Label utils module
The module kili.utils.labels provides a set of helpers to convert point, bounding box, polygon and segmentation labels.
Info
In Kili json response format, a normalized vertex is a dictionary with keys x and y and values between 0 and 1. The origin is always the top left corner of the image. The x-axis is horizontal and the y-axis is vertical with the y-axis pointing down. You can find more information about the Kili data format here.
Points
kili.utils.labels.point
Helpers to create point annotations.
normalized_point_to_point(point, img_width=None, img_height=None, origin_location='bottom_left')
Convert a Kili normalized vertex to a 2D point.
It is the inverse of the method point_to_normalized_point.
A point is a dict with keys "x" and "y", and corresponding values in pixels (int or float).
Conventions for the input point:
- The origin is the top left corner of the image.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical and goes from top to bottom.
Conventions for the output point:
- The origin is defined by the
origin_locationargument. - x-axis is horizontal and goes from left to right.
- y-axis is vertical. If
origin_locationis"top_left", it goes from top to bottom. Iforigin_locationis"bottom_left", it goes from bottom to top.
If the image width and height are provided, the output point coordinates will be scaled to the image size. If not, the method will return a point with normalized coordinates.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
point |
Dict[str, float] |
Point to convert. |
required |
img_width |
Union[int, float] |
Width of the image the point is defined in. |
None |
img_height |
Union[int, float] |
Height of the image the point is defined in. |
None |
origin_location |
typing_extensions.Literal['top_left', 'bottom_left'] |
Location of the origin of output point coordinate system. Can be either |
'bottom_left' |
Returns:
| Type | Description |
|---|---|
Dict[typing_extensions.Literal['x', 'y'], float] |
A dict with keys |
Source code in kili/utils/labels/point.py
def normalized_point_to_point(
point: Dict[str, float],
img_width: Optional[Union[int, float]] = None,
img_height: Optional[Union[int, float]] = None,
origin_location: Literal["top_left", "bottom_left"] = "bottom_left",
) -> Dict[Literal["x", "y"], float]:
# pylint: disable=line-too-long
"""Convert a Kili normalized vertex to a 2D point.
It is the inverse of the method `point_to_normalized_point`.
A point is a dict with keys `"x"` and `"y"`, and corresponding values in pixels (`int` or `float`).
Conventions for the input point:
- The origin is the top left corner of the image.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical and goes from top to bottom.
Conventions for the output point:
- The origin is defined by the `origin_location` argument.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical. If `origin_location` is `"top_left"`, it goes from top to bottom. If `origin_location` is `"bottom_left"`, it goes from bottom to top.
If the image width and height are provided, the output point coordinates will be scaled to the image size.
If not, the method will return a point with normalized coordinates.
Args:
point: Point to convert.
img_width: Width of the image the point is defined in.
img_height: Height of the image the point is defined in.
origin_location: Location of the origin of output point coordinate system. Can be either `top_left` or `bottom_left`.
Returns:
A dict with keys `"x"` and `"y"`, and corresponding values in pixels.
"""
if (img_width is None) != (img_height is None):
raise ValueError("img_width and img_height must be both None or both not None.")
if origin_location == "bottom_left":
point = {"x": point["x"], "y": 1 - point["y"]}
img_height = img_height or 1
img_width = img_width or 1
return {"x": point["x"] * img_width, "y": point["y"] * img_height}
point_to_normalized_point(point, img_width=None, img_height=None, origin_location='bottom_left')
Converts a 2D point to a Kili normalized vertex.
The output can be used to create object detection annotations. See the documentation for more details.
A point is a dict with keys "x" and "y", and corresponding values in pixels (int or float).
Conventions for the input point:
- The origin is defined by the
origin_locationargument. - x-axis is horizontal and goes from left to right.
- y-axis is vertical. If
origin_locationis"top_left", it goes from top to bottom. Iforigin_locationis"bottom_left", it goes from bottom to top.
Conventions for the output point:
- The origin is the top left corner of the image.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical and goes from top to bottom.
If the image width and height are provided, the input point coordinates will be normalized to [0, 1].
