_runvista2d.py +++ b/plugins/cellprofiler/test_runvista2d.py @@ -1,3 +1,5 @@ +import os + import cellprofiler_core.image import cellprofiler_core.measurement import cellprofiler_core.object @@ -5,9 +7,8 @@ import cellprofiler_core.setting import cellprofiler_core.workspace import numpy -import os import pytest -from active_plugins.runvista2d import RunVISTA2D, MONAILabelClient +from active_plugins.runvista2d import MONAILabelClient, RunVISTA2D IMAGE_NAME = "my_image" OBJECTS_NAME = "my_objects" From 0a20ba15cd20bc1471393ec954dea135e7e56de7 Mon Sep 17 00:00:00 2001 From: "Bin Liu (SW-GPU)" Date: Wed, 14 Aug 2024 16:25:42 +0800 Subject: [PATCH 15/30] add unit test Signed-off-by: Bin Liu (SW-GPU) --- .../cellprofiler/resources/vista2d_test.tiff | Bin 0 -> 65670 bytes plugins/cellprofiler/test_runvista2d.py | 49 ++++++++++++++---- 2 files changed, 38 insertions(+), 11 deletions(-) create mode 100644 plugins/cellprofiler/resources/vista2d_test.tiff diff --git a/plugins/cellprofiler/resources/vista2d_test.tiff b/plugins/cellprofiler/resources/vista2d_test.tiff new file mode 100644 index 0000000000000000000000000000000000000000..bb1aa9b08d77803a1b9622110eca8d08e764ad85 GIT binary patch literal 65670 zcmeIuF$#n*6a>&8BO-R&tc``WH?iIeZRNT5uSCcpULY_GlN9efkC-E3i6~=~Oy9hv z%R{Mmzh_@t{_mzPzuNxF{@uM@Th1S2>-L|PYwjHa1PBlyK!5-N0t5&UAV7cs0RjXF z5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk z1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs z0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZ zfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&U zAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C7 K2oNCf2>bwhG6U=Y literal 0 HcmV?d00001 diff --git a/plugins/cellprofiler/test_runvista2d.py b/plugins/cellprofiler/test_runvista2d.py index 80214fed1..fb22e0a40 100644 --- a/plugins/cellprofiler/test_runvista2d.py +++ b/plugins/cellprofiler/test_runvista2d.py @@ -8,7 +8,7 @@ import cellprofiler_core.workspace import numpy import pytest -from active_plugins.runvista2d import MONAILabelClient, RunVISTA2D +from runvista2d import MONAILabelClient, MONAILabelClientException, MONAILabelUtils, RunVISTA2D IMAGE_NAME = "my_image" OBJECTS_NAME = "my_objects" @@ -25,6 +25,33 @@ def infer(*args, **kwargs): return image, {} +class MockErrResponse: + @staticmethod + def http_multipart(*args, **kwargs): + return 400, {}, {}, {} + + +def test_mock_failed(): + x = RunVISTA2D() + x.y_name.value = OBJECTS_NAME + x.x_name.value = IMAGE_NAME + x.server_address.value = SERVER_ADDRESS + x.model_name.value = MODEL_NAME + + img = numpy.zeros((128, 128, 3)) + image = cellprofiler_core.image.Image(img) + image_set_list = cellprofiler_core.image.ImageSetList() + image_set = image_set_list.get_image_set(0) + image_set.providers.append(cellprofiler_core.image.VanillaImage(IMAGE_NAME, image)) + object_set = cellprofiler_core.object.ObjectSet() + measurements = cellprofiler_core.measurement.Measurements() + pipeline = cellprofiler_core.pipeline.Pipeline() + + pytest.MonkeyPatch().setattr(MONAILabelUtils, "http_multipart", MockErrResponse.http_multipart) + with pytest.raises(MONAILabelClientException): + x.run(cellprofiler_core.workspace.Workspace(pipeline, x, image_set, object_set, measurements, None)) + + def test_mock_successful(): x = RunVISTA2D() x.y_name.value = OBJECTS_NAME @@ -41,24 +68,24 @@ def test_mock_successful(): measurements = cellprofiler_core.measurement.Measurements() pipeline = cellprofiler_core.pipeline.Pipeline() - pytest.MonkeyPatch.setattr(MONAILabelClient, "infer", MockResponse.infer) + pytest.MonkeyPatch().setattr(MONAILabelClient, "infer", MockResponse.infer) x.run(cellprofiler_core.workspace.Workspace(pipeline, x, image_set, object_set, measurements, None)) assert len(object_set.object_names) == 1 - assert "my_object" in object_set.object_names - objects = object_set.get_objects("my_object") + assert OBJECTS_NAME in object_set.object_names + objects = object_set.get_objects(OBJECTS_NAME) segmented = objects.segmented assert numpy.all(segmented == 0) assert "Image" in measurements.get_object_names() - assert "my_object" in measurements.get_object_names() + assert OBJECTS_NAME in measurements.get_object_names() - assert "Count_my_object" in measurements.get_feature_names("Image") - count = measurements.get_current_measurement("Image", "Count_my_object") + assert f"Count_{OBJECTS_NAME}" in measurements.get_feature_names("Image") + count = measurements.get_current_measurement("Image", f"Count_{OBJECTS_NAME}") assert count == 0 - assert "Location_Center_X" in measurements.get_feature_names("my_object") - location_center_x = measurements.get_current_measurement("my_object", "Location_Center_X") + assert "Location_Center_X" in measurements.get_feature_names(OBJECTS_NAME) + location_center_x = measurements.get_current_measurement(OBJECTS_NAME, "Location_Center_X") assert isinstance(location_center_x, numpy.ndarray) assert numpy.product(location_center_x.shape) == 0 - assert "Location_Center_Y" in measurements.get_feature_names("my_object") - location_center_y = measurements.get_current_measurement("my_object", "Location_Center_Y") + assert "Location_Center_Y" in measurements.get_feature_names(OBJECTS_NAME) + location_center_y = measurements.get_current_measurement(OBJECTS_NAME, "Location_Center_Y") assert isinstance(location_center_y, numpy.ndarray) assert numpy.product(location_center_y.shape) == 0 From 1f1045dbc1958f1040fb4721a7dbd952ccb20d5e Mon Sep 17 00:00:00 2001 From: binliu Date: Thu, 15 Aug 2024 15:16:02 +0000 Subject: [PATCH 16/30] add readme for cellprofiler plugin Signed-off-by: binliu --- plugins/cellprofiler/README.md | 48 ++++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) diff --git a/plugins/cellprofiler/README.md b/plugins/cellprofiler/README.md index e69de29bb..7ef3a9532 100644 --- a/plugins/cellprofiler/README.md +++ b/plugins/cellprofiler/README.md @@ -0,0 +1,48 @@ + + +# MONAI Label Plugin for CellProfiler +CellProfiler is a free open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. + +CellProfiler is designed to serve biologists as well as bioimage analysts who want a flexible system that is easy to deploy to collaborators who lack computational skills. It is commonly used for small-scale experiments involving a few images but is also routinely run on millions of images using cluster or cloud computing resources at some of the largest pharmaceutical companies and academic screening centers in the world. + + + +### Table of Contents +- [Supported Applications](#supported-applications) +- [Installing CellProfiler](#installing-cellprofiler) +- [Install MONAI Label Extension From Binaries](#install-monai-label-extension) +- [Using the Plugin](#using-the-plugin) + +### Supported Applications +Users can find supported applications in the [sample-apps](../../sample-apps/monaibundle/) folder and use the cell_vista_segmentation bundle. Currently, only this bundle can be used to create and refine labels for various medical imaging tasks in CellProfiler. + +### Installing CellProfiler + +To use MONAILabel with CellProfiler, you first need to install CellProfiler from source code following the installation part of [CellProfiler WiKi](https://github.com/CellProfiler/CellProfiler/wiki). Once you have CellProfiler installed, you can install the MONAILabel plugin as shown in the `Install MONAI Label Extension` part. + +Please note these tips when installing the software: +1. After cloned the source code, you must switch to a specific version tag of the repo to start installation. +1. For Macbook with Apple Silicon, please note [this issue](https://github.com/CellProfiler/CellProfiler/issues/4932) before you start to install. +1. Before actually running the command `pip install -e .`, you may need to install numpy if it doesn't exist in your environment. + +### Install MONAI Label Extension +1. Save the runvista2d.py file to a local path. +1. Open the File-->preferences option and fill in the `CellProfiler plugin directory` parameter with your local path. + +## Using the Plugin + +1. Start the MONAI Label server with cell_vista_segmentation bundle. +1. Make sure the MONAILabel Server URL is correctly set in the plugin input. +1. Click the `Analyse Images` or `Start Test Mode` button to execute the pipeline. +1. Update masks and perform the sequential modules to get measurement results. 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Supported Applications -Users can find supported applications in the [sample-apps](../../sample-apps/monaibundle/) folder and use the cell_vista_segmentation bundle. Currently, only this bundle can be used to create and refine labels for various medical imaging tasks in CellProfiler. +Users can find supported applications in the [sample-apps](../../sample-apps/monaibundle/) folder and use the cell_vista_segmentation bundle. Currently, only this bundle can be used to create and refine labels for medical imaging tasks in CellProfiler. ### Installing CellProfiler @@ -33,16 +33,19 @@ To use MONAILabel with CellProfiler, you first need to install CellProfiler from Please note these tips when installing the software: 1. After cloned the source code, you must switch to a specific version tag of the repo to start installation. -1. For Macbook with Apple Silicon, please note [this issue](https://github.com/CellProfiler/CellProfiler/issues/4932) before you start to install. +1. For Macbooks with Apple Silicon, please note [this issue](https://github.com/CellProfiler/CellProfiler/issues/4932) before you start to install. 1. Before actually running the command `pip install -e .`, you may need to install numpy if it doesn't exist in your environment. ### Install MONAI Label Extension 1. Save the runvista2d.py file to a local path. -1. Open the File-->preferences option and fill in the `CellProfiler plugin directory` parameter with your local path. +1. Start CellProfiler from CLI, open the `File-->preferences` option and fill in the `CellProfiler plugin directory` parameter with your local path. ## Using the Plugin 1. Start the MONAI Label server with cell_vista_segmentation bundle. +1. Add the `RunVISTA2D` module to your pipeline for object processing. 1. Make sure the MONAILabel Server URL is correctly set in the plugin input. 1. Click the `Analyse Images` or `Start Test Mode` button to execute the pipeline. 1. Update masks and perform the sequential modules to get measurement results. + + From 02bbc82c3d268bab50915f59d918a5fb04e32f38 Mon Sep 17 00:00:00 2001 From: binliu Date: Sun, 18 Aug 2024 03:52:37 +0000 Subject: [PATCH 19/30] remove the unnecessary file Signed-off-by: binliu --- monailabel/tasks/infer/vista.py | 16 ---------------- 1 file changed, 16 deletions(-) delete mode 100644 monailabel/tasks/infer/vista.py diff --git a/monailabel/tasks/infer/vista.py b/monailabel/tasks/infer/vista.py deleted file mode 100644 index e328bbedf..000000000 --- a/monailabel/tasks/infer/vista.py +++ /dev/null @@ -1,16 +0,0 @@ -def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, Any]: - d = dict(data) - output_dir = d.get("output_dir", "") - output_ext = d.get("output_ext", "") - - os.makedirs(self.output_dir, exist_ok=True) - - img = d.get(self.image_key, None) - filename = img.meta.get(ImageMetaKey.FILENAME_OR_OBJ) if img is not None else None - basename = os.path.splitext(os.path.basename(filename))[0] if filename else "mask" - logger.info(f"File: {filename}; Base: {basename}") - - for key in self.key_iterator(d): - label = d[key] - output_filename = f"{basename}{'_' + self.output_postfix if self.output_postfix else ''}{output_ext}" - output_filepath = os.path.join(output_dir, output_filename) From ed36f3afb6f1295086bc22d5951d58c455c63219 Mon Sep 17 00:00:00 2001 From: binliu Date: Mon, 19 Aug 2024 06:38:25 +0000 Subject: [PATCH 20/30] fix import Signed-off-by: binliu --- plugins/cellprofiler/runvista2d.