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run_HRA_basic.py
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run_HRA_basic.py
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"""Script for single image inference using the Human-Rights-Archive-CNNs.
Example
--------
>>> python run_HRA_basic.py --img_path path/to/your/image/xxx.jpg --pre_trained_model VGG19 --pooling_mode avg
"""
from __future__ import print_function
import os
from utils.predict import *
from applications.hra_baseline import baseline_model
from applications.hra_resnet50 import HRA_ResNet50
from applications.hra_vgg16 import HRA_VGG16
from applications.hra_vgg19 import HRA_VGG19
from applications.hra_vgg16_places365 import HRA_VGG16_Places365
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--img_path", type = str, help = 'path to image file')
parser.add_argument("--pre_trained_model", type = str,help = 'One of `VGG16`, `VGG19`, `ResNet50`, `VGG16_Places365` or `baseline`')
parser.add_argument("--pooling_mode", type = str, help = 'One of `avg`, `max`, or `flatten`')
parser.add_argument("--data_augm_enabled", type = bool, default = False, help = 'Whether to augment the samples during training or not')
parser.add_argument("--nb_predictions", type=int, default=3,
help='Number of predictions returned by the model')
args = parser.parse_args()
return args
# model = baseline_model(classes=9, epochs=40, weights='HRA')
args = get_args()
if args.pre_trained_model == 'VGG16':
model = HRA_VGG16(weights = 'HRA', mode = 'fine_tuning', pooling_mode = args.pooling_mode)
elif args.pre_trained_model == 'VGG19':
model = HRA_VGG19(weights='HRA', mode='fine_tuning', pooling_mode=args.pooling_mode)
elif args.pre_trained_model == 'ResNet50':
model = HRA_ResNet50(weights='HRA', mode='fine_tuning', pooling_mode=args.pooling_mode)
elif args.pre_trained_model == 'VGG16_Places365':
model = HRA_VGG16_Places365(weights='HRA', mode='fine_tuning', pooling_mode=args.pooling_mode)
elif args.pre_trained_model == 'baseline':
model = baseline_model(weights = 'HRA', epochs = 20)
img = image.load_img(args.img_path, target_size=(224, 224))
# preprocess image
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)[0]
top_preds = np.argsort(preds)[::-1][0:args.nb_predictions]
# load the class label
file_name = 'categories_HRA.txt'
if not os.access(file_name, os.W_OK):
synset_url = 'https://github.com/GKalliatakis/Human-Rights-Archive-CNNs/releases/download/v1.0/categories_HRA.txt'
os.system('wget ' + synset_url)
classes = list()
with open(file_name) as class_file:
for line in class_file:
classes.append(line.strip().split(' ')[0][3:])
classes = tuple(classes)
print ('\n')
print('--PREDICTED HUMAN RIGHTS VIOLATIONS CATEGORIES:')
# output the prediction
for i in range(0, args.nb_predictions):
print(classes[top_preds[i]], '->', preds[top_preds[i]])