Function plot_evaluation_results

deeplabcut/utils/visualization.py:440–606

Creates labeled images using the results of inference, and saves them to an output folder. Args: df_combined: dataframe with multiindex rows ("labeled-data", video_name, image_name) and columns ("scorer", "individuals", "bodyparts", "coords"). There should be

(
    df_combined: pd.DataFrame,
    project_root: str,
    scorer: str,
    model_name: str,
    output_folder: str,
    in_train_set: bool,
    plot_unique_bodyparts: bool = False,
    mode: str = "bodypart",
    colormap: str = "rainbow",
    dot_size: int = 12,
    alpha_value: float = 0.7,
    p_cutoff: float = 0.6,
    bounding_boxes: dict | None = None,
    bboxes_cutoff: float = 0.6,
    bounding_boxes_color: str = "auto",
)

def plot_evaluation_results(
    df_combined: pd.DataFrame,
    project_root: str,
    scorer: str,
    model_name: str,
    output_folder: str,
    in_train_set: bool,
    plot_unique_bodyparts: bool = False,
    mode: str = "bodypart",
    colormap: str = "rainbow",
    dot_size: int = 12,
    alpha_value: float = 0.7,
    p_cutoff: float = 0.6,
    bounding_boxes: dict | None = None,
    bboxes_cutoff: float = 0.6,
    bounding_boxes_color: str = "auto",
) -> None:
    """Creates labeled images using the results of inference, and saves them to an
    output folder.

    Args:
        df_combined: dataframe with multiindex rows ("labeled-data", video_name,
            image_name) and columns ("scorer", "individuals", "bodyparts", "coords").
            There should be two scorers: scorer (for ground truth data) and model_name
            (for prediction data)
        project_root: the project root path
        scorer: the name of the scorer for ground truth data in df_combined
        model_name: the name of the model for predictions in df_combined
        output_folder: the name of the folder where images should be saved
        in_train_set: whether df_combined is for train set images
        plot_unique_bodyparts: whether we should plot unique bodyparts
        mode: one of {"bodypart", "individual"}. Determines the keypoint color grouping
        colormap: the colormap to use for keypoints
        dot_size: the dot size to use for keypoints
        alpha_value: the alpha value to use for keypoints
        p_cutoff: the p-cutoff for "confident" keypoints
        bounding_boxes: dictionary with df_combined rows as keys and bounding boxes
            (np array for coordinates and np array for confidence).
            None corresponds to no bounding boxes.
        bboxes_cutoff: bounding boxes confidence cutoff threshold.
        bounding_boxes_color: If plotting bounding boxes, this is the color that will be used for bounding boxes.
            If set to "auto" (default value):
                - if mode is "bodypart", the bbox color will be a default color
                - if mode is "individual", each individual's color will be used for its bounding box
    """
    if bounding_boxes is None:
        bounding_boxes = {}

    for row_index, row in df_combined.iterrows():
        if isinstance(row_index, str):
            image_rel_path = Path(row_index)
            data_folder = image_rel_path.parent.parent.name
            video = image_rel_path.parent.name
            image = image_rel_path.name
        else:
            data_folder, video, image = row_index

        image_path = Path(project_root) / data_folder / video / image