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13
docs/hyperdatasets/annotations.md
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@ -27,21 +27,22 @@ Frame labels are applied to an entire frame, not a region in a frame.
### Adding a Frame Object ### Adding a Frame Object
To add a frame object annotation to a SingleFrame, use the `SingleFrame.add_annotation` method. To add a frame object annotation to a SingleFrame, use the [`SingleFrame.add_annotation`](../references/hyperdataset/singleframe.md#add_annotation)
method.
```python ```python
# a bounding box labeled "test" at x=10,y=10 with width of 30px and height of 20px # a bounding box labeled "test" at x=10,y=10 with width of 30px and height of 20px
frame.add_annotation(box2d_xywh=(10, 10, 30, 20), labels=['test']) frame.add_annotation(box2d_xywh=(10, 10, 30, 20), labels=['test'])
``` ```
The argument `box2d_xywh` specifies the coordinates of the annotation's bounding box, and the argument `labels` specifies The `box2d_xywh` argument specifies the coordinates of the annotation's bounding box, and the `labels` argument specifies
a list of labels for the annotation. a list of labels for the annotation.
When adding an annotation there are a few options for entering the annotation's boundaries, including: When adding an annotation there are a few options for entering the annotation's boundaries, including:
* `poly2d_xy` - A list of floating points (x,y) to create for single polygon, or a list of Floating points lists for a * `poly2d_xy` - A list of floating points (x,y) to create for single polygon, or a list of floating points lists for a
complex polygon complex polygon.
* `ellipse2d_xyrrt` - A List consisting of cx, cy, rx, ry, and theta for an ellipse * `ellipse2d_xyrrt` - A List consisting of cx, cy, rx, ry, and theta for an ellipse.
* And more! See `SingleFrame.add_annotation` for further options. * And more! See [`SingleFrame.add_annotation`](../references/hyperdataset/singleframe.md#add_annotation) for further options.
### Adding a Frame Label ### Adding a Frame Label

2
docs/hyperdatasets/custom_metadata.md
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@ -30,7 +30,7 @@ frame.metadata['dangerous'] = 'no'
### Adding ROI Metadata ### Adding ROI Metadata
Metadata can be added to individual ROIs when adding an annotation to a `frame`, using the `add_annotation` Metadata can be added to individual ROIs when adding an annotation to a `frame`, using the [`SingleFrame.add_annotation`](../references/hyperdataset/singleframe.md#add_annotation)
method. method.
```python ```python

103
docs/hyperdatasets/dataset.md
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@ -39,7 +39,8 @@ with an "Example" banner in the WebApp (UI).
### Creating Datasets ### Creating Datasets
Use the `Dataset.create` method to create a Dataset. It will contain an empty version named `Current`. Use the [`Dataset.create`](../references/hyperdataset/hyperdataset.md#datasetcreate) method to create a Dataset. It will
contain an empty version named `Current`.
```python ```python
from allegroai import Dataset from allegroai import Dataset
@ -47,7 +48,8 @@ from allegroai import Dataset
myDataset = Dataset.create(dataset_name='myDataset') myDataset = Dataset.create(dataset_name='myDataset')
``` ```
Or, use the `DatasetVersion.create_new_dataset` method. Or, use the [`DatasetVersion.create_new_dataset`](../references/hyperdataset/hyperdatasetversion.md#datasetversioncreate_new_dataset)
method.
```python ```python
from allegroai import DatasetVersion from allegroai import DatasetVersion
@ -77,14 +79,17 @@ except ValueError:
Additionally, create a Dataset with tags and a description. Additionally, create a Dataset with tags and a description.
```python ```python
myDataset = DatasetVersion.create_new_dataset(dataset_name='myDataset', myDataset = DatasetVersion.create_new_dataset(
tags=['One Tag', 'Another Tag', 'And one more tag'], dataset_name='myDataset',
description='some description text') tags=['One Tag', 'Another Tag', 'And one more tag'],
description='some description text'
)
``` ```
### Accessing Current Dataset ### Accessing Current Dataset
To get the current Dataset, use the `DatasetVersion.get_current` method. To get the current Dataset, use the [`DatasetVersion.get_current`](../references/hyperdataset/hyperdatasetversion.md#datasetversionget_current)
method.
