code: def yolo4_output_processing(feature_maps):
      STRIDES = [8, 16, 32]
      ANCHORS = get_anchors(
          [
              12,
              16,
              19,
              36,
              40,
              28,
              36,
              75,
              76,
              55,
              72,
              146,
              142,
              110,
              192,
              243,
              459,
              401,
          ]
      )
      NUM_CLASS = 80
      XYSCALE = [1.2, 1.1, 1.05]
      iou_threshold = 0.45
      score_threshold = 0.25
  
      bbox_tensors = []
      prob_tensors = []
      score_thres = 0.2
      for i, fm in enumerate(feature_maps):
          if i == 0:
              output_tensors = decode(
                  fm, 416 // 8, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE
              )
          elif i == 1:
              output_tensors = decode(
                  fm, 416 // 16, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE
              )
          else:
              output_tensors = decode(
                  fm, 416 // 32, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE
              )
          bbox_tensors.append(output_tensors[0])
          prob_tensors.append(output_tensors[1])
      pred_bbox = tf.concat(bbox_tensors, axis=1)
      pred_prob = tf.concat(prob_tensors, axis=1)
      boxes, pred_conf = filter_boxes(
          pred_bbox,
          pred_prob,
          score_threshold=score_thres,
          input_shape=tf.constant([416, 416]),
      )
      pred = {"concat": tf.concat([boxes, pred_conf], axis=-1)}
  
      for key, value in pred.items():
          boxes = value[:, :, 0:4]
          pred_conf = value[:, :, 4:]
  
      (
          boxes,
          scores,
          classes,
          valid_detections,
      ) = tf.image.combined_non_max_suppression(
          boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
          scores=tf.reshape(
              pred_conf,
              (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1]),
          ),
          max_output_size_per_class=50,
          max_total_size=50,
          iou_threshold=iou_threshold,
          score_threshold=score_threshold,
      )
      output = [
          boxes.numpy(),
          scores.numpy(),
          classes.numpy(),
          valid_detections.numpy(),
      ]
      return output
  
file_path: TensorLayer/tensorlayer/app/computer_vision_object_detection/common.py
imports: import tensorflow
  import colorsys, random, cv2
  import numpy
label_desc: Use image node_id 9 for calling a global function with example usage: yolo4_output_processing(feature_maps) and returns: output