Annotation¶
- class Annotation[source]¶
Bases:
objectAnnotation for a single image.
Annotationobject is immutable.- Parameters
- img_size : Tuple[int, int] or List[int, int]
Size of the image (height, width).
- labels : List[Label]
List of Label objects.
- img_tags : TagCollection or List[Tag]
TagCollection object or list of Tag objects.
- img_description : str, optional
Image description.
- pixelwise_scores_labels : List[Label]
List of Label objects.
- custom_data : dict, optional
Custom data.
- image_id : int, optional
Id of the image.
- Raises
TypeError, if image size is not tuple or list- Usage example
# Simple Annotation example import supervisely as sly height, width = 500, 700 ann = sly.Annotation((height, width)) # More complex Annotation example # TagCollection meta_lemon = sly.TagMeta('lemon_tag', sly.TagValueType.ANY_STRING) tag_lemon = sly.Tag(meta_lemon, 'Hello') tags = sly.TagCollection([tag_lemon]) # or tags = [tag_lemon] # ObjClass class_lemon = sly.ObjClass('lemon', sly.Rectangle) # Label label_lemon = sly.Label(sly.Rectangle(100, 100, 200, 200), class_lemon) # Annotation height, width = 300, 400 ann = sly.Annotation((height, width), [label_lemon], tags, 'example annotaion') # 'points': {'exterior': [[100, 100], [200, 200]], 'interior': []} # If Label geometry is out of image size bounds, it will be cropped label_lemon = sly.Label(sly.Rectangle(100, 100, 700, 900), class_lemon) height, width = 300, 400 ann = sly.Annotation((height, width), [label_lemon], tags, 'example annotaion') # 'points': {'exterior': [[100, 100], [399, 299]], 'interior': []}
Methods
Clones Annotation and adds a new Label.
Clones Annotation and adds a new Labels.
Add label to the pixelwise_scores_labels and return the copy of the current Annotation object.
Add_pixelwise_score_labels extend list of the labels of the pixelwise_scores_labels and return the copy of the current Annotation object.
Clones Annotation and adds a new Tag.
Clones Annotation and adds a new list of Tags.
Convert current annotation objects boxes to BoundingBoxesOnImage format.
Makes a copy of Annotation with new fields, if fields are given, otherwise it will use fields of the original Annotation.
Crops Labels of the current Annotation.
Clones Annotation with removed Label.
Clones Annotation with removed Tag.
Clones Annotation with removed Tag by it's name.
Clones Annotation with removed Tags.
Clones Annotation and removes Tags by their names.
Remove binding keys from all labels.
Draws current Annotation on image.
Draws current Annotation on render.
Draws geometry contour of Annotation on image.
Draws current Annotation on image with contour.
Filter annotation labels by given classes names.
Filters Labels of the current Annotation by side.
Flips the current Annotation horizontally.
Flips the current Annotation vertically.
Creates empty Annotation from image.
Create Annotation from image and SegmentationMapsOnImage, BoundingBoxesOnImage data or ProjectMeta.
Convert a json dict to Annotation.
Returns dictionary with bindings keys as keys and list of labels as values.
Get Label from current Annotation by sly_id.
Check whether annotation contains labels or tags, or not.
Loads json file and converts it to Annotation.
Convert current annotation objects masks to SegmentationMapsOnImage format.
Merge current Annotation with another Annotation.
Crops current Annotation and with image size (height, width) changes.
Resizes current Annotation.
Rotates current Annotation.
Scales current Annotation with the given factor.
Get statistics about color area representation on the given render for the current Annotation.
Get statistics about number of each class in Annotation.
Convert Supervisely annotation to COCO format annotation ("annotations" field).
Convert Annotation classes geometries according to mapping dict and checking nonoverlapping masks.
Draw current Annotation on image and save it in PIL format.
Convert the Annotation to a json dict.
Create new annotation with non-overlapping labels masks.
Convert Supervisely annotation to Pascal VOC format annotation ("annotations" field).
Convert Annotation classes by joining labels with same object classes to one label.
Convert Supervisely annotation to YOLO annotation format.
Transform labels and change image size in current Annotation object and return the copy of the current Annotation object.
Attributes
Id of the image.
Image description.
Size of the image (height, width).
Image tags.
Labels on annotation.
- add_label(label)[source]¶
Clones Annotation and adds a new Label.
- Parameters
- label : Label
Label to be added.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly ann = sly.Annotation((300, 600)) # Create Label class_kiwi = sly.ObjClass('kiwi', sly.Rectangle) label_kiwi = sly.Label(sly.Rectangle(0, 0, 300, 300), class_kiwi) # Add label to Annotation # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable new_ann = ann.add_label(label_kiwi)
- add_labels(labels)[source]¶
Clones Annotation and adds a new Labels.
- Parameters
- labels : List[Label]
List of Label objects to be added.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly ann = sly.Annotation((300, 600)) # Create Labels class_kiwi = sly.ObjClass('kiwi', sly.Rectangle) label_kiwi = sly.Label(sly.Rectangle(0, 0, 300, 300), class_kiwi) class_lemon = sly.ObjClass('lemon', sly.Rectangle) label_lemon = sly.Label(sly.Rectangle(0, 0, 500, 600), class_lemon) # Add labels to Annotation # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable new_ann = ann.add_labels([label_kiwi, label_lemon])
- add_pixelwise_score_label(label)[source]¶
Add label to the pixelwise_scores_labels and return the copy of the current Annotation object. :type label:
Label:param label: Label class object to be added :rtype:Annotation:return: Annotation class object with the new list of the pixelwise_scores_labels
- add_pixelwise_score_labels(labels)[source]¶
Add_pixelwise_score_labels extend list of the labels of the pixelwise_scores_labels and return the copy of the current Annotation object. :type labels:
List[Label] :param labels: list of the Label class objects to be added :rtype:Annotation:return: Annotation class object with the new list of the pixelwise_scores_labels
- add_tag(tag)[source]¶
Clones Annotation and adds a new Tag.