```python ```python
myDataset = DatasetVersion.get_current(dataset_name='myDataset') myDataset = DatasetVersion.get_current(dataset_name='myDataset')
@ -92,11 +97,11 @@ myDataset = DatasetVersion.get_current(dataset_name='myDataset')
### Deleting Datasets ### Deleting Datasets
Use the `Dataset.delete` method to delete a Dataset. Use the [`Dataset.delete`](../references/hyperdataset/hyperdataset.md#datasetdelete) method to delete a Dataset.
Delete an empty Dataset (no versions). Delete an empty Dataset (no versions).
```python ```python
Dataset.delete(dataset_name='MyDataset', delete_all_versions=False, force=False) Dataset.delete(dataset_name='MyDataset', delete_all_versions=False, force=False)
``` ```
@ -187,8 +192,10 @@ a snapshot named `snapshot` with the timestamp appended.
The newly created version (with a new version ID) is the current version, and its name is `NewCurrentVersionName`. The newly created version (with a new version ID) is the current version, and its name is `NewCurrentVersionName`.
```python ```python
myDataset = DatasetVersion.create_snapshot(dataset_name='MyDataset', myDataset = DatasetVersion.create_snapshot(
child_name='NewCurrentVersionName') dataset_name='MyDataset',
child_name='NewCurrentVersionName'
)
``` ```
#### Adding Metadata and Comments #### Adding Metadata and Comments
@ -198,17 +205,19 @@ Add a metadata dictionary and / or comment to a snapshot.
For example: For example:
```python ```python
myDataset = DatasetVersion.create_snapshot(dataset_name='MyDataset', myDataset = DatasetVersion.create_snapshot(
child_metadata={'abc':'1234','def':'5678'}, dataset_name='MyDataset',
child_comment='some text comment') child_metadata={'abc':'1234','def':'5678'},
child_comment='some text comment'
)
``` ```
### Creating Child Versions ### Creating Child Versions
Create a new version from any version whose status is *Published*. Create a new version from any version whose status is *Published*.
To create a new version, call the `DatasetVersion.create_version` method, and To create a new version, call the [`DatasetVersion.create_version`](../references/hyperdataset/hyperdataset.md#datasetversioncreate_version)
provide: method, and provide:
* Either the Dataset name or ID * Either the Dataset name or ID
* The parent version name or ID from which the child inherits frames * The parent version name or ID from which the child inherits frames
* The new version's name. * The new version's name.
@ -218,18 +227,22 @@ where the new version inherits the frames of the existing version. If `NewChildV
it is returned. it is returned.
```python ```python
myVersion = DatasetVersion.create_version(dataset_name='MyDataset', myVersion = DatasetVersion.create_version(
parent_version_names=['PublishedVersion'], dataset_name='MyDataset',
version_name='NewChildVersion') parent_version_names=['PublishedVersion'],
version_name='NewChildVersion'
)
``` ```
To raise a ValueError exception if `NewChildVersion` exists, set `raise_if_exists` to `True`. To raise a ValueError exception if `NewChildVersion` exists, set `raise_if_exists` to `True`.
```python ```python
myVersion = DatasetVersion.create_version(dataset_name='MyDataset', myVersion = DatasetVersion.create_version(
parent_version_names=['PublishedVersion'], dataset_name='MyDataset',
version_name='NewChildVersion', parent_version_names=['PublishedVersion'],
raise_if_exists=True)) version_name='NewChildVersion',
raise_if_exists=True
)
``` ```
### Creating Root-level Parent Versions ### Creating Root-level Parent Versions
@ -237,13 +250,16 @@ myVersion = DatasetVersion.create_version(dataset_name='MyDataset',
Create a new version at the root-level. This is a version without a parent, and it contains no frames. Create a new version at the root-level. This is a version without a parent, and it contains no frames.
