NEURAL NET OBJECT RECOGNITION NOTES 2017-03-15
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# Install tensorflow_hub
cd
mkdir imageRetraining
cd imageRetraining
pip install "tensorflow>=1.7.0"
pip install tensorflow-hub
mkdir example_code
cd example_code
curl -LO https://github.com/tensorflow/hub/raw/master/examples/image_retraining/retrain.py
python retrain.py --image_dir ../flower_photos
curl -LO https://github.com/tensorflow/tensorflow/raw/master/tensorflow/examples/label_image/label_image.py
./classifyImages.sh
# Save the retrained model locally.
mkdir theNewRetrainedModel
cd theNewRetrainedModel
mv /tmp/output_graph.pb .
mv /tmp/output_labels.txt .
# Test this with some pretty flower pictures ..
i=SOME_NICE_IMAGE_OF_A_FLOWER
# Daisy, Daisy, give me your answer, do ...
python label_image.py --graph=$DIR/theNewRetrainedModel/output_graph.pb --labels=$DIR/theNewRetrainedModel/output_labels.txt --input_layer=Placeholder --output_layer=final_result --image=$i
# Excellent!

# Now we want to set up a sub-folder of kangaroos, sheep ..
cd $HOME/openImages
# Sheep
grep 07bgp train-annotations-bbox.csv | awk -F ',' '{ print "mv */" $1 ".jpg $HOME/imageRetraining/animal_photos/sheep/" }' | bash
# Kangaroos
grep 07bgp train-annotations-bbox.csv | awk -F ',' '{ print "mv */" $1 ".jpg $HOMOE/imageRetraining/animal_photos/sheep/" }' | bash

# But not everything is a kangaroo or a sheep, so create an 'unknown' category.

mkdir animal_photos/unknown

cp $HOME/openImages/train_01/c0*jpg animal_photos/unknown/
# Hmm .. some of these might be kangaroos or sheep, so remove those.
find animal_photos/kangaroo/ -name \*jpg | sed 's/kangaroo/unknown/' | sed 's/^/rm -f /'
find animal_photos/sheep/ -name \*jpg | sed 's/sheep/unknown/' | sed 's/^/rm -f /'

# Retrain the model ..
python retrain.py --image_dir ../animal_photos
cd theNewRetrainedModel
mv /tmp/output_graph.pb .
mv /tmp/output_labels.txt .
# See if we can recognize some skippys in test_photos
DIR=$HOME/imageRetraining
for i in $DIR/test_photos/*jpg
         do
             echo ----- Look at image $i
             echo python label_image.py --graph=$DIR/theNewRetrainedModel/output_graph.pb --labels=$DIR/theNewRetrainedModel/output_labels.txt --input_layer=Placeholder --output_layer=final_result --image=$i
             python label_image.py --graph=$DIR/theNewRetrainedModel/output_graph.pb --labels=$DIR/theNewRetrainedModel/output_labels.txt --input_layer=Placeholder --output_layer=final_result --image=$i
done