code: def draw_detections(
      image,
      boxes,
      scores,
      labels,
      color=None,
      label_to_name=None,
      score_threshold=0.5,
  ):
      """Draws detections in an image.
  
      # Arguments
          image           : The image to draw on.
          boxes           : A [N, 4] matrix (x1, y1, x2, y2).
          scores          : A list of N classification scores.
          labels          : A list of N labels.
          color           : The color of the boxes. By default the color from keras_retinanet.utils.colors.label_color will be used.
          label_to_name   : (optional) Functor for mapping a label to a name.
          score_threshold : Threshold used for determining what detections to draw.
      """
      selection = np.where(scores > score_threshold)[0]
  
      for i in selection:
          c = color if color is not None else label_color(labels[i])
          draw_box(image, boxes[i, :], color=c)
  
          # draw labels
          caption = (
              label_to_name(labels[i]) if label_to_name else labels[i]
          ) + ": {0:.2f}".format(scores[i])
          draw_caption(image, boxes[i, :], caption)
  
file_path: ImageAI/imageai_tf_deprecated/Detection/keras_retinanet/utils/visualization.py
imports: import cv2
  import numpy
  from .colors import label_color
label_desc: Use image node_id 4 for calling a global function with example usage: draw_detections(image, boxes, scores, labels, color, label_to_name, score_threshold) without return types