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/plugins/cellprofiler/runvista2d.py b/plugins/cellprofiler/runvista2d.py index 1500406ab..9580bae08 100644 --- a/plugins/cellprofiler/runvista2d.py +++ b/plugins/cellprofiler/runvista2d.py @@ -21,7 +21,7 @@ from cellprofiler_core.module.image_segmentation import ImageSegmentation from cellprofiler_core.object import Objects from cellprofiler_core.setting.choice import Choice -from cellprofiler_core.setting.text import URL, Text +from cellprofiler_core.setting.text import Text ################################# # @@ -720,8 +720,9 @@ class RunVISTA2D(ImageSegmentation): def create_settings(self): super().create_settings() - self.server_address = URL( + self.server_address = Text( text="MONAI label server address", + value="http://127.0.0.1:8000", doc="""\ Please set up the MONAI label server in local/cloud environment and fill the server address here. """, From ec832fddf4e4f637b235d8dd039bc52599380cd7 Mon Sep 17 00:00:00 2001 From: binliu Date: Mon, 19 Aug 2024 06:46:36 +0000 Subject: [PATCH 21/30] add return Signed-off-by: binliu --- sample-apps/vista/lib/infers/vista.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/sample-apps/vista/lib/infers/vista.py b/sample-apps/vista/lib/infers/vista.py index b206f7550..4035b9f40 100644 --- a/sample-apps/vista/lib/infers/vista.py +++ b/sample-apps/vista/lib/infers/vista.py @@ -3,7 +3,6 @@ from typing import Any, Dict, Tuple from monai.utils import ImageMetaKey - from monailabel.tasks.infer.bundle import BundleInferTask logger = logging.getLogger(__name__) @@ -30,4 +29,4 @@ def writer(self, data: Dict[str, Any], extension=None, dtype=None) -> Tuple[Any, logger.info(f"Reusing the bundle output {output_filepath}.") return output_filepath, {} else: - super().writer(data=data, extension=extension, dtype=dtype) + return super().writer(data=data, extension=extension, dtype=dtype) From ad41a8dd36712abec56f3565d7afc3202fedc9e4 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 19 Aug 2024 06:47:43 +0000 Subject: [PATCH 22/30] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- sample-apps/vista/lib/infers/vista.py | 1 + 1 file changed, 1 insertion(+) diff --git a/sample-apps/vista/lib/infers/vista.py b/sample-apps/vista/lib/infers/vista.py index 4035b9f40..81c805fc2 100644 --- a/sample-apps/vista/lib/infers/vista.py +++ b/sample-apps/vista/lib/infers/vista.py @@ -3,6 +3,7 @@ from typing import Any, Dict, Tuple from monai.utils import ImageMetaKey + from monailabel.tasks.infer.bundle import BundleInferTask logger = logging.getLogger(__name__) From becf05cf55cf9a19b50b950788d0c2b6b94b411f Mon Sep 17 00:00:00 2001 From: binliu Date: Mon, 19 Aug 2024 07:33:12 +0000 Subject: [PATCH 23/30] update vista app readme Signed-off-by: binliu --- sample-apps/vista/README.md | 189 ++++++------------------------------ 1 file changed, 29 insertions(+), 160 deletions(-) diff --git a/sample-apps/vista/README.md b/sample-apps/vista/README.md index 9b9b67191..9687eb920 100644 --- a/sample-apps/vista/README.md +++ b/sample-apps/vista/README.md @@ -11,190 +11,59 @@ See the License for the specific language governing permissions and limitations under the License. --> -# MONAI Bundle Application -The MONAIBundle App allows you to easily pull any MONAI Bundle from the [MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo/tree/dev/models) and import it into MONAI Label. However, it's important to note that any MONAI Bundle used with MONAI Label must meet the following constraints: - -- It must comply with the [MONAI Bundle Specification](https://docs.monai.io/en/latest/mb_specification.html). -- For inference, the bundle must define either an `inference.json` or `inference.yaml` file, and it must include the keys described in the bundle.py file located in the `monailabel/tasks/infer/` directory. -- For training, the bundle must define either a `train.json` or `train.yaml file`, and it must include the keys described in the bundle.py file located in the `monailabel/tasks/train/` directory. -- For multi-GPU training, the bundle must define either a `multi_gpu_train.json` or `multi_gpu_train.yaml` file. - -These constraints ensure that any MONAI Bundle used with MONAI Label is compatible with the platform and can be seamlessly integrated into your workflow. +# VISTA Application +A reference app to run the inference task to segment cells. This app works in CellProfiler for now. All samples in the CellProfiler are provided from local path. ### Table of Contents -- [Supported Models](#supported-models) +- [Supported Viewers](#supported-viewers) +- [Pretrained Models](#pretrained-models) - [How To Use the App](#how-to-use-the-app) -- [Epistemic Scoring using MONAI Bundles](#epistemic-Scoring-for-monaibundle-app)\ -- [Detection Model](#Detection-Model) - -### Supported Models - -The MONAIBundle App currently supports most labeling models in the Model-Zoo. You can