# construct the argument parser and parse the command line arguments
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--conf", required=True, help="path to configuration file")
args = vars(ap.parse_args())
# load the configuration, label encoder, and classifier
print("[INFO] loading model...")
conf = Conf(args["conf"])
le = cPickle.loads(open(conf["label_encoder_path"]).read())
model = cPickle.loads(open(conf["classifier_path"]+ conf["model"] + ".cpickle").read())
imagePaths = list(paths.list_images(conf["evaluation_path"]))
oe = OverfeatExtractor()
(truelabels, images) = dataset.build_batch(imagePaths, conf["overfeat_fixed_size"])
features = oe.describe(images)
controlImages = dataset.build_batch_control_evaluation(imagePaths, conf["overfeat_fixed_size"])
featuresControl = oe.describe(controlImages)
features = [x+y for (x,y) in zip(features,featuresControl)]
truelabels = [x[0:x.find(':')] for x in truelabels]
labels = []
for (label, vector) in zip(truelabels, features):
prediction = model.predict(np.atleast_2d(vector))[0]
print(prediction)
prediction = le.inverse_transform(prediction)
labels.append(prediction)
print("[INFO] predicted: {}, true label: {}".format(prediction,label))
print(accuracy_score(truelabels,labels))
# label encoder
print("[INFO] encoding labels...")
le = LabelEncoder()
le.fit([p.split("/")[-2] for p in imagePaths])
# initialize the Overfeat extractor and the Overfeat indexer
print("[INFO] initializing network...")
oe = GoogleNetExtractor()
oi = GoogleNetIndexer(conf["features_path"], estNumImages=len(imagePaths))
print("[INFO] starting feature extraction...")
# loop over the image paths in batches
for (i, paths) in enumerate(
dataset.chunk(imagePaths, conf["googlenet_batch_size"])):
# load the set of images from disk and describe them
(labels, images) = dataset.build_batch(paths, conf["googlenet_fixed_size"])
controlImages = dataset.build_batch_control(paths,
conf["googlenet_fixed_size"])
features = oe.describe(images)
featuresControl = oe.describe(controlImages)
#features = [x+y for (x,y) in zip(featuresImages,featuresControl)]
# loop over each set of (label, vector) pair and add them to the indexer
for (label, vector) in zip(labels, features):
oi.add(label, vector)
# check to see if progress should be displayed
if i > 0:
oi._debug("processed {} images".format(
(i + 1) * conf["googlenet_batch_size"], msgType="[PROGRESS]"))