main_train_your_own.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Feb 23 18:08:34 2020

@author: craig
"""

from keras_segmentation.models.unet import vgg_unet
from keras_segmentation.predict import model_from_checkpoint_path

model = vgg_unet(n_classes=51 ,  input_height=416, input_width=608  )

model=model_from_checkpoint_path("weights/vgg_unet_1")


model.train(
    train_images =  "dataset1/images_prepped_train/",
    train_annotations = "dataset1/annotations_prepped_train/",
    val_images="dataset1/images_prepped_test/" ,
    val_annotations="dataset1/annotations_prepped_test/",
    verify_dataset=True,
#    load_weights="weights/vgg_unet_1.4" ,
    optimizer_name='adadelta' , do_augment=True , augmentation_name="aug_all",    
    checkpoints_path = "weights/vgg_unet_1" , epochs=10
)

# Display the model's architecture
model.summary()

# Save the entire model to a HDF5 file.
# The '.h5' extension indicates that the model should be saved to HDF5.
model.save('vgg_unet_1.h5') 


#predict an image from the training data
out = model.predict_segmentation(
    checkpoints_path="weights/vgg_unet_1" , 
    inp="dataset1/images_prepped_test/43.jpg",
    out_fname="newout.png"
)

from keras_segmentation.predict import predict_multiple


predict_multiple( 
	checkpoints_path="weights/vgg_unet_1" , 
	inp_dir="dataset1/images_prepped_test/" , 
	out_dir="weights/out/" 
)





#import matplotlib.pyplot as plt
#plt.imshow(out)
#
## evaluating the model 
#print(model.evaluate_segmentation( inp_images_dir="dataset1/images_prepped_test/"  , annotations_dir="dataset1/annotations_prepped_test/" ) )