find a table of supported labeling tasks below. Please note that the list of supported tasks is updated based on the latest release from the [Model-Zoo](https://github.com/Project-MONAI/model-zoo/tree/dev/models). +### Supported Viewers -| Bundle | Model | Objects | Modality | Note | -|:----:|:-----:|:-------:|:--------:|:----:| -| [spleen_ct_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation) | UNet | Spleen | CT | A model for (3D) segmentation of the spleen | -| [swin_unetr_btcv_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/swin_unetr_btcv_segmentation) | SwinUNETR | Multi-Organ | CT | A model for (3D) multi-organ segmentation | -| [prostate_mri_anatomy](https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy) | UNet | Prostate | MRI | A model for (3D) prostate segmentation from MRI image | -| [pancreas_ct_dints_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/pancreas_ct_dints_segmentation) | DiNTS | Pancreas/Tumor | CT | An automl method for (3D) pancreas/tumor segmentation | -| [renalStructures_UNEST_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/renalStructures_UNEST_segmentation) | UNesT | Kidney Substructure | CT | A pre-trained for inference (3D) kidney cortex/medulla/pelvis segmentation | -| [wholeBrainSeg_UNEST_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/wholeBrainSeg_Large_UNEST_segmentation) | UNesT | Whole Brain | MRI T1 | A pre-trained for inference (3D) 133 whole brain structures segmentation | -| [spleen_deepedit_annotation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_deepedit_annotation) | DeepEdit | Spleen| CT | An interactive method for 3D spleen Segmentation | -| [lung_nodule_ct_detection](https://github.com/Project-MONAI/model-zoo/tree/dev/models/lung_nodule_ct_detection) | RetinaNet | Lung Nodule| CT | The detection model for 3D CT images | -| [wholeBody_ct_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/wholeBody_ct_segmentation) | SegResNet | 104 body structures| CT | The segmentation model for 104 tissue from 3D CT images (TotalSegmentator Dataset) | +The VISTA Application supports the following viewer: +- [CellProfiler](../../plugins/cellprofiler/) -### How To Use the App - -```bash -# skip this if you have already downloaded the app or using github repository (dev mode) -monailabel apps --download --name monaibundle --output workspace +### Pretrained Models -# List all available models from zoo -monailabel start_server --app workspace/monaibundle --studies workspace/images +The following are the models which are currently added into Pathology App: -# Pick spleen_ct_segmentation_v0.1.0 model -monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.1.0 +| Name | Description | +|------|-------------| +| Cell VISTA Segmentation | An example of instance segmentation for the cell segmentation. | -# Pick spleen_ct_segmentation_v0.1.0 model and preload -monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.1.0 --conf preload true +

+ Model Details (dataset, input, outputs) -# Pick DeepEdit And Segmentation model (multiple models) -monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models "spleen_ct_segmentation_v0.1.0,spleen_deepedit_annotation_v0.1.0" +#### Dataset -# Skip Training Tasks or Infer only mode -monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.1.0 --conf skip_trainers true -``` +You can use the [cellpose dataset](https://www.cellpose.org/dataset) for inference. -**Specify bundle version** (Optional) -Above command will download the latest bundles from Model-Zoo by default. If a specific or older bundle version is used, users can add version `_v` followed by the bundle name. Example: +#### Inputs -```bash -monailabel start_server --app workspace/monaibundle --studies workspace/images --conf models spleen_ct_segmentation_v0.3.7 -``` -Note: bundles in Model-Zoo are continuously updated, old bundles may contain deprecated API, we recommend using the latest realeased MONAI Label and bundle for the best - experience. +TIFF Images -### Epistemic Scoring for monaibundle app -The MONAIBundle App supports epistemic scoring using bundle models. To use epistemic scoring, you can specify a valid scoring bundle model as the `epistemic_model` configuration parameter when running the app, E.g., `--conf epistemic_model `. If a valid scoring bundle model is provided, scoring inference will be triggered. The loaded scoring bundle model can be either a model from the Model Zoo or a local bundle, but it must support the `dropout` argument. +#### Output -With epistemic scoring, MONAIBundle can provide measures of uncertainty or confidence in the model's predictions, which can be useful in a variety of applications. +Segmentation Masks -```bash -# Use the UNet in spleen_ct_segmentation_v0.2.0 bundle as epistemic scoring model. -# Manual define epistemic scoring parameters -monailabel start_server \ - --app workspace/monaibundle \ - --studies workspace/images \ - --conf models spleen_ct_segmentation_v0.2.0,swin_unetr_btcv_segmentation_v0.2.0 \ - --conf epistemic_model spleen_ct_segmentation_v0.2.0 - --conf epistemic_max_samples 0 \ - --conf epistemic_simulation_size 5 - --conf epistemic_dropout 0.2 +
