modelConfig.py

import os
import tensorflow as tf
import numpy as np
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

# lbael
category_index = label_map_util.create_category_index_from_labelmap(os.getcwd()+"/tf/ssd_mobilenet_v2_320x320_coco17_tpu-8/mscoco_label_map.pbtxt", use_display_name=True)

# model
tf.keras.backend.clear_session()
a = os.getcwd()+"/tf/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model/"
model = tf.saved_model.load(f'{a}')


def run_inference_for_single_image(model, image):

  # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
  input_tensor = tf.convert_to_tensor(image)
  # The model expects a batch of images, so add an axis with `tf.newaxis`.
  input_tensor = input_tensor[tf.newaxis,...]

  # Run inference
  model_fn = model.signatures['serving_default']
  output_dict = model_fn(input_tensor)

  # All outputs are batches tensors.
  # Convert to numpy arrays, and take index [0] to remove the batch dimension.
  # We're only interested in the first num_detections.
  num_detections = int(output_dict.pop('num_detections'))
  output_dict = {key:value[0, :num_detections].numpy() 
                 for key,value in output_dict.items()}
  output_dict['num_detections'] = num_detections

  # detection_classes should be ints.
  output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)
   
  # Handle models with masks:
  if 'detection_masks' in output_dict:
    # Reframe the the bbox mask to the image size.
    detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
              output_dict['detection_masks'], output_dict['detection_boxes'],
               image.shape[0], image.shape[1])      
    detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,
                                       tf.uint8)
    output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()
    
  return output_dict


def main(img_np):

    output_dict = run_inference_for_single_image(model, img_np)

    vis_util.visualize_boxes_and_labels_on_image_array(
      img_np,
      output_dict['detection_boxes'],
      output_dict['detection_classes'],
      output_dict['detection_scores'],
      category_index,
      instance_masks=output_dict.get('detection_masks_reframed', None),
      use_normalized_coordinates=True,
      line_thickness=3)

    return img_np