```python ```python
myDataset = DatasetVersion.create_version(dataset_name='MyDataset', myDataset = DatasetVersion.create_version(
version_name='NewRootVersion') dataset_name='MyDataset',
version_name='NewRootVersion'
)
``` ```
### Getting Versions ### Getting Versions
To get a version or versions, use the `DatasetVersion.get_version` and `DatasetVersion.get_versions` To get a version or versions, use the [`DatasetVersion.get_version`](../references/hyperdataset/hyperdatasetversion.md#datasetversionget_version)
and [`DatasetVersion.get_versions`](../references/hyperdataset/hyperdatasetversion.md#datasetversionget_versions)
methods, respectively. methods, respectively.
**Getting a list of all versions** **Getting a list of all versions**
@ -255,15 +271,19 @@ myDatasetversion = DatasetVersion.get_versions(dataset_name='MyDataset')
**Getting a list of all _published_ versions** **Getting a list of all _published_ versions**
```python ```python
myDatasetversion = DatasetVersion.get_versions(dataset_name='MyDataset', myDatasetversion = DatasetVersion.get_versions(
only_published=True) dataset_name='MyDataset',
only_published=True
)
``` ```
**Getting a list of all _drafts_ versions** **Getting a list of all _drafts_ versions**
```python ```python
myDatasetversion = DatasetVersion.get_versions(dataset_name='MyDataset', myDatasetversion = DatasetVersion.get_versions(
only_draft=True) dataset_name='MyDataset',
only_draft=True
)
``` ```
**Getting the current version** **Getting the current version**
@ -277,13 +297,16 @@ myDatasetversion = DatasetVersion.get_version(dataset_name='MyDataset')
**Getting a specific version** **Getting a specific version**
```python ```python
myDatasetversion = DatasetVersion.get_version(dataset_name='MyDataset', myDatasetversion = DatasetVersion.get_version(
version_name='VersionName') dataset_name='MyDataset',
version_name='VersionName'
)
``` ```
### Deleting Versions ### Deleting Versions
Delete versions which are status *Draft* using the `Dataset.delete_version` method. Delete versions which are status *Draft* using the [`Dataset.delete_version`](../references/hyperdataset/hyperdataset.md#delete_version)
method.
```python ```python
from allegroai import Dataset from allegroai import Dataset
@ -295,12 +318,14 @@ myDataset.delete_version(version_name='VersionToDelete')
### Publishing Versions ### Publishing Versions
Publish (make read-only) versions which are status *Draft* using the `Dataset.publish_version` method. This includes the current version, if the Dataset is in Publish (make read-only) versions which are status *Draft* using the [`DatasetVersion.publish_version`](../references/hyperdataset/hyperdatasetversion.md#publish_version)
the simple version structure. method. This includes the current version, if the Dataset is in the simple version structure.
```python ```python
myVersion = DatasetVersion.get_version(dataset_name='MyDataset', myVersion = DatasetVersion.get_version(
version_name='VersionToPublish') dataset_name='MyDataset',
version_name='VersionToPublish'
)
myVersion.publish_version() myVersion.publish_version()
``` ```
@ -312,8 +337,8 @@ myVersion.publish_version()
In order to visualize masks in a dataset version, the mask values need to be mapped to their labels. Mask-label In order to visualize masks in a dataset version, the mask values need to be mapped to their labels. Mask-label
mapping is stored in a version's metadata. mapping is stored in a version's metadata.
To define the DatasetVersion level mask-label mapping, use the `set_masks_labels` method of the [DatasetVersion](dataset.md#dataset-versioning) To define the DatasetVersion level mask-label mapping, use the [`DatasetVersion.set_masks_labels`](../references/hyperdataset/hyperdatasetversion.md#set_masks_labels)
class, and input a dictionary of RGB-value tuple keys and label-list values. method, and input a dictionary of RGB-value tuple keys and label-list values.
```python ```python
from allegroai import DatasetVersion from allegroai import DatasetVersion
@ -337,7 +362,7 @@ myDatasetversion.set_masks_labels(
The mask values and labels are stored as a property in a dataset version's metadata. The mask values and labels are stored as a property in a dataset version's metadata.
