예제 #1
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def _load_rectangles(job: Job, image_id: str, term: int,
                     detections: dict) -> None:

    progress = 10
    job.update(
        progress=progress,
        status=Job.RUNNING,
        statusComment=
        f"Uploading detections of type rectangles to image {image_id} with terms {term}"
    )

    rectangles = _generate_rectangles(detections)

    # Upload annotations to server
    delta = 85 / len(rectangles)
    annotations = AnnotationCollection()
    for rectangle in rectangles:
        annotations.append(
            Annotation(location=rectangle.wkt,
                       id_image=image_id,
                       id_terms=[term]))
        progress += delta
        job.update(progress=int(progress), status=Job.RUNNING)

    annotations.save()
    progress = 100
    job.update(progress=progress,
               status=Job.TERMINATED,
               statusComment="All detections have been uploaded")
예제 #2
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    def upload_annotation(self, predicted_data, project_id):
        self.cj.job.update(progress=95, statusComment="Uploading annotations")

        annotations = AnnotationCollection()
        components = ObjectFinder(predicted_data).find_components()
        locations = []
        for component in components:
            location = Polygon(component[0], component[1])

            if location.is_valid:
                locations.append(location)
            else:
                fixed = fix_geometry(location)

                if fixed.is_valid and not fixed.is_empty:
                    locations.append(fixed)

        for idx, loc in enumerate(locations):
            if not loc.is_valid:
                fixed = fix_geometry(loc)
                if fixed.is_valid and not fixed.is_empty:
                    loc[idx] = fixed

        annotations.extend([
            create_annotation_from_location(loc, self.image_instance.id,
                                            self.image_instance.height,
                                            project_id) for loc in locations
        ])

        annotations.save(chunk=20)
예제 #3
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def main(argv):
    with CytomineJob.from_cli(argv) as job:
        model_path = os.path.join(str(Path.home()), "models", "thyroid-unet")
        model_filepath = pick_model(model_path, job.parameters.tile_size,
                                    job.parameters.cytomine_zoom_level)
        device = torch.device(job.parameters.device)
        unet = Unet(job.parameters.init_fmaps, n_classes=1)
        unet.load_state_dict(torch.load(model_filepath, map_location=device))
        unet.to(device)
        unet.eval()

        segmenter = UNetSegmenter(device=job.parameters.device,
                                  unet=unet,
                                  classes=[0, 1],
                                  threshold=job.parameters.threshold)

        working_path = os.path.join(str(Path.home()), "tmp")
        tile_builder = CytomineTileBuilder(working_path)
        builder = SSLWorkflowBuilder()
        builder.set_n_jobs(1)
        builder.set_overlap(job.parameters.tile_overlap)
        builder.set_tile_size(job.parameters.tile_size,
                              job.parameters.tile_size)
        builder.set_tile_builder(tile_builder)
        builder.set_border_tiles(Workflow.BORDER_TILES_EXTEND)
        builder.set_background_class(0)
        builder.set_distance_tolerance(1)
        builder.set_seg_batch_size(job.parameters.batch_size)
        builder.set_segmenter(segmenter)
        workflow = builder.get()

        slide = CytomineSlide(img_instance=ImageInstance().fetch(
            job.parameters.cytomine_id_image),
                              zoom_level=job.parameters.cytomine_zoom_level)
        results = workflow.process(slide)

        print("-------------------------")
        print(len(results))
        print("-------------------------")

        collection = AnnotationCollection()
        for obj in results:
            wkt = shift_poly(obj.polygon,
                             slide,
                             zoom_level=job.parameters.cytomine_zoom_level).wkt
            collection.append(
                Annotation(location=wkt,
                           id_image=job.parameters.cytomine_id_image,
                           id_terms=[154005477],
                           id_project=job.project.id))
        collection.save(n_workers=job.parameters.n_jobs)

        return {}
예제 #4
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def run(cyto_job, parameters):

    job = cyto_job.job
    project_id = cyto_job.project
    term_id = parameters.terms_list

    logging.info(f"########### Parameters = {str(parameters)}")
    logging.info(f"########### Term {str(term_id)}")
    logging.info(f"########### Project {str(project_id)}")

    annotations = AnnotationCollection()
    annotations.project = project_id
    annotations.terms = [term_id]
    annotations.fetch()

    progress = 0
    progress_delta = 1.0 / (1.50 * len(annotations))

    job.update(
        progress=progress,
        statusComment=f"Converting annotations from project {project_id}")

    new_annotations = AnnotationCollection()
    for a in annotations:
        if a.location is None:
            a.fetch()
        new_annotations.append(
            Annotation(a.location, a.image, a.term, a.project))
    new_annotations.save(chunk=None)

    job.update(progress=0.25, statusComment=f"Deleting old annotations...")

    for a in annotations:
        a.delete()
        progress += progress_delta
        job.update(progress=progress)
예제 #5
0
파일: run.py 프로젝트: zhang-free/S_Test
def main(argv):
    print(argv)
    with CytomineJob.from_cli(argv) as cj:

        images = ImageInstanceCollection().fetch_with_filter("project", cj.parameters.cytomine_id_project)
        for image in cj.monitor(images, prefix="Running detection on image", period=0.1):
            # Resize image if needed
            resize_ratio = max(image.width, image.height) / cj.parameters.max_image_size
            if resize_ratio < 1:
                resize_ratio = 1

            resized_width = int(image.width / resize_ratio)
            resized_height = int(image.height / resize_ratio)

            image.dump(dest_pattern="/tmp/{id}.jpg", max_size=max(resized_width, resized_height), bits=image.bitDepth)
            img = cv2.imread(image.filename, cv2.IMREAD_GRAYSCALE)

            thresholded_img = cv2.adaptiveThreshold(img, 2**image.bitDepth, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                                    cv2.THRESH_BINARY, cj.parameters.threshold_blocksize,
                                                    cj.parameters.threshold_constant)

            kernel = np.ones((5, 5), np.uint8)
            eroded_img = cv2.erode(thresholded_img, kernel, iterations=cj.parameters.erode_iterations)
            dilated_img = cv2.dilate(eroded_img, kernel, iterations=cj.parameters.dilate_iterations)

            extension = 10
            extended_img = cv2.copyMakeBorder(dilated_img, extension, extension, extension, extension,
                                              cv2.BORDER_CONSTANT, value=2**image.bitDepth)

            components = find_components(extended_img)
            zoom_factor = image.width / float(resized_width)
            for i, component in enumerate(components):
                converted = []
                for point in component[0]:
                    x = int((point[0] - extension) * zoom_factor)
                    y = int(image.height - ((point[1] - extension) * zoom_factor))
                    converted.append((x, y))

                components[i] = Polygon(converted)

            # Find largest component (whole image)
            largest = max(components, key=attrgetter('area'))
            components.remove(largest)

            # Only keep components greater than 5% of whole image
            min_area = int(0.05 * image.width * image.height)

            annotations = AnnotationCollection()
            for component in components:
                if component.area > min_area:
                    annotations.append(Annotation(location=component.wkt, id_image=image.id,
                                                  id_terms=[cj.parameters.cytomine_id_predicted_term],
                                                  id_project=cj.parameters.cytomine_id_project))

                    if len(annotations) % 100 == 0:
                        annotations.save()
                        annotations = AnnotationCollection()

            annotations.save()

        cj.job.update(statusComment="Finished.")
def main(argv):
    with CytomineJob.from_cli(argv) as conn:
        conn.job.update(status=Job.RUNNING,
                        progress=0,
                        statusComment="Initialization...")
        # base_path = "{}".format(os.getenv("HOME")) # Mandatory for Singularity
        base_path = "/home/mmu/Desktop"
        working_path = os.path.join(base_path, str(conn.job.id))

        #Loading pre-trained Stardist model
        np.random.seed(17)
        lbl_cmap = random_label_cmap()
        #Stardist H&E model downloaded from https://github.com/mpicbg-csbd/stardist/issues/46
        #Stardist H&E model downloaded from https://drive.switch.ch/index.php/s/LTYaIud7w6lCyuI
        model = StarDist2D(
            None, name='2D_versatile_HE', basedir='/models/'
        )  #use local model file in ~/models/2D_versatile_HE/

        #Select images to process
        images = ImageInstanceCollection().fetch_with_filter(
            "project", conn.parameters.cytomine_id_project)
        list_imgs = []
        if conn.parameters.cytomine_id_images == 'all':
            for image in images:
                list_imgs.append(int(image.id))
        else:
            list_imgs = [
                int(id_img)
                for id_img in conn.parameters.cytomine_id_images.split(',')
            ]

