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Save Detections

CSV Sink

A utility class for saving detection data to a CSV file. This class is designed to efficiently serialize detection objects into a CSV format, allowing for the inclusion of bounding box coordinates and additional attributes like confidence, class_id, and tracker_id.

Tip

CSVSink allow to pass custom data alongside the detection fields, providing flexibility for logging various types of information.

Parameters:

Name Type Description Default
file_name str

The name of the CSV file where the detections will be stored. Defaults to 'output.csv'.

 'output.csv'
Example 
import supervision as sv
from ultralytics import YOLO

model = YOLO(<SOURCE_MODEL_PATH>)
csv_sink = sv.CSVSink(<RESULT_CSV_FILE_PATH>)
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with csv_sink as sink:
    for frame in frames_generator:
        result = model(frame)[0]
        detections = sv.Detections.from_ultralytics(result)
        sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
Source code in supervision/detection/tools/csv_sink.py 
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class CSVSink:
    """
    A utility class for saving detection data to a CSV file. This class is designed to
    efficiently serialize detection objects into a CSV format, allowing for the
    inclusion of bounding box coordinates and additional attributes like `confidence`,
    `class_id`, and `tracker_id`.

    !!! tip

        CSVSink allow to pass custom data alongside the detection fields, providing
        flexibility for logging various types of information.

    Args:
        file_name (str): The name of the CSV file where the detections will be stored.
            Defaults to 'output.csv'.

    Example:
        ```python
        import supervision as sv
        from ultralytics import YOLO

        model = YOLO(<SOURCE_MODEL_PATH>)
        csv_sink = sv.CSVSink(<RESULT_CSV_FILE_PATH>)
        frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

        with csv_sink as sink:
            for frame in frames_generator:
                result = model(frame)[0]
                detections = sv.Detections.from_ultralytics(result)
                sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
        ```
    """  # noqa: E501 // docs

    def __init__(self, file_name: str = "output.csv") -> None:
        """
        Initialize the CSVSink instance.

        Args:
            file_name (str): The name of the CSV file.

        Returns:
            None
        """
        self.file_name = file_name
        self.file: Optional[open] = None
        self.writer: Optional[csv.writer] = None
        self.header_written = False
        self.field_names = []

    def __enter__(self) -> CSVSink:
        self.open()
        return self

    def __exit__(
        self,
        exc_type: Optional[type],
        exc_val: Optional[Exception],
        exc_tb: Optional[Any],
    ) -> None:
        self.close()

    def open(self) -> None:
        """
        Open the CSV file for writing.

        Returns:
            None
        """
        parent_directory = os.path.dirname(self.file_name)
        if parent_directory and not os.path.exists(parent_directory):
            os.makedirs(parent_directory)

        self.file = open(self.file_name, "w", newline="")
        self.writer = csv.writer(self.file)

    def close(self) -> None:
        """
        Close the CSV file.

        Returns:
            None
        """
        if self.file:
            self.file.close()

    @staticmethod
    def parse_detection_data(
        detections: Detections, custom_data: Dict[str, Any] = None
    ) -> List[Dict[str, Any]]:
        parsed_rows = []
        for i in range(len(detections.xyxy)):
            row = {
                "x_min": detections.xyxy[i][0],
                "y_min": detections.xyxy[i][1],
                "x_max": detections.xyxy[i][2],
                "y_max": detections.xyxy[i][3],
                "class_id": ""
                if detections.class_id is None
                else str(detections.class_id[i]),
                "confidence": ""
                if detections.confidence is None
                else str(detections.confidence[i]),
                "tracker_id": ""
                if detections.tracker_id is None
                else str(detections.tracker_id[i]),
            }

            if hasattr(detections, "data"):
                for key, value in detections.data.items():
                    if value.ndim == 0:
                        row[key] = value
                    else:
                        row[key] = value[i]

            if custom_data:
                row.update(custom_data)
            parsed_rows.append(row)
        return parsed_rows

    def append(
        self, detections: Detections, custom_data: Dict[str, Any] = None
    ) -> None:
        """
        Append detection data to the CSV file.

        Args:
            detections (Detections): The detection data.
            custom_data (Dict[str, Any]): Custom data to include.

