import glob
from PIL import Image
from segmenter import Segmenter
image_paths = glob.glob("input/*.png") # get all images in the input folder
segmenter = Segmenter() # create an instance of the classifier
predictions = segmenter.run(
image_paths) # run the classifier to get predictions
segmenter.save_predictions(predictions,
image_paths) # save the results to output folder
Image.fromarray(
predictions[0]).show() # show a single prediction (the first image)
generator = LinkGen()
generator.run(categories)
downloader = Downloader(categories, config.BATCH_SIZE)
segmenter = Segmenter()
remover = Remover()
logger = Logger()
processed_video_count = utils.get_processed_video_count()
while processed_video_count < config.NUMBER_OF_LINKS:
processed_video_count = utils.get_processed_video_count()
load = utils.get_checkpoints_flag()
downloader.run()
segmenter.run()
source = config.NEURAL_NETWORK_PATH
tfrecords_command = 'python2.7 %s/utils/generate_tfrecords_dataset.py' \
' --videos_dir %s --save_dir %s' % (config.NEURAL_NETWORK_PATH, config.PROCESSED_VIDEOS_PATH,
config.TFRECORDS_PATH)
print(tfrecords_command + '\n')
start_time = logger.get_current_timestamp()
subprocess.run(tfrecords_command, shell=True)
end_time = logger.get_current_timestamp()
logger.log_tfrecords_generation_time(start_time, end_time)
starter = NeuralNetworkStarter(config.LEARNING_RATE, len(categories), load)