        #Go over images
        for id_image in conn.monitor(list_imgs,
                                     prefix="Running detection on image",
                                     period=0.1):
            #Dump ROI annotations in img from Cytomine server to local images
            #conn.job.update(status=Job.RUNNING, progress=0, statusComment="Fetching ROI annotations...")
            roi_annotations = AnnotationCollection()
            roi_annotations.project = conn.parameters.cytomine_id_project
            roi_annotations.term = conn.parameters.cytomine_id_roi_term
            roi_annotations.image = id_image  #conn.parameters.cytomine_id_image
            roi_annotations.showWKT = True
            roi_annotations.fetch()
            print(roi_annotations)
            #Go over ROI in this image
            #for roi in conn.monitor(roi_annotations, prefix="Running detection on ROI", period=0.1):
            for roi in roi_annotations:
                #Get Cytomine ROI coordinates for remapping to whole-slide
                #Cytomine cartesian coordinate system, (0,0) is bottom left corner
                print(
                    "----------------------------ROI------------------------------"
                )
                roi_geometry = wkt.loads(roi.location)
                print("ROI Geometry from Shapely: {}".format(roi_geometry))
                print("ROI Bounds")
                print(roi_geometry.bounds)
                minx = roi_geometry.bounds[0]
                miny = roi_geometry.bounds[3]
                #Dump ROI image into local PNG file
                roi_path = os.path.join(
                    working_path,
                    str(roi_annotations.project) + '/' +
                    str(roi_annotations.image) + '/' + str(roi.id))
                roi_png_filename = os.path.join(roi_path + '/' + str(roi.id) +
                                                '.png')
                print("roi_png_filename: %s" % roi_png_filename)
                roi.dump(dest_pattern=roi_png_filename, mask=True, alpha=True)
                #roi.dump(dest_pattern=os.path.join(roi_path,"{id}.png"), mask=True, alpha=True)

                #Stardist works with TIFF images without alpha channel, flattening PNG alpha mask to TIFF RGB
                im = Image.open(roi_png_filename)
                bg = Image.new("RGB", im.size, (255, 255, 255))
                bg.paste(im, mask=im.split()[3])
                roi_tif_filename = os.path.join(roi_path + '/' + str(roi.id) +
                                                '.tif')
                bg.save(roi_tif_filename, quality=100)
                X_files = sorted(glob(roi_path + '/' + str(roi.id) + '*.tif'))
                X = list(map(imread, X_files))
                n_channel = 3 if X[0].ndim == 3 else X[0].shape[-1]
                axis_norm = (
                    0, 1
                )  # normalize channels independently  (0,1,2) normalize channels jointly
                if n_channel > 1:
                    print("Normalizing image channels %s." %
                          ('jointly' if axis_norm is None or 2 in axis_norm
                           else 'independently'))

                #Going over ROI images in ROI directory (in our case: one ROI per directory)
                for x in range(0, len(X)):
                    print("------------------- Processing ROI file %d: %s" %
                          (x, roi_tif_filename))
                    img = normalize(X[x],
                                    conn.parameters.stardist_norm_perc_low,
                                    conn.parameters.stardist_norm_perc_high,
                                    axis=axis_norm)
                    #Stardist model prediction with thresholds
                    labels, details = model.predict_instances(
                        img,
                        prob_thresh=conn.parameters.stardist_prob_t,
                        nms_thresh=conn.parameters.stardist_nms_t)
                    print("Number of detected polygons: %d" %
                          len(details['coord']))
                    cytomine_annotations = AnnotationCollection()
                    #Go over detections in this ROI, convert and upload to Cytomine
                    for pos, polygroup in enumerate(details['coord'], start=1):
                        #Converting to Shapely annotation
                        points = list()
                        for i in range(len(polygroup[0])):
                            #Cytomine cartesian coordinate system, (0,0) is bottom left corner
                            #Mapping Stardist polygon detection coordinates to Cytomine ROI in whole slide image
                            p = Point(minx + polygroup[1][i],
                                      miny - polygroup[0][i])
                            points.append(p)

                        annotation = Polygon(points)
                        #Append to Annotation collection
                        cytomine_annotations.append(
                            Annotation(
                                location=annotation.wkt,
                                id_image=
                                id_image,  #conn.parameters.cytomine_id_image,
                                id_project=conn.parameters.cytomine_id_project,
                                id_terms=[
                                    conn.parameters.cytomine_id_cell_term
                                ]))
                        print(".", end='', flush=True)

                    #Send Annotation Collection (for this ROI) to Cytomine server in one http request
                    ca = cytomine_annotations.save()

        conn.job.update(status=Job.TERMINATED,
                        progress=100,
                        statusComment="Finished.")
예제 #7
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                path_to_landmarks = os.path.join(params.landmarks, tissue,
                                                 scale, f"{original_name}.csv")

                with open(path_to_landmarks, 'r') as csvfile:

                    f_csv = csv.reader(csvfile,
                                       delimiter=str(','),
                                       quotechar=str('|'))
                    headers = next(f_csv)
                    annotations = AnnotationCollection()

                    for row_landmarks in f_csv:

                        id_landmark = int(row_landmarks[0])

                        # due to Cytomine
                        point = Point(float(row_landmarks[1]),
                                      height - float(row_landmarks[2]))

                        a = Annotation(location=point.wkt,
                                       id_image=image_id,
                                       id_project=params.id_project)
                        a.property = [{
                            "key": "ANNOTATION_GROUP_ID",
                            "value": id_landmark
                        }]
                        annotations.append(a)

                    annotations.save()
예제 #8
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def main(argv):
    with CytomineJob.from_cli(argv) as cj:
        cj.job.update(progress=1, statusComment="Initialisation")
        cj.log(str(cj.parameters))

        term_ids = [cj.parameters.cytomine_id_predicted_term] \
            if hasattr(cj.parameters, "cytomine_id_predicted_term") else None

        image_ids = [
            int(image_id)
            for image_id in cj.parameters.cytomine_id_images.split(",")
        ]
        images = ImageInstanceCollection().fetch_with_filter(
            "project", cj.parameters.cytomine_id_project)
        images = [image for image in images if image.id in image_ids]

        tile_size = cj.parameters.tile_size
        tile_overlap = cj.parameters.tile_overlap
        filter_func = _get_filter(cj.parameters.filter)
        projection = cj.parameters.projection
        if projection not in ('min', 'max', 'average'):
            raise ValueError("Projection {} is not found".format(projection))

        cj.log("Filter: {}".format(cj.parameters.filter))
        cj.log("Projection: {}".format(projection))
        for image in cj.monitor(images,
                                prefix="Running detection on image",
                                start=5,
                                end=99):

            def worker_tile_func(tile):
                window = tile.np_image
                threshold = filter_func(window)
                return window, threshold

            cj.log("Get tiles for image {}".format(image.instanceFilename))
            sldc_image = CytomineProjectionSlide(image, projection)
            tile_builder = CytomineProjectionTileBuilder("/tmp")
            topology = sldc_image.tile_topology(tile_builder, tile_size,
                                                tile_size, tile_overlap)

            results = generic_parallel(topology, worker_tile_func)
            thresholds = list()
            for result in results:
                tile, output = result
                window, threshold = output
                thresholds.append(threshold)

            global_threshold = int(np.mean(thresholds))
            cj.log("Mean threshold is {}".format(global_threshold))

            def worker_annotations_func(tile):
                filtered = img_as_uint(tile.np_image > global_threshold)
                return mask_to_objects_2d(filtered, offset=tile.abs_offset)

            cj.log(
                "Extract annotations from filtered tiles for image {}".format(
                    image.instanceFilename))
            results = generic_parallel(topology, worker_annotations_func)
            ids, geometries = list(), list()
            for result in results:
                tile, tile_geometries = result
                # Workaround for slow SemanticMerger but geometries shouldn't be filtered at this stage.
                tile_geometries = [
                    g for g in tile_geometries
                    if g.area > cj.parameters.min_area
                ]
                ids.append(tile.identifier)
                geometries.append(tile_geometries)

            cj.log("Merge annotations from filtered tiles for image {}".format(
                image.instanceFilename))
            merged_geometries = SemanticMerger(tolerance=1).merge(
                ids, geometries, topology)
            cj.log("{} merged geometries".format(len(merged_geometries)))

            if cj.parameters.annotation_slices == 'median':
                # By default, if no slice is given, an annotation is added to the median slice
                slice_ids = [None]
            else:
                slices = SliceInstanceCollection().fetch_with_filter(
                    "imageinstance", image.id)
                if cj.parameters.annotation_slices == 'first':
                    slice_ids = [slices[0].id]
                else:
                    slice_ids = [sl.id for sl in slices]

            ac = AnnotationCollection()
            for geometry in merged_geometries:
                if geometry.area > cj.parameters.min_area:
                    for slice_id in slice_ids:
                        ac.append(
                            Annotation(location=change_referential(
                                geometry, image.height).wkt,
                                       id_image=image.id,
                                       id_terms=term_ids,
                                       id_slice=slice_id))
            ac.save()

        cj.job.update(statusComment="Finished.", progress=100)
예제 #9
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def main(argv):
    base_path = str(Path.home())

    #Available filters
    filters = {
        'binary': BinaryFilter(),
        'adaptive': AdaptiveThresholdFilter(),
        'otsu': OtsuFilter()
    }