        Returns:
            None
        """
        if not self.writer:
            raise Exception(
                f"Cannot append to CSV: The file '{self.file_name}' is not open."
            )
        field_names = CSVSink.parse_field_names(detections, custom_data)
        if not self.header_written:
            self.field_names = field_names
            self.writer.writerow(field_names)
            self.header_written = True

        if field_names != self.field_names:
            print(
                f"Field names do not match the header. "
                f"Expected: {self.field_names}, given: {field_names}"
            )

        parsed_rows = CSVSink.parse_detection_data(detections, custom_data)
        for row in parsed_rows:
            self.writer.writerow(
                [row.get(field_name, "") for field_name in self.field_names]
            )

    @staticmethod
    def parse_field_names(
        detections: Detections, custom_data: Dict[str, Any]
    ) -> List[str]:
        dynamic_header = sorted(
            set(custom_data.keys()) | set(getattr(detections, "data", {}).keys())
        )
        return BASE_HEADER + dynamic_header

Functions

__init__(file_name='output.csv')

Initialize the CSVSink instance.

Parameters:

Name Type Description Default
file_name str

The name of the CSV file.

 'output.csv'

Returns:

Type Description
None

None
Source code in supervision/detection/tools/csv_sink.py 
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def __init__(self, file_name: str = "output.csv") -> None:
    """
    Initialize the CSVSink instance.

    Args:
        file_name (str): The name of the CSV file.

    Returns:
        None
    """
    self.file_name = file_name
    self.file: Optional[open] = None
    self.writer: Optional[csv.writer] = None
    self.header_written = False
    self.field_names = []

append(detections, custom_data=None)

Append detection data to the CSV file.

Parameters:

Name Type Description Default
detections Detections

The detection data.

 required
custom_data Dict[str, Any]

Custom data to include.

 None

Returns:

Type Description
None

None
Source code in supervision/detection/tools/csv_sink.py 
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def append(
    self, detections: Detections, custom_data: Dict[str, Any] = None
) -> None:
    """
    Append detection data to the CSV file.

    Args:
        detections (Detections): The detection data.
        custom_data (Dict[str, Any]): Custom data to include.

    Returns:
        None
    """
    if not self.writer:
        raise Exception(
            f"Cannot append to CSV: The file '{self.file_name}' is not open."
        )
    field_names = CSVSink.parse_field_names(detections, custom_data)
    if not self.header_written:
        self.field_names = field_names
        self.writer.writerow(field_names)
        self.header_written = True

    if field_names != self.field_names:
        print(
            f"Field names do not match the header. "
            f"Expected: {self.field_names}, given: {field_names}"
        )

    parsed_rows = CSVSink.parse_detection_data(detections, custom_data)
    for row in parsed_rows:
        self.writer.writerow(
            [row.get(field_name, "") for field_name in self.field_names]
        )

close()

Close the CSV file.

Returns:

Type Description
None

None
Source code in supervision/detection/tools/csv_sink.py 
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def close(self) -> None:
    """
    Close the CSV file.

    Returns:
        None
    """
    if self.file:
        self.file.close()

open()

Open the CSV file for writing.

Returns:

Type Description
None

None
Source code in supervision/detection/tools/csv_sink.py 
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def open(self) -> None:
    """
    Open the CSV file for writing.

    Returns:
        None
    """
    parent_directory = os.path.dirname(self.file_name)
    if parent_directory and not os.path.exists(parent_directory):
        os.makedirs(parent_directory)

    self.file = open(self.file_name, "w", newline="")
    self.writer = csv.writer(self.file)

JSON Sink

A utility class for saving detection data to a JSON file. This class is designed to efficiently serialize detection objects into a JSON format, allowing for the inclusion of bounding box coordinates and additional attributes like confidence, class_id, and tracker_id.

Tip

JSONsink allow to pass custom data alongside the detection fields, providing flexibility for logging various types of information.

Parameters:

Name Type Description Default
file_name str

The name of the JSON file where the detections will be stored. Defaults to 'output.json'.

 'output.json'
Example 
import supervision as sv
from ultralytics import YOLO

model = YOLO(<SOURCE_MODEL_PATH>)
json_sink = sv.JSONSink(<RESULT_JSON_FILE_PATH>)
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with json_sink as sink:
    for frame in frames_generator:
        result = model(frame)[0]
        detections = sv.Detections.from_ultralytics(result)
        sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
Source code in supervision/detection/tools/json_sink.py 
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class JSONSink:
    """
    A utility class for saving detection data to a JSON file. This class is designed to
    efficiently serialize detection objects into a JSON format, allowing for the
    inclusion of bounding box coordinates and additional attributes like `confidence`,
    `class_id`, and `tracker_id`.

    !!! tip

        JSONsink allow to pass custom data alongside the detection fields, providing
        flexibility for logging various types of information.

    Args:
        file_name (str): The name of the JSON file where the detections will be stored.
            Defaults to 'output.json'.

