def main(argv):
# Parse arguments
args = parse_args(argv)
set_verbose_level(args.verbose)
print_verbose("Args: %s" % str(args), 1)
# Adapted from
# https://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb
np.set_printoptions(threshold=np.nan)
caffe.set_mode_cpu()
net = caffe.Net(args.proto, args.model, caffe.TEST)
# input preprocessing: 'data' is the name of the input blob == net.inputs[0]
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2, 0, 1))
transformer.set_mean('data', VGG_MEAN_PIXEL) # mean pixel
transformer.set_raw_scale(
'data', 255
) # the reference model operates on images in [0,255] range instead of [0,1]
transformer.set_channel_swap(
'data',
(2, 1,
0)) # the reference model has channels in BGR order instead of RGB
# Read image names
with open(args.list) as f:
allnames = f.read().splitlines()
for sub in xrange(0, len(allnames), CAFFE_BATCH_SIZE):
fnames = allnames[sub:sub + CAFFE_BATCH_SIZE]
# Reshape input data
print net.blobs['data'].data.shape
net.blobs['data'].reshape(len(fnames), *net.blobs['data'].shape[1:])
print net.blobs['data'].data.shape
# Preprocess images
for idx, fname in enumerate(fnames):
fpath = os.path.join(args.input, fname)
print "Processing image %s ..." % fpath
img = transformer.preprocess('data', caffe.io.load_image(fpath))
net.blobs['data'].data[idx] = img
# Extract features
print "Extracting features ..."
out = net.forward()
# Write extracted features
for idx, fname in enumerate(fnames):
path = os.path.join(args.output, os.path.dirname(fname))
if not os.path.exists(path):
os.makedirs(path)
fpath = os.path.join(args.output, fname + ".feat")
print "Writing features to %s ..." % fpath
np.savetxt(fpath, net.blobs['fc7'].data[idx])
print "Done!"
def main(argv):
# Parse arguments
args = parse_args(argv)
set_verbose_level(args.verbose)
set_n_cores(args.cores)
print_verbose("Args: %s" % str(args), 1)
# Prepare data
data, labels, classes = gen_data(args.dir)
print_verbose('Data: %s' % str(data), 5)
print_verbose('Labels: %s' % str(labels), 4)
print_verbose('Classes: %s' % str(classes), 4)
print_verbose('Data shape: %s' % str(data.shape), 2)
print_verbose('Labels shape: %s' % str(labels.shape), 2)
print_verbose('Classes shape: %s' % str(classes.shape), 2)
print_verbose('Data bytes: %s' % str(data.nbytes), 2)
# Calculate distances
dist = calc_dist(args.model, data)
# Generate score model
model = generate_model(dist, classes, args)
print_verbose('Model: %s' % str(model), 0)
# Export
print_verbose('Saving model to %s' % args.score, 0)
with open(args.score, "wb") as f:
pickle.dump(model, f)
print_verbose('Done!', 0)
def main(argv):
# Parse arguments
args = parse_args(argv)
set_verbose_level(args.verbose)
print_verbose("Args: %s" % str(args), 1)
# Resize images
resize_from_csv(args.csv, args.original, args.resized, args.density)
def main(argv):
# Parse arguments
args = parse_args(argv)
set_verbose_level(args.verbose)
print_verbose("Args: %s" % str(args), 1)
# Download images
download_from_csv(args.csv, args.directory)
def main(argv):
# Parse arguments
args = parse_args(argv)
set_verbose_level(args.verbose)
print_verbose("Args: %s" % str(args), 1)
# Crawl URL
crawl(args.url)
def main(argv):
# Parse arguments
args = parse_args(argv)
set_verbose_level(args.verbose)
set_n_cores(args.cores)
print_verbose("Args: %s" % str(args), 1)
# Extract patches
patch_extract(args.image, args.window, args.step, args.dir)
def main(argv):
# Parse arguments
args = parse_args(argv)
set_verbose_level(args.verbose)
set_n_cores(args.cores)
print_verbose("Args: %s" % str(args), 1)
data, labels, classes = gen_data(args.dir)
print_verbose('Data: %s' % str(data), 5)
print_verbose('Labels: %s' % str(labels), 4)
print_verbose('Classes: %s' % str(classes), 4)
print_verbose('Data shape: %s' % str(data.shape), 2)
print_verbose('Labels shape: %s' % str(labels.shape), 2)
print_verbose('Classes shape: %s' % str(classes.shape), 2)
def main(argv):
# Parse arguments
args = parse_args(argv)
set_verbose_level(args.verbose)
print_verbose("Args: %s" % str(args), 1)
# Crawl URL
result = crawl(args.url)
# Extract data
raw_data = extract_data(result)
# Sort (optional)
raw_data.sort()
# Save to CSV
gen_csv(args.csv, raw_data)
def main(argv):
# Parse arguments
args = parse_args(argv)
set_verbose_level(args.verbose)
set_n_cores(args.cores)
print_verbose("Args: %s" % str(args), 1)
# Some tests
data, labels = gen_data(args.dir, False)
print_verbose('Data: %s' % str(data), 5)
print_verbose('Labels: %s' % str(labels), 4)
print_verbose('Data shape: %s' % str(data.shape), 2)
print_verbose('Labels shape: %s' % str(labels.shape), 2)
classification = classify(data, labels, args)
print_verbose('Final classification: %s' % str(classification), 0)
# Evaluate performance
if args.gtruth:
eval_perf(classification)