    #Connect to Cytomine
    with CytomineJob.from_cli(argv) as cj:
        cj.job.update(status=Job.RUNNING,
                      progress=0,
                      statusComment="Initialisation...")

        working_path = os.path.join(base_path, "data", str(cj.job.id))
        if not os.path.exists(working_path):
            os.makedirs(working_path)

        filter = filters.get(cj.parameters.cytomine_filter)

        #Initiatlize the reader to browse the whole image
        whole_slide = WholeSlide(
            cj.get_image_instance(cj.parameters.cytomine_id_image, True))
        reader = CytomineReader(whole_slide,
                                window_position=Bounds(
                                    0, 0, cj.parameters.cytomine_tile_size,
                                    cj.parameters.cytomine_tile_size),
                                zoom=cj.parameters.cytomine_zoom_level,
                                overlap=cj.parameters.cytomine_tile_overlap)
        reader.window_position = Bounds(0, 0, reader.window_position.width,
                                        reader.window_position.height)

        #Browse the slide using reader
        i = 0
        geometries = []
        cj.job.update(progress=1, status_comment="Browsing big image...")

        while True:
            #Read next tile
            reader.read()
            image = reader.data
            #Saving tile image locally
            tile_filename = "%s/image-%d-zoom-%d-tile-%d-x-%d-y-%d.png" % (
                working_path, cj.parameters.cytomine_id_image,
                cj.parameters.cytomine_zoom_level, i, reader.window_position.x,
                reader.window_position.y)
            image.save(tile_filename, "PNG")
            #Apply filtering
            cv_image = np.array(reader.result())
            filtered_cv_image = filter.process(cv_image)
            i += 1
            #Detect connected components
            components = ObjectFinder(filtered_cv_image).find_components()
            #Convert local coordinates (from the tile image) to global coordinates (the whole slide)
            components = whole_slide.convert_to_real_coordinates(
                components, reader.window_position, reader.zoom)
            geometries.extend(
                get_geometries(components, cj.parameters.cytomine_min_area,
                               cj.parameters.cytomine_max_area))

            #Upload annotations (geometries corresponding to connected components) to Cytomine core
            #Upload each geometry and predicted term
            annotations = AnnotationCollection()
            for geometry in geometries:
                pol = shapely.wkt.loads(geometry)
                if pol.is_valid:
                    annotations.append(
                        Annotation(
                            location=geometry,
                            id_image=cj.parameters.cytomine_id_image,
                            id_project=cj.parameters.cytomine_id_project,
                            id_terms=[
                                cj.parameters.cytomine_id_predicted_term
                            ]))
                #Batches of 100 annotations
                if len(annotations) % 100 == 0:
                    annotations.save()
                    annotations = AnnotationCollection()

            annotations.save()
            geometries = []
            if not reader.next(): break

        cj.job.update(progress=50,
                      status_comment=
                      "Detection done, starting Union over whole big image...")

        #Perform Union of geometries (because geometries are computed locally in each tile but objects (e.g. cell clusters) might overlap several tiles)
        host = cj.parameters.cytomine_host.replace("http://", "")
        unioncommand = "groovy -cp \"/lib/jars/*\" /app/union4.groovy http://%s %s %s %d %d %d %d %d %d %d %d %d %d" % (
            host,
            cj._public_key,
            cj._private_key,
            cj.parameters.cytomine_id_image,
            cj.job.userJob,
            cj.parameters.cytomine_id_predicted_term,  #union_term
            cj.parameters.cytomine_union_min_length,  #union_minlength,
            cj.parameters.cytomine_union_bufferoverlap,  #union_bufferoverlap,
            cj.parameters.
            cytomine_union_min_point_for_simplify,  #union_minPointForSimplify,
            cj.parameters.cytomine_union_min_point,  #union_minPoint,
            cj.parameters.cytomine_union_max_point,  #union_maxPoint,
            cj.parameters.cytomine_union_nb_zones_width,  #union_nbzonesWidth,
            cj.parameters.cytomine_union_nb_zones_height
        )  #union_nbzonesHeight)

        os.chdir(base_path)
        print(unioncommand)
        os.system(unioncommand)

    cj.job.update(status=Job.TERMINATED,
                  progress=100,
                  statusComment="Finished.")
def main():
    with CytomineJob.from_cli(sys.argv) as conn:
        base_path = "{}".format(os.getenv("HOME"))
        working_path = os.path.join(base_path, str(conn.job.id))
        in_path = os.path.join(working_path, "in/")
        out_path = os.path.join(working_path, "out/")

        tr_working_path = os.path.join(base_path,
                                       str(conn.parameters.model_to_use))
        tr_out_path = os.path.join(tr_working_path, "out/")

        if not os.path.exists(working_path):
            os.makedirs(working_path)
            os.makedirs(in_path)

        images = ImageInstanceCollection().fetch_with_filter(
            "project", conn.parameters.cytomine_id_project)
        list_imgs = []
        if conn.parameters.images_to_predict == 'all':
            for image in images:
                list_imgs.append(int(image.id))
                image.dump(os.path.join(in_path, '%d.jpg' % (image.id)))
        else:
            list_imgs = [
                int(id_img)
                for id_img in conn.parameters.images_to_predict.split(',')
            ]
            for image in images:
                if image.id in list_imgs:
                    image.dump(os.path.join(in_path, '%d.jpg' % (image.id)))

        annotation_collection = AnnotationCollection()
        train_job = Job().fetch(conn.parameters.model_to_use)
        properties = PropertyCollection(train_job).fetch()
        str_terms = ""
        for prop in properties:
            if prop.fetch(key='id_terms') != None:
                str_terms = prop.fetch(key='id_terms').value
        term_list = [int(x) for x in str_terms.split(' ')]
        attached_files = AttachedFileCollection(train_job).fetch()

        for id_term in conn.monitor(term_list,
                                    start=10,
                                    end=90,
                                    period=0.05,
                                    prefix="Finding landmarks for terms..."):
            model_file = find_by_attribute(attached_files, "filename",
                                           "%d_model.joblib" % id_term)
            model_filepath = os.path.join(in_path, "%d_model.joblib" % id_term)
            model_file.download(model_filepath, override=True)
            cov_file = find_by_attribute(attached_files, 'filename',
                                         '%d_cov.joblib' % id_term)
            cov_filepath = os.path.join(in_path, "%d_cov.joblib" % id_term)
            cov_file.download(cov_filepath, override=True)
            parameters_file = find_by_attribute(
                attached_files, 'filename', '%d_parameters.joblib' % id_term)
            parameters_filepath = os.path.join(
                in_path, '%d_parameters.joblib' % id_term)
            parameters_file.download(parameters_filepath, override=True)

            model = joblib.load(model_filepath)
            [mx, my, cm] = joblib.load(cov_filepath)
            parameters_hash = joblib.load(parameters_filepath)
            feature_parameters = None
            if parameters_hash['feature_type'] in ['haar', 'gaussian']:
                fparameters_file = find_by_attribute(
                    attached_files, 'filename',
                    "%d_fparameters.joblib" % id_term)
                fparametersl_filepath = os.path.join(
                    in_path, "%d_fparameters.joblib" % id_term)
                fparameters_file.download(fparametersl_filepath, override=True)
                feature_parameters = joblib.load(fparametersl_filepath)
            for id_img in list_imgs:
                (x, y) = searchpoint_cytomine(
                    in_path, id_img, model, mx, my, cm,
                    1. / (2.**np.arange(parameters_hash['model_depth'])),
                    parameters_hash['window_size'],
                    parameters_hash['feature_type'], feature_parameters, 'jpg',
                    parameters_hash['model_npred'])
                circle = Point(x, y)
                annotation_collection.append(
                    Annotation(location=circle.wkt,
                               id_image=id_img,
                               id_terms=[id_term],
                               id_project=conn.parameters.cytomine_id_project))

        annotation_collection.save()
예제 #11
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def run(argv):
    # CytomineJob.from_cli() uses the descriptor.json to automatically create the ArgumentParser
    with CytomineJob.from_cli(argv) as cj:
        cj.job.update(statusComment="Initialization...")
        id_project = cj.parameters.cytomine_id_project
        id_terms = cj.parameters.cytomine_id_terms
        id_tags_for_images = cj.parameters.cytomine_id_tags_for_images
        working_path = cj.parameters.working_path

        terms = TermCollection().fetch_with_filter("project", id_project)
        if id_terms:
            filtered_term_ids = [
                int(id_term) for id_term in id_terms.split(',')
            ]
            filtered_terms = TermCollection()
            for term in terms:
                if term.id in filtered_term_ids:
                    filtered_terms.append(term)
        else:
            filtered_terms = terms

        # Associate YOLO class index to Cytomine term
        classes_filename = os.path.join(working_path, CLASSES_FILENAME)
        with open(classes_filename, 'r') as f:
            classes = f.readlines()
            indexes_terms = {}
            for i, _class in enumerate(classes):
                _class = _class.strip()
                indexes_terms[i] = filtered_terms.find_by_attribute(
                    "name", _class)

        cj.job.update(statusComment="Open model...", progress=1)
        # TODO...