-``` - -### Detection Model - -MONAI Label now supports detection models. We demonstrate an example of lung nodule detection model from model-zoo. - -3D Slicer plugin needs to be updated or use the latest developer mode. - -#### Step 1: **Prerequisites** - -**Get latest MONAI Label Slicer plugin** (Optional) - -If you are using older release of 3D Slicer MONAI Label plugin, you can update to the latest plugin which supports ***drawing ROI nodes from MONAI Label server prediction***. - -Or to use the developer mode of MONAI Label plugin by: - - - Pull latest MONAI Label repo: - ```bash - git clone https://github.com/Project-MONAI/MONAILabel.git - ``` - - Go to `MONAILabel/plugins/slicer/` - - Follow the Readme page to use developer mode of Slicer plugin. - https://github.com/Project-MONAI/MONAILabel/tree/main/plugins/slicer#install-plugin-in-developer-mode - - Install Plugin in Developer Mode - - Open 3D Slicer: Go to Edit -> Application Settings -> Modules -> Additional Module Paths - - Add New Module Path: /plugins/slicer/MONAILabel - - Restart 3D Slicer - -#### Step 2: Download `monaibundle` App - -Sample-app with built-in versions are in the downloads, fetch the `monaibundle` app using monailabel API - -```bash - monailabel apps --download --name monaibundle --output apps -``` - -#### Step 3: Prepare Sample Data - -The lung nodule detection model are trained and evaluated using [LUNA16](https://luna16.grand-challenge.org/Home/) dataset. - -LUNA16 is a public dataset of CT lung nodule detection. Using raw CT scans, the goal is to identify locations of possible nodules, and to assign a probability for being a nodule to each location. - -Disclaimer: We are not the host of the data. Please make sure to read the requirements and usage policies of the data and give credit to the authors of the dataset! We acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study. - - - If you are going to use full datset of LUNA16, please refer to the dataset link, download the data, create and preprocess the images following [this page]( https://github.com/Project-MONAI/model-zoo/tree/dev/models/lung_nodule_ct_detection#data -). - - - In this tutorial, we prepared a sample subset, resampled and ready to use. The subset is only for demonstration. Download [here](https://github.com/Project-MONAI/MONAILabel/releases/download/data/detection_dataset.zip). - - Or use these commands to download and extract directly. +### How To Use the App ```bash -mkdir datasets -wget "https://github.com/Project-MONAI/MONAILabel/releases/download/data/detection_dataset.zip" -O datasets/detection_dataset.zip -unzip datasets/detection_dataset.zip -d datasets/detection_dataset -``` - - - The detection model also supports MSD Task06_Lung dataset for inference. Note this dataset is created for nodule segmentation task. We do not use the segmentation mask, only the images for inference. Use the following monailabel API to automatically download the dataset. +# skip this if you have already downloaded the app or using github repository (dev mode) +monailabel apps --download --name vista --output apps -```bash -monailabel datasets --download --name Task06_Lung --output datasets +# Start server with cell_vista_segmentation_v0.2.0 model +monailabel start_server --app apps/vista --studies datasets --conf models cell_vista_segmentation --conf preload true --conf skip_trainers true ``` -#### Step 4: Start MONAI Label server and 3D Slicer to Annotate - -All models, datasets, and plugins are ready, start the MONAI Label server and open 3D Slicer to annotate! +**Specify bundle version** (Optional) +Above command will download the latest bundles from Model-Zoo by default. If a specific or older bundle version is used, users can add version `_v` followed by the bundle name. Example: ```bash -# 1: Use LUNA16 sample data -monailabel start_server --app apps/monaibundle --studies datasets/Task06_Lung/imagesTr --conf models lung_nodule_ct_detection -# 2: Use Task06_Lung data for inference demonstration -monailabel start_server --app apps/monaibundle --studies datasets/detection_dataset --conf models lung_nodule_ct_detection +monailabel start_server --app apps/vista --studies datasets --conf models cell_vista_segmentation_v0.2.0 --conf preload true --conf skip_trainers true ``` -#### Step 5: Modify Detection Parameters in the Bundle (Optional) - -The lung_nodule_ct_detection bundle can set the box prediction probability threshold. In `apps/monaibundle/model/lung_nodule_ct_detection/configs/inference.json`, it's define here `score_thresh=0.02`: - -```json - "detector_ops": [ - "$@detector.set_target_keys(box_key='box', label_key='label')", - "$@detector.set_box_selector_parameters(score_thresh=0.02,topk_candidates_per_level=1000,nms_thresh=0.22,detections_per_img=300)", - "$@detector.set_sliding_window_inferer(roi_size=@infer_patch_size,overlap=0.25,sw_batch_size=1,mode='constant',device='cpu')" - ], - -``` - -This parameter will impact the number of boxes in the final prediction output. Then show all boxes ROIs in 3D Slicer. - -If users want to limit the number of predicted boxes showing on 3D Slicer, users can set higher `score_thresh`, e.g., `score_thresh=0.5`, `score_thresh=0.6`, or even higher. - - -#### Additional Configs - -To set configuration parameters for MONAI Label Server, use the `--conf ` flag followed by the parameter name and value while starting the MONAI Label Server. - -| Name | Values | Description | -|---------------------------|-----------------|---------------------------------------------------------------------------------------------| -| zoo_source | string | _Default value:_ github | -| zoo_repo | string | _Default value:_ Project-MONAI/model-zoo/hosting_storage_v1 | -| preload | true, **false** | Preload model into GPU | -| skip_trainers | true, **false** | Skip adding training tasks (Run in Infer mode only) | -| epistemic_max_samples | int | _Default value:_ 0 ; Epistemic scoring parameters | -| epistemic_simulation_size | int | _Default value:_ 5 ; Epistemic simulation size parameters | -| epistemic_dropout | float | _Default value:_ 0.2 ; Epistemic scoring parameters: Dropout rate for scoring models | | From 4679ac7a2ae8bea1bce03a56975db89ba5e523f0 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 19 Aug 2024 07:34:31 +0000 Subject: [PATCH 24/30] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- sample-apps/vista/README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/sample-apps/vista/README.md b/sample-apps/vista/README.md index 9687eb920..141db357c 100644 --- a/sample-apps/vista/README.md +++ b/sample-apps/vista/README.md @@ -66,4 +66,3 @@ Above command will download the latest bundles from Model-Zoo by default. If a s ```bash monailabel start_server --app apps/vista --studies datasets --conf models cell_vista_segmentation_v0.2.0 --conf preload true --conf skip_trainers true ``` - From 79de3fe31cd0a50dcb1f90131a006b52de67bd2f Mon Sep 17 00:00:00 2001 From: binliu Date: Wed, 21 Aug 2024 13:30:30 +0000 Subject: [PATCH 25/30] rename the vista app to vista2d app Signed-off-by: binliu --- sample-apps/{vista => vista2d}/README.md | 10 +++++----- sample-apps/{vista => vista2d}/__init__.py | 0 sample-apps/{vista => vista2d}/lib/__init__.py | 0 .../{vista => vista2d}/lib/activelearning/__init__.py | 0 sample-apps/{vista => vista2d}/lib/infers/__init__.py | 0 sample-apps/{vista => vista2d}/lib/infers/vista.py | 0 .../{vista => vista2d}/lib/trainers/__init__.py | 0 sample-apps/{vista => vista2d}/main.py | 0 sample-apps/{vista => vista2d}/requirements.txt | 0 9 files changed, 5 insertions(+), 5 deletions(-) rename sample-apps/{vista => vista2d}/README.md (81%) rename sample-apps/{vista => vista2d}/__init__.py (100%) rename sample-apps/{vista => vista2d}/lib/__init__.py (100%) rename sample-apps/{vista => vista2d}/lib/activelearning/__init__.py (100%) rename sample-apps/{vista => vista2d}/lib/infers/__init__.py (100%) rename sample-apps/{vista => vista2d}/lib/infers/vista.py (100%) rename sample-apps/{vista => vista2d}/lib/trainers/__init__.py (100%) rename sample-apps/{vista => vista2d}/main.py (100%) rename sample-apps/{vista => vista2d}/requirements.txt (100%) diff --git a/sample-apps/vista/README.md b/sample-apps/vista2d/README.md similarity index 81% rename from sample-apps/vista/README.md rename to sample-apps/vista2d/README.md index 141db357c..f11c433e5 100644 --- a/sample-apps/vista/README.md +++ b/sample-apps/vista2d/README.md @@ -11,7 +11,7 @@ See the License for the specific language governing permissions and limitations under the License. --> -# VISTA Application +# VISTA2D Application A reference app to run the inference task to segment cells. This app works in CellProfiler for now. All samples in the CellProfiler are provided from local path. ### Table of Contents @@ -21,7 +21,7 @@ A reference app to run the inference task to segment cells. This app works in Ce ### Supported Viewers -The VISTA Application supports the following viewer: +The VISTA2D Application supports the following viewer: - [CellProfiler](../../plugins/cellprofiler/) @@ -54,15 +54,15 @@ Segmentation Masks ```bash # skip this if you have already downloaded the app or using github repository (dev mode) -monailabel apps --download --name vista --output apps +monailabel apps --download --name vista2d --output apps # Start server with cell_vista_segmentation_v0.2.0 model -monailabel start_server --app apps/vista --studies datasets --conf models cell_vista_segmentation --conf preload true --conf skip_trainers true +monailabel start_server --app apps/vista2d --studies datasets --conf models cell_vista_segmentation --conf preload true --conf skip_trainers true ``` **Specify bundle version** (Optional) Above command will download the latest bundles from Model-Zoo by default. If a specific or older bundle version is used, users can add version `_v` followed by the bundle name. Example: ```bash -monailabel start_server --app apps/vista --studies datasets --conf models cell_vista_segmentation_v0.2.0 --conf preload true --conf skip_trainers true +monailabel start_server --app apps/vista2d --studies datasets --conf models cell_vista_segmentation_v0.2.0 --conf preload true --conf skip_trainers true ``` diff --git a/sample-apps/vista/__init__.py b/sample-apps/vista2d/__init__.py similarity index 100% rename from sample-apps/vista/__init__.py rename to sample-apps/vista2d/__init__.py diff --git a/sample-apps/vista/lib/__init__.py b/sample-apps/vista2d/lib/__init__.py similarity index 100% rename from sample-apps/vista/lib/__init__.py rename to sample-apps/vista2d/lib/__init__.py diff --git a/sample-apps/vista/lib/activelearning/__init__.py b/sample-apps/vista2d/lib/activelearning/__init__.py similarity index 100% rename from sample-apps/vista/lib/activelearning/__init__.py rename to sample-apps/vista2d/lib/activelearning/__init__.py diff --git a/sample-apps/vista/lib/infers/__init__.py b/sample-apps/vista2d/lib/infers/__init__.py similarity index 100% rename from sample-apps/vista/lib/infers/__init__.py rename to sample-apps/vista2d/lib/infers/__init__.py diff --git a/sample-apps/vista/lib/infers/vista.py b/sample-apps/vista2d/lib/infers/vista.py similarity index 100% rename from sample-apps/vista/lib/infers/vista.py rename to sample-apps/vista2d/lib/infers/vista.py diff --git a/sample-apps/vista/lib/trainers/__init__.py b/sample-apps/vista2d/lib/trainers/__init__.py similarity index 100% rename from sample-apps/vista/lib/trainers/__init__.py rename to sample-apps/vista2d/lib/trainers/__init__.py diff --git a/sample-apps/vista/main.py