If not, the method expects the input point coordinates to be already normalized.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
point |
Dict[str, Union[int, float]] |
Point to convert. |
required |
img_width |
Union[int, float] |
Width of the image the point is defined in. |
None |
img_height |
Union[int, float] |
Height of the image the point is defined in. |
None |
origin_location |
typing_extensions.Literal['top_left', 'bottom_left'] |
Location of the origin of input point coordinate system. Can be either |
'bottom_left' |
Returns:
| Type | Description |
|---|---|
Dict[typing_extensions.Literal['x', 'y'], float] |
A dict with keys |
Example
from kili.utils.labels.point import point_to_normalized_point
normalized_point = point_to_normalized_point({"x": 5, "y": 40}, img_width=100, img_height=100)
json_response = {
"OBJECT_DETECTION_JOB": {
"annotations": [
{
"point": normalized_point,
"categories": [{"name": "CLASS_A"}],
"type": "marker",
}
]
}
}
Source code in kili/utils/labels/point.py
def point_to_normalized_point(
point: Dict[str, Union[int, float]],
img_width: Optional[Union[int, float]] = None,
img_height: Optional[Union[int, float]] = None,
origin_location: Literal["top_left", "bottom_left"] = "bottom_left",
) -> Dict[Literal["x", "y"], float]:
# pylint: disable=line-too-long
"""Converts a 2D point to a Kili normalized vertex.
The output can be used to create object detection annotations. See the [documentation](https://docs.kili-technology.com/reference/export-object-entity-detection-and-relation) for more details.
A point is a dict with keys `"x"` and `"y"`, and corresponding values in pixels (`int` or `float`).
Conventions for the input point:
- The origin is defined by the `origin_location` argument.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical. If `origin_location` is `"top_left"`, it goes from top to bottom. If `origin_location` is `"bottom_left"`, it goes from bottom to top.
Conventions for the output point:
- The origin is the top left corner of the image.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical and goes from top to bottom.
If the image width and height are provided, the input point coordinates will be normalized to `[0, 1]`.
If not, the method expects the input point coordinates to be already normalized.
Args:
point: Point to convert.
img_width: Width of the image the point is defined in.
img_height: Height of the image the point is defined in.
origin_location: Location of the origin of input point coordinate system. Can be either `top_left` or `bottom_left`.
Returns:
A dict with keys `"x"` and `"y"`, and corresponding normalized values.
!!! Example
```python
from kili.utils.labels.point import point_to_normalized_point
normalized_point = point_to_normalized_point({"x": 5, "y": 40}, img_width=100, img_height=100)
json_response = {
"OBJECT_DETECTION_JOB": {
"annotations": [
{
"point": normalized_point,
"categories": [{"name": "CLASS_A"}],
"type": "marker",
}
]
}
}
```
"""
if (img_width is None) != (img_height is None):
raise ValueError("img_width and img_height must be both None or both not None.")
if img_width is not None and img_height is not None:
point = {
"x": point["x"] / img_width,
"y": point["y"] / img_height,
}
if origin_location == "bottom_left":
point = {"x": point["x"], "y": 1 - point["y"]}
assert 0 <= point["x"] <= 1, f"Point x coordinate {point['x']} should be in [0, 1]."
assert 0 <= point["y"] <= 1, f"Point y coordinate {point['y']} should be in [0, 1]."
return {"x": point["x"], "y": point["y"]}
Bounding boxes
kili.utils.labels.bbox
Helpers to create boundingPoly rectangle annotations.
bbox_points_to_normalized_vertices(*, bottom_left, bottom_right, top_right, top_left, img_width=None, img_height=None, origin_location='bottom_left')
Converts a bounding box defined by its 4 points to normalized vertices.
The output can be used to create a boundingPoly rectangle annotation. See the documentation for more details.
A point is a dict with keys "x" and "y", and corresponding values in pixels (int or float).
Conventions for the input points:
- The origin is defined by the
origin_locationargument. - x-axis is horizontal and goes from left to right.
- y-axis is vertical. If
origin_locationis"top_left", it goes from top to bottom. Iforigin_locationis"bottom_left", it goes from bottom to top.
Conventions for the output vertices:
- The origin is the top left corner of the image.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical and goes from top to bottom.
If the image width and height are provided, the input point coordinates will be normalized to [0, 1].