        cj.job.update(statusComment="Predictions...", progress=5)
        images = ImageInstanceCollection(
            tags=id_tags_for_images).fetch_with_filter("project", id_project)
        for image in images:
            print("Prediction for image {}".format(image.instanceFilename))
            # TODO: get predictions from YOLO
            # TODO: I suppose here for the sake of the demo that the output format is the same as input, which is not sure
            # <class> <x_center> <y_center> <width> <height> <proba>
            sample_predictions = [(0, 0.604000000000, 0.493846153846,
                                   0.105600000000, 0.461538461538, 0.9),
                                  (0, 0.409200000000, 0.606153846154,
                                   0.050400000000, 0.095384615385, 0.5)]

            ac = AnnotationCollection()
            for pred in sample_predictions:
                _class, xcenter, ycenter, width, height, proba = pred
                term_ids = [indexes_terms[_class].id
                            ] if _class in indexes_terms.keys() else None
                if term_ids is None:
                    print("No term found for class {}".format(_class))
                geometry = yolo_to_geometry((xcenter, ycenter, width, height),
                                            image.width, image.height)
                properties = [{"key": "probability", "value": proba}]
                ac.append(
                    Annotation(id_image=image.id,
                               id_terms=term_ids,
                               location=geometry.wkt,
                               properties=properties))

            ac.save()

        cj.job.update(statusComment="Finished", progress=100)
예제 #12
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def main(argv):
    with CytomineJob.from_cli(argv) as conn:
        conn.job.update(progress=0, statusComment="Initialization..")
        base_path = "{}".format(os.getenv("HOME"))  # Mandatory for Singularity
        working_path = os.path.join(base_path, str(conn.job.id))

        # Load pretrained model (assume the best of all)
        conn.job.update(progress=0,
                        statusComment="Loading segmentation model..")

        with open("/models/resnet50b_fpn256/config.json") as f:
            config = json.load(f)
        model = FPN.build_resnet_fpn(
            name=config['name'],
            input_size=conn.parameters.dataset_patch_size,  # must be / by 16
            input_channels=1 if config['input']['mode'] == 'grayscale' else 3,
            output_channels=config['fpn']['out_channels'],
            num_classes=2,  # legacy
            in_features=config['fpn']['in_features'],
            out_features=config['fpn']['out_features'])
        model.to(_DEVICE)
        model_dict = torch.load(config['weights'],
                                map_location=torch.device(_DEVICE))
        model.load_state_dict(model_dict['model'])

        # Select images to process
        images = ImageInstanceCollection().fetch_with_filter(
            "project", conn.parameters.cytomine_id_project)

        if conn.parameters.cytomine_id_images != 'all':
            images = [
                _ for _ in images
                if _.id in map(lambda x: int(x.strip()),
                               conn.parameters.cytomine_id_images.split(','))
            ]
        images_id = [image.id for image in images]

        # Download selected images into "working_directory"
        img_path = os.path.join(working_path, "images")
        os.makedirs(img_path)
        for image in conn.monitor(
                images,
                start=2,
                end=50,
                period=0.1,
                prefix="Downloading images into working directory.."):
            fname, fext = os.path.splitext(image.filename)
            if image.download(dest_pattern=os.path.join(
                    img_path, "{}{}".format(image.id, fext))) is not True:

                print("Failed to download image {}".format(image.filename))

        # create a file that lists all images (used by PatchBasedDataset
        conn.job.update(progress=50,
                        statusComment="Preparing data for execution..")
        images = os.listdir(img_path)
        images = list(map(lambda x: x + '\n', images))
        with open(os.path.join(working_path, 'images.txt'), 'w') as f:
            f.writelines(images)

        # Prepare dataset and dataloader objects
        ImgTypeBits = {'.dcm': 16}
        channel_bits = ImgTypeBits.get(fext.lower(), 8)
        mean, std = compute_mean_and_std(img_path, bits=channel_bits)

        dataset = InferencePatchBasedDataset(
            path=working_path,
            subset='images',
            patch_size=conn.parameters.dataset_patch_size,
            mode=config['input']['mode'],
            bits=channel_bits,
            mean=mean,
            std=std)

        dataloader = DataLoader(
            dataset=dataset,
            batch_size=conn.parameters.model_batch_size,
            drop_last=False,
            shuffle=False,
            num_workers=0,
            collate_fn=InferencePatchBasedDataset.collate_fn)

        # Go over images
        conn.job.update(status=Job.RUNNING,
                        progress=55,
                        statusComment="Running inference on images..")
        results = inference_on_segmentation(
            model, dataloader, conn.parameters.postprocess_p_threshold)

        for id_image in conn.monitor(
                images_id,
                start=90,
                end=95,
                prefix="Deleting old annotations on images..",
                period=0.1):
            # Delete old annotations
            del_annotations = AnnotationCollection()
            del_annotations.image = id_image
            del_annotations.user = conn.job.id
            del_annotations.project = conn.parameters.cytomine_id_project
            del_annotations.term = conn.parameters.cytomine_id_predict_term,
            del_annotations.fetch()
            for annotation in del_annotations:
                annotation.delete()

        conn.job.update(
            status=Job.RUNNING,
            progress=95,
            statusComment="Uploading new annotations to Cytomine server..")
        annotations = AnnotationCollection()
        for instance in results:
            idx, _ = os.path.splitext(instance['filename'])
            width, height = instance['size']

            for box in instance['bbox']:
                points = [
                    Point(box[0], height - 1 - box[1]),
                    Point(box[0], height - 1 - box[3]),
                    Point(box[2], height - 1 - box[3]),
                    Point(box[2], height - 1 - box[1])
                ]
                annotation = Polygon(points)

                annotations.append(
                    Annotation(
                        location=annotation.wkt,
                        id_image=int(idx),
                        id_terms=[conn.parameters.cytomine_id_predict_term],
                        id_project=conn.parameters.cytomine_id_project))
        annotations.save()

        conn.job.update(status=Job.TERMINATED,
                        status_comment="Finish",
                        progress=100)
        point = Point(10, 10)
        annotation_point = Annotation(location=point.wkt, id_image=params.id_image_instance).save()
        if params.id_term:
            AnnotationTerm(annotation_point.id, params.id_term).save()

        # Then, we add a rectangle as annotation
        rectangle = box(20, 20, 100, 100)
        annotation_rectangle = Annotation(location=rectangle.wkt, id_image=params.id_image_instance).save()
        if params.id_term:
            AnnotationTerm(annotation_rectangle.id, params.id_term).save()

        # We can also add a property (key-value pair) to an annotation
        Property(annotation_rectangle, key="my_property", value=10).save()

        # Print the list of annotations in the given image:
        annotations = AnnotationCollection()
        annotations.image = params.id_image_instance
        annotations.fetch()
        print(annotations)

        # We can also add multiple annotation in one request:
        annotations = AnnotationCollection()
        annotations.append(Annotation(location=point.wkt, id_image=params.id_image_instance, id_project=params.id_project))
        annotations.append(Annotation(location=rectangle.wkt, id_image=params.id_image_instance, id_project=params.id_project))
        annotations.save()

        # Print the list of annotations in the given image:
        annotations = AnnotationCollection()
        annotations.image = params.id_image_instance
        annotations.fetch()
        print(annotations)
예제 #14
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    def end_successful_import(self, path: Path, image: Image, *args, **kwargs):
        uf = self.get_uf(path)

        ai = AbstractImage()
        ai.uploadedFile = uf.id
        ai.originalFilename = uf.originalFilename
        ai.width = image.width
        ai.height = image.height
        ai.depth = image.depth
        ai.duration = image.duration
        ai.channels = image.n_intrinsic_channels
        ai.extrinsicChannels = image.n_channels
        if image.physical_size_x:
            ai.physicalSizeX = round(
                convert_quantity(image.physical_size_x, "micrometers"), 6)
        if image.physical_size_y:
            ai.physicalSizeY = round(
                convert_quantity(image.physical_size_y, "micrometers"), 6)
        if image.physical_size_z:
            ai.physicalSizeZ = round(
                convert_quantity(image.physical_size_z, "micrometers"), 6)
        if image.frame_rate:
            ai.fps = round(convert_quantity(image.frame_rate, "Hz"), 6)
        ai.magnification = parse_int(image.objective.nominal_magnification)
        ai.bitPerSample = dtype_to_bits(image.pixel_type)
        ai.samplePerPixel = image.n_channels / image.n_intrinsic_channels
        ai.save()
        self.abstract_images.append(ai)

        asc = AbstractSliceCollection()
        set_channel_names = image.n_intrinsic_channels == image.n_channels
        for c in range(image.n_intrinsic_channels):
            name = None
            color = None
            if set_channel_names:
                name = image.channels[c].suggested_name
                color = image.channels[c].hex_color
            for z in range(image.depth):
                for t in range(image.duration):
                    mime = "image/pyrtiff"  # TODO: remove
                    asc.append(
                        AbstractSlice(ai.id,
                                      uf.id,
                                      mime,
                                      c,
                                      z,
                                      t,
                                      channelName=name,
                                      channelColor=color))
        asc.save()

        properties = PropertyCollection(ai)
        for metadata in image.raw_metadata.values():
            if metadata.value is not None and str(metadata.value) != '':
                properties.append(
                    Property(ai, metadata.namespaced_key, str(metadata.value)))
        try:
            properties.save()
        except CollectionPartialUploadException:
            pass  # TODO: improve handling of this exception, but prevent to fail the import

        uf.status = UploadedFile.DEPLOYED
        uf.update()

        properties = PropertyCollection(ai)
        for k, v in self.user_properties:
            if v is not None and str(v) != '':
                properties.append(Property(ai, k, v))
        try:
            properties.save()
        except CollectionPartialUploadException:
            pass  # TODO: improve handling of this exception, but prevent to fail the import

        instances = []
        for p in self.projects:
            instances.append(ImageInstance(ai.id, p.id).save())
        self.images.append((ai, instances))