- Parameters
- tag : Tag
Tag object to be added.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly ann = sly.Annotation((300, 600)) # Create Tag meta_message = sly.TagMeta('Message', sly.TagValueType.ANY_STRING) tag_message = sly.Tag(meta_message, 'Hello') # Add Tag to Annotation # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable new_ann = ann.add_tag(tag_message)
- add_tags(tags)[source]¶
Clones Annotation and adds a new list of Tags.
- Parameters
- tags : List[Tag]
List of Tags to be added.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly ann = sly.Annotation((300, 600)) # Create Tags meta_message = sly.TagMeta('Message', sly.TagValueType.ANY_STRING) meta_alert = sly.TagMeta('Alert', sly.TagValueType.NONE) tag_message = sly.Tag(meta_message, 'Hello') tag_alert = sly.Tag(meta_alert) # Add Tags to Annotation # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable new_ann = ann.add_tags([tag_message, tag_alert])
- bboxes_to_imgaug()[source]¶
Convert current annotation objects boxes to BoundingBoxesOnImage format.
- Returns
BoundingBoxesOnImage, otherwise
None- Return type
BoundingBoxesOnImageorNoneType
-
clone(img_size=
None, labels=None, img_tags=None, img_description=None, pixelwise_scores_labels=None, custom_data=None, image_id=None)[source]¶ Makes a copy of Annotation with new fields, if fields are given, otherwise it will use fields of the original Annotation.
- Parameters
- img_size : Tuple[int, int] or List[int, int]
Size of the image (height, width).
- labels : List[Label]
List of Label objects.
- img_tags : TagCollection or List[Tag]
TagCollection object or list of Tag objects.
- img_description : str, optional
Image description.
- pixelwise_scores_labels : List[Label]
List of Label objects.
- custom_data : dict, optional
Custom data.
- image_id : int, optional
Id of the image.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly ann = sly.Annotation((300, 400)) # Let's clone our Annotation with Label class_kiwi = sly.ObjClass('kiwi', sly.Rectangle) label_kiwi = sly.Label(sly.Rectangle(0, 0, 300, 300), class_kiwi) # Assign cloned annotation to a new variable ann_clone_1 = ann.clone(labels=[label_kiwi]) # Let's clone our Annotation with Label, TagCollection and description meta_lemon = sly.TagMeta('lemon', sly.TagValueType.ANY_STRING) tag_lemon = sly.Tag(meta_lemon, 'juicy') tags = sly.TagCollection([tag_lemon]) # Assign cloned annotation to a new variable ann_clone_2 = ann.clone(labels=[label_kiwi], img_tags=tags, img_description='Juicy')
- crop_labels(rect)[source]¶
Crops Labels of the current Annotation.
- Parameters
- rect : Rectangle
Rectangle object for crop.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) new_img = copy.deepcopy(img) # Draw Annotation on image before crop ann.draw_pretty(img, thickness=3) # Crop Labels for current Annotation # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable cropped_ann = ann.crop_labels(sly.Rectangle(0, 0, 600, 700)) # Draw Annotation on image after crop cropped_ann.draw_pretty(new_img, thickness=3)
Before¶
After¶
- delete_label(label)[source]¶
Clones Annotation with removed Label.
- Parameters
- label : Label
Label to be deleted.
- Raises
KeyError, if there is no deleted Label in current Annotation object- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly ann = sly.Annotation((300, 600)) # Create Labels class_kiwi = sly.ObjClass('kiwi', sly.Rectangle) label_kiwi = sly.Label(sly.Rectangle(0, 0, 300, 300), class_kiwi) class_lemon = sly.ObjClass('lemon', sly.Rectangle) label_lemon = sly.Label(sly.Rectangle(0, 0, 500, 600), class_lemon) # Add labels to Annotation ann = ann.add_labels([label_kiwi, label_lemon]) print(len(ann.labels)) # Output: 2 # Run through all labels in Annotation objects for label in ann.labels: if label.obj_class.name == 'lemon': # label obj_class name we want to delete # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable new_ann = ann.delete_label(label) print(len(new_ann.labels)) # Output: 1
- delete_tag(tag)[source]¶
Clones Annotation with removed Tag.
- Parameters
- tag : Tag
Tag to be deleted.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly ann = sly.Annotation((300, 600)) meta_dog = sly.TagMeta('dog', sly.TagValueType.ANY_STRING) meta_cat = sly.TagMeta('cat', sly.TagValueType.NONE) tag_dog = sly.Tag(meta_dog, 'Woof!') tag_cat = sly.Tag(meta_cat) ann = ann.add_tags([tag_dog, tag_cat]) print(len(ann.img_tags)) # Output: 2 new_ann = ann.delete_tag(tag_dog) print(len(new_ann.img_tags)) # Output: 1
- delete_tag_by_name(tag_name)[source]¶
Clones Annotation with removed Tag by it’s name.
- Parameters
- tag_name : str
Tag name to be delete.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly ann = sly.Annotation((300, 600)) # Create Tag meta_alert = sly.TagMeta('Alert', sly.TagValueType.ANY_STRING) tag_alert = sly.Tag(meta_alert, 'Hello') tag_ann = ann.add_tag(tag_alert) # Delete Tag from Annotation # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable new_ann = tag_ann.delete_tag_by_name('Alert')
- delete_tags(tags)[source]¶
Clones Annotation with removed Tags.
- Parameters
- tags : List[Tag]
List of Tags to be deleted.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly ann = sly.Annotation((300, 600)) meta_message = sly.TagMeta('Message', sly.TagValueType.ANY_STRING) meta_alert = sly.TagMeta('Alert', sly.TagValueType.NONE) tag_message = sly.Tag(meta_message, 'Hello') tag_alert = sly.Tag(meta_alert) ann = ann.add_tags([tag_message, tag_alert]) print(len(ann.img_tags)) # Output: 2 new_ann = ann.delete_tags([tag_message, tag_alert]) print(len(new_ann.img_tags)) # Output: 0
- delete_tags_by_name(tag_names)[source]¶
Clones Annotation and removes Tags by their names.
