Print probabilities and class label tensor flow object detetcion API

Question

I am following tensorflow tutorial and got predictions against every image what i want is to get the class and prediction probabilities

https://github.com/tensorflow/models

I am follwing the above tutorial using this piece of code i got detection box ,label and probability in my image

code:

   for image_path in TEST_IMAGE_PATHS:
  image = Image.open(image_path)
  # the array based representation of the image will be used later in order to prepare the
  # result image with boxes and labels on it.
  image_np = load_image_into_numpy_array(image)
  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
  image_np_expanded = np.expand_dims(image_np, axis=0)
  # Actual detection.
  output_dict = run_inference_for_single_image(image_np, detection_graph)
  # Visualization of the results of a detection.
  vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,
      output_dict['detection_boxes'],
      output_dict['detection_classes'],
      output_dict['detection_scores'],
      category_index,
      instance_masks=output_dict.get('detection_masks'),
      use_normalized_coordinates=True,
      line_thickness=8)
  plt.figure(figsize=IMAGE_SIZE)

  plt.imshow(image_np)

want output like

{class: p, prediction:99% , boundigbox: filename,width,height,class,xmin,ymin,xmax,ymax}

Answer 1

This code should work fine:

from tensorflow.models.research.object_detection.utils import label_map_util


width = image_np.shape[1]  # Number of columns
height = image_np.shape[0]  # number of rows
category_index = label_map_util.create_category_index(categories)
for i in range(len(output_dict['detection_boxes'])):
    class_name = category_index[output_dict['detection_classes'][i]]['name']
    print("{class: %s, prediction: %s, boundingbox: %s,%i,%i,%i,%i,%i,%i,%i}"
          % (class_name,
             output_dict['detection_scores'][i],
             image_path,
             width,
             height,
             output_dict['detection_classes'][i],
             int(width * output_dict['detection_boxes'][i][1]),  # The boxes are given normalized and in row/col order
             int(height * output_dict['detection_boxes'][i][0]),
             int(width * output_dict['detection_boxes'][i][3]),
             int(height * output_dict['detection_boxes'][i][2])
             ))