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📚 documentation cleanup

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SkalskiP committed 2023-06-28 14:52:13 +02:00
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commit a0c808cfdf
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supervision/dataset/core.py
+41 -39
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@@ -152,8 +152,7 @@ class DetectionDataset(BaseDataset):
) -> None:
"""
Exports the dataset to PASCAL VOC format. This method saves the images and their corresponding annotations in
PASCAL VOC format, which consists of XML files. The method allows filtering the detections based on their area
percentage.
PASCAL VOC format.
Args:
images_directory_path (Optional[str]): The path to the directory where the images should be saved.
@@ -161,10 +160,11 @@ class DetectionDataset(BaseDataset):
annotations_directory_path (Optional[str]): The path to the directory where the annotations in
PASCAL VOC format should be saved. If not provided, annotations will not be saved.
min_image_area_percentage (float): The minimum percentage of detection area relative to
the image area for a detection to be included.
the image area for a detection to be included. Argument is used only for segmentation datasets.
max_image_area_percentage (float): The maximum percentage of detection area relative to
the image area for a detection to be included.
approximation_percentage (float): The percentage of polygon points to be removed from the input polygon, in the range [0, 1).
the image area for a detection to be included. Argument is used only for segmentation datasets.
approximation_percentage (float): The percentage of polygon points to be removed from the input polygon,
in the range [0, 1). Argument is used only for segmentation datasets.
"""
if images_directory_path:
images_path = Path(images_directory_path)
@@ -323,10 +323,7 @@ class DetectionDataset(BaseDataset):
) -> None:
"""
Exports the dataset to YOLO format. This method saves the images and their corresponding
annotations in YOLO format, which is a simple text file that describes an object in the image. It also allows
for the optional saving of a data.yaml file, used in YOLOv5, that contains metadata about the dataset.
The method allows filtering the detections based on their area percentage and offers an option for polygon approximation.
annotations in YOLO format.
Args:
images_directory_path (Optional[str]): The path to the directory where the images should be saved.
@@ -336,11 +333,12 @@ class DetectionDataset(BaseDataset):
data_yaml_path (Optional[str]): The path where the data.yaml file should be saved.
If not provided, the file will not be saved.
min_image_area_percentage (float): The minimum percentage of detection area relative to
the image area for a detection to be included.
the image area for a detection to be included. Argument is used only for segmentation datasets.
max_image_area_percentage (float): The maximum percentage of detection area relative to
the image area for a detection to be included.
the image area for a detection to be included. Argument is used only for segmentation datasets.
approximation_percentage (float): The percentage of polygon points to be removed from the input polygon,
in the range [0, 1). This is useful for simplifying the annotations.
in the range [0, 1). This is useful for simplifying the annotations. Argument is used only for
segmentation datasets.
"""
if images_directory_path is not None:
save_dataset_images(
@@ -366,7 +364,7 @@ class DetectionDataset(BaseDataset):
force_masks: bool = False,
) -> DetectionDataset:
"""
Creates a Dataset instance from YOLO formatted data.
Creates a Dataset instance from COCO formatted data.
Args:
images_directory_path (str): The path to the directory containing the images.
@@ -411,30 +409,23 @@ class DetectionDataset(BaseDataset):
annotations_path: Optional[str] = None,
min_image_area_percentage: float = 0.0,
max_image_area_percentage: float = 1.0,
approximation_percentage: float = 0.0,
licenses: Optional[list] = None,
info: Optional[dict] = None,
approximation_percentage: float = 0.0
) -> None:
"""
Exports the dataset to COCO format. This method saves the images and their corresponding
annotations in COCO format, which is a simple json file that describes an object in the image.
Annotation json file also include category maps.
The method allows filtering the detections based on their area percentage and offers an option for polygon approximation.
annotations in COCO format.
Args:
images_directory_path (Optional[str]): The path to the directory where the images should be saved.
If not provided, images will not be saved.
annotations_directory_path (Optional[str]): The path to the directory where the annotations in
YOLO format should be saved. If not provided, annotations will not be saved.
annotations_path (Optional[str]): The path to COCO annotation file.
min_image_area_percentage (float): The minimum percentage of detection area relative to
the image area for a detection to be included.
the image area for a detection to be included. Argument is used only for segmentation datasets.
max_image_area_percentage (float): The maximum percentage of detection area relative to
the image area for a detection to be included.
the image area for a detection to be included. Argument is used only for segmentation datasets.
approximation_percentage (float): The percentage of polygon points to be removed from the input polygon,
in the range [0, 1). This is useful for simplifying the annotations.
licenses (Optional[str]): List of licenses for images
info (Optional[dict]): Information of Dataset as dictionary
in the range [0, 1). This is useful for simplifying the annotations. Argument is used only for
segmentation datasets.
"""
if images_directory_path is not None:
save_dataset_images(
@@ -448,33 +439,44 @@ class DetectionDataset(BaseDataset):
classes=self.classes,
min_image_area_percentage=min_image_area_percentage,
max_image_area_percentage=max_image_area_percentage,
approximation_percentage=approximation_percentage,
licenses=licenses,
info=info,
approximation_percentage=approximation_percentage
)
@classmethod
def merge(cls, dataset_list: List[DetectionDataset]) -> DetectionDataset:
"""
Merge a list of DetectionDataset objects into a single DetectionDataset object.
Merge a list of `DetectionDataset` objects into a single `DetectionDataset` object.
This method takes a list of DetectionDataset objects and combines their respective fields (`classes`, `images`,
`annotations`) into a single DetectionDataset object.
This method takes a list of `DetectionDataset` objects and combines their respective fields (`classes`, `images`,
`annotations`) into a single `DetectionDataset` object.
Args:
dataset_list (List[DetectionDataset]): A list of DetectionDataset objects to merge.
dataset_list (List[DetectionDataset]): A list of `DetectionDataset` objects to merge.
Returns:
(DetectionDataset): A single DetectionDataset object containing the merged data from the input list.
(DetectionDataset): A single `DetectionDataset` object containing the merged data from the input list.
Example:
```python
>>> from supervision import DetectionDataset
>>> import supervision as sv
>>> ds_1 = DetectionDataset(...)
>>> ds_2 = DetectionDataset(...)
>>> ds_1 = sv.DetectionDataset(...)
>>> len(ds_1)
100
>>> ds_1.classes
['dog', 'person']
>>> merged_detection_dataset = DetectionDataset.merge([ds_1, ds_2])
>>> ds_2 = sv.DetectionDataset(...)
>>> len(ds_2)
200
>>> ds_2.classes
['cat']
>>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
>>> len(ds_merged)
300
>>> ds_merged.classes
['cat', 'dog', 'person']
```
"""
merged_images, merged_annotations = {}, {}
supervision/dataset/formats/coco.py
+9 -13
View File
@@ -161,21 +161,17 @@ def save_coco_annotations(
classes: List[str],
min_image_area_percentage: float = 0.0,
max_image_area_percentage: float = 1.0,
approximation_percentage: float = 0.75,
licenses: List[dict] = None,
info: dict = None,
approximation_percentage: float = 0.75
) -> None:
Path(annotation_path).parent.mkdir(parents=True, exist_ok=True)
if not info:
info = {}
if not licenses:
licenses = [
{
"id": 1,
"url": "https://creativecommons.org/licenses/by/4.0/",
"name": "CC BY 4.0",
}
]
info = {}
licenses = [
{
"id": 1,
"url": "https://creativecommons.org/licenses/by/4.0/",
"name": "CC BY 4.0",
}
]
coco_annotations = []
coco_images = []