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Commit 8cf8446b authored by Yukun Zhu's avatar Yukun Zhu Committed by aquariusjay
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Adding panoptic evaluation tools and update internal changes. (#6320) 

* Internal changes

PiperOrigin-RevId: 237183552

* update readme

PiperOrigin-RevId: 237184584
parent 05a79f5a
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Showing with 86 additions and 78 deletions
research/deeplab/export_model.py
View file @ 8cf8446b
......@@ -129,10 +129,10 @@ def main(unused_argv):
model_options=model_options,
eval_scales=FLAGS.inference_scales,
add_flipped_images=FLAGS.add_flipped_images)
predictions = tf.cast(predictions[common.OUTPUT_TYPE], tf.float32)
raw_predictions = tf.identity(
tf.cast(predictions[common.OUTPUT_TYPE], tf.float32),
_RAW_OUTPUT_NAME)
# Crop the valid regions from the predictions.
raw_predictions = tf.identity(predictions, _RAW_OUTPUT_NAME)
semantic_predictions = tf.slice(
raw_predictions,
[0, 0, 0],
......
research/deeplab/g3doc/installation.md
View file @ 8cf8446b
......@@ -26,8 +26,9 @@ The remaining libraries can be installed on Ubuntu 14.04 using via apt-get:
```bash
sudo apt-get install python-pil python-numpy
sudo pip install jupyter
sudo pip install matplotlib
pip install --user jupyter
pip install --user matplotlib
pip install --user PrettyTable
```
## Add Libraries to PYTHONPATH
......@@ -38,7 +39,13 @@ tensorflow/models/research/:
```bash
# From tensorflow/models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
# [Optional] for panoptic evaluation, you might need panopticapi:
# https://github.com/cocodataset/panopticapi
# Please clone it to a local directory ${PANOPTICAPI_DIR}
touch ${PANOPTICAPI_DIR}/panopticapi/__init__.py
export PYTHONPATH=$PYTHONPATH:${PANOPTICAPI_DIR}/panopticapi
```
Note: This command needs to run from every new terminal you start. If you wish
......
research/deeplab/train.py
View file @ 8cf8446b
......@@ -485,6 +485,7 @@ def main(unused_argv):
config=session_config,
scaffold=scaffold,
checkpoint_dir=FLAGS.train_logdir,
summary_dir=FLAGS.train_logdir,
log_step_count_steps=FLAGS.log_steps,
save_summaries_steps=FLAGS.save_summaries_secs,
save_checkpoint_secs=FLAGS.save_interval_secs,
......
research/marco/Automated_Marco.py
View file @ 8cf8446b
#!/usr/bin/python
# 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.
# ==============================================================================
import tensorflow as tf
import csv
import os
import argparse
"""
usage:
Processes all .jpg, .png, .bmp and .gif files found in the specified directory and its subdirectories.
--PATH ( Path to directory of images or path to directory with subdirectory of images). e.g Path/To/Directory/
--Model_PATH path to the tensorflow model
"""
parser = argparse.ArgumentParser(description='Crystal Detection Program')
parser.add_argument('--PATH', type=str, help='path to image directory. Recursively finds all image files in directory and sub directories') # path to image directory or containing sub directories.
parser.add_argument('--MODEL_PATH', type=str, default='./savedmodel',help='the file path to the tensorflow model ')
args = vars(parser.parse_args())
PATH = args['PATH']
model_path = args['MODEL_PATH']
crystal_images = [os.path.join(dp, f) for dp, dn, filenames in os.walk(PATH) for f in filenames if os.path.splitext(f)[1] in ['.jpg','png','bmp','gif']]
size = len(crystal_images)
def load_images(file_list):
for i in file_list:
files = open(i,'rb')
yield {"image_bytes":[files.read()]},i
iterator = load_images(crystal_images)
with open(PATH +'results.csv', 'w') as csvfile:
Writer = csv.writer(csvfile, delimiter=' ',quotechar=' ', quoting=csv.QUOTE_MINIMAL)
predicter= tf.contrib.predictor.from_saved_model(model_path)
dic = {}
k = 0
for _ in range(size):
data,name = next(iterator)
results = predicter(data)
vals =results['scores'][0]
classes = results['classes'][0]
dictionary = dict(zip(classes,vals))
print('Image path: '+ name+' Crystal: '+str(dictionary[b'Crystals'])+' Other: '+ str(dictionary[b'Other'])+' Precipitate: '+ str(dictionary[b'Precipitate'])+' Clear: '+ str(dictionary[b'Clear']))
Writer.writerow(['Image path: '+ name,'Crystal: '+str(dictionary[b'Crystals']),'Other: '+ str(dictionary[b'Other']),'Precipitate: '+ str(dictionary[b'Precipitate']),'Clear: '+ str(dictionary[b'Clear'])])
#!/usr/bin/python
# 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.
# ==============================================================================
import tensorflow as tf
import csv
import os
import argparse
"""
usage:
Processes all .jpg, .png, .bmp and .gif files found in the specified directory and its subdirectories.
--PATH ( Path to directory of images or path to directory with subdirectory of images). e.g Path/To/Directory/
--Model_PATH path to the tensorflow model
"""
parser = argparse.ArgumentParser(description='Crystal Detection Program')
parser.add_argument('--PATH', type=str, help='path to image directory. Recursively finds all image files in directory and sub directories') # path to image directory or containing sub directories.
parser.add_argument('--MODEL_PATH', type=str, default='./savedmodel',help='the file path to the tensorflow model ')
args = vars(parser.parse_args())
PATH = args['PATH']
model_path = args['MODEL_PATH']
crystal_images = [os.path.join(dp, f) for dp, dn, filenames in os.walk(PATH) for f in filenames if os.path.splitext(f)[1] in ['.jpg','png','bmp','gif']]
size = len(crystal_images)
def load_images(file_list):
for i in file_list:
files = open(i,'rb')
yield {"image_bytes":[files.read()]},i
iterator = load_images(crystal_images)
with open(PATH +'results.csv', 'w') as csvfile:
Writer = csv.writer(csvfile, delimiter=' ',quotechar=' ', quoting=csv.QUOTE_MINIMAL)
predicter= tf.contrib.predictor.from_saved_model(model_path)
dic = {}
k = 0
for _ in range(size):
data,name = next(iterator)
results = predicter(data)
vals =results['scores'][0]
classes = results['classes'][0]
dictionary = dict(zip(classes,vals))
print('Image path: '+ name+' Crystal: '+str(dictionary[b'Crystals'])+' Other: '+ str(dictionary[b'Other'])+' Precipitate: '+ str(dictionary[b'Precipitate'])+' Clear: '+ str(dictionary[b'Clear']))
Writer.writerow(['Image path: '+ name,'Crystal: '+str(dictionary[b'Crystals']),'Other: '+ str(dictionary[b'Other']),'Precipitate: '+ str(dictionary[b'Precipitate']),'Clear: '+ str(dictionary[b'Clear'])])
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