-
Notifications
You must be signed in to change notification settings - Fork 0
/
classify_image_using_trained_net.py
75 lines (58 loc) · 2.89 KB
/
classify_image_using_trained_net.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
#!/usr/bin/python
from argparse import ArgumentParser
from keras.models import model_from_json
import numpy as np
from model import *
from keras.preprocessing import image as kerasImage
import logging
from datetime import datetime
DEFAULT_DATE_TIME_FORMAT = "%Y%m%d-%H%M%S.%s"
classification_time_file = "./classificationTime.txt"
classification_results = "./classificationresults.txt"
header = ["Start Time", "End Time", "Duration (s)"]
header2 = ["Image Name", "Category", "Percentage"]
append_row_to_csv(classification_results, header2)
logging.basicConfig(level=logging.DEBUG, format='[%(asctime)s] %(levelname)s - %(message)s', )
# format= '[%(asctime)s] {%(pathname)s:%(lineno)d} %(levelname)s - %(message)s',
logger = logging.getLogger(__name__)
def runCNN(cnn_name, image):
logger.info('Running Against {} neural network'.format(cnn_name))
with open('model_' + cnn_name + '_architecture.json', 'r') as f:
net_model = model_from_json(f.read())
logger.info('Loaded model')
net_model.load_weights('model-' + cnn_name + '-final.h5')
logger.info('Loaded weights')
# preprocess input
raw_image = kerasImage.load_img(image, target_size=IMAGE_SIZE)
x = kerasImage.img_to_array(raw_image)
x = np.expand_dims(x, axis=0)
post_processed_input_images = np.vstack([x])
# predict output
t_start = datetime.now()
row = [t_start.strftime(DEFAULT_DATE_TIME_FORMAT)]
output_probability = net_model.predict(post_processed_input_images)
output_classes = output_probability.argmax(axis=-1)
logger.debug("Output classes: %s", output_classes)
logger.debug("Output probabilities: %s", output_probability)
for idx, output_class in enumerate(output_classes):
logger.info("Image {} was a {}".format(image, CLASS_LABEL[output_class]))
logger.info("Probability: {} ".format(output_probability[idx][output_class]))
row2 = []
row2.append(image + "," + CLASS_LABEL[output_class] + "," + str(output_probability[idx][output_class]))
append_row_to_csv(classification_results, row2)
t_end = datetime.now()
difference_in_seconds = get_difference_in_seconds(t_start, t_end)
row.append(t_end.strftime(DEFAULT_DATE_TIME_FORMAT))
row.append(str(difference_in_seconds))
append_row_to_csv(classification_time_file, header)
append_row_to_csv(classification_time_file, row)
if __name__ == "__main__":
models = [cls.__name__ for cls in vars()[BaseModel.__name__].__subclasses__()]
parser = ArgumentParser()
parser.add_argument("-n", "--net", required=True, choices=models,
help="Neural network model that should be used for classification")
parser.add_argument("-i", "--image", required=True, help="Path to image that should be classified")
args = parser.parse_args()
logger.info('CNN_TO_RUN: %s', args.net)
logger.info('TEST_IMAGE: %s', args.image)
runCNN(args.net, args.image)