        # TODO: temporary add annotations for backwards compatibility.
        #  BUT it should be done by core when an image instance is created.
        if image.n_planes == 1 and len(instances) > 0:
            # TODO: currently only supports metadata annots on 2D images

            metadata_annots = image.annotations
            if len(metadata_annots) > 0:
                metadata_terms = [
                    ma.terms for ma in metadata_annots if len(ma.terms) > 0
                ]
                metadata_terms = set(flatten(metadata_terms))

                for instance in instances:
                    project_id = instance.project
                    project = self.projects.find_by_attribute('id', project_id)
                    ontology_id = project.ontology  # noqa
                    ontology_terms = TermCollection().fetch_with_filter(
                        "project", project_id)
                    terms_id_mapping = {t.name: t.id for t in ontology_terms}

                    for metadata_term in metadata_terms:
                        if metadata_term not in terms_id_mapping:
                            # TODO: user must have ontology rights !
                            term = Term(name=metadata_term,
                                        id_ontology=ontology_id,
                                        color="#AAAAAA").save()
                            terms_id_mapping[term.name] = term.id

                    annots = AnnotationCollection()
                    for metadata_annot in metadata_annots:
                        term_ids = [
                            terms_id_mapping[t] for t in metadata_annot.terms
                        ]
                        properties = [
                            dict(key=k, value=v)
                            for k, v in metadata_annot.properties.items()
                        ]
                        annots.append(
                            Annotation(location=metadata_annot.wkt,
                                       id_image=instance.id,
                                       id_terms=term_ids
                                       if len(term_ids) > 0 else None,
                                       properties=properties
                                       if len(properties) > 0 else None,
                                       user=uf.user))

                    try:
                        annots.save()
                    except CollectionPartialUploadException:
                        pass
예제 #15
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def upload_data(problemclass,
                nj,
                inputs,
                out_path,
                monitor_params=None,
                **kwargs):
    """Upload annotations or any other related results to the server.

    Parameters
    ----------
    problemclass: str
        The problem class
    nj: CytomineJob|BiaflowsJob
        The CytomineJob or BiaflowsJob object. Ignored if do_export is True.
    inputs: list
        Input data as returned by the prepare_data
    out_path: str
        Output path
    monitor_params: dict|None
        A dictionnary of parameters to be passed to the data upload loop monitor.
    kwargs: dict
        Additional parameters for:
        * ObjDet/SptCnt: see function 'extract_annotations_objdet'
        * ObjSeg: see function 'extract_annotations_objseg'
    """
    if "is_2d" in kwargs:
        warnings.warn(
            "As of version 0.9.3, the 'is_2d' parameter is not needed anymore in function 'upload_data' and "
            "is now ignored.", DeprecationWarning)
    if not nj.flags["do_upload_annotations"]:
        return
    if nj.flags["tiling"] and (problemclass != CLASS_OBJSEG
                               and problemclass != CLASS_PIXCLA):
        print(
            "Annot. upload is only supported for one of {ObjSeg, PixCla} in 2D when tiling is enabled.. skipping !"
        )
        return
    if monitor_params is None:
        monitor_params = dict()

    annotations = AnnotationCollection()

    if nj.flags["tiling"]:
        annotations.extend(
            extract_tiled_annotations(
                inputs,
                out_path,
                nj,
                label_merging=problemclass == CLASS_PIXCLA))
    else:
        if problemclass == CLASS_OBJSEG:
            extract_fn = extract_annotations_objseg
        elif problemclass == CLASS_PIXCLA:
            extract_fn = extract_annotations_pixcla
        elif problemclass == CLASS_OBJDET or problemclass == CLASS_SPTCNT or problemclass == CLASS_LNDDET:
            extract_fn = extract_annotations_objdet
        elif problemclass == CLASS_LOOTRC or problemclass == CLASS_TRETRC:
            extract_fn = extract_annotations_lootrc
        elif problemclass == CLASS_PRTTRK:
            extract_fn = extract_annotations_prttrk
        elif problemclass == CLASS_OBJTRK:
            extract_fn = extract_annotations_objtrk
        else:
            raise NotImplementedError(
                "Upload data does not support problem class '{}' yet.".format(
                    problemclass))

        tracks = TrackCollection()
        monitor_params["prefix"] = "Extract masks/points/... from output data"
        for in_image in nj.monitor(inputs, **monitor_params):
            curr_tracks, curr_annots = extract_fn(out_path,
                                                  in_image,
                                                  nj.project.id,
                                                  track_prefix=str(nj.job.id),
                                                  **kwargs)
            tracks.extend(curr_tracks)
            annotations.extend(curr_annots)

    nj.job.update(
        statusComment="Upload extracted annotations (total: {})".format(
            len(annotations)))
    annotations.save(chunk=20, n_workers=min(4, cpu_count() * 2))
예제 #16
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def run(debug=False):
    """
    Gets project image from cytomine

    Args:
        debug (bool): If true will save annotations individually and plot any error

    Example:
      python main.py --cytomine_host 'localhost-core' --cytomine_public_key 'dadb7d7a-5822-48f7-ab42-59bce27750ae' --cytomine_private_key 'd73f4602-51d2-4d15-91e4-d4cc175d65fd' --cytomine_id_project 187 --cytomine_id_image_instance 375 --cytomine_id_software 228848

      python main.py --cytomine_host 'localhost-core' --cytomine_public_key 'b6ebb23c-00ff-427b-be24-87b2a82490df' --cytomine_private_key '6812f09b-3f33-4938-82ca-b23032d377fd' --cytomine_id_project 154 --cytomine_id_image_instance 3643

      python main.py --cytomine_host 'localhost-core' --cytomine_public_key 'd2be8bd7-2b0b-40c3-9e81-5ad5765568f3' --cytomine_private_key '6dfe27d7-2ad1-4ca2-8ee9-6321ec3f1318' --cytomine_id_project 197 --cytomine_id_image_instance 2140 --cytomine_id_software 2633

      docker run --gpus all -it --rm --mount type=bind,source=/home/giussepi/Public/environments/Cytomine/cyto_CRLM/,target=/CRLM,bind-propagation=private --network=host ttt --cytomine_host 'localhost-core' --cytomine_public_key 'd2be8bd7-2b0b-40c3-9e81-5ad5765568f3' --cytomine_private_key '6dfe27d7-2ad1-4ca2-8ee9-6321ec3f1318' --cytomine_id_project 197 --cytomine_id_image_instance 31296 --cytomine_id_software 79732
    """

    parser = ArgumentParser(prog="Cytomine Python client example")

    # Cytomine connection parameters
    parser.add_argument('--cytomine_host',
                        dest='host',
                        default='demo.cytomine.be',
                        help="The Cytomine host")
    parser.add_argument('--cytomine_public_key',
                        dest='public_key',
                        help="The Cytomine public key")
    parser.add_argument('--cytomine_private_key',
                        dest='private_key',
                        help="The Cytomine private key")
    parser.add_argument('--cytomine_id_project',
                        dest='id_project',
                        help="The project from which we want the images")
    parser.add_argument('--cytomine_id_software',
                        dest='id_software',
                        help="The software to be used to process the image")
    parser.add_argument('--cytomine_id_image_instance',
                        dest='id_image_instance',
                        help="The image to which the annotation will be added")

    params, _ = parser.parse_known_args(sys.argv[1:])

    with CytomineJob.from_cli(sys.argv[1:]) as cytomine:
        # TODO: To be tested on TITANx
        img = ImageInstance().fetch(params.id_image_instance)
        download_image(img)
        process_wsi_and_save(get_container_image_path(img))
        new_annotations = generate_polygons(get_container_image_path(img),
                                            adapt_to_cytomine=True)
        annotation_collection = None

        for label_key in new_annotations:
            # Sending annotation batches to the server
            for sub_list in chunks(new_annotations[label_key],
                                   ANNOTATION_BATCH):
                if not debug:
                    annotation_collection = AnnotationCollection()

                for exterior_points in sub_list:
                    if debug:
                        annotation_collection = AnnotationCollection()

                    annotation_collection.append(
                        Annotation(location=Polygon(
                            exterior_points.astype(int).reshape(
                                exterior_points.shape[0],
                                exterior_points.shape[2]).tolist()).wkt,
                                   id_image=params.id_image_instance,
                                   id_project=params.id_project,
                                   id_terms=[CYTOMINE_LABELS[label_key]]))

                    if debug:
                        try:
                            annotation_collection.save()
                        except Exception as e:
                            print(
                                exterior_points.astype(int).reshape(
                                    exterior_points.shape[0],
                                    exterior_points.shape[2]).tolist())
                            plt.plot(*Polygon(
                                exterior_points.astype(int).reshape(
                                    exterior_points.shape[0], exterior_points.
                                    shape[2])).exterior.coords.xy)
                            plt.show()
                            # raise(e)
                            print(e)
                        finally:
                            time.sleep(1)

                if not debug:
                    annotation_collection.save()
                    time.sleep(ANNOTATION_SLEEP_TIME)