```python ```python
mappping = myDatasetversion.get_metadata()['mask_labels'] mapping = myDatasetversion.get_metadata()['mask_labels']
print(mapping) print(mapping)
``` ```

172
docs/hyperdatasets/dataviews.md
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@ -143,8 +143,8 @@ myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=
### Adding Queries ### Adding Queries
To add a query to a DataView, use the `DataView.add_query` method and specify Dataset versions, To add a query to a DataView, use the [`DataView.add_query`](../references/hyperdataset/dataview.md#add_query) method
ROI and / or frame queries, and other criteria. and specify Dataset versions, ROI and / or frame queries, and other criteria.
The `dataset_name` and `version_name` arguments specify the Dataset Version. The `roi_query` and `frame_query` arguments The `dataset_name` and `version_name` arguments specify the Dataset Version. The `roi_query` and `frame_query` arguments
specify the queries. specify the queries.
@ -165,8 +165,11 @@ This example is an ROI query filtering for frames containing at least one ROI wi
myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True) myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True)
# Add a query for a Dataset version # Add a query for a Dataset version
myDataView.add_query(dataset_name='myDataset', myDataView.add_query(
version_name='myVersion', roi_query='cat') dataset_name='myDataset',
version_name='myVersion',
roi_query='cat'
)
``` ```
* ROI query for one label OR another * ROI query for one label OR another
@ -175,11 +178,17 @@ This example is an ROI query filtering for frames containing at least one ROI wi
```python ```python
# Add a query for a Dataset version # Add a query for a Dataset version
myDataView.add_query(dataset_name='myDataset', version_name='myVersion', myDataView.add_query(
roi_query='cat') dataset_name='myDataset',
version_name='myVersion',
roi_query='cat'
)
myDataView.add_query(dataset_name='myDataset', version_name='myVersion', myDataView.add_query(
roi_query='dog') dataset_name='myDataset',
version_name='myVersion',
roi_query='dog'
)
``` ```
* ROI query for one label AND another label * ROI query for one label AND another label
@ -188,8 +197,10 @@ This example is an ROI query filtering for frames containing at least one ROI wi
```python ```python
# Add a query for a Dataset version # Add a query for a Dataset version
myDataView.add_query(dataset_name='myDataset', version_name='training', myDataView.add_query(
roi_query=['Car','partly_occluded']) dataset_name='myDataset',
version_name='training',
roi_query=['Car','partly_occluded'])
``` ```
* ROI query for one label AND NOT another (Lucene query). * ROI query for one label AND NOT another (Lucene query).
@ -202,8 +213,11 @@ This example is an ROI query filtering for frames containing at least one ROI wi
# Use a Lucene Query # Use a Lucene Query
# "label" is a key in the rois dictionary of a frame # "label" is a key in the rois dictionary of a frame
# In this Lucene Query, specify two values for the label key and use a Logical AND NOT # In this Lucene Query, specify two values for the label key and use a Logical AND NOT
myDataView.add_query(dataset_name='myDataset', version_name='training', myDataView.add_query(
roi_query='label.keyword:\"Car\" AND NOT label.keyword:\"partly_occluded\"') dataset_name='myDataset',
version_name='training',
roi_query='label.keyword:\"Car\" AND NOT label.keyword:\"partly_occluded\"'
)
``` ```
#### Querying Multiple Datasets and Versions #### Querying Multiple Datasets and Versions
@ -215,23 +229,28 @@ from two versions of one Dataset, and one version of another Dataset.