If not, the method expects the input points' coordinates to be already normalized.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
bottom_left |
Dict[str, Union[int, float]] |
Bottom left point of the bounding box. |
required |
bottom_right |
Dict[str, Union[int, float]] |
Bottom right point of the bounding box. |
required |
top_right |
Dict[str, Union[int, float]] |
Top right point of the bounding box. |
required |
top_left |
Dict[str, Union[int, float]] |
Top left point of the bounding box. |
required |
img_width |
Union[int, float] |
Width of the image the bounding box is defined in. |
None |
img_height |
Union[int, float] |
Height of the image the bounding box is defined in. |
None |
origin_location |
typing_extensions.Literal['top_left', 'bottom_left'] |
Location of the origin of input point coordinate system. Can be either |
'bottom_left' |
Returns:
| Type | Description |
|---|---|
List[Dict[typing_extensions.Literal['x', 'y'], float]] |
A list of normalized vertices. |
Example
from kili.utils.labels.bbox import bbox_points_to_normalized_vertices
inputs = {
bottom_left = {"x": 0, "y": 0},
bottom_right = {"x": 10, "y": 0},
top_right = {"x": 10, "y": 10},
top_left = {"x": 0, "y": 10},
img_width = 100,
img_height = 100,
}
normalized_vertices = bbox_points_to_normalized_vertices(**inputs)
json_response = {
"OBJECT_DETECTION_JOB": {
"annotations": [
{
"boundingPoly": [{"normalizedVertices": normalized_vertices}],
"categories": [{"name": "CLASS_A"}],
"type": "rectangle",
}
]
}
}
Source code in kili/utils/labels/bbox.py
def bbox_points_to_normalized_vertices(
*,
bottom_left: Dict[str, Union[int, float]],
bottom_right: Dict[str, Union[int, float]],
top_right: Dict[str, Union[int, float]],
top_left: Dict[str, Union[int, float]],
img_width: Optional[Union[int, float]] = None,
img_height: Optional[Union[int, float]] = None,
origin_location: Literal["top_left", "bottom_left"] = "bottom_left",
) -> List[Dict[Literal["x", "y"], float]]:
# pylint: disable=line-too-long
"""Converts a bounding box defined by its 4 points to normalized vertices.
The output can be used to create a boundingPoly rectangle annotation. See the [documentation](https://docs.kili-technology.com/reference/export-object-entity-detection-and-relation#standard-object-detection) for more details.
A point is a dict with keys `"x"` and `"y"`, and corresponding values in pixels (`int` or `float`).
Conventions for the input points:
- The origin is defined by the `origin_location` argument.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical. If `origin_location` is `"top_left"`, it goes from top to bottom. If `origin_location` is `"bottom_left"`, it goes from bottom to top.
Conventions for the output vertices:
- The origin is the top left corner of the image.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical and goes from top to bottom.
If the image width and height are provided, the input point coordinates will be normalized to `[0, 1]`.
If not, the method expects the input points' coordinates to be already normalized.
Args:
bottom_left: Bottom left point of the bounding box.
bottom_right: Bottom right point of the bounding box.
top_right: Top right point of the bounding box.
top_left: Top left point of the bounding box.
img_width: Width of the image the bounding box is defined in.
img_height: Height of the image the bounding box is defined in.
origin_location: Location of the origin of input point coordinate system. Can be either `top_left` or `bottom_left`.
Returns:
A list of normalized vertices.
!!! Example
```python
from kili.utils.labels.bbox import bbox_points_to_normalized_vertices
inputs = {
bottom_left = {"x": 0, "y": 0},
bottom_right = {"x": 10, "y": 0},
top_right = {"x": 10, "y": 10},
top_left = {"x": 0, "y": 10},
img_width = 100,
img_height = 100,
}
normalized_vertices = bbox_points_to_normalized_vertices(**inputs)
json_response = {
"OBJECT_DETECTION_JOB": {
"annotations": [
{
"boundingPoly": [{"normalizedVertices": normalized_vertices}],
"categories": [{"name": "CLASS_A"}],
"type": "rectangle",
}
]
}
}
```
"""
assert bottom_left["x"] <= bottom_right["x"], "bottom_left.x must be <= bottom_right.x"
assert top_left["x"] <= top_right["x"], "top_left.x must be <= top_right.x"
if origin_location == "bottom_left":
assert bottom_left["y"] <= top_left["y"], "bottom_left.y must be <= top_left.y"
assert bottom_right["y"] <= top_right["y"], "bottom_right.y must be <= top_right.y"
elif origin_location == "top_left":
assert bottom_left["y"] >= top_left["y"], "bottom_left.y must be >= top_left.y"
assert bottom_right["y"] >= top_right["y"], "bottom_right.y must be >= top_right.y"
if (img_width is None) != (img_height is None):
raise ValueError("img_width and img_height must be both None or both not None.")