        # Adding pie chart labels data as image property
        # TODO: Change delete_results_file to True for final test on titanX
        num_pixels_per_label = get_pie_chart_data(
            get_container_image_path(img), delete_results_file=False)

        for percentage, label_ in zip(num_pixels_per_label, Label.names):
            Property(img, key=label_, value='{}%'.format(percentage)).save()

        remove_image_local_copy(img)

        cytomine.job.update(statusComment="Finished.")
예제 #17
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def main(argv):
    with CytomineJob.from_cli(argv) as conn:
        conn.job.update(status=Job.RUNNING, progress=0, statusComment='Intialization...')
        base_path = "{}".format(os.getenv('HOME'))  # Mandatory for Singularity
        working_path = os.path.join(base_path, str(conn.job.id))

        # Loading models from models directory
        with tf.device('/cpu:0'):
            h_model = load_model('/models/head_dice_sm_9976.hdf5', compile=False)  # head model
            h_model.compile(optimizer='adam', loss=dice_coef_loss,
                            metrics=['accuracy'])
            op_model = load_model('/models/op_ce_sm_9991.hdf5', compile=True)  # operculum model
            #op_model.compile(optimizer='adam', loss=dice_coef_loss,
                            #metrics=['accuracy'])

        # Select images to process
        images = ImageInstanceCollection().fetch_with_filter('project', conn.parameters.cytomine_id_project)
        if conn.parameters.cytomine_id_images != 'all':  # select only given image instances = [image for image in image_instances if image.id in id_list]
            images = [_ for _ in images if _.id
                      in map(lambda x: int(x.strip()),
                             conn.parameters.cytomine_id_images.split(','))]
        images_id = [image.id for image in images]

        # Download selected images into 'working_directory'
        img_path = os.path.join(working_path, 'images')
        # if not os.path.exists(img_path):
        os.makedirs(img_path)

        for image in conn.monitor(
                images, start=2, end=50, period=0.1,
                prefix='Downloading images into working directory...'):
            fname, fext = os.path.splitext(image.filename)
            if image.download(dest_pattern=os.path.join(
                    img_path,
                    "{}{}".format(image.id, fext))) is not True:  # images are downloaded with image_ids as names
                print('Failed to download image {}'.format(image.filename))

        # Prepare image file paths from image directory for execution
        conn.job.update(progress=50,
                        statusComment="Preparing data for execution..")
        image_paths = glob.glob(os.path.join(img_path, '*'))
        std_size = (1032,1376)   #maximum size that the model can handle
        model_size = 256
        for i in range(len(image_paths)):

            org_img = Image.open(image_paths[i]) 
            
            filename = os.path.basename(image_paths[i])
            fname, fext = os.path.splitext(filename)
            fname = int(fname)
            org_img = img_to_array(org_img)
            img = org_img.copy()
            org_size = org_img.shape[:2]
            asp_ratio = org_size[0] / org_size[1]  #for cropping and upscaling to original size
            if org_size[1] > std_size[1]:
                img = tf.image.resize(img, (675,900), method='nearest')
                img = tf.image.resize_with_crop_or_pad(img, std_size[0],std_size[1])
                h_mask = predict_mask(img, h_model,model_size)
                h_mask = crop_to_aspect(h_mask, asp_ratio)
                h_mask = tf.image.resize(h_mask, std_size, method='nearest')
                h_up_mask = tf.image.resize_with_crop_or_pad(h_mask, 675,900)
                h_up_mask = tf.image.resize(h_up_mask, org_size, method='nearest')
                h_up_mask = np.asarray(h_up_mask).astype(np.uint8)
                _, h_up_mask = cv.threshold(h_up_mask, 0.001, 255, 0)
                kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (17, 17))
                h_up_mask = cv.morphologyEx(h_up_mask, cv.MORPH_OPEN, kernel, iterations=5)
                h_up_mask = cv.morphologyEx(h_up_mask, cv.MORPH_CLOSE, kernel, iterations=1)
                #h_up_mask = cv.erode(h_up_mask ,kernel,iterations = 3)
                #h_up_mask = cv.dilate(h_up_mask ,kernel,iterations = 3)
                h_up_mask = np.expand_dims(h_up_mask, axis=-1)
                
            else:
                h_mask = predict_mask(img, h_model, model_size)
                h_mask = crop_to_aspect(h_mask, asp_ratio)
                h_up_mask = tf.image.resize(h_mask, org_size, method='nearest')
                h_up_mask = np.asarray(h_up_mask).astype(np.uint8)
                _, h_up_mask = cv.threshold(h_up_mask, 0.001, 255, 0)
                kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (5, 5))
                #kernel = np.ones((9,9),np.uint8)
                h_up_mask = cv.morphologyEx(h_up_mask, cv.MORPH_CLOSE, kernel, iterations=3)
                h_up_mask = np.expand_dims(h_up_mask, axis=-1)
        
            box = bb_pts(h_up_mask)  # bounding box points for operculum (x_min, y_min, x_max, y_max)
            w = box[0]
            h = box[1]
            tr_h = box[3] - box[1]  # target height
            tr_w = box[2] - box[0]  # target width
            crop_op_img = tf.image.crop_to_bounding_box(org_img, h, w, tr_h, tr_w)

            op_asp_ratio = crop_op_img.shape[0] / crop_op_img.shape[1]
            op_mask = predict_mask(crop_op_img, op_model, model_size)
            op_mask = crop_to_aspect(op_mask, op_asp_ratio)
            op_mask = tf.image.resize(op_mask, (crop_op_img.shape[0], crop_op_img.shape[1]), method='nearest')
            op_up_mask = np.zeros((org_img.shape[0],org_img.shape[1],1)).astype(np.uint8) # array of zeros to be filled with op mask
            op_up_mask[box[1]:box[3], box[0]:box[2]] = op_mask # paste op_mask in org_img (reversing the crop operation)
            #op_up_mask = tf.image.resize_with_crop_or_pad(op_mask, org_size[0], org_size[1])
        

            h_polygon = h_make_polygon(h_up_mask)
            op_polygon = o_make_polygon(op_up_mask)

            conn.job.update(
                status=Job.RUNNING, progress=95,
                statusComment="Uploading new annotations to Cytomine server..")

            annotations = AnnotationCollection()
            annotations.append(Annotation(location=h_polygon[0].wkt, id_image=fname, id_terms=143971108,
                                          id_project=conn.parameters.cytomine_id_project))
            annotations.append(Annotation(location=op_polygon[0].wkt, id_image=fname, id_term=143971084,
                                          id_project=conn.parameters.cytomine_id_project))
            annotations.save()

        conn.job.update(status=Job.TERMINATED, status_comment="Finish", progress=100)  # 524787186
예제 #18
0
def main(argv):
    with CytomineJob.from_cli(argv) as conn:
        # with Cytomine(argv) as conn:
        print(conn.parameters)

        conn.job.update(status=Job.RUNNING,
                        progress=0,
                        statusComment="Initialization...")
        base_path = "{}".format(os.getenv("HOME"))  # Mandatory for Singularity
        working_path = os.path.join(base_path, str(conn.job.id))

        # with Cytomine(host=params.host, public_key=params.public_key, private_key=params.private_key,
        #           verbose=logging.INFO) as cytomine:

        # ontology = Ontology("classPNcells"+str(conn.parameters.cytomine_id_project)).save()
        # ontology_collection=OntologyCollection().fetch()
        # print(ontology_collection)
        # ontology = Ontology("CLASSPNCELLS").save()
        # terms = TermCollection().fetch_with_filter("ontology", ontology.id)
        terms = TermCollection().fetch_with_filter(
            "project", conn.parameters.cytomine_id_project)
        conn.job.update(status=Job.RUNNING,
                        progress=1,
                        statusComment="Terms collected...")
        print(terms)

        # term_P = Term("PositiveCell", ontology.id, "#FF0000").save()
        # term_N = Term("NegativeCell", ontology.id, "#00FF00").save()
        # term_P = Term("PositiveCell", ontology, "#FF0000").save()
        # term_N = Term("NegativeCell", ontology, "#00FF00").save()