# Add queries: # Add queries:
# The 1st Dataset version # The 1st Dataset version
myDataView.add_query(dataset_name='dataset_1', myDataView.add_query(
version_name='version_1', dataset_name='dataset_1',
roi_query='label.keyword:\"car\" OR label.keyword:\"truck\" OR ' version_name='version_1',
'label.keyword:\"bicycle\"') roi_query='label.keyword:\"car\" OR label.keyword:\"truck\" OR '
'label.keyword:\"bicycle\"'
)
# The 1st Dataset, but a different version # The 1st Dataset, but a different version
myDataView.add_query(dataset_name='dataset_1', myDataView.add_query(
version_name='version_2', dataset_name='dataset_1',
roi_query='label.keyword:\"car\" OR label.keyword:\"truck\" OR ' version_name='version_2',
'label.keyword:\"bicycle\"') roi_query='label.keyword:\"car\" OR label.keyword:\"truck\" OR '
'label.keyword:\"bicycle\"'
)
# A 2nd Dataset (version) # A 2nd Dataset (version)
myDataView.add_query(dataset_name='dataset_2', myDataView.add_query(
version_name='some_version', dataset_name='dataset_2',
roi_query='label.keyword:\"car\" OR label.keyword:\"truck\" OR ' version_name='some_version',
'label.keyword:\"bicycle\"') roi_query='label.keyword:\"car\" OR label.keyword:\"truck\" OR '
'label.keyword:\"bicycle\"'
)
``` ```
#### Frame Queries #### Frame Queries
@ -246,16 +265,18 @@ This example demonstrates a frame query filtering for frames containing the meta
```python ```python
# Add a frame query for frames with the meta key "city" value of "bremen" # Add a frame query for frames with the meta key "city" value of "bremen"
myDataView.add_query(dataset_name='myDataset', myDataView.add_query(
version_name='version', dataset_name='myDataset',
frame_query='meta.city:"bremen"') version_name='version',
frame_query='meta.city:"bremen"'
)
``` ```
### Controlling Query Iteration ### Controlling Query Iteration
Use `DataView.set_iteration_parameters` to manage the order, number, timing, and reproducibility of frames Use [`DataView.set_iteration_parameters`](../references/hyperdataset/dataview.md#set_iteration_parameters) to manage the
for training. order, number, timing, and reproducibility of frames for training.
#### Iterate Frames Infinitely #### Iterate Frames Infinitely
@ -280,11 +301,16 @@ myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=
# Set Iteration Parameters (overrides parameters in constructing the DataView object # Set Iteration Parameters (overrides parameters in constructing the DataView object
myDataView.set_iteration_parameters( myDataView.set_iteration_parameters(
order=IterationOrder.random, infinite=False) order=IterationOrder.random,
infinite=False
)
# Add a query for a Dataset version # Add a query for a Dataset version
myDataView.add_query(dataset_name='myDataset', myDataView.add_query(
version_name='myVersion', roi_query='cat') dataset_name='myDataset',
version_name='myVersion',
roi_query='cat'
)
``` ```
#### Iterate a Maximum Number of Frames #### Iterate a Maximum Number of Frames
@ -297,14 +323,17 @@ myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=
# Set Iteration Parameters (overrides parameters in constructing the DataView object # Set Iteration Parameters (overrides parameters in constructing the DataView object
myDataView.set_iteration_parameters( myDataView.set_iteration_parameters(
order=IterationOrder.random, infinite=False, order=IterationOrder.random,
maximum_number_of_frames=5000) infinite=False,
maximum_number_of_frames=5000
)
``` ```
### Debiasing Input Data ### Debiasing Input Data
Debias input data using the `DataView.add_query` method's `weight` argument to add weights. This Debias input data using the [`DataView.add_query`](../references/hyperdataset/dataview.md#add_query) method's `weight`
is the same `DataView.add_query` that can be used to specify Dataset versions, and ROI queries and frame queries. argument to add weights. This is the same `DataView.add_query` that can be used to specify Dataset versions, and ROI
queries and frame queries.
This example adjusts an imbalance in the input data to improve training for `Car` ROIs that are also `largely occluded` This example adjusts an imbalance in the input data to improve training for `Car` ROIs that are also `largely occluded`
(obstructed). For every frame containing at least one ROI labeled `Car`, approximately five frames containing at least (obstructed). For every frame containing at least one ROI labeled `Car`, approximately five frames containing at least
@ -334,21 +363,33 @@ The example maps `Car` (upper case "C") to `car` (lower case "c").
myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True) myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True)
# The 1st Dataset (version) - "car" with lowercase "c" # The 1st Dataset (version) - "car" with lowercase "c"
myDataView.add_query(dataset_name='myDataset', version_name='myVersion' roi_query='car') myDataView.add_query(
dataset_name='myDataset',
version_name='myVersion',
roi_query='car'
)
# The 2nd Dataset (version) - "car" with lowercase "c" # The 2nd Dataset (version) - "car" with lowercase "c"
myDataView.add_query(dataset_name='dataset_2', version_name='aVersion', myDataView.add_query(
roi_query='car') dataset_name='dataset_2',
version_name='aVersion',
roi_query='car'
)
# A 3rd Dataset (version) - "Car" with uppercase "C" # A 3rd Dataset (version) - "Car" with uppercase "C"
myDataView.add_query(dataset_name='dataset_3', version_name='training', myDataView.add_query(
roi_query='Car') dataset_name='dataset_3',
version_name='training',
roi_query='Car'
)
# Use a mapping rule to translate "Car" (uppercase) to "car" (lowercase) # Use a mapping rule to translate "Car" (uppercase) to "car" (lowercase)
myDataView.add_mapping_rule(dataset_name='dataset_3', myDataView.add_mapping_rule(
version_name='training', dataset_name='dataset_3',
from_labels=['Car'], version_name='training',
to_label='car') from_labels=['Car'],
to_label='car'
)
``` ```
### Setting Label Enumeration Values ### Setting Label Enumeration Values
@ -369,21 +410,38 @@ For example, if the labels `truck`, `van`, and `car` are mapped **to** `vehicle`
myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True) myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True)
# Add a query for a Dataset version # Add a query for a Dataset version
myDataView.add_query(dataset_name='myDataset', version_name='myVersion', myDataView.add_query(
roi_query='cat') dataset_name='myDataset',
version_name='myVersion',
roi_query='cat'
)
myDataView.add_query(dataset_name='myDataset', version_name='myVersion', myDataView.add_query(
roi_query='dog') dataset_name='myDataset',
version_name='myVersion',
roi_query='dog'
)
myDataView.add_query(dataset_name='myDataset', version_name='myVersion', myDataView.add_query(
roi_query='bird') dataset_name='myDataset',
version_name='myVersion',
roi_query='bird'
)
myDataView.add_query(dataset_name='myDataset', version_name='myVersion', myDataView.add_query(
roi_query='sheep') dataset_name='myDataset',
version_name='myVersion',
roi_query='sheep'
)
myDataView.add_query(dataset_name='myDataset', version_name='myVersion', myDataView.add_query(
roi_query='cow') dataset_name='myDataset',
version_name='myVersion',
roi_query='cow'
)
# Set the enumeration label values # Set the enumeration label values
myDataView.set_labels({"cat": 1, "dog": 2, "bird": 3, "sheep": 4, "cow": 5, "ignore": -1,}) myDataView.set_labels(
{"cat": 1, "dog": 2, "bird": 3, "sheep": 4, "cow": 5, "ignore": -1,}
)
``` ```

37
docs/hyperdatasets/frame_groups.md
View File

@ -31,8 +31,11 @@ from allegroai import FrameGroup, SingleFrame
frame_group = FrameGroup() frame_group = FrameGroup()
# Create a SingleFrame # Create a SingleFrame
frame = SingleFrame(source='https://allegro-datasets.s3.amazonaws.com/tutorials/000012.jpg', frame = SingleFrame(
width=512, height=512, preview_uri='https://allegro-datasets.s3.amazonaws.com/tutorials/000012.jpg') source='https://allegro-datasets.s3.amazonaws.com/tutorials/000012.jpg',
width=512, height=512,
preview_uri='https://allegro-datasets.s3.amazonaws.com/tutorials/000012.jpg'
)
# Add the first SingleFrame to the FrameGroup. # Add the first SingleFrame to the FrameGroup.
frame_group['FrameOne'] = frame frame_group['FrameOne'] = frame
@ -54,7 +57,9 @@ To add FrameGroups to a Dataset Version:
frame_group = FrameGroup() frame_group = FrameGroup()
# Create SingleFrame # Create SingleFrame
single_frame = SingleFrame(source='https://allegro-datasets.s3.amazonaws.com/tutorials/000012.jpg') single_frame = SingleFrame(
source='https://allegro-datasets.s3.amazonaws.com/tutorials/000012.jpg'
)