- Parameters
- tag_names : List[str]
List of Tags names to be deleted.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly ann = sly.Annotation((300, 600)) # Create Tags meta_message = sly.TagMeta('Message', sly.TagValueType.ANY_STRING) meta_alert = sly.TagMeta('Alert', sly.TagValueType.NONE) tag_message = sly.Tag(meta_message, 'Hello') tag_alert = sly.Tag(meta_alert) # Add Tags to Annotation tags_ann = ann.add_tags([tag_message, tag_alert]) # Delete Tags from Annotation # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable new_ann = tags_ann.delete_tags_by_name(['Message', 'Alert'])
-
draw(bitmap, color=
None, thickness=1, draw_tags=False, fill_rectangles=True, draw_class_names=False)[source]¶ Draws current Annotation on image. Modifies mask.
- Parameters
- bitmap : np.ndarray
Image.
- color : List[int, int, int], optional
Drawing color in
[R, G, B].- thickness : int, optional
Thickness of the drawing figure.
- draw_tags : bool, optional
Determines whether to draw tags on bitmap or not.
- fill_rectangles : int, optional
Choose False if you want to draw only contours of bboxes. By default, True.
- draw_class_names : int, optional
Determines whether to draw class names on bitmap or not.
- Returns
None- Return type
NoneType- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) # Draw Annotation on image ann.draw(img)
- draw_class_idx_rgb(render, name_to_index)[source]¶
Draws current Annotation on render.
- Parameters
- render : np.ndarray
Target render to draw classes.
- name_to_index : dict
Dict where keys are class names and values are class indices to draw on render.
- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) # Draw Annotation on image name_to_index = {'lemon': 90, 'kiwi': 195} ann.draw_class_idx_rgb(img, name_to_index)
- Return type
-
draw_contour(bitmap, color=
None, thickness=1, draw_tags=False)[source]¶ Draws geometry contour of Annotation on image. Modifies mask.
- Parameters
- Returns
None- Return type
NoneType- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) # Draw Annotation contour on image ann.draw_contour(img)
-
draw_pretty(bitmap, color=
None, thickness=None, opacity=0.5, draw_tags=False, output_path=None, fill_rectangles=True)[source]¶ Draws current Annotation on image with contour. Modifies mask.
- Parameters
- bitmap : np.ndarray
Image.
- color : List[int, int, int], optional
Drawing color in
[R, G, B].- thickness : int, optional
Thickness of the drawing figure.
- opacity : float, optional
Opacity of the drawing figure.
- draw_tags : bool, optional
Determines whether to draw tags on bitmap or not.
- output_path : str, optional
Saves modified image to the given path.
- Returns
None- Return type
NoneTypeimport supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) # Draw pretty Annotation on image ann.draw_pretty(img, thickness=3)
- filter_labels_by_classes(keep_classes)[source]¶
Filter annotation labels by given classes names.
- Parameters
- keep_classes : List[str]
List with classes names.
- Returns
New instance of Annotation
- Return type
-
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image annotation from API project_id = 7473 image_id = 2223200 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) print(json.dumps(ann_info.annotation, indent=4)) # Output: { # "description": "", # "tags": [], # "size": { # "height": 800, # "width": 1067 # }, # "objects": [ # { # "id": 57388829, # "classId": 121405, # "description": "", # "geometryType": "bitmap", # "labelerLogin": "alex", # "createdAt": "2022-01-02T08:06:05.183Z", # "updatedAt": "2022-01-02T08:07:12.219Z", # "tags": [], # "classTitle": "kiwi", # "bitmap": { # "data": "eJyNlWs4lHkYxv/z8qp5NZuYSZes1KZm6CDSTuP0mh2ZWYdmULakdBUzkRqRrDG8M1vZZg8WIy..., # "origin": [ # 481, # 543 # ] # } # }, # { # "id": 57388831, # "classId": 121404, # "description": "", # "geometryType": "bitmap", # "labelerLogin": "alex", # "createdAt": "2022-01-02T08:06:59.133Z", # "updatedAt": "2022-01-02T08:07:12.219Z", # "tags": [], # "classTitle": "lemon", # "bitmap": { # "data": "eJwdV388k/sXfzwmz3TVNm3l94z5saQoXFSG+c26tM1GYjWUISWKK201SchvIcq30tX2rDuTdE..., # "origin": [ # 523, # 119 # ] # } # }, # { # "id": 57388832, # "classId": 121405, # "description": "", # "geometryType": "bitmap", # "labelerLogin": "alex", # "createdAt": "2022-01-02T08:07:12.104Z", # "updatedAt": "2022-01-02T08:07:12.104Z", # "tags": [], # "classTitle": "kiwi", # "bitmap": { # "data": "eJw1VglQU8kWTWISHzH6AzwwIEsSCBr2AApBloAJhB0eCEIYDJFN2WHACC7sRghbIAiIMuKj..., # "origin": [ # 773, # 391 # ] # } # } # ] # } ann = sly.Annotation.from_json(ann_info.annotation, meta) # Let's filter 'lemon' class keep_classes = ['lemon'] filter_ann = ann.filter_labels_by_classes(keep_classes) print(json.dumps(filter_ann.to_json(), indent=4)) # Output: { # "description": "", # "size": { # "height": 800, # "width": 1067 # }, # "tags": [], # "objects": [ # { # "classTitle": "lemon", # "description": "", # "tags": [], # "bitmap": { # "origin": [ # 523, # 119 # ], # "data": "eJwBOAPH/IlQTkcNChoKAAAADUlIRFIAAAEsAAABCQEDAAAAFNKIswAAAAZQTFRFAAAA..., # }, # "shape": "bitmap", # "geometryType": "bitmap", # "labelerLogin": "alex", # "updatedAt": "2022-01-02T08:07:12.219Z", # "createdAt": "2022-01-02T08:06:59.133Z", # "id": 57388831, # "classId": 121404 # } # ], # "customBigData": {} # }
- filter_labels_by_min_side(thresh, filter_operator=<built-in function lt>, classes=None)[source]¶
Filters Labels of the current Annotation by side. If minimal side is smaller than Label threshold it will be ignored.