vertices = [
point_to_normalized_point(
point, img_width=img_width, img_height=img_height, origin_location=origin_location
)
for point in (bottom_left, top_left, top_right, bottom_right)
]
return vertices
normalized_vertices_to_bbox_points(normalized_vertices, img_width=None, img_height=None, origin_location='bottom_left')
Converts a rectangle normalizedVertices annotation to a bounding box defined by 4 points.
It is the inverse of the method bbox_points_to_normalized_vertices.
A point is a dict with keys "x" and "y", and corresponding values in pixels (int or float).
Conventions for the input vertices:
- The origin is the top left corner of the image.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical and goes from top to bottom.
Conventions for the output points (top_left, bottom_left, bottom_right, top_right):
- The origin is defined by the
origin_locationargument. - x-axis is horizontal and goes from left to right.
- y-axis is vertical. If
origin_locationis"top_left", it goes from top to bottom. Iforigin_locationis"bottom_left", it goes from bottom to top.
If the image width and height are provided, the output point coordinates will be scaled to the image size.
If not, the method will return the output points' coordinates normalized to [0, 1].
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
normalized_vertices |
List[Dict[str, float]] |
A list of normalized vertices. |
required |
img_width |
Union[int, float] |
Width of the image the bounding box is defined in. |
None |
img_height |
Union[int, float] |
Height of the image the bounding box is defined in. |
None |
origin_location |
typing_extensions.Literal['top_left', 'bottom_left'] |
Location of the origin of output point coordinate system. Can be either |
'bottom_left' |
Returns:
| Type | Description |
|---|---|
Dict[typing_extensions.Literal['top_left', 'bottom_left', 'bottom_right', 'top_right'], Dict[typing_extensions.Literal['x', 'y'], float]] |
A dict with keys |
Example
from kili.utils.labels.bbox import normalized_vertices_to_bbox_points
# if using raw dict label:
normalized_vertices = label["jsonResponse"]["OBJECT_DETECTION_JOB"]["annotations"][0]["boundingPoly"][0]["normalizedVertices"]
# if using parsed label:
normalized_vertices = label.jobs["OBJECT_DETECTION_JOB"].annotations[0].bounding_poly[0].normalized_vertices
img_height, img_width = 1080, 1920
bbox_points = normalized_vertices_to_bbox_points(normalized_vertices, img_width, img_height)
Source code in kili/utils/labels/bbox.py
def normalized_vertices_to_bbox_points(
normalized_vertices: List[Dict[str, float]],
img_width: Optional[Union[int, float]] = None,
img_height: Optional[Union[int, float]] = None,
origin_location: Literal["top_left", "bottom_left"] = "bottom_left",
) -> Dict[
Literal["top_left", "bottom_left", "bottom_right", "top_right"], Dict[Literal["x", "y"], float]
]:
# pylint: disable=line-too-long
"""Converts a rectangle normalizedVertices annotation to a bounding box defined by 4 points.
It is the inverse of the method `bbox_points_to_normalized_vertices`.
A point is a dict with keys `"x"` and `"y"`, and corresponding values in pixels (`int` or `float`).
Conventions for the input vertices:
- The origin is the top left corner of the image.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical and goes from top to bottom.
Conventions for the output points (`top_left`, `bottom_left`, `bottom_right`, `top_right`):
- The origin is defined by the `origin_location` argument.
- x-axis is horizontal and goes from left to right.
- y-axis is vertical. If `origin_location` is `"top_left"`, it goes from top to bottom. If `origin_location` is `"bottom_left"`, it goes from bottom to top.
If the image width and height are provided, the output point coordinates will be scaled to the image size.
If not, the method will return the output points' coordinates normalized to `[0, 1]`.