        # Get all the terms of our ontology
        # terms = TermCollection().fetch_with_filter("ontology", ontology.id)
        # terms = TermCollection().fetch_with_filter("ontology", ontology)
        # print(terms)

        # #Loading pre-trained Stardist model
        # np.random.seed(17)
        # lbl_cmap = random_label_cmap()
        # #Stardist H&E model downloaded from https://github.com/mpicbg-csbd/stardist/issues/46
        # #Stardist H&E model downloaded from https://drive.switch.ch/index.php/s/LTYaIud7w6lCyuI
        # model = StarDist2D(None, name='2D_versatile_HE', basedir='/models/')   #use local model file in ~/models/2D_versatile_HE/

        #Select images to process
        images = ImageInstanceCollection().fetch_with_filter(
            "project", conn.parameters.cytomine_id_project)
        conn.job.update(status=Job.RUNNING,
                        progress=2,
                        statusComment="Images gathered...")

        list_imgs = []
        if conn.parameters.cytomine_id_images == 'all':
            for image in images:
                list_imgs.append(int(image.id))
        else:
            list_imgs = [
                int(id_img)
                for id_img in conn.parameters.cytomine_id_images.split(',')
            ]
            print(list_imgs)

        #Go over images
        conn.job.update(status=Job.RUNNING,
                        progress=10,
                        statusComment="Running PN classification on image...")
        #for id_image in conn.monitor(list_imgs, prefix="Running PN classification on image", period=0.1):
        for id_image in list_imgs:

            roi_annotations = AnnotationCollection()
            roi_annotations.project = conn.parameters.cytomine_id_project
            roi_annotations.term = conn.parameters.cytomine_id_cell_term
            roi_annotations.image = id_image  #conn.parameters.cytomine_id_image
            roi_annotations.job = conn.parameters.cytomine_id_annotation_job
            roi_annotations.user = conn.parameters.cytomine_id_user_job
            roi_annotations.showWKT = True
            roi_annotations.fetch()
            print(roi_annotations)

            #Go over ROI in this image
            #for roi in conn.monitor(roi_annotations, prefix="Running detection on ROI", period=0.1):
            for roi in roi_annotations:
                #Get Cytomine ROI coordinates for remapping to whole-slide
                #Cytomine cartesian coordinate system, (0,0) is bottom left corner
                print(
                    "----------------------------Cells------------------------------"
                )
                roi_geometry = wkt.loads(roi.location)
                # print("ROI Geometry from Shapely: {}".format(roi_geometry))
                #                 print("ROI Bounds")
                #                 print(roi_geometry.bounds)
                minx = roi_geometry.bounds[0]
                miny = roi_geometry.bounds[3]
                #Dump ROI image into local PNG file
                # roi_path=os.path.join(working_path,str(roi_annotations.project)+'/'+str(roi_annotations.image)+'/'+str(roi.id))
                roi_path = os.path.join(
                    working_path,
                    str(roi_annotations.project) + '/' +
                    str(roi_annotations.image) + '/')
                #                 print(roi_path)
                roi_png_filename = os.path.join(roi_path + str(roi.id) +
                                                '.png')
                conn.job.update(status=Job.RUNNING,
                                progress=20,
                                statusComment=roi_png_filename)
                #                 print("roi_png_filename: %s" %roi_png_filename)
                roi.dump(dest_pattern=roi_png_filename, alpha=True)
                #roi.dump(dest_pattern=os.path.join(roi_path,"{id}.png"), mask=True, alpha=True)

                # im=Image.open(roi_png_filename)

                J = cv2.imread(roi_png_filename, cv2.IMREAD_UNCHANGED)
                J = cv2.cvtColor(J, cv2.COLOR_BGRA2RGBA)
                [r, c, h] = J.shape
                # print("J: ",J)

                if r < c:
                    blocksize = r
                else:
                    blocksize = c
                # print("blocksize:",blocksize)
                rr = np.zeros((blocksize, blocksize))
                cc = np.zeros((blocksize, blocksize))

                zz = [*range(1, blocksize + 1)]
                # print("zz:", zz)
                for i in zz:
                    rr[i - 1, :] = zz
                # print("rr shape:",rr.shape)

                zz = [*range(1, blocksize + 1)]
                for i in zz:
                    cc[:, i - 1] = zz
                # print("cc shape:",cc.shape)

                cc1 = np.asarray(cc) - 16.5
                rr1 = np.asarray(rr) - 16.5
                cc2 = np.asarray(cc1)**2
                rr2 = np.asarray(rr1)**2
                rrcc = np.asarray(cc2) + np.asarray(rr2)

                weight = np.sqrt(rrcc)
                # print("weight: ",weight)
                weight2 = 1. / weight
                # print("weight2: ",weight2)
                #                 print("weight2 shape:",weight2.shape)
                coord = [c / 2, r / 2]
                halfblocksize = blocksize / 2

                y = round(coord[1])
                x = round(coord[0])

                # Convert the RGB image to HSV
                Jalpha = J[:, :, 3]
                Jalphaloc = Jalpha / 255
                Jrgb = cv2.cvtColor(J, cv2.COLOR_RGBA2RGB)
                Jhsv = cv2.cvtColor(Jrgb, cv2.COLOR_RGB2HSV_FULL)
                Jhsv = Jhsv / 255
                Jhsv[:, :, 0] = Jhsv[:, :, 0] * Jalphaloc
                Jhsv[:, :, 1] = Jhsv[:, :, 1] * Jalphaloc
                Jhsv[:, :, 2] = Jhsv[:, :, 2] * Jalphaloc
                # print("Jhsv: ",Jhsv)

                # print("Jhsv size:",Jhsv.shape)
                # print("Jhsv class:",Jhsv.dtype)

                currentblock = Jhsv[0:blocksize, 0:blocksize, :]
                # print("currentblock: ",currentblock)
                #                 print(currentblock.dtype)
                currentblockH = currentblock[:, :, 0]
                currentblockV = 1 - currentblock[:, :, 2]
                hue = sum(sum(currentblockH * weight2))
                val = sum(sum(currentblockV * weight2))
                #                 print("hue:", hue)
                #                 print("val:", val)

                if hue < 2:
                    cellclass = 1
                elif val < 15:
                    cellclass = 2
                else:
                    if hue < 30 or val > 40:
                        cellclass = 1
                    else:
                        cellclass = 2

                # tags = TagCollection().fetch()
                # tags = TagCollection()
                # print(tags)

                if cellclass == 1:
                    #                     print("Positive (H: ", str(hue), ", V: ", str(val), ")")
                    id_terms = conn.parameters.cytomine_id_positive_term
                    # tag = Tag("Positive (H: ", str(hue), ", V: ", str(val), ")").save()
                    # print(tag)
                    # id_terms=Term("PositiveCell", ontology.id, "#FF0000").save()
                elif cellclass == 2:
                    #                     print("Negative (H: ", str(hue), ", V: ", str(val), ")")
                    id_terms = conn.parameters.cytomine_id_negative_term
                    # for t in tags:
                    # tag = Tag("Negative (H: ", str(hue), ", V: ", str(val), ")").save()
                    # print(tag)
                    # id_terms=Term("NegativeCell", ontology.id, "#00FF00").save()

                    # First we create the required resources

                cytomine_annotations = AnnotationCollection()
                # property_collection = PropertyCollection(uri()).fetch("annotation",id_image)
                # property_collection = PropertyCollection().uri()
                # print(property_collection)
                # print(cytomine_annotations)

                # property_collection.append(Property(Annotation().fetch(id_image), key="Hue", value=str(hue)))
                # property_collection.append(Property(Annotation().fetch(id_image), key="Val", value=str(val)))
                # property_collection.save()

                # prop1 = Property(Annotation().fetch(id_image), key="Hue", value=str(hue)).save()
                # prop2 = Property(Annotation().fetch(id_image), key="Val", value=str(val)).save()

                # prop1.Property(Annotation().fetch(id_image), key="Hue", value=str(hue)).save()
                # prop2.Property(Annotation().fetch(id_image), key="Val", value=str(val)).save()

                # for pos, polygroup in enumerate(roi_geometry,start=1):
                #     points=list()
                #     for i in range(len(polygroup[0])):
                #         p=Point(minx+polygroup[1][i],miny-polygroup[0][i])
                #         points.append(p)

                annotation = roi_geometry

                # tags.append(TagDomainAssociation(Annotation().fetch(id_image, tag.id))).save()

                # association = append(TagDomainAssociation(Annotation().fetch(id_image, tag.id))).save()
                # print(association)

                cytomine_annotations.append(
                    Annotation(
                        location=annotation.wkt,  #location=roi_geometry,
                        id_image=id_image,  #conn.parameters.cytomine_id_image,
                        id_project=conn.parameters.cytomine_id_project,
                        id_terms=[id_terms]))
                print(".", end='', flush=True)

