def main():
tl.check_arguments(sys.argv, 1, "You have to specify settings file!\n./classify.py settings_file")
settings_filename = sys.argv[1]
settings = tl.load_settings(settings_filename)
caffe_prototxt = settings["caffe_prototxt"]
caffe_model = settings["caffe_model"]
test_list = settings["test_list"]
caffe.set_mode_gpu()
net = caffe.Net(caffe_prototxt, caffe_model, caffe.TEST)
transformer = create_transformer(net)
img_test_names, img_test_labels = tl.read_img_names_from_csv(test_list,
skip_header=False,
delimiter=',')
submission_file, confusion_matrix, accuracy = classify_images(net,
transformer,
img_test_names,
img_test_labels,
settings)
if (settings["print_csv"]):
print "Results written to " + submission_file
print "Accuracy: " + str(accuracy)
# Confusion matrix
if (settings["conf_matrix"]):
plt.figure(figsize=(2,2), dpi=200)
plt.clf()
plt.imshow(confusion_matrix, norm=LogNorm())
#plt.colorbar()
plt.savefig(settings["conf_matrix_path"])
def prepare_data(settings):
db_names = settings["db_names"]
input_img_lists = settings["input_img_lists"]
input_img_dirs = settings["input_img_dirs"]
for db, input_list, input_dir in zip(db_names, input_img_lists, input_img_dirs):
img_names, img_labels = tl.read_img_names_from_csv(input_list, skip_header=False, delimiter=",", append_string=".png")
img_idxs = [li.labels[cls] for cls in img_labels]
if not tl.exist_dir(db):
print "Creating " + db + " lmdb database."
tl.imgs_to_lmdb(input_dir, img_names, db, labels=img_idxs)
else:
print "Database " + db + " already exists!"
