third_party/tflite_support/src/tensorflow_lite_support/examples/task/vision/desktop/python/image_classifier_demo.py

# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Python demo tool for Image Classification."""

import inspect
import os.path as _os_path
import subprocess
import sys

from absl import app
from absl import flags

FLAGS = flags.FLAGS

flags.DEFINE_string('model_path', None,
                    'Absolute path to the ".tflite" image classifier model.')
flags.DEFINE_string(
    'image_path', None,
    'Absolute path to the image to classify. The image must be RGB or '
    'RGBA (grayscale is not supported). The image EXIF orientation '
    'flag, if any, is NOT taken into account.')
flags.DEFINE_integer('max_results', 5,
                     'Maximum number of classification results to display.')
flags.DEFINE_float(
    'score_threshold', 0,
    'Classification results with a confidence score below this value are '
    'rejected. If >= 0, overrides the score threshold(s) provided in the '
    'TFLite Model Metadata. Ignored otherwise.')
flags.DEFINE_string(
    'class_name_whitelist', '',
    'Comma-separated list of class names that acts as a whitelist. If '
    'non-empty, classification results whose "class_name" is not in this list '
    'are filtered out. Mutually exclusive with "class_name_blacklist".')
flags.DEFINE_string(
    'class_name_blacklist', '',
    'Comma-separated list of class names that acts as a blacklist. If '
    'non-empty, classification results whose "class_name" is in this list '
    'are filtered out. Mutually exclusive with "class_name_whitelist".')
flags.DEFINE_bool(
    'use_coral', False,
    'If true, inference will be delegated to a connected Coral Edge TPU '
    'device.')
# Required flag.
flags.mark_flag_as_required('model_path')
flags.mark_flag_as_required('image_path')

_IMAGE_CLASSIFICATION_NATIVE_PATH = _os_path.join(
    _os_path.dirname(inspect.getfile(inspect.currentframe())),
    '../image_classifier_demo')


def classify(model_path, image_path, max_results, score_threshold,
             class_name_whitelist, class_name_blacklist, use_coral):
  """Classifies input image into different categories.

  Args:
      model_path: Path to model
      image_path: Absolute path to the image to classify
      max_results: Maximum number of classification results to display
      score_threshold: Optional; Classification results with a confidence 
        score below this value are rejected
      class_name_whitelist: Optional; Comma-separated list of class names 
        that acts as a whitelist
      class_name_blacklist: Optional; Comma-separated list of class names 
        that acts as a blacklist
      use_coral: Optional; If true, inference will be delegated to a 
        connected Coral Edge TPU device
  """
  # Run the classification tool:
  subprocess.run([
      _IMAGE_CLASSIFICATION_NATIVE_PATH + ' --model_path=' + model_path +
      ' --image_path=' + image_path + ' --max_results=' + str(max_results) +
      ' --score_threshold=' + str(score_threshold) +
      ' --class_name_whitelist="' + str(class_name_whitelist) +
      '" --class_name_blacklist="' + str(class_name_blacklist) +
      '" --use_coral=' + str(use_coral)
  ],
                 shell=True,
                 check=True)


def run_main(argv):
  del argv  # Unused.
  classify(FLAGS.model_path, FLAGS.image_path, FLAGS.max_results,
           FLAGS.score_threshold, FLAGS.class_name_whitelist,
           FLAGS.class_name_blacklist, FLAGS.use_coral)


# Simple wrapper to make the code pip-friendly
def main():
  app.run(main=run_main, argv=sys.argv)


if __name__ == '__main__':
  main()