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launcher.py
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launcher.py
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import argparse
import numpy as np
from keras.models import load_model
from keras.preprocessing import image
from evaluate_model import evaluate_model, extract_hard_samples
from transfer_learning import train_simpler_inception_based_model
from video_annotation import video_fire_detection
from keras.applications.inception_v3 import preprocess_input as inception_preprocess_input
if __name__ == '__main__':
classes = ['fire', 'no_fire', 'start_fire']
parser = argparse.ArgumentParser(description='Convolutional neural network for forest fire detection',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
subparsers = parser.add_subparsers(title='',
description='Network can be trained on a provided dataset or predictions can be'
'made using a pre-trained model. Models can also be evaluated.',
help='', dest='mode')
subparsers.required = True
parser_train = subparsers.add_parser('train',
help='Create and train the simpler InceptionV3-based model.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser_train.add_argument('-data',
type=str,
action='store',
dest='dataset',
help='Path to the dataset on which to train.',
default=argparse.SUPPRESS,
required=True)
parser_train.add_argument('-prop',
type=float,
action='store',
dest='proportion',
help='Proportion of the dataset to be used for training (the rest is for validation).',
default=argparse.SUPPRESS,
required=True)
parser_train.add_argument('-freeze',
type=bool,
action='store',
dest='freeze',
help='Whether to freeze every layer except the last fully connected ones.',
default=argparse.SUPPRESS,
required=True)
parser_train.add_argument('-epochs',
type=int,
action='store',
dest='epochs',
help='Number of epochs.',
default=10,
required=False)
parser_train.add_argument('-batch',
type=int,
action='store',
dest='batch_size',
help='Size of a batch.',
default=32,
required=False)
parser_tune = subparsers.add_parser('tune', help='Fine-tune a pre-trained Inception-V3-based model.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser_tune.add_argument('-model',
type=str,
action='store',
dest='model_path',
help='Path to the pre-trained model.',
default=argparse.SUPPRESS,
required=True)
parser_tune.add_argument('-lr',
type=float,
action='store',
dest='learning_rate',
help='Learning rate to be used for fine-tuning.',
default=0.001,
required=False)
parser_tune.add_argument('-data',
type=str,
action='store',
dest='dataset',
help='Path to the dataset on which to train.',
default=argparse.SUPPRESS,
required=True)
parser_tune.add_argument('-prop',
type=float,
action='store',
dest='proportion',
help='Proportion of the dataset to be used for training (the rest is for validation).',
default=argparse.SUPPRESS,
required=True)
parser_tune.add_argument('-freeze',
type=bool,
action='store',
dest='freeze',
help='Whether to freeze every layer except the last fully connected ones.',
default=argparse.SUPPRESS,
required=True)
parser_tune.add_argument('-epochs',
type=int,
action='store',
dest='epochs',
help='Number of epochs.',
default=10,
required=False)
parser_tune.add_argument('-batch',
type=int,
action='store',
dest='batch_size',
help='Size of a batch.',
default=32,
required=False)
parser_predict = subparsers.add_parser('predict',
help='Perform prediction on a provided picture.')
parser_predict.add_argument('-path',
type=str,
action='store',
dest='image_path',
help='Path to an image.',
default=argparse.SUPPRESS,
required=True)
parser_predict.add_argument('-model',
type=str,
action='store',
dest='model_path',
help='Path to a trained model.',
default=argparse.SUPPRESS,
required=True)
parser_video = subparsers.add_parser('video',
help='Perform prediction on a video.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser_video.add_argument('-in',
type=str,
action='store',
dest='input_video_path',
help='Path to an mp4 video.',
default=argparse.SUPPRESS,
required=True)
parser_video.add_argument('-out',
type=str,
action='store',
dest='output_video_path',
help='Path to output the annotated mp4 video.',
default=argparse.SUPPRESS,
required=True)
parser_video.add_argument('-model',
type=str,
action='store',
dest='model_path',
help='Path to a trained model.',
default=argparse.SUPPRESS,
required=True)
parser_video.add_argument('-freq',
type=int,
action='store',
dest='freq',
help='Prediction is to be made every freq frames.',
default=12,
required=False)
parser_extract = subparsers.add_parser('extract',
help='Extract hard examples from a dataset (samples classified with low '
'confidence).')
parser_extract.add_argument('-data',
type=str,
action='store',
dest='dataset',
help='Path to a dataset.',
default=argparse.SUPPRESS,
required=True)
parser_extract.add_argument('-model',
type=str,
action='store',
dest='model_path',
help='Path to a trained model.',
default=argparse.SUPPRESS,
required=True)
parser_extract.add_argument('-threshold',
type=float,
action='store',
dest='extract_threshold',
help='Threshold for the hard examples.',
default=argparse.SUPPRESS,
required=True)
parser_test = subparsers.add_parser('test',
help='Test a model on a test set of images.')
parser_test.add_argument('-data',
type=str,
action='store',
dest='dataset',
help='Path to a test set.',
default=argparse.SUPPRESS,
required=True)
parser_test.add_argument('-model',
type=str,
action='store',
dest='model_path',
help='Path to a trained model.',
default=argparse.SUPPRESS,
required=True)
parsed = parser.parse_args()
if parsed.mode == "train":
train_simpler_inception_based_model(parsed.dataset,
fine_tune_existing=None,
freeze=parsed.freeze,
learning_rate=0.001,
percentage=parsed.proportion,
nbr_epochs=parsed.epochs,
batch_size=parsed.batch_size)
elif parsed.mode == "tune":
train_simpler_inception_based_model(parsed.dataset,
fine_tune_existing=parsed.model_path,
freeze=parsed.freeze,
learning_rate=parsed.learning_rate,
percentage=parsed.proportion,
nbr_epochs=parsed.epochs,
batch_size=parsed.batch_size)
elif parsed.mode == "predict":
model = load_model(parsed.model_path)
img = image.load_img(parsed.image_path, target_size=(224, 224, 3))
# processed image to feed the network
processed_img = image.img_to_array(img)
processed_img = np.expand_dims(processed_img, axis=0)
processed_img = inception_preprocess_input(processed_img)
# get prediction using the network
predictions = model.predict(processed_img)[0]
print(predictions)
elif parsed.mode == "video":
video_fire_detection(parsed.input_video_path,
parsed.output_video_path,
parsed.model_path,
inception_preprocess_input,
(224, 224),
parsed.freq)
elif parsed.mode == "extract":
print(extract_hard_samples(parsed.model_path,
inception_preprocess_input,
parsed.dataset,
parsed.extract_threshold))
elif parsed.mode == "test":
print(evaluate_model(parsed.model_path,
classes,
inception_preprocess_input,
parsed.dataset))