b/sample-apps/vista2d/main.py similarity index 100% rename from sample-apps/vista/main.py rename to sample-apps/vista2d/main.py diff --git a/sample-apps/vista/requirements.txt b/sample-apps/vista2d/requirements.txt similarity index 100% rename from sample-apps/vista/requirements.txt rename to sample-apps/vista2d/requirements.txt From 3bba98b05875f0783642e494f835bf217aa937af Mon Sep 17 00:00:00 2001 From: binliu Date: Sun, 25 Aug 2024 11:48:25 +0000 Subject: [PATCH 26/30] fix vista to vista2d Signed-off-by: binliu --- sample-apps/vista2d/lib/infers/__init__.py | 2 +- sample-apps/vista2d/lib/infers/{vista.py => vista2d.py} | 2 +- sample-apps/vista2d/main.py | 8 ++++---- 3 files changed, 6 insertions(+), 6 deletions(-) rename sample-apps/vista2d/lib/infers/{vista.py => vista2d.py} (97%) diff --git a/sample-apps/vista2d/lib/infers/__init__.py b/sample-apps/vista2d/lib/infers/__init__.py index db78968da..57ae33bec 100644 --- a/sample-apps/vista2d/lib/infers/__init__.py +++ b/sample-apps/vista2d/lib/infers/__init__.py @@ -9,4 +9,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -from .vista import VISTAInfer +from .vista2d import VISTA2DInfer diff --git a/sample-apps/vista2d/lib/infers/vista.py b/sample-apps/vista2d/lib/infers/vista2d.py similarity index 97% rename from sample-apps/vista2d/lib/infers/vista.py rename to sample-apps/vista2d/lib/infers/vista2d.py index 81c805fc2..e7be48143 100644 --- a/sample-apps/vista2d/lib/infers/vista.py +++ b/sample-apps/vista2d/lib/infers/vista2d.py @@ -9,7 +9,7 @@ logger = logging.getLogger(__name__) -class VISTAInfer(BundleInferTask): +class VISTA2DInfer(BundleInferTask): """ This provides Inference Engine for pre-trained VISTA segmentation model. """ diff --git a/sample-apps/vista2d/main.py b/sample-apps/vista2d/main.py index 05148ba5a..c423b1e62 100644 --- a/sample-apps/vista2d/main.py +++ b/sample-apps/vista2d/main.py @@ -13,7 +13,7 @@ import os from typing import Dict -from lib.infers import VISTAInfer +from lib.infers import VISTA2DInfer from monai.transforms import Invertd, SaveImaged import monailabel @@ -32,7 +32,7 @@ logger = logging.getLogger(__name__) -class VISTAApp(MONAILabelApp): +class VISTA2DApp(MONAILabelApp): def __init__(self, app_dir, studies, conf): self.models = get_bundle_models(app_dir, conf) # Add Epistemic model for scoring @@ -61,7 +61,7 @@ def init_infers(self) -> Dict[str, InferTask]: ################################################# for n, b in self.models.items(): - i = VISTAInfer(b, self.conf, model_state_dict="state_dict") + i = VISTA2DInfer(b, self.conf, model_state_dict="state_dict") logger.info(f"+++ Adding Inferer:: {n} => {i}") infers[n] = i @@ -155,7 +155,7 @@ def main(): "preload": "false", } - app = VISTAApp(app_dir, studies, conf) + app = VISTA2DApp(app_dir, studies, conf) # Infer if args.test == "infer": From 99e9e35349c6aa5ab3edf2b4c551cf9546d1da4e Mon Sep 17 00:00:00 2001 From: binliu Date: Sun, 25 Aug 2024 11:52:46 +0000 Subject: [PATCH 27/30] fix the bundle name from cell_vista_segmentation to vista2d Signed-off-by: binliu --- plugins/cellprofiler/README.md | 4 ++-- plugins/cellprofiler/runvista2d.py | 4 ++-- plugins/cellprofiler/test_runvista2d.py | 2 +- sample-apps/vista2d/README.md | 4 ++-- sample-apps/vista2d/main.py | 2 +- 5 files changed, 8 insertions(+), 8 deletions(-) diff --git a/plugins/cellprofiler/README.md b/plugins/cellprofiler/README.md index 7b0ed1e90..4bc8d5a65 100644 --- a/plugins/cellprofiler/README.md +++ b/plugins/cellprofiler/README.md @@ -25,7 +25,7 @@ CellProfiler is designed to serve biologists as well as bioimage analysts who wa - [Using the Plugin](#using-the-plugin) ### Supported Applications -Users can find supported applications in the [sample-apps](../../sample-apps/monaibundle/) folder and use the cell_vista_segmentation bundle. Currently, only this bundle can be used to create and refine labels for medical imaging tasks in CellProfiler. +Users can find supported applications in the [sample-apps](../../sample-apps/vista2d/) folder and use the vista2d bundle. Currently, only this bundle can be used to create and refine labels for medical imaging tasks in CellProfiler. ### Installing CellProfiler @@ -42,7 +42,7 @@ Please note these tips when installing the software: ## Using the Plugin -1. Start the MONAI Label server with cell_vista_segmentation bundle. +1. Start the MONAI Label server with vista2d bundle. 1. Add the `RunVISTA2D` module to your pipeline for object processing. 1. Make sure the MONAILabel Server URL is correctly set in the plugin input. 1. Click the `Analyse Images` or `Start Test Mode` button to execute the pipeline. diff --git a/plugins/cellprofiler/runvista2d.py b/plugins/cellprofiler/runvista2d.py index 9580bae08..9aa33ccbe 100644 --- a/plugins/cellprofiler/runvista2d.py +++ b/plugins/cellprofiler/runvista2d.py @@ -730,8 +730,8 @@ def create_settings(self): self.model_name = Choice( text="The model for running the inference", - choices=["cell_vista_segmentation"], - value="cell_vista_segmentation", + choices=["vista2d"], + value="vista2d", doc=""" Pick the model for running infernce. Now only VISTA2D is available. """, diff --git a/plugins/cellprofiler/test_runvista2d.py b/plugins/cellprofiler/test_runvista2d.py index fb22e0a40..47ac1f929 100644 --- a/plugins/cellprofiler/test_runvista2d.py +++ b/plugins/cellprofiler/test_runvista2d.py @@ -12,7 +12,7 @@ IMAGE_NAME = "my_image" OBJECTS_NAME = "my_objects" -MODEL_NAME = "cell_vista_segmentation" +MODEL_NAME = "vista2d" SERVER_ADDRESS = "http://127.0.0.1:8000" diff --git a/sample-apps/vista2d/README.md