    Example:
        ```python
        import supervision as sv
        from ultralytics import YOLO

        model = YOLO(<SOURCE_MODEL_PATH>)
        json_sink = sv.JSONSink(<RESULT_JSON_FILE_PATH>)
        frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

        with json_sink as sink:
            for frame in frames_generator:
                result = model(frame)[0]
                detections = sv.Detections.from_ultralytics(result)
                sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
        ```
    """  # noqa: E501 // docs

    def __init__(self, file_name: str = "output.json") -> None:
        """
        Initialize the JSONSink instance.

        Args:
            file_name (str): The name of the JSON file.

        Returns:
            None
        """
        self.file_name = file_name
        self.file: Optional[open] = None
        self.data: List[Dict[str, Any]] = []

    def __enter__(self) -> JSONSink:
        self.open()
        return self

    def __exit__(
        self,
        exc_type: Optional[type],
        exc_val: Optional[Exception],
        exc_tb: Optional[Any],
    ) -> None:
        self.write_and_close()

    def open(self) -> None:
        """
        Open the JSON file for writing.

        Returns:
            None
        """
        parent_directory = os.path.dirname(self.file_name)
        if parent_directory and not os.path.exists(parent_directory):
            os.makedirs(parent_directory)

        self.file = open(self.file_name, "w")

    def write_and_close(self) -> None:
        """
        Write and close the JSON file.

        Returns:
            None
        """
        if self.file:
            json.dump(self.data, self.file, indent=4)
            self.file.close()

    @staticmethod
    def parse_detection_data(
        detections: Detections, custom_data: Dict[str, Any] = None
    ) -> List[Dict[str, Any]]:
        parsed_rows = []
        for i in range(len(detections.xyxy)):
            row = {
                "x_min": float(detections.xyxy[i][0]),
                "y_min": float(detections.xyxy[i][1]),
                "x_max": float(detections.xyxy[i][2]),
                "y_max": float(detections.xyxy[i][3]),
                "class_id": ""
                if detections.class_id is None
                else int(detections.class_id[i]),
                "confidence": ""
                if detections.confidence is None
                else float(detections.confidence[i]),
                "tracker_id": ""
                if detections.tracker_id is None
                else int(detections.tracker_id[i]),
            }

            if hasattr(detections, "data"):
                for key, value in detections.data.items():
                    row[key] = (
                        str(value[i])
                        if hasattr(value, "__getitem__") and value.ndim != 0
                        else str(value)
                    )

            if custom_data:
                row.update(custom_data)
            parsed_rows.append(row)
        return parsed_rows

    def append(
        self, detections: Detections, custom_data: Dict[str, Any] = None
    ) -> None:
        """
        Append detection data to the JSON file.

        Args:
            detections (Detections): The detection data.
            custom_data (Dict[str, Any]): Custom data to include.

        Returns:
            None
        """
        parsed_rows = JSONSink.parse_detection_data(detections, custom_data)
        self.data.extend(parsed_rows)

Functions

__init__(file_name='output.json')

Initialize the JSONSink instance.

Parameters:

Name Type Description Default
file_name str

The name of the JSON file.

 'output.json'

Returns:

Type Description
None

None
Source code in supervision/detection/tools/json_sink.py 
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def __init__(self, file_name: str = "output.json") -> None:
    """
    Initialize the JSONSink instance.

    Args:
        file_name (str): The name of the JSON file.

    Returns:
        None
    """
    self.file_name = file_name
    self.file: Optional[open] = None
    self.data: List[Dict[str, Any]] = []

append(detections, custom_data=None)

Append detection data to the JSON file.

Parameters:

Name Type Description Default
detections Detections

The detection data.

 required
custom_data Dict[str, Any]

Custom data to include.

 None

Returns:

Type Description
None

None
Source code in supervision/detection/tools/json_sink.py 
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def append(
    self, detections: Detections, custom_data: Dict[str, Any] = None
) -> None:
    """
    Append detection data to the JSON file.

    Args:
        detections (Detections): The detection data.
        custom_data (Dict[str, Any]): Custom data to include.

    Returns:
        None
    """
    parsed_rows = JSONSink.parse_detection_data(detections, custom_data)
    self.data.extend(parsed_rows)

open()

Open the JSON file for writing.

Returns:

Type Description
None

None
Source code in supervision/detection/tools/json_sink.py 
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def open(self) -> None:
    """
    Open the JSON file for writing.

    Returns:
        None
    """
    parent_directory = os.path.dirname(self.file_name)
    if parent_directory and not os.path.exists(parent_directory):
        os.makedirs(parent_directory)

    self.file = open(self.file_name, "w")

write_and_close()

Write and close the JSON file.

Returns:

Type Description
None

None
Source code in supervision/detection/tools/json_sink.py 
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def write_and_close(self) -> None:
    """
    Write and close the JSON file.

    Returns:
        None
    """
    if self.file:
        json.dump(self.data, self.file, indent=4)
        self.file.close()

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