help='Enable or not cuda')
parser.add_argument('--test_filenames',
default='test_images/*.jpg',
type=str,
help='Regex of filenames')
args = parser.parse_args()
net = SSD(cuda=args.cuda,
architecture='300_VGG16',
num_classes=len(LogoDataset.CLASSES))
has_cuda = args.cuda and torch.cuda.is_available()
if has_cuda:
weights = torch.load(args.weights)['model']
else:
weights = torch.load(args.weights, map_location='cpu')['model']
net = SSD.load(weights=weights)
COLORMAP = [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
images = []
images = [cv2.imread(filename) for filename in glob.glob(args.test_filenames)]
results = net.predict(images)
for im, result_image in zip(images, results):
for i, result in enumerate(result_image):
print(LogoDataset.CLASSES[result['class']])
class_ = LogoDataset.CLASSES[result['class']]
position = result['position']
confidence = int(100 * result['confidence'])
cv2.rectangle(im, (int(position[0]), int(position[1])),
