.PHONY: help

VENV_NAME?=env
PYTHON=${VENV_NAME}/bin/python3
LINT_FILES=linear_regression.py \
		   lenet5.py \
		   vgg16_prepare_train_model.py \
		   vgg16_test_model.py \
		   yolov3.py

.DEFAULT: help
help:
	@echo "make linear_regression"
	@echo "       runs a linear regression example"
	@echo "make lenet5"
	@echo "       runs a LeNet-5 character recognition example"
	@echo "make vgg16"
	@echo "       runs a VGG16 object detection example"
	@echo "make yolov3"
	@echo "       runs the YOLO v3 object detection example"
	@echo "make env"
	@echo "       creates and prepares the environment"
	@echo "make data"
	@echo "       downloads and unpacks the data"
	@echo "make lint"
	@echo "       runs pylint"
	@echo "make clean"
	@echo "       cleans the development environment"

data: images
images:
	wget https://s3.us-east-2.amazonaws.com/naturalimages02/images.tar.gz
	tar -xzf images.tar.gz

env: $(VENV_NAME)/bin/activate
$(VENV_NAME)/bin/activate:
	test -d $(VENV_NAME) || python3 -m venv $(VENV_NAME)
	${PYTHON} -m pip install -U pip
	${PYTHON} -m pip install --upgrade tensorflow
	${PYTHON} -m pip install keras
	${PYTHON} -m pip install matplotlib
	${PYTHON} -m pip install keras_vggface
	${PYTHON} -m pip install pylint
	${PYTHON} -m pip install mypy
	${PYTHON} -m pip install pandas
	${PYTHON} -m pip install sklearn
	touch $(VENV_NAME)/bin/activate

linear_regression: env
	${PYTHON} linear_regression.py

lenet5: env
	${PYTHON} lenet5.py

vgg16_training: fine_tune.h5
fine_tune.h5:
	${PYTHON} vgg16_prepare_train_model.py
vgg16: env data vgg16_training
	${PYTHON} vgg16_test_model.py

yolov3: env data
	${PYTHON} yolov3.py

lint: env
	${PYTHON} -m pylint --rcfile=pylintrc $(LINT_FILES)

clean:
	rm -rf $(VENV_NAME)
	rm -f data.csv
	rm -rf images
	rm -f images.tar.gz
	rm -f fine_tune.h5
	rm -rf yolov3.h5
	rm -rf lenet5.h5
	rm -rf .mypy_cache
	rm -rf *.gz