- Parameters
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) # Filter Labels # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable filtered_ann = ann.filter_labels_by_min_side(200) # Draw filtered Annotation on image filtered_ann.draw(img)
Before¶
After¶
- fliplr()[source]¶
Flips the current Annotation horizontally.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) new_img = copy.deepcopy(img) new_img = sly.imaging.image.fliplr(new_img) # Draw Annotation on image before horizontal flip ann.draw_pretty(img, thickness=3) # Flip # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable fliplr_ann = ann.fliplr() # Draw Annotation on image after horizontal flip fliplr_ann.draw_pretty(new_img, thickness=3)
Before¶
After¶
- flipud()[source]¶
Flips the current Annotation vertically.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) new_img = copy.deepcopy(img) new_img = sly.imaging.image.flipud(new_img) # Draw Annotation on image before vertical flip ann.draw_pretty(img, thickness=3) # Flip # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable flipud_ann = ann.flipud() # Draw Annotation on image after vertical flip flipud_ann.draw_pretty(new_img, thickness=3)
Before¶
After¶
- classmethod from_img_path(img_path)[source]¶
Creates empty Annotation from image.
- Parameters
- img_path : str
Path to the input image.
- Returns
Annotation
- Return type
- Usage Example
import supervisely as sly img_path = "/home/admin/work/docs/my_dataset/img/example.jpeg" ann = sly.Annotation.from_img_path(img_path)
-
classmethod from_imgaug(img, ia_boxes=
None, ia_masks=None, index_to_class=None, meta=None)[source]¶ Create Annotation from image and SegmentationMapsOnImage, BoundingBoxesOnImage data or ProjectMeta.
- Parameters
- img : np.ndarray
Image in numpy format.
- ia_boxes : List[BoundingBoxesOnImage], optional
List of BoundingBoxesOnImage data.
- ia_masks : List[SegmentationMapsOnImage], optional
List of SegmentationMapsOnImage data.
- index_to_class : Dict[int, str], optional
Dictionary specifying index match of class name.
- meta : ProjectMeta, optional
ProjectMeta.
- Raises
ValueError, if ia_boxes or ia_masks and meta is None- Raises
KeyError, if processed ObjClass not found in meta- Returns
Annotation object
- Return type
- classmethod from_json(data, project_meta)[source]¶
Convert a json dict to Annotation. Read more about Supervisely format.
- Parameters
- data : dict
Annotation in json format as a dict.
- project_meta : ProjectMeta
Input
ProjectMeta.
- Returns
Annotation object
- Return type
- Raises
- Usage example
import supervisely as sly meta = sly.ProjectMeta() ann_json = { "size": { "height": 500, "width": 700 }, "tags": [], "objects": [] } ann = sly.Annotation.from_json(ann_json, meta)
- get_label_by_id(sly_id)[source]¶
Get Label from current Annotation by sly_id.
- Parameters
- sly_id : int
Label ID from Supervisely server.
- Returns
Label or None
- Return type
LabelorNoneType- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # To get Label ID you must first access ProjectMeta PROJECT_ID = 999 meta_json = api.project.get_meta(PROJECT_ID) meta = sly.ProjectMeta.from_json(meta_json) # Get desired image id to which label belongs to download annotation image_id = 376728 ann_info = api.annotation.download(image_id) ann_json = ann_info.annotation ann = sly.Annotation.from_json(ann_json, meta) # Get Label by it's ID label_by_id = ann.get_label_by_id(sly_id=2263842) print(label_by_id.to_json()) # Output: { # "classTitle":"kiwi", # "description":"", # "tags":[], # "points":{ # "exterior":[ # [481, 549], # [641, 703] # ], # "interior":[] # }, # "labelerLogin":"cxnt", # "updatedAt":"2020-12-11T08:11:43.249Z", # "createdAt":"2020-12-10T09:38:57.969Z", # "id":2263842, # "classId":7370, # "geometryType":"rectangle", # "shape":"rectangle" # } # Returns None if Label ID doesn't exist on the given image ID label_by_id = ann.get_label_by_id(sly_id=9999999) # Output: None
- is_empty()[source]¶
Check whether annotation contains labels or tags, or not.
- Returns
True if annotation is empty, False otherwise.
- Return type
- classmethod load_json_file(path, project_meta)[source]¶
Loads json file and converts it to Annotation.
- Parameters
- path : str
Path to the json file.
- project_meta : ProjectMeta
Input ProjectMeta object.
- Returns
Annotation object
- Return type
- Usage example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) team_name = 'Vehicle Detection' workspace_name = 'Cities' project_name = 'London' team = api.team.get_info_by_name(team_name) workspace = api.workspace.get_info_by_name(team.id, workspace_name) project = api.project.get_info_by_name(workspace.id, project_name) meta = api.project.get_meta(project.id) # Load json file path = "/home/admin/work/docs/my_dataset/ann/annotation.json" ann = sly.Annotation.load_json_file(path, meta)
- masks_to_imgaug(class_to_index)[source]¶
Convert current annotation objects masks to SegmentationMapsOnImage format.
- Parameters
- class_to_index : dict
Dict matching class name to index.
- Returns
SegmentationMapsOnImage, otherwise
None- Return type
SegmentationMapsOnImageorNoneType
- merge(other)[source]¶
Merge current Annotation with another Annotation.
