#!/usr/bin/env python3
# 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()
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