Args:
normalized_vertices: A list of normalized vertices.
img_width: Width of the image the bounding box is defined in.
img_height: Height of the image the bounding box is defined in.
origin_location: Location of the origin of output point coordinate system. Can be either `top_left` or `bottom_left`.
Returns:
A dict with keys `"top_left"`, `"bottom_left"`, `"bottom_right"`, `"top_right"`, and corresponding points.
!!! Example
```python
from kili.utils.labels.bbox import normalized_vertices_to_bbox_points
# if using raw dict label:
normalized_vertices = label["jsonResponse"]["OBJECT_DETECTION_JOB"]["annotations"][0]["boundingPoly"][0]["normalizedVertices"]
# if using parsed label:
normalized_vertices = label.jobs["OBJECT_DETECTION_JOB"].annotations[0].bounding_poly[0].normalized_vertices
img_height, img_width = 1080, 1920
bbox_points = normalized_vertices_to_bbox_points(normalized_vertices, img_width, img_height)
```
"""
if len(normalized_vertices) != 4:
raise ValueError(f"normalized_vertices must have length 4. Got {len(normalized_vertices)}.")
if (img_width is None) != (img_height is None):
raise ValueError("img_width and img_height must be both None or both not None.")
img_height = img_height or 1
img_width = img_width or 1
ret = {}
for vertex, point_name in zip(
normalized_vertices, ("bottom_left", "top_left", "top_right", "bottom_right")
):
ret[point_name] = normalized_point_to_point(
vertex, img_width=img_width, img_height=img_height, origin_location=origin_location
)
return ret
Polygon and segmentation masks
kili.utils.labels.image
OpenCV
It is recommended to install the image dependencies to use the image helpers.
pip install kili[image-utils]
Helpers to create boundingPoly polygon and semantic annotations.
mask_to_normalized_vertices(image)
Converts a binary mask to a list of normalized vertices using OpenCV cv2.findContours.
The output can be used to create "boundingPoly" polygon or semantic annotations. See the documentation for more details.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ndarray |
Binary mask. Should be an array of shape (height, width) with values in {0, 255}. |
required |
Returns:
| Type | Description |
|---|---|
Tuple |
A tuple containing a list of normalized vertices and the hierarchy of the contours (see OpenCV documentation). |
Example
import urllib.request
import cv2
from kili.utils.labels.image import mask_to_normalized_vertices
mask_url = "https://raw.githubusercontent.com/kili-technology/kili-python-sdk/main/recipes/img/HUMAN.mask.png"
urllib.request.urlretrieve(mask_url, "mask.png")
img = cv2.imread("mask.png")[:, :, 0] # keep only height and width
img[200:220, 200:220] = 0 # add a hole in the mask to test the hierarchy
contours, hierarchy = mask_to_normalized_vertices(img)
# hierarchy tells us that the first contour is the outer contour
# and the second one is the inner contour
json_response = {
"OBJECT_DETECTION_JOB": {
"annotations": [
{
"boundingPoly": [
{"normalizedVertices": contours[0]}, # outer contour
{"normalizedVertices": contours[1]}, # inner contour
],
"categories": [{"name": "A"}],
"type": "semantic",
}
]
}
}
Source code in kili/utils/labels/image.py
def mask_to_normalized_vertices(
image: np.ndarray,
) -> Tuple[List[List[Dict[str, float]]], np.ndarray]:
# pylint: disable=line-too-long
"""Converts a binary mask to a list of normalized vertices using OpenCV [cv2.findContours](https://docs.opencv.org/4.7.0/d3/dc0/group__imgproc__shape.html#gadf1ad6a0b82947fa1fe3c3d497f260e0).
The output can be used to create "boundingPoly" polygon or semantic annotations.
See the [documentation](https://docs.kili-technology.com/reference/export-object-entity-detection-and-relation#standard-object-detection) for more details.
Args:
image: Binary mask. Should be an array of shape (height, width) with values in {0, 255}.
Returns:
Tuple: A tuple containing a list of normalized vertices and the hierarchy of the contours (see [OpenCV documentation](https://docs.opencv.org/4.7.0/d9/d8b/tutorial_py_contours_hierarchy.html)).
