omar16100 / tf_object_detection_cm

Confusion Matrix in Object Detection with TensorFlow

Confusion Matrix in Object Detection with TensorFlow

This script generates a confusion matrix using the detections record file generated by the /object_detection/inference/infer_detections.py script.

For a full description of the format of the matrix, and how it is computed, take a look at the Confusion Matrix in Object Detection with TensorFlow article.

To run this script you need a couple of things:

• The label map used by your model — This is the proto-buff file that you created in order to train your model.

• A detection record file — This is the file generated by using the /object_detection/inference/infer_detections.py. This script runs a TFRecord file through your model and saves the results in a detection record file.

Here is an example of running the script:

python confusion_matrix.py --detections_record=testing_detections.record --label_map=label_map.pbtxt --output_path=confusion_matrix.csv

The script will print the confusion matrix along with precision and recall information to the standard output and save the precision/recall results to the specified output area.

Sometimes things fail, so read up

Since I published this repository, the single most reported issue is some flavor of the following error:

Traceback (most recent call last):
  File "confusion_matrix.py", line 133, in <module>
    tf.app.run(main)
  File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "confusion_matrix.py", line 128, in main
    confusion_matrix = process_detections(FLAGS.detections_record, categories)
  File "confusion_matrix.py", line 45, in process_detections
    decoded_dict = data_parser.parse(example)
  File "/home/ubuntu/tensorflow/models/research/object_detection/metrics/tf_example_parser.py", line 153, in parse
    results_dict[key] = parser.parse(tf_example)
  File "/home/ubuntu/tensorflow/models/research/object_detection/metrics/tf_example_parser.py", line 49, in parse
    self.field_name].HasField("bytes_list") else None
TypeError: sequence item 0: expected str instance, bytes found

For some weird reason, I've been too lazy to add an answer to this documentation, so I've gotten a bunch of reports and I've kept pointing everyone to issue #1 where I explained how to solve the problem. That ends today.

The problem is with the TensorFlow library and the solution is explained here. Unfortunately, you'll have to patch your local copy of TensorFlow to get around it.

Here is the relevant section of the link that explains what you need to do (quote below):

I had same error with Python3.5 and TF1.5. This causes tf_example.features.feature[self.field_name].bytes_list.value returns byte type instead of string type in metrics/tf_example_parser.StringParser.

So I changed tf_example_parser.StringParser below

class StringParser(data_parser.DataToNumpyParser):
  """Tensorflow Example string parser."""
  def __init__(self, field_name):
    self.field_name = field_name
  def parse(self, tf_example):
    if tf_example.features.feature[self.field_name].HasField("bytes_list"):
        result = tf_example.features.feature[self.field_name].bytes_list.value
        result = "".join([x if type(x)=='str' else x.decode('utf-8') for x in result])
    else:
        result = None
    return result

I don't like to patch things, and probably you neither. But I promise this one is extremely simple. Find metrics/tf_example_parser, and make the above change. It'll take you a minute to be up and running.