Image_Fracture_Organic_Detection Readme

object detection

Steps for fracture and OM detection

1. Create xml files for SEM images using Lableimg : https://github.com/ritaxiaotian/labelImg

2. Creating TFRecords:

• xml_to_csv.py: generate train.csv, test.csv

  • python3 xml_to_csv.py

  modify code:

  def main(): for directory in ['train','test']: image_path = os.path.join(os.getcwd(), 'images/{}'.format(directory)) xml_df = xml_to_csv(image_path) xml_df.to_csv('data/{}_labels.csv'.format(directory), index=None) print('Successfully converted xml to csv.')

• generate_tfrecord.py: generate tfrecord

  • From tensorflow/models/
  • Create train data:
  • python3 generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=data/train.record
  • Create test data:
  • python3 generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=data/test.record

3. Training fracture and organic matter object detector: set up configure files & pick up a model

• 3a configuring the object detection training pipeline: configure your own model, Github
• 3b use ssd_mobilenet model (https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs)

file configure

Object-Detection
-data/
--test_labels.csv
--train_labels.csv
-images/
--test/
---testingimages.jpg
--train/
---testingimages.jpg
--...yourimages.jpg
-training
-xml_to_csv.py

---

• under models/research
  protoc object_detection/protos/*.proto --python_out=.

• under models/research
  *under models/research/object_detection
  export PYTHONPATH=$PYTHONPATH:pwd:pwd/slim

• under models/research
  python3 train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config

python3 train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config

python3 train.py --logtostderr --pipeline_config_path=${PATH_TO_YOUR_PIPELINE_CONFIG} --train_dir=${PATH_TO_TRAIN_DIR}

python3 train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_resnet_v2_atrous_coco.config

Evaluation Job

PATH_TO_TRAIN_DIR = training/

PATH_TO_YOUR_PIPELINE_CONFIG = training/ssd_mobilenet_v1_pets.config

python3 eval.py --logtostderr --pipeline_config_path=${PATH_TO_YOUR_PIPELINE_CONFIG} --checkpoint_dir=${PATH_TO_TRAIN_DIR}
--eval_dir=${PATH_TO_EVAL_DIR}

python3 eval.py --logtostderr --pipeline_config_path=training/ssd_mobilenet_v1_pets.config --checkpoint_dir=training/ --eval_dir=eval/

python3 train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_coco.config

look at tensor flow training log

at terminal: models/object_detection

tensorboard --logdir=training

tensorboard --logdir=eval

4. Testing fracture and organic matter object detector

**export_inference_graph.py: **

python3 export_inference_graph.py
--input_type image_tensor
--pipeline_config_path training/ssd_mobilenet_v1_pets.config
--trained_checkpoint_prefix training/model.ckpt-100252
--output_directory ssd_mobilenet_v1_OverviewPlusFrac_inference_graph

5. Test

What model to download.

MODEL_NAME = 'ssd_mobilenet_v1_OverviewPlusFrac_inference_graph'

Path to frozen detection graph. This is the actual model that is used for the object detection.

PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

List of the strings that is used to add correct label for each box.

PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt')

NUM_CLASSES = 1 Next, we can just delete the entire Download Model section, since we don't need to download anymore.

Finally, in the Detection section, change the TEST_IMAGE_PATHS var to:

TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i))

Google Cloud: cd ..
cd xiaoran
sudo jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root