# Add the first SingleFrame to the FrameGroup. # Add the first SingleFrame to the FrameGroup.
frame_group['FrameOne'] = single_frame frame_group['FrameOne'] = single_frame
@ -74,9 +79,11 @@ To access a FrameGroup, use the `DatasetVersion.get_single_frame` method, just l
```python ```python
# Get the FrameGroup # Get the FrameGroup
frame_group = DatasetVersion.get_single_frame(frame_id='f3ed0e09bf23fc947f426a0d254c652c', frame_group = DatasetVersion.get_single_frame(
dataset_name='MyDataset', frame_id='f3ed0e09bf23fc947f426a0d254c652c',
version_name='FrameGroup') dataset_name='MyDataset',
version_name='FrameGroup'
)
``` ```
### Updating FrameGroups ### Updating FrameGroups
@ -88,8 +95,11 @@ SingleFrame needs to be referenced using its name as the key in the FrameGroup.
frames = [] frames = []
# Get the FrameGroup # Get the FrameGroup
frame_group = DatasetVersion.get_single_frame(frame_id='f3ed0e09bf23fc947f426a0d254c652c', frame_group = DatasetVersion.get_single_frame(
dataset_name='MyDataset', version_name='FrameGroup') frame_id='f3ed0e09bf23fc947f426a0d254c652c',
dataset_name='MyDataset',
version_name='FrameGroup'
)
# Add metadata by referencing the name of the SingleFrame in the FrameGroup # Add metadata by referencing the name of the SingleFrame in the FrameGroup
frame_group['FrameOne'].metadata['new_key'] = 'new_value' frame_group['FrameOne'].metadata['new_key'] = 'new_value'
@ -102,15 +112,18 @@ myVersion.update_frames(frames)
### Deleting Frames ### Deleting Frames
To delete a FrameGroup, use the `DatasetVersion.delete_frames` method, just like when deleting a To delete a FrameGroup, use the [`DatasetVersion.delete_frames`](../references/hyperdataset/hyperdatasetversion.md#delete_frames)
SingleFrame, except that a FrameGroup is being referenced. method, just like when deleting a SingleFrame, except that a FrameGroup is being referenced.
```python ```python
frames = [] frames = []
# Get the FrameGroup # Get the FrameGroup
frame_group = DatasetVersion.get_single_frame(frame_id='f3ed0e09bf23fc947f426a0d254c652c', frame_group = DatasetVersion.get_single_frame(
dataset_name='MyDataset', version_name='FrameGroup') frame_id='f3ed0e09bf23fc947f426a0d254c652c',
dataset_name='MyDataset',
version_name='FrameGroup'
)
# Delete the FrameGroup # Delete the FrameGroup
frames.append(frame_group) frames.append(frame_group)

4
docs/hyperdatasets/masks.md
View File

@ -243,8 +243,8 @@ This example shows two masks for video from a camera. The masks label cars and t
### Adding Mask Annotations ### Adding Mask Annotations
To add a mask annotation to a frame, use the `add_annotation` method of the [SingleFrame](single_frames.md) class. This To add a mask annotation to a frame, use the [`SingleFrame.add_annotation`](../references/hyperdataset/singleframe.md#add_annotation).
method is generally used to add ROI annotations, but it can also be used to add frame specific mask labels. Input the This method is generally used to add ROI annotations, but it can also be used to add frame specific mask labels. Input the
mask value as a list with the RGB values in the `mask_rgb` parameter, and a list of labels in the `labels` parameter. mask value as a list with the RGB values in the `mask_rgb` parameter, and a list of labels in the `labels` parameter.
