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@tensorflower-gardener @scottamain @nairb774 @angerson
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# Copyright 2018 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.
# ==============================================================================
"""label_image for tflite."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import numpy as np
from PIL import Image
import tensorflow as tf # TF2
def load_labels(filename):
with open(filename, 'r') as f:
return [line.strip() for line in f.readlines()]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'-i',
'--image',
default='/tmp/grace_hopper.bmp',
help='image to be classified')
parser.add_argument(
'-m',
'--model_file',
default='/tmp/mobilenet_v1_1.0_224_quant.tflite',
help='.tflite model to be executed')
parser.add_argument(
'-l',
'--label_file',
default='/tmp/labels.txt',
help='name of file containing labels')
parser.add_argument(
'--input_mean',
default=127.5, type=float,
help='input_mean')
parser.add_argument(
'--input_std',
default=127.5, type=float,
help='input standard deviation')
args = parser.parse_args()
interpreter = tf.lite.Interpreter(model_path=args.model_file)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# check the type of the input tensor
floating_model = input_details[0]['dtype'] == np.float32
# NxHxWxC, H:1, W:2
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
img = Image.open(args.image).resize((width, height))
# add N dim
input_data = np.expand_dims(img, axis=0)
if floating_model:
input_data = (np.float32(input_data) - args.input_mean) / args.input_std
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
results = np.squeeze(output_data)
top_k = results.argsort()[-5:][::-1]
labels = load_labels(args.label_file)
for i in top_k:
if floating_model:
print('{:08.6f}: {}'.format(float(results[i]), labels[i]))
else:
print('{:08.6f}: {}'.format(float(results[i] / 255.0), labels[i]))
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