- Parameters
- other : Annotation
Annotation to merge.
- Returns
New instance of Annotation
- Return type
-
import supervisely as sly # Create annotation meta_lemon = sly.TagMeta('lemon_tag', sly.TagValueType.ANY_STRING) tag_lemon = sly.Tag(meta_lemon, 'lemon') tags_lemon = sly.TagCollection([tag_lemon]) class_lemon = sly.ObjClass('lemon', sly.Rectangle) label_lemon = sly.Label(sly.Rectangle(100, 100, 200, 200), class_lemon) height, width = 300, 400 ann_lemon = sly.Annotation((height, width), [label_lemon], tags_lemon) # Create annotation to merge meta_kiwi= sly.TagMeta('kiwi_tag', sly.TagValueType.ANY_STRING) tag_kiwi = sly.Tag(meta_kiwi, 'kiwi') tags_kiwi = sly.TagCollection([tag_kiwi]) class_kiwi = sly.ObjClass('kiwi', sly.Rectangle) label_kiwi = sly.Label(sly.Rectangle(200, 100, 700, 200), class_kiwi) height, width = 700, 500 ann_kiwi = sly.Annotation((height, width), [label_kiwi], tags_kiwi) # Merge annotations ann_merge = ann_lemon.merge(ann_kiwi) for label in ann_merge.labels: print(label.obj_class.name) # Output: lemon # Output: kiwi
- relative_crop(rect)[source]¶
Crops current Annotation and with image size (height, width) changes.
- Parameters
- rect : Rectangle
Rectangle object for crop.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) new_img = copy.deepcopy(img) new_img = sly.imaging.image.crop(new_img, sly.Rectangle(200, 300, 600, 700)) # Draw Annotation on image before relative crop ann.draw_pretty(img, thickness=3) # Relative Crop Labels for current Annotation # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable r_cropped_ann = ann.relative_crop(sly.Rectangle(200, 300, 600, 700)) # Draw Annotation on image after relative crop r_cropped_ann.draw_pretty(new_img, thickness=3)
Before¶
After¶
-
resize(out_size, skip_empty_masks=
False)[source]¶ Resizes current Annotation.
- Parameters
- Returns
New instance of Annotation
- Return type
- class
Annotation
- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) new_img = copy.deepcopy(img) new_img = sly.imaging.image.resize(new_img, (100, 200)) # Draw Annotation on image before resize ann.draw_pretty(img, thickness=3) # Resize # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable resized_ann = ann.resize((100, 200)) # Draw Annotation on image after resize resized_ann.draw_pretty(new_img, thickness=3)
Before¶
After¶
- rotate(rotator)[source]¶
Rotates current Annotation.
- Parameters
- rotator : ImageRotator
ImageRotator object.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly from supervisely.geometry.image_rotator import ImageRotator address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) new_img = copy.deepcopy(img) new_img = sly.imaging.image.rotate(new_img, 10) # Draw Annotation on image before rotation ann.draw_pretty(img, thickness=3) # Rotate Labels for current Annotation # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable rotator = ImageRotator(annotation.img_size, 10) rotated_ann = ann.rotate(rotator) # Draw Annotation on image after rotation rotated_ann.draw_pretty(new_img, thickness=3)
Before¶
After¶
- scale(factor)[source]¶
Scales current Annotation with the given factor.
- Parameters
- factor : float
Scale size.
- Returns
New instance of Annotation
- Return type
- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) new_img = copy.deepcopy(img) new_img = sly.imaging.image.scale(new_img, 0.55) # Draw Annotation on image before rescale ann.draw_pretty(img, thickness=3) # Scale # Remember that Annotation object is immutable, and we need to assign new instance of Annotation to a new variable rescaled_ann = ann.scale(0.55) # Draw Annotation on image after rescale rescaled_ann.draw_pretty(new_img, thickness=3)
Before¶
After¶
- classmethod stat_area(render, names, colors)[source]¶
Get statistics about color area representation on the given render for the current Annotation.
- Parameters
- Returns
Colors area representation on the given render
- Return type
- Raises
RuntimeErrorif len(names) != len(colors)- Usage Example
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image and annotation from API project_id = 888 image_id = 555555 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) ann = sly.Annotation.from_json(ann_info.annotation, meta) img = api.image.download_np(image_id) class_names = [] class_colors = [] for label in ann.labels: class_names.append(label.obj_class.name) class_colors.append(label.obj_class.color) ann.draw_pretty(img, thickness=3) ann_stats = ann.stat_area(img, class_names, class_colors) print(ann_stats) # Output: { # "lemon":0.45548266166822865, # "kiwi":0.5697047797563262, # "unlabeled":98.97481255857544, # "height":800, # "width":1067, # "channels":3 # } print(stat_area)
-
stat_class_count(class_names=
None)[source]¶ Get statistics about number of each class in Annotation.
- Parameters
- class_names : List[str], optional
List of classes names.
- Returns
Number of each class in Annotation and total number of classes
- Return type
defaultdict- Usage Example
import supervisely as sly # Create object classes class_kiwi = sly.ObjClass('kiwi', sly.Rectangle) class_lemon = sly.ObjClass('lemon', sly.Rectangle) # Create labels label_kiwi = sly.Label(sly.Rectangle(100, 100, 700, 900), class_kiwi) label_lemon = sly.Label(sly.Rectangle(200, 200, 500, 600), class_lemon) labels_arr = [label_kiwi, label_lemon] # Create annotation height, width = 300, 400 ann = sly.Annotation((height, width), labels_arr) stat_class = ann.stat_class_count(['lemon', 'kiwi']) # Output: defaultdict(<class 'int'>, {'lemon': 1, 'kiwi': 1, 'total': 2})
-
to_coco(coco_image_id, class_mapping, coco_ann=
None, last_label_id=None, coco_captions=None, last_caption_id=None)[source]¶ Convert Supervisely annotation to COCO format annotation (“annotations” field).