b/sample-apps/vista2d/README.md index f11c433e5..5d4c04260 100644 --- a/sample-apps/vista2d/README.md +++ b/sample-apps/vista2d/README.md @@ -57,12 +57,12 @@ Segmentation Masks monailabel apps --download --name vista2d --output apps # Start server with cell_vista_segmentation_v0.2.0 model -monailabel start_server --app apps/vista2d --studies datasets --conf models cell_vista_segmentation --conf preload true --conf skip_trainers true +monailabel start_server --app apps/vista2d --studies datasets --conf models vista2d --conf preload true --conf skip_trainers true ``` **Specify bundle version** (Optional) Above command will download the latest bundles from Model-Zoo by default. If a specific or older bundle version is used, users can add version `_v` followed by the bundle name. Example: ```bash -monailabel start_server --app apps/vista2d --studies datasets --conf models cell_vista_segmentation_v0.2.0 --conf preload true --conf skip_trainers true +monailabel start_server --app apps/vista2d --studies datasets --conf models vista2d_v0.2.1 --conf preload true --conf skip_trainers true ``` diff --git a/sample-apps/vista2d/main.py b/sample-apps/vista2d/main.py index c423b1e62..2380f1ff8 100644 --- a/sample-apps/vista2d/main.py +++ b/sample-apps/vista2d/main.py @@ -144,7 +144,7 @@ def main(): parser = argparse.ArgumentParser() parser.add_argument("-s", "--studies", default=studies) - parser.add_argument("-m", "--model", default="cell_vista_segmentation") + parser.add_argument("-m", "--model", default="vista2d") parser.add_argument("-t", "--test", default="infer", choices=("train", "infer", "batch_infer")) args = parser.parse_args() From 99752653a015686ba4a9186c4062e65761f57447 Mon Sep 17 00:00:00 2001 From: binliu Date: Sun, 25 Aug 2024 12:12:42 +0000 Subject: [PATCH 28/30] add vista2d model Signed-off-by: binliu --- monailabel/utils/others/modelzoo_list.py | 1 + sample-apps/vista2d/README.md | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/monailabel/utils/others/modelzoo_list.py b/monailabel/utils/others/modelzoo_list.py index cce2daf29..fc42e2219 100644 --- a/monailabel/utils/others/modelzoo_list.py +++ b/monailabel/utils/others/modelzoo_list.py @@ -32,4 +32,5 @@ "wholeBrainSeg_Large_UNEST_segmentation", # whole brain segmentation for T1 MRI brain images. Added Oct 2022 "lung_nodule_ct_detection", # The first lung nodule detection task can be used for MONAI Label. Added Dec 2022 "wholeBody_ct_segmentation", # The SegResNet trained TotalSegmentator dataset with 104 tissues. Added Feb 2023 + "vista2d", # The VISTA segmentation trained on a collection of 15K public microscopy images. Added Aug 2024 ] diff --git a/sample-apps/vista2d/README.md b/sample-apps/vista2d/README.md index 5d4c04260..4aa8f54f9 100644 --- a/sample-apps/vista2d/README.md +++ b/sample-apps/vista2d/README.md @@ -31,7 +31,7 @@ The following are the models which are currently added into Pathology App: | Name | Description | |------|-------------| -| Cell VISTA Segmentation | An example of instance segmentation for the cell segmentation. | +| VISTA2D | An example of instance segmentation for the cell segmentation. |
Model Details (dataset, input, outputs) @@ -56,7 +56,7 @@ Segmentation Masks # skip this if you have already downloaded the app or using github repository (dev mode) monailabel apps --download --name vista2d --output apps -# Start server with cell_vista_segmentation_v0.2.0 model +# Start server with vista2d model monailabel start_server --app apps/vista2d --studies datasets --conf models vista2d --conf preload true --conf skip_trainers true ``` From 57d027d094c5438359cd8dad90ad34b7d04baf1a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 25 Aug 2024 12:37:49 +0000 Subject: [PATCH 29/30] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- monailabel/utils/others/modelzoo_list.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monailabel/utils/others/modelzoo_list.py b/monailabel/utils/others/modelzoo_list.py index fc42e2219..ebdc9afe0 100644 --- a/monailabel/utils/others/modelzoo_list.py +++ b/monailabel/utils/others/modelzoo_list.py @@ -32,5 +32,5 @@ "wholeBrainSeg_Large_UNEST_segmentation", # whole brain segmentation for T1 MRI brain images. Added Oct 2022 "lung_nodule_ct_detection", # The first lung nodule detection task can be used for MONAI Label. Added Dec 2022 "wholeBody_ct_segmentation", # The SegResNet trained TotalSegmentator dataset with 104 tissues. Added Feb 2023 - "vista2d", # The VISTA segmentation trained on a collection of 15K public microscopy images. Added Aug 2024 + "vista2d", # The VISTA segmentation trained on a collection of 15K public microscopy images. Added Aug 2024 ] From 71bbd07b4948141501badf4b1c3e5d41a1d6f5eb Mon Sep 17 00:00:00 2001 From: binliu Date: Wed, 28 Aug 2024 15:50:38 +0000 Subject: [PATCH 30/30] fix readme issue Signed-off-by: binliu --- plugins/cellprofiler/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plugins/cellprofiler/README.md b/plugins/cellprofiler/README.md index 4bc8d5a65..308683e83 100644 --- a/plugins/cellprofiler/README.md +++ b/plugins/cellprofiler/README.md @@ -21,7 +21,7 @@ CellProfiler is designed to serve biologists as well as bioimage analysts who wa ### Table of Contents - [Supported Applications](#supported-applications) - [Installing CellProfiler](#installing-cellprofiler) -- [Install MONAI Label Extension From Binaries](#install-monai-label-extension) +- [Install MONAI Label Extension](#install-monai-label-extension) - [Using the Plugin](#using-the-plugin) ### Supported Applications

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