code: def evaluate(
      generator,
      model,
      iou_threshold=0.5,
      score_threshold=0.05,
      max_detections=100,
      save_path=None,
  ):
      """Evaluate a given dataset using a given model.
  
      # Arguments
          generator       : The generator that represents the dataset to evaluate.
          model           : The model to evaluate.
          iou_threshold   : The threshold used to consider when a detection is positive or negative.
          score_threshold : The score confidence threshold to use for detections.
          max_detections  : The maximum number of detections to use per image.
          save_path       : The path to save images with visualized detections to.
      # Returns
          A dict mapping class names to mAP scores.
      """
      # gather all detections and annotations
      all_detections, all_inferences = _get_detections(
          generator,
          model,
          score_threshold=score_threshold,
          max_detections=max_detections,
          save_path=save_path,
      )
      all_annotations = _get_annotations(generator)
      average_precisions = {}
  
      # all_detections = pickle.load(open('all_detections.pkl', 'rb'))
      # all_annotations = pickle.load(open('all_annotations.pkl', 'rb'))
      # pickle.dump(all_detections, open('all_detections.pkl', 'wb'))
      # pickle.dump(all_annotations, open('all_annotations.pkl', 'wb'))
  
      # process detections and annotations
      for label in range(generator.num_classes()):
          if not generator.has_label(label):
              continue
  
          false_positives = np.zeros((0,))
          true_positives = np.zeros((0,))
          scores = np.zeros((0,))
          num_annotations = 0.0
  
          for i in range(generator.size()):
              detections = all_detections[i][label]
              annotations = all_annotations[i][label]
              num_annotations += annotations.shape[0]
              detected_annotations = []
  
              for d in detections:
                  scores = np.append(scores, d[4])
  
                  if annotations.shape[0] == 0:
                      false_positives = np.append(false_positives, 1)
                      true_positives = np.append(true_positives, 0)
                      continue
  
                  overlaps = compute_overlap(
                      np.expand_dims(d, axis=0), annotations
                  )
                  assigned_annotation = np.argmax(overlaps, axis=1)
                  max_overlap = overlaps[0, assigned_annotation]
  
                  if (
                      max_overlap >= iou_threshold
                      and assigned_annotation
                      not in detected_annotations
                  ):
                      false_positives = np.append(false_positives, 0)
                      true_positives = np.append(true_positives, 1)
                      detected_annotations.append(assigned_annotation)
                  else:
                      false_positives = np.append(false_positives, 1)
                      true_positives = np.append(true_positives, 0)
  
          # no annotations -> AP for this class is 0 (is this correct?)
          if num_annotations == 0:
              average_precisions[label] = 0, 0
              continue
  
          # sort by score
          indices = np.argsort(-scores)
          false_positives = false_positives[indices]
          true_positives = true_positives[indices]
  
          # compute false positives and true positives
          false_positives = np.cumsum(false_positives)
          true_positives = np.cumsum(true_positives)
  
          # compute recall and precision
          recall = true_positives / num_annotations
          precision = true_positives / np.maximum(
              true_positives + false_positives, np.finfo(np.float64).eps
          )
  
          # compute average precision
          average_precision = _compute_ap(recall, precision)
          average_precisions[label] = average_precision, num_annotations
  
      # inference time
      inference_time = np.sum(all_inferences) / generator.size()
  
      return average_precisions, inference_time
  
file_path: ImageAI/imageai_tf_deprecated/Detection/keras_retinanet/utils/eval.py
imports: from .anchors import compute_overlap
  from .visualization import draw_detections, draw_annotations
  from tensorflow import keras
  import numpy
  import os
  import time
  import cv2
  import progressbar
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate(generator, model, iou_threshold, score_threshold, max_detections, save_path) and returns: average_precisions,  inference_time