def demo(args):
hyps = hyp_parse(args.hyp)
ds = DATASETS[args.dataset](level=1)
model = RetinaNet(backbone=args.backbone, hyps=hyps)
if args.weight.endswith('.pth'):
chkpt = torch.load(args.weight)
# load model
if 'model' in chkpt.keys():
model.load_state_dict(chkpt['model'])
else:
model.load_state_dict(chkpt)
print('load weight from: {}'.format(args.weight))
model.eval()
# if os.path.exists('outputs'):
# shutil.rmtree('outputs')
# os.mkdir('outputs')
t0 = time.time()
if not args.dataset == 'DOTA':
ims_list = [x for x in os.listdir(args.ims_dir) if is_image(x)]
for idx, im_name in enumerate(ims_list):
s = ''
t = time.time()
im_path = os.path.join(args.ims_dir, im_name)
s += 'image %g/%g %s: ' % (idx, len(ims_list), im_path)
src = cv2.imread(im_path, cv2.IMREAD_COLOR)
im = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
cls_dets = im_detect(model, im, target_sizes=args.target_size)
for j in range(len(cls_dets)):
cls, scores = cls_dets[j, 0], cls_dets[j, 1]
bbox = cls_dets[j, 2:]
if len(bbox) == 4:
draw_caption(src, bbox, '{:1.3f}'.format(scores))
cv2.rectangle(src, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
color=(0, 0, 255),
thickness=2)
else:
pts = np.array([rbox_2_quad(bbox[:5]).reshape((4, 2))],
dtype=np.int32)
cv2.drawContours(src,
pts,
0,
color=(0, 255, 0),
thickness=2)
put_label = True
if put_label:
label = ds.return_class(cls) + str(' %.2f' % scores)
fontScale = 0.7
font = cv2.FONT_HERSHEY_COMPLEX
thickness = 1
t_size = cv2.getTextSize(label,
font,
fontScale=fontScale,
thickness=thickness)[0]
c1 = tuple(bbox[:2].astype('int'))
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 5
cv2.rectangle(src, c1, c2, [0, 255, 0], -1) # filled
cv2.putText(src,
label, (c1[0], c1[1] - 5),
font,
fontScale, [0, 0, 0],
thickness=thickness,
lineType=cv2.LINE_AA)
# display original anchors
# if len(bbox) > 5:
# pts = np.array([rbox_2_quad(bbox[5:]).reshape((4, 2))], dtype=np.int32)
# cv2.drawContours(src, pts, 0, color=(0, 0, 255), thickness=2)
# resize for better shown
# im = cv2.resize(src, (800, 800), interpolation=cv2.INTER_LINEAR)
# cv2.imshow('Detection Results', im)
# cv2.waitKey(0)
print('%sDone. (%.3fs) %d objs' %
(s, time.time() - t, len(cls_dets)))
# save image
out_path = os.path.join('outputs', os.path.split(im_path)[1])
cv2.imwrite(out_path, src)
## DOTA detct on large image
else:
evaluate(args.target_size,
args.ims_dir,
'DOTA',
args.backbone,
args.weight,
hyps=hyps,
conf=0.05)
if os.path.exists('outputs/dota_out'):
shutil.rmtree('outputs/dota_out')
os.mkdir('outputs/dota_out')
exec(
'cd outputs && rm -rf detections && rm -rf integrated && rm -rf merged'
)
ResultMerge('outputs/detections', 'outputs/integrated',
'outputs/merged', 'outputs/dota_out')
img_path = os.path.join(args.ims_dir, 'images')
label_path = 'outputs/dota_out'
save_imgs = False
if save_imgs:
show_dota_results(img_path, label_path)
print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Hyperparams')
parser.add_argument('--backbone',
dest='backbone',
default='res50',
type=str)
parser.add_argument('--weight', type=str, default='weights/best.pth')
parser.add_argument('--target_size',
dest='target_size',
default=[800],
type=int)
parser.add_argument('--hyp',
type=str,
default='hyp.py',
help='hyper-parameter path')
# parser.add_argument('--dataset', nargs='?', type=str, default='NWPU_VHR')
# parser.add_argument('--test_path', type=str, default='NWPU_VHR/test.txt')
parser.add_argument('--dataset', nargs='?', type=str, default='HRSC2016')
parser.add_argument('--test_path', type=str, default='./HRSC2016/test.txt')
arg = parser.parse_args()
hyps = hyp_parse(arg.hyp)
evaluate(arg.target_size,
arg.test_path,
arg.dataset,
arg.backbone,
arg.weight,
hyps=hyps)