- Parameters
- coco_image_id : int
Image id in COCO format.
- class_mapping : Dict[str, int]
Dictionary that maps class names to class ids.
- coco_ann : Union[Dict, List], optional
COCO annotation in dictionary or list format to append new annotations.
- last_label_id : int, optional
Last label id in COCO format to continue counting.
- coco_captions : Union[Dict, List], optional
COCO captions in dictionary or list format to append new captions.
- Returns
Tuple with list of COCO objects and list of COCO captions.
- Return type
- Usage example
import supervisely as sly coco_instances = dict( info=dict( description="COCO dataset converted from Supervisely", url="None", version=str(1.0), year=2025, contributor="Supervisely", date_created="2025-01-01 00:00:00", ), licenses=[dict(url="None", id=0, name="None")], images=[], annotations=[], categories=get_categories_from_meta(meta), # [{"supercategory": "lemon", "id": 0, "name": "lemon"}, ...] ) ann = sly.Annotation.from_json(ann_json, meta) image_id = 11 label_id = 222 class_mapping = {obj_cls.name: idx for idx, obj_cls in enumerate(meta.obj_classes)} curr_coco_ann, _ = ann.to_coco(image_id, class_mapping, coco_instances, label_id) # or # curr_coco_ann, _ = ann.to_coco(image_id, class_mapping, label_id=label_id) # coco_instances["annotations"].extend(curr_coco_ann) label_id += len(curr_coco_ann) image_id += 1
- to_detection_task(mapping)[source]¶
Convert Annotation classes geometries according to mapping dict and checking nonoverlapping masks. Converting possible only to Bitmap or Rectangle.
- Returns
New instance of Annotation
- Return type
-
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image annotation from API project_id = 7548 image_id = 2254942 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) print(json.dumps(ann_info.annotation, indent=4)) # Output: { # "description": "", # "tags": [], # "size": { # "height": 800, # "width": 1067 # }, # "objects": [ # { # "id": 56656282, # "classId": 122357, # "description": "", # "geometryType": "bitmap", # "labelerLogin": "alex", # "createdAt": "2021-10-15T11:01:40.805Z", # "updatedAt": "2021-10-15T11:01:40.805Z", # "tags": [], # "classTitle": "lemon", # "bitmap": { # "data": "eJwBuwJE/YlQTkcNChoKAAAADUlIRFIAAAE3AAAApgEDAAAAhaFaIwAAAAZQTFRFAAAA..., # "origin": [ # 589, # 372 # ] # } # }, # { # "id": 56656281, # "classId": 122356, # "description": "", # "geometryType": "polygon", # "labelerLogin": "alex", # "createdAt": "2021-10-15T13:22:58.178Z", # "updatedAt": "2021-10-15T13:22:58.178Z", # "tags": [], # "classTitle": "kiwi", # "points": { # "exterior": [ # [ # 719, # 115 # ], # ... # [ # 732, # 123 # ] # ], # "interior": [] # } # }, # { # "id": 56656280, # "classId": 122356, # "description": "", # "geometryType": "polygon", # "labelerLogin": "alex", # "createdAt": "2021-10-15T13:22:58.178Z", # "updatedAt": "2021-10-15T13:22:58.178Z", # "tags": [], # "classTitle": "kiwi", # "points": { # "exterior": [ # [ # 250, # 216 # ], # ... # [ # 278, # 212 # ] # ], # "interior": [] # } # }, # { # "id": 56656279, # "classId": 122356, # "description": "", # "geometryType": "polygon", # "labelerLogin": "alex", # "createdAt": "2021-10-15T13:22:58.177Z", # "updatedAt": "2021-10-15T13:22:58.177Z", # "tags": [], # "classTitle": "kiwi", # "points": { # "exterior": [ # [ # 554, # 581 # ], # ... # [ # 560, # 587 # ] # ], # "interior": [] # } # } # ] # } ann = sly.Annotation.from_json(ann_info.annotation, meta) # Create mapping for classes converting. Let's convert classes to Rectangle. mapping = {} for label in ann.labels: if label.obj_class.name not in mapping: new_obj_class = sly.ObjClass(label.obj_class.name, Rectangle) mapping[label.obj_class] = new_obj_class det_ann = ann.to_detection_task(mapping) print(json.dumps(det_ann.to_json(), indent=4)) # Output: { # "description": "", # "size": { # "height": 800, # "width": 1067 # }, # "tags": [], # "objects": [ # { # "classTitle": "lemon", # "description": "", # "tags": [], # "points": { # "exterior": [ # [ # 589, # 372 # ], # [ # 899, # 537 # ] # ], # "interior": [] # }, # "geometryType": "rectangle", # "shape": "rectangle" # }, # { # "classTitle": "kiwi", # "description": "", # "tags": [], # "points": { # "exterior": [ # [ # 612, # 110 # ], # [ # 765, # 282 # ] # ], # "interior": [] # }, # "geometryType": "rectangle", # "shape": "rectangle" # }, # { # "classTitle": "kiwi", # "description": "", # "tags": [], # "points": { # "exterior": [ # [ # 196, # 212 # ], # [ # 352, # 380 # ] # ], # "interior": [] # }, # "geometryType": "rectangle", # "shape": "rectangle" # }, # { # "classTitle": "kiwi", # "description": "", # "tags": [], # "points": { # "exterior": [ # [ # 425, # 561 # ], # [ # 576, # 705 # ] # ], # "interior": [] # }, # "geometryType": "rectangle", # "shape": "rectangle" # } # ], # "customBigData": {} # }
-
to_indexed_color_mask(mask_path, palette=
1, colors=256)[source]¶ Draw current Annotation on image and save it in PIL format.
- to_json()[source]¶
Convert the Annotation to a json dict. Read more about Supervisely format.