                #Send Annotation Collection (for this ROI) to Cytomine server in one http request
                ca = cytomine_annotations.save()

        conn.job.update(status=Job.TERMINATED,
                        progress=100,
                        statusComment="Finished.")
예제 #19
0
def main(argv):
    with CytomineJob.from_cli(argv) as cj:
        # use only images from the current project
        cj.job.update(progress=1, statusComment="Preparing execution")

        # extract images to process
        if cj.parameters.cytomine_zoom_level > 0 and (
                cj.parameters.cytomine_tile_size != 256
                or cj.parameters.cytomine_tile_overlap != 0):
            raise ValueError(
                "when using zoom_level > 0, tile size should be 256 "
                "(given {}) and overlap should be 0 (given {})".format(
                    cj.parameters.cytomine_tile_size,
                    cj.parameters.cytomine_tile_overlap))

        cj.job.update(
            progress=1,
            statusComment="Preparing execution (creating folders,...).")
        # working path
        root_path = str(Path.home())
        working_path = os.path.join(root_path, "images")
        os.makedirs(working_path, exist_ok=True)

        # load training information
        cj.job.update(progress=5,
                      statusComment="Extract properties from training job.")
        train_job = Job().fetch(cj.parameters.cytomine_id_job)
        properties = PropertyCollection(train_job).fetch().as_dict()
        binary = str2bool(properties["binary"].value)
        classes = parse_domain_list(properties["classes"].value)

        cj.job.update(progress=10, statusComment="Download the model file.")
        attached_files = AttachedFileCollection(train_job).fetch()
        model_file = attached_files.find_by_attribute("filename",
                                                      "model.joblib")
        model_filepath = os.path.join(root_path, "model.joblib")
        model_file.download(model_filepath, override=True)
        pyxit = joblib.load(model_filepath)

        # set n_jobs
        pyxit.base_estimator.n_jobs = cj.parameters.n_jobs
        pyxit.n_jobs = cj.parameters.n_jobs

        cj.job.update(progress=45, statusComment="Build workflow.")
        builder = SSLWorkflowBuilder()
        builder.set_tile_size(cj.parameters.cytomine_tile_size,
                              cj.parameters.cytomine_tile_size)
        builder.set_overlap(cj.parameters.cytomine_tile_overlap)
        builder.set_tile_builder(
            CytomineTileBuilder(working_path, n_jobs=cj.parameters.n_jobs))
        builder.set_logger(StandardOutputLogger(level=Logger.INFO))
        builder.set_n_jobs(1)
        builder.set_background_class(0)
        # value 0 will prevent merging but still requires to run the merging check
        # procedure (inefficient)
        builder.set_distance_tolerance(2 if cj.parameters.union_enabled else 0)
        builder.set_segmenter(
            ExtraTreesSegmenter(
                pyxit=pyxit,
                classes=classes,
                prediction_step=cj.parameters.pyxit_prediction_step,
                background=0,
                min_std=cj.parameters.tile_filter_min_stddev,
                max_mean=cj.parameters.tile_filter_max_mean))
        workflow = builder.get()

        area_checker = AnnotationAreaChecker(
            min_area=cj.parameters.min_annotation_area,
            max_area=cj.parameters.max_annotation_area)

        def get_term(label):
            if binary:
                if "cytomine_id_predict_term" not in cj.parameters:
                    return []
                else:
                    return [int(cj.parameters.cytomine_id_predict_term)]
            # multi-class
            return [label]

        zones = extract_images_or_rois(cj.parameters)
        for zone in cj.monitor(zones,
                               start=50,
                               end=90,
                               period=0.05,
                               prefix="Segmenting images/ROIs"):
            results = workflow.process(zone)

            annotations = AnnotationCollection()
            for obj in results:
                if not area_checker.check(obj.polygon):
                    continue
                polygon = obj.polygon
                if isinstance(zone, ImageWindow):
                    polygon = affine_transform(
                        polygon,
                        [1, 0, 0, 1, zone.abs_offset_x, zone.abs_offset_y])
                polygon = change_referential(polygon, zone.base_image.height)
                if cj.parameters.cytomine_zoom_level > 0:
                    zoom_mult = (2**cj.parameters.cytomine_zoom_level)
                    polygon = affine_transform(
                        polygon, [zoom_mult, 0, 0, zoom_mult, 0, 0])
                annotations.append(
                    Annotation(location=polygon.wkt,
                               id_terms=get_term(obj.label),
                               id_project=cj.project.id,
                               id_image=zone.base_image.image_instance.id))
            annotations.save()

        cj.job.update(status=Job.TERMINATED,
                      status_comment="Finish",
                      progress=100)
def main(argv):
    with CytomineJob.from_cli(argv) as cj:
        # prepare paths
        working_path = str(Path.home())
        data_path = os.path.join(working_path, "pred_data")
        if not os.path.exists(data_path):
            os.makedirs(data_path)

        model_filename = "model.pkl"

        cj.job.update(progress=5, statusComment="Download model ...")
        model_job = Job().fetch(cj.parameters.cytomine_model_job_id)
        attached_files = AttachedFileCollection(model_job).fetch_with_filter(
            "project", cj.project.id)
        if not (0 < len(attached_files) < 2):
            raise ValueError(
                "More or less than 1 file attached to the Job (found {} file(s))."
                .format(len(attached_files)))
        attached_file = attached_files[0]
        if attached_file.filename != model_filename:
            raise ValueError(
                "Expected model file name is '{}' (found: '{}').".format(
                    model_filename, attached_file.filename))
        model_path = os.path.join(working_path, model_filename)
        attached_file.download(model_path)

        # load model
        with open(model_path, "rb") as file:
            data = pickle.load(file)
            model = data["model"]
            classifier = data["classifier"]
            network = data["network"]
            reduction = data["reduction"]

        # load and dump annotations
        cj.job.update(progress=10, statusComment="Download annotations.")
        annotations = get_annotations(
            project_id=cj.parameters.cytomine_project_id,
            images=parse_list_or_none(cj.parameters.cytomine_images_ids),
            users=parse_list_or_none(cj.parameters.cytomine_users_ids),
            showWKT=True)

        cj.job.update(statusComment="Fetch crops.", progress=15)
        n_samples = len(annotations)
        x = np.zeros([n_samples], dtype=np.object)
        for i, annotation in cj.monitor(enumerate(annotations),
                                        start=15,
                                        end=40,
                                        prefix="Fetch crops",
                                        period=0.1):
            file_format = os.path.join(data_path, "{id}.png")
            if not annotation.dump(dest_pattern=file_format):
                raise ValueError("Download error for annotation '{}'.".format(
                    annotation.id))
            x[i] = file_format.format(id=annotation.id)

        available_nets = {
            MODEL_RESNET50, MODEL_VGG19, MODEL_VGG16, MODEL_INCEPTION_V3,
            MODEL_INCEPTION_RESNET_V2, MODEL_MOBILE, MODEL_DENSE_NET_201,
            MODEL_NASNET_LARGE, MODEL_NASNET_MOBILE
        }

        if network not in available_nets:
            raise ValueError(
                "Invalid value (='{}'} for parameter 'network'.".format(
                    network))
        if reduction not in {"average_pooling"}:
            raise ValueError(
                "Invalid value (='{}') for parameter 'reduction'.".format(
                    reduction))
        if classifier not in {"svm"}:
            raise ValueError(
                "Invalid value (='{}') for parameter 'classifier'.".format(
                    classifier))

        # prepare network
        cj.job.update(statusComment="Load neural network '{}'".format(network),
                      progress=40)
        features = PretrainedModelFeatures(model=network,
                                           layer="last",
                                           reduction=reduction,
                                           weights="imagenet")
        height, width, _ = features._get_input_shape(network)
        loader = ImageLoader(load_size_range=(height, height),
                             crop_size=height,
                             random_crop=False)

        cj.job.update(statusComment="Transform features.", progress=50)
        x_feat = batch_transform(loader,
                                 features,
                                 x,
                                 logger=cj.logger(start=50, end=70,
                                                  period=0.1),
                                 batch_size=128)

        cj.job.update(statusComment="Prediction with '{}'.".format(classifier),
                      progress=70)
        if hasattr(model, "n_jobs"):
            model.n_jobs = cj.parameters.n_jobs

        probas = None
        if hasattr(model, "predict_proba"):
            probas = model.predict_proba(x_feat)
            y_pred = model.classes_.take(np.argmax(probas, axis=1), axis=0)
        else:
            y_pred = model.predict(x_feat)

        cj.job.update(statusComment="Upload annotations.", progress=80)
        annotation_collection = AnnotationCollection()
        for i, annotation in cj.monitor(enumerate(annotations),
                                        start=80,
                                        end=100,
                                        period=0.1,
                                        prefix="Upload annotations"):
            annotation_collection.append(
                Annotation(location=annotation.location,
                           id_image=annotation.image,
                           id_project=annotation.project,
                           term=[int(y_pred[i])],
                           rate=float(probas[i])
                           if probas is not None else 1.0).save())
        annotation_collection.save()

        cj.job.update(statusComment="Finished.", progress=100)