```python ```python

47
docs/hyperdatasets/single_frames.md
View File

@ -194,7 +194,7 @@ For more information about using Frames in the WebApp, see [Working with Frames]
### Creating a SingleFrame ### Creating a SingleFrame
To create a `SingleFrame`, instantiate a `SingleFrame` class and populate it with: To create a [`SingleFrame`](../references/hyperdataset/singleframe.md), instantiate a `SingleFrame` class and populate it with:
* The URI link to the source file of the data frame * The URI link to the source file of the data frame
* A preview URI that is accessible by browser, so you will be able to visualize the data frame in the web UI * A preview URI that is accessible by browser, so you will be able to visualize the data frame in the web UI
@ -248,14 +248,16 @@ myDatasetversion.add_frames(frames)
### Accessing SingleFrames ### Accessing SingleFrames
To access a SingleFrame, use the `DatasetVersion.get_single_frame` method. To access a SingleFrame, use the [`DatasetVersion.get_single_frame`](../references/hyperdataset/hyperdatasetversion.md#datasetversionget_single_frame)
method.
```python ```python
from allegroai import DatasetVersion from allegroai import DatasetVersion
frame = DatasetVersion.get_single_frame(frame_id='dcd81d094ab44e37875c13f2014530ae', frame = DatasetVersion.get_single_frame(
dataset_name='MyDataset', # OR dataset_id='80ccb3ae11a74b91b1c6f25f98539039' frame_id='dcd81d094ab44e37875c13f2014530ae',
version_name='SingleFrame' # OR version_id='b07b626e3b6f4be7be170a2f39e14bfb' dataset_name='MyDataset', # OR dataset_id='80ccb3ae11a74b91b1c6f25f98539039'
) version_name='SingleFrame' # OR version_id='b07b626e3b6f4be7be170a2f39e14bfb'
)
``` ```
To access a SingleFrame, the following must be specified: To access a SingleFrame, the following must be specified:
@ -266,23 +268,30 @@ To access a SingleFrame, the following must be specified:
### Updating SingleFrames ### Updating SingleFrames
To update a SingleFrame: To update a SingleFrame:
* Access the SingleFrame by calling the `DatasetVersion.get_single_frame` method, * Access the SingleFrame by calling the [`DatasetVersion.get_single_frame`](../references/hyperdataset/hyperdatasetversion.md#datasetversionget_single_frame)
method
* Make changes to the frame * Make changes to the frame
* Update the frame in a DatasetVersion using the `DatasetVersion.update_frames` method. * Update the frame in a DatasetVersion using the [`DatasetVersion.update_frames`](../references/hyperdataset/hyperdatasetversion.md##update_frames)
method.
```python ```python
frames = [] frames = []
# get the SingleFrame # get the SingleFrame
frame = DatasetVersion.get_single_frame(frame_id='dcd81d094ab44e37875c13f2014530ae', frame = DatasetVersion.get_single_frame(
dataset_name='MyDataset', frame_id='dcd81d094ab44e37875c13f2014530ae',
version_name='SingleFrame') dataset_name='MyDataset',
version_name='SingleFrame'
)
# make changes to the frame # make changes to the frame
## add a new annotation ## add a new annotation
frame.add_annotation(poly2d_xy=[154, 343, 209, 343, 209, 423, 154, 423], frame.add_annotation(
labels=['tire'], metadata={'alive': 'no'}, poly2d_xy=[154, 343, 209, 343, 209, 423, 154, 423],
confidence=0.5) labels=['tire'],
metadata={'alive': 'no'},
confidence=0.5
)
## add metadata ## add metadata
frame.meta['road_hazard'] = 'yes' frame.meta['road_hazard'] = 'yes'
@ -296,14 +305,18 @@ myDatasetVersion.update_frames(frames)
### Deleting Frames ### Deleting Frames
To delete a SingleFrame, use the `DatasetVersion.delete_frames` method. To delete a SingleFrame, use the [`DatasetVersion.delete_frames`](../references/hyperdataset/hyperdatasetversion.md#delete_frames)
method.
```python ```python
frames = [] frames = []
# get the SingleFrame # get the SingleFrame
frame = DatasetVersion.get_single_frame(frame_id='f3ed0e09bf23fc947f426a0d254c652c', frame = DatasetVersion.get_single_frame(
dataset_name='MyDataset', version_name='FrameGroup') frame_id='f3ed0e09bf23fc947f426a0d254c652c',
dataset_name='MyDataset',
version_name='FrameGroup'
)
# delete the SingleFrame # delete the SingleFrame
frames.append(frame) frames.append(frame)