!!! Example
```python
import urllib.request
import cv2
from kili.utils.labels.image import mask_to_normalized_vertices
mask_url = "https://raw.githubusercontent.com/kili-technology/kili-python-sdk/main/recipes/img/HUMAN.mask.png"
urllib.request.urlretrieve(mask_url, "mask.png")
img = cv2.imread("mask.png")[:, :, 0] # keep only height and width
img[200:220, 200:220] = 0 # add a hole in the mask to test the hierarchy
contours, hierarchy = mask_to_normalized_vertices(img)
# hierarchy tells us that the first contour is the outer contour
# and the second one is the inner contour
json_response = {
"OBJECT_DETECTION_JOB": {
"annotations": [
{
"boundingPoly": [
{"normalizedVertices": contours[0]}, # outer contour
{"normalizedVertices": contours[1]}, # inner contour
],
"categories": [{"name": "A"}],
"type": "semantic",
}
]
}
}
```
"""
if image.ndim > 2:
raise ValueError(f"Image should be a 2D array, got {image.ndim}D array")
unique_values = np.unique(image).tolist()
if not all(value in [0, 255] for value in unique_values):
raise ValueError(f"Image should be binary with values in {{0, 255}}, got {unique_values}")
img_height, img_width = image.shape
# pylint:disable=no-member
contours, hierarchy = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # type: ignore
contours = [
_opencv_contour_to_normalized_vertices(contour, img_width, img_height)
for contour in contours
]
hierarchy = hierarchy[0]
return contours, hierarchy
normalized_vertices_to_mask(normalized_vertices, img_width, img_height)
Converts a Kili label with normalized vertices to a binary mask.
It is the inverse of the method mask_to_normalized_vertices.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
normalized_vertices |
List[Dict[str, float]] |
A list of normalized vertices. |
required |
img_width |
Union[int, float] |
Width of the image the segmentation is defined in. |
required |
img_height |
Union[int, float] |
Height of the image the segmentation is defined in. |
required |
Returns:
| Type | Description |
|---|---|
ndarray |
A numpy array of shape (height, width) with values in {0, 255}. |
Example
from kili.utils.labels.image import normalized_vertices_to_mask
# if using raw dict label:
normalized_vertices = label["jsonResponse"]["OBJECT_DETECTION_JOB"]["annotations"][0]["boundingPoly"][0]["normalizedVertices"]
# if using parsed label:
normalized_vertices = label.jobs["OBJECT_DETECTION_JOB"].annotations[0].bounding_poly[0].normalized_vertices
img_height, img_width = 1080, 1920
mask = normalized_vertices_to_mask(normalized_vertices, img_width, img_height)
plt.imshow(mask)
plt.show()
Source code in kili/utils/labels/image.py
def normalized_vertices_to_mask(
normalized_vertices: List[Dict[str, float]],
img_width: Union[int, float],
img_height: Union[int, float],
) -> np.ndarray:
# pylint: disable=line-too-long
"""Converts a Kili label with normalized vertices to a binary mask.
It is the inverse of the method `mask_to_normalized_vertices`.
Args:
normalized_vertices: A list of normalized vertices.
img_width: Width of the image the segmentation is defined in.
img_height: Height of the image the segmentation is defined in.
Returns:
A numpy array of shape (height, width) with values in {0, 255}.
!!! Example
```python
from kili.utils.labels.image import normalized_vertices_to_mask
# if using raw dict label:
normalized_vertices = label["jsonResponse"]["OBJECT_DETECTION_JOB"]["annotations"][0]["boundingPoly"][0]["normalizedVertices"]
# if using parsed label:
normalized_vertices = label.jobs["OBJECT_DETECTION_JOB"].annotations[0].bounding_poly[0].normalized_vertices
img_height, img_width = 1080, 1920
mask = normalized_vertices_to_mask(normalized_vertices, img_width, img_height)
plt.imshow(mask)
plt.show()
```
"""
mask = np.zeros((img_height, img_width), dtype=np.uint8)
polygon = [
[
int(round(vertice["x"] * img_width)),
int(round(vertice["y"] * img_height)),
]
for vertice in normalized_vertices
]
polygon = np.array([polygon])
cv2.fillPoly(img=mask, pts=polygon, color=255) # type: ignore # pylint:disable=no-member
return mask