- Returns
Json format as a dict
- Return type
- Usage example
import supervisely as sly ann = sly.Annotation((500, 700)) ann_json = ann.to_json() print(ann_json) # Output: { # "description": "", # "size": { # "height": 500, # "width": 700 # }, # "tags": [], # "objects": [], # "customBigData": {} # }
- to_nonoverlapping_masks(mapping)[source]¶
Create new annotation with non-overlapping labels masks. Convert classes to Bitmap or skip them.
- Parameters
- Returns
New instance of Annotation
- Return type
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image annotation from API project_id = 7548 image_id = 2254937 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) print(json.dumps(ann_info.annotation, indent=4)) # Output: { # "description": "", # "tags": [], # "size": { # "height": 800, # "width": 1067 # }, # "objects": [ # { # "id": 56656282, # "classId": 122357, # "description": "", # "geometryType": "bitmap", # "labelerLogin": "alex", # "createdAt": "2021-10-15T11:01:40.805Z", # "updatedAt": "2021-10-15T11:01:40.805Z", # "tags": [], # "classTitle": "lemon", # "bitmap": { # "data": "eJwBuwJE/YlQTkcNChoKAAAADUlIRFIAAAE3AAAApgEDAAAAhaFaIwAAAAZQTFRFAAAA..., # "origin": [ # 589, # 372 # ] # } # }, # { # "id": 56656281, # "classId": 122356, # "description": "", # "geometryType": "polygon", # "labelerLogin": "alex", # "createdAt": "2021-10-15T13:22:58.178Z", # "updatedAt": "2021-10-15T13:22:58.178Z", # "tags": [], # "classTitle": "kiwi", # "points": { # "exterior": [ # [ # 719, # 115 # ], # ... # [ # 732, # 123 # ] # ], # "interior": [] # } # }, # { # "id": 56656280, # "classId": 122356, # "description": "", # "geometryType": "polygon", # "labelerLogin": "alex", # "createdAt": "2021-10-15T13:22:58.178Z", # "updatedAt": "2021-10-15T13:22:58.178Z", # "tags": [], # "classTitle": "kiwi", # "points": { # "exterior": [ # [ # 250, # 216 # ], # ... # [ # 278, # 212 # ] # ], # "interior": [] # } # }, # { # "id": 56656279, # "classId": 122356, # "description": "", # "geometryType": "polygon", # "labelerLogin": "alex", # "createdAt": "2021-10-15T13:22:58.177Z", # "updatedAt": "2021-10-15T13:22:58.177Z", # "tags": [], # "classTitle": "kiwi", # "points": { # "exterior": [ # [ # 554, # 581 # ], # ... # [ # 560, # 587 # ] # ], # "interior": [] # } # } # ] # } ann = sly.Annotation.from_json(ann_info.annotation, meta) # Create mapping. Let's check 'kiwi' classes and skip 'lemon' classes. mapping = {} for label in ann.labels: if label.obj_class.name not in mapping: if label.obj_class.name == 'lemon': mapping[label.obj_class] = None else: new_obj_class = sly.ObjClass(label.obj_class.name, Bitmap) mapping[label.obj_class] = new_obj_class nonoverlap_ann = ann.to_nonoverlapping_masks(mapping) print(json.dumps(nonoverlap_ann.to_json(), indent=4)) # Output: { # "description": "", # "size": { # "height": 800, # "width": 1067 # }, # "tags": [], # "objects": [ # { # "classTitle": "kiwi", # "description": "", # "tags": [], # "bitmap": { # "origin": [ # 187, # 396 # ], # "data": "eJwBLALT/YlQTkcNChoKAAAADUlIRFIAAACuAAAAzgEDAAAAnTar9wAAAAZQTFRFAAAA... # }, # "shape": "bitmap", # "geometryType": "bitmap" # }, # { # "classTitle": "kiwi", # "description": "", # "tags": [], # "bitmap": { # "origin": [ # 365, # 385 # ], # "data": "eJwB4gEd/olQTkcNChoKAAAADUlIRFIAAACbAAAAwgEDAAAAZC4i8AAAAAZQTFRFAAAA... # }, # "shape": "bitmap", # "geometryType": "bitmap" # }, # { # "classTitle": "kiwi", # "description": "", # "tags": [], # "bitmap": { # "origin": [ # 469, # 506 # ], # "data": "eJwBHgLh/YlQTkcNChoKAAAADUlIRFIAAAC1AAAArQEDAAAAzBisHAAAAAZQTFRFAAAA... # }, # "shape": "bitmap", # "geometryType": "bitmap" # } # ], # "customBigData": {} # }
- to_pascal_voc(image_name)[source]¶
Convert Supervisely annotation to Pascal VOC format annotation (“annotations” field).
- Parameters
- ann :
Annotation Supervisely annotation.
- image_name :
str Image name.
- ann :
- Returns
Tuple with xml tree and instance and class masks in PIL.Image format.
- Return type
Tuple- Usage example
import supervisely as sly from supervisely.convert.image.pascal_voc.pascal_voc_helper import sly_ann_to_pascal_voc ann = sly.Annotation.from_json(ann_json, meta) xml_tree, instance_mask, class_mask = sly_ann_to_pascal_voc(ann, image_name)
- to_segmentation_task()[source]¶
Convert Annotation classes by joining labels with same object classes to one label. Applies to Bitmap only.
- Returns
New instance of Annotation
- Return type
-
import supervisely as sly address = 'https://app.supervise.ly/' token = 'Your Supervisely API Token' api = sly.Api(address, token) # Get image annotation from API project_id = 7473 image_id = 2223200 meta_json = api.project.get_meta(project_id) meta = sly.ProjectMeta.from_json(meta_json) ann_info = api.annotation.download(image_id) print(json.dumps(ann_info.annotation, indent=4)) # Output: { # "description": "", # "tags": [], # "size": { # "height": 800, # "width": 1067 # }, # "objects": [ # { # "id": 57388829, # "classId": 121405, # "description": "", # "geometryType": "bitmap", # "labelerLogin": "alex", # "createdAt": "2022-01-02T08:06:05.183Z", # "updatedAt": "2022-01-02T08:07:12.219Z", # "tags": [], # "classTitle": "kiwi", # "bitmap": { # "data": "eJyNlWs4lHkYxv/z8qp5NZuYSZes1KZm6CDSTuP0mh2ZWYdmULakdBUzkRqRrDG8M1vZZg8W..., # "origin": [ # 481, # 543 # ] # } # }, # { # "id": 57388831, # "classId": 121404, # "description": "", # "geometryType": "bitmap", # "labelerLogin": "alex", # "createdAt": "2022-01-02T08:06:59.133Z", # "updatedAt": "2022-01-02T08:07:12.219Z", # "tags": [], # "classTitle": "lemon", # "bitmap": { # "data": "eJwdV388k/sXfzwmz3TVNm3l94z5saQoXFSG+c26tM1GYjWUISWKK201SchvIcq30tX2rD..., # "origin": [ # 523, # 119 # ] # } # }, # { # "id": 57388832, # "classId": 121405, # "description": "", # "geometryType": "bitmap", # "labelerLogin": "alex", # "createdAt": "2022-01-02T08:07:12.104Z", # "updatedAt": "2022-01-02T08:07:12.104Z", # "tags": [], # "classTitle": "kiwi", # "bitmap": { # "data": "eJw1VglQU8kWTWISHzH6AzwwIEsSCBr2AApBloAJhB0eCEIYDJFN2WHACC7sRghbIAiIMuKj..., # "origin": [ # 773, # 391 # ] # } # } # ] # } ann = sly.Annotation.from_json(ann_info.annotation, meta) segm_ann = ann.to_segmentation_task() print(json.dumps(segm_ann.to_json(), indent=4)) # Output: { # "description": "", # "size": { # "height": 800, # "width": 1067 # }, # "tags": [], # "objects": [ # { # "classTitle": "kiwi", # "description": "", # "tags": [], # "bitmap": { # "origin": [ # 481, # 391 # ], # "data": "eJwBagSV+4lQTkcNChoKAAAADUlIRFIAAAHpAAABOQEDAAAAjj5K+wAAAAZQTFRFAAAA... # }, # "shape": "bitmap", # "geometryType": "bitmap" # }, # { # "classTitle": "lemon", # "description": "", # "tags": [], # "bitmap": { # "origin": [ # 523, # 119 # ], # "data": "eJwBOAPH/IlQTkcNChoKAAAADUlIRFIAAAEsAAABCQEDAAAAFNKIswAAAAZQTFRFAAAA... # }, # "shape": "bitmap", # "geometryType": "bitmap" # } # ], # "customBigData": {} # }
-
to_yolo(class_names, task_type=
'detection')[source]¶ Convert Supervisely annotation to YOLO annotation format. Returns a list of strings, each string represents one object.
- Parameters
- Returns
List of objects in YOLO format.
- Return type
- Usage example
import supervisely as sly ann = sly.Annotation.from_json(ann_json, meta) class_names = [obj_cls.name for obj_cls in meta.obj_classes] yolo_lines = ann.to_yolo(class_names, task_type="segmentation")
-
transform_labels(label_transform_fn, new_size=
None)[source]¶ Transform labels and change image size in current Annotation object and return the copy of the current Annotation object. :param label_transform_fn: function for transform labels :type new_size:
Optional[Tuple[int,int]] :param new_size: new image size :rtype:Annotation:return: Annotation class object with new labels and image size
- property custom_data¶
- property image_id¶
Id of the image.
- Returns
Image id
- Return type
- Usage example
height, width = 300, 400 image_id = 12345 ann = sly.Annotation((height, width), image_id=image_id) print(ann.image_id) # Output: 12345
- property img_description¶
Image description.
- Returns
Image description
- Return type
- Usage example
ann = sly.Annotation((500, 700), img_description='Annotation for this image is empty') print(ann.img_description) # Output: Annotation for this image is empty
- property img_size¶
Size of the image (height, width).
- Returns
Image size
- Return type
Tuple[int, int]- Usage example
height, width = 300, 400 ann = sly.Annotation((height, width)) print(ann.img_size) # Output: (300, 400)
- property img_tags¶
Image tags.
- Returns
TagCollection object
- Return type
- Usage example
# Create TagCollection meta_weather = sly.TagMeta('weather', sly.TagValueType.ANY_STRING) tag_weather = sly.Tag(meta_weather, 'cloudy') tags = sly.TagCollection([tag_weather]) ann = sly.Annotation((300, 400), img_tags=tags) print(ann.img_tags) # Output: # Tags: # +----------------+------------+--------+ # | Name | Value type | Value | # +----------------+------------+--------+ # | weather | any_string | cloudy | # +----------------+------------+--------+
- property labels¶
Labels on annotation.
- Returns
Copy of list with image labels
- Return type
- Usage example
# Create Labels and add them to Annotation class_kiwi = sly.ObjClass('kiwi', sly.Rectangle) label_kiwi = sly.Label(sly.Rectangle(0, 0, 300, 300), class_kiwi) class_lemon = sly.ObjClass('lemon', sly.Rectangle) label_lemon = sly.Label(sly.Rectangle(0, 0, 500, 600), class_lemon) labels_arr = [label_kiwi, label_lemon] height, width = 300, 400 ann = sly.Annotation((height, width), labels_arr) # Note that ann.labels return a COPY of list with image labels class_potato = sly.ObjClass('potato', sly.Rectangle) label_potato = sly.Label(sly.Rectangle(0, 0, 200, 400), class_potato) ann.labels.append(label_potato) print(len(ann.labels)) # Output: 2 ann_arr = ann.labels ann_arr.append(label_potato) print(len(ann_arr)) # Output: 3